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Frameworks Support Matrix

Abstract

This support matrix is for NVIDIA® optimized frameworks. The matrix provides a single view into the supported software and specific versions that come packaged with the frameworks based on the container image.

1. 25.xx Framework Containers Support Matrix

Important:

Content that is included in <<>> brackets indicates new content from the previously published version.

Note:

The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 25.01 release of an image was released in January 2025.

25.xx container images

Container Image	25.06	25.05	25.04	25.03	25.02	25.01
DGX
DGX System	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX™ H100 NVIDIA DGX™ H200 NVIDIA DGX™ B200 GB200 NVL72	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX™ H100 NVIDIA DGX™ H200 NVIDIA DGX™ B200 GB200 NVL72	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX™ H100 NVIDIA DGX™ H200 NVIDIA DGX™ B200 GB200 NVL72	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX™ H100 NVIDIA DGX™ H200 NVIDIA DGX™ B200 GB200 NVL72	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX™ H100 NVIDIA DGX™ H200 NVIDIA DGX™ B200 GB200 NVL72	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX™ H100 NVIDIA DGX™ H200 NVIDIA DGX™ B200 GB200 NVL72
Operating System	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9² EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9³ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9⁴ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9⁵ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9⁶ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}
System Requirements
NVIDIA Driver	Release 25.06 is based on CUDA 12.9.1.010 which requires NVIDIA Driver release 575 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520, R530, R545, R555, and R560 drivers, which are not forward-compatible with CUDA 12.8. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 25.05 is based on CUDA 12.9.0.43 which requires NVIDIA Driver release 575 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520, R530, R545, R555, and R560 drivers, which are not forward-compatible with CUDA 12.8. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 25.04 is based on CUDA 12.9.0.036 which requires NVIDIA Driver release 575 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520, R530, R545, R555, and R560 drivers, which are not forward-compatible with CUDA 12.8. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 25.03 is based on CUDA 12.8.1.012 which requires NVIDIA Driver release 570 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520, R530, R545, R555, and R560 drivers, which are not forward-compatible with CUDA 12.8. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 25.02 is based on CUDA 12.8.0 which requires NVIDIA Driver release 570 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520, R530, R545, R555, and R560 drivers, which are not forward-compatible with CUDA 12.8. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 25.01 is based on CUDA 12.8.0 which requires NVIDIA Driver release 570 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520, R530, R545, R555, and R560 drivers, which are not forward-compatible with CUDA 12.8. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.
GPU Model	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Blackwell™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Blackwell™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Blackwell™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Blackwell™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Blackwell™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Blackwell™
CUDA Deep Learning Base Container Image (included in all containers)
Container OS	Ubuntu 24.04	Ubuntu 24.04	Ubuntu 24.04	Ubuntu 24.04	Ubuntu 24.04	Ubuntu 24.04
CUDA	NVIDIA CUDA 12.9.1	NVIDIA CUDA 12.9.0	NVIDIA CUDA 12.9.0	NVIDIA CUDA 12.8.1.012	NVIDIA CUDA 12.8.0.038	NVIDIA CUDA 12.8.0.038
cuBLAS	NVIDIA cuBLAS 12.9.1.4	NVIDIA cuBLAS 12.9.0.13	NVIDIA cuBLAS 12.9.0.2	NVIDIA cuBLAS 12.8.4.1	NVIDIA cuBLAS 12.8.3.14	NVIDIA cuBLAS 12.8.3.14
cuDNN	9.10.2.21	9.10.1.3	9.9.0.52	9.8.0.87	9.7.1.26	9.7.0.66
cuTENSOR	-	-	-	-	2.1.1.1	2.1.0.9
DALI	1.50	1.49	1.48	1.47	1.46	1.45
NCCL	2.27.3	2.26.5	2.26.3	2.25.1	2.25.1	2.25.1
TensorRT	TensorRT 10.11.0.33	TensorRT 10.10.0.31	TensorRT 10.9.0.34	TensorRT 10.9.0.34	TensorRT 10.8.0.43	TensorRT 10.8.0.43
rdma-core	50.0	50.0	50.0	50.0	50.0	50.0
NVIDIA HPC-X	2.23	2.23	2.22.1	2.21 with OpenMPI 4.1.7 UCX 1.18.0	2.21 with OpenMPI 4.1.7 UCX 1.18.0	2.21 with OpenMPI 4.1.7 UCX 1.18.0
GDRcopy			2.4.1
Nsight Compute	2025.2.1.3	2025.2.0.11	2025.2.0.11	2025.1.1.2	2025.1.0.14	2025.1.0.14
Nsight Systems	2025.3.1.90	2025.3.1.90	2025.2.1.130	2025.1.1.110	2025.1.1.65	2024.6.2.225
	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA
	Docker image size: 10.5 GB	Docker image size: 10 GB	Docker image size: 10 GB	Docker image size: 10 GB	Docker image size: 10 GB	Docker image size: 10 GB
NVIDIA Optimized Frameworks
DGL	-	2.4.0 (including DGL-Graphbolt, a recently released GNN dataloader library which has achieved state-of-the-art performance on NVIDIA GPUs). RAPIDS 25.04 PyTorch 2.8.0a0+5228986c39 Apex GDRCopy 2.3 TensorBoard 2.12.0 Torch-TensorRT 2.8.0.a0 MAGMA 2.6.2 JupyterLab 4.3.4 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v2.3 NVSHMEM 2.10.1	-	2.4.0 (including DGL-Graphbolt, a recently released GNN dataloader library which has achieved state-of-the-art performance on NVIDIA GPUs). RAPIDS 25.02 PyTorch 2.7.0a0+7c8ec84dab Apex GDRCopy 2.3 TensorBoard 2.12.0 Torch-TensorRT 2.7.0.a0 MAGMA 2.6.2 JupyterLab 4.3.4 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.14 NVSHMEM 2.10.1	-	2.4.0 (including DGL-Graphbolt, a recently released GNN dataloader library which has achieved state-of-the-art performance on NVIDIA GPUs). RAPIDS 24.10 PyTorch 2.6.0a0+ecf3bae40a Apex GDRCopy 2.3 TensorBoard 2.12.0 Torch-TensorRT 2.6.0.a0 MAGMA 2.6.2 JupyterLab 4.3.4 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.14 NVSHMEM 2.10.1
	-	Multi arch support: x86, Arm SBSA	-	Multi arch support: x86, Arm SBSA	-	Multi arch support: x86, Arm SBSA
	-	Docker image size: 29.8 GB	-	Docker image size: 27.8 GB	-	Docker image size: 30.4 GB
JAX	-	-	JAX v0.5.3 including: Ubuntu 22.04.2 LTS with Python 3.12 NVIDIA cuDNN 9.9.0.52 NVIDIA NCCL 2.26.3 NVIDIA HPC-X 2.20 Nsight Compute 2025.2.0.11 Nsight Systems 2025.2.1.130 TransformerEngine 2.2	-	-	JAX v0.4.26 including: Ubuntu 22.04.2 LTS with Python 3.12 NVIDIA cuDNN 9.7.0.66 NVIDIA NCCL 2.25.1 NVIDIA HPC-X 2.20 Nsight Compute 2025.1.0.14 Nsight Systems 2024.6.2.225 TransformerEngine 1.14
	-	-	Multi arch support: x86 only		-	Multi arch support: x86 only
	-	-	Docker image size: 16.7 GB		-	Docker image size: 15.9 GB
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet	-	-	-	-	-	-
	-		-	-	-	-
	-		-	-	-	-
PaddlePaddle	-	-	-	-	-	-
	-	-	-	-	-	-
	-	-	-	-	-	-
PyG	-	PyG 2.7 PyTorch 2.8.0a0+5228986c39 including Python 3.12 APEX TensorBoard 2.9.0 Torch-TensorRT 2.8.0a0 MAGMA 2.6.2 RAPIDS 25.04 JupyterLab 4.3.5 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v2.3	-	PyG 2.6.1 PyTorch 2.7.0a0+7c8ec84dab including Python 3.12 APEX TensorBoard 2.9.0 Torch-TensorRT 2.7.0a0 MAGMA 2.6.2 RAPIDS 25.02 JupyterLab 4.3.5 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v2.1	-	PyG 2.6.1 PyTorch 2.6.0a0+ecf3bae40a including Python 3.12 APEX TensorBoard 2.9.0 Torch-TensorRT 2.6.0a0 MAGMA 2.6.2 RAPIDS 24.10 JupyterLab 4.3.4 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.14
	-	Multi arch support: x86, Arm SBSA	-	Multi arch support: x86, Arm SBSA	-	Multi arch support: x86, Arm SBSA
	-	Docker image size: 27.3 GB	-	Docker image size: 25.5 GB	-	Docker image size: 28.1 GB
PyTorch	2.8.0a0+5228986c39including Python 3.12 APEX NVIDIA cuBLASMp 0.4.0 TensorBoard 2.16.2 Torch-TensorRT 2.8.0a0 MAGMA 2.6.2 RAPIDS 25.04 JupyterLab 4.3.6 including Jupyter-TensorBoard TensorRT Model Optimizer 0..25 TransformerEngine v2.4	2.8.0a0+5228986c39including Python 3.12 APEX NVIDIA cuBLASMp 0.4.0 TensorBoard 2.16.2 Torch-TensorRT 2.8.0a0 MAGMA 2.6.2 RAPIDS 25.04 JupyterLab 4.3.6 including Jupyter-TensorBoard TensorRT Model Optimizer 0..25 TransformerEngine v2.3	2.7.0a0+79aa174including Python 3.12 APEX NVIDIA cuBLASMp 0.4.0 TensorBoard 2.16.2 Torch-TensorRT 2.7.0a0 MAGMA 2.6.2 RAPIDS 25.02 JupyterLab 4.3.6 including Jupyter-TensorBoard TensorRT Model Optimizer 0..25 TransformerEngine v2.2	2.7.0a0+7c8ec84dab including Python 3.12 APEX NVIDIA cuBLASMp 0.4.0 TensorBoard 2.16.2 Torch-TensorRT 2.7.0a0 MAGMA 2.6.2 RAPIDS 25.02 JupyterLab 4.3.5 including Jupyter-TensorBoard TensorRT Model Optimizer 0.17.1 TransformerEngine v2.1	2.7.0a0+6c54963f75 including Python 3.12 APEX TensorBoard 2.16.2 Torch-TensorRT 2.6.0a0 MAGMA 2.6.2 RAPIDS 24.12 JupyterLab 4.3.5 including Jupyter-TensorBoard TensorRT Model Optimizer 0.17.1 TransformerEngine v2.0	2.6.0a0+ecf3bae40a including Python 3.12 APEX TensorBoard 2.16.2 Torch-TensorRT 2.6.0a0 MAGMA 2.6.2 RAPIDS 24.10 JupyterLab 4.3.4 including Jupyter-TensorBoard TensorRT Model Optimizer 0.17.1 TransformerEngine v1.14
	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA
	Docker image size: 26.7 GB	Docker image size: 25.7 GB	Docker image size: 24.7 GB	Docker image size: 24 GB	Docker image size: 24.7 GB	Docker image size: 26.5 GB
TensorFlow	-	-	-	-	2.17.0 including Python 3.12 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 24.12 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 4.3.4 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.16.1 including Python 3.12 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 24.10 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 4.3.4 JupyterLab Server 1.0.0 Jupyter-TensorBoard
	-	-	-	-	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA
	-	-	-	-	Docker image size: 18 GB	Docker image size: 20.4 GB
TensorRT	TensorRT 10.11.0.33	TensorRT 10.10.0.31	TensorRT 10.9.034	TensorRT 10.9.0.34	TensorRT 10.8.0.43	TensorRT 10.8.0.43
	Docker image size: 10.8 GB	Docker image size: 11 GB	Docker image size: 10.7 GB		Docker image size: 10.5 GB	Docker image size: 12.4 GB
Triton Inference Server	Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.12 2.58 including PyTorch 2.8.0a0+5228986c39 NVIDIA CUDA 12.9.1.010 NVIDIA cuBLAS 12.9.1.4 cuDNN 9.10.2.21 NVIDIA NCCL 2.27.3 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.11.0.33 UCX 1.18.0 GDRCopy 2.4.1 NVIDIA HPC-X 2.23 OpenMPI 4.1.7 FIL NVIDIA DALI® 1.50 ONNX Runtime 1.21.0 Intel OpenVINO 2025.0.0 DCGM 3.2.6 TensorRT-LLM version release/0.20.0 vLLM version 0.9.0.1 nvImageCodec 0.2.0.7	Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.12 2.58 including PyTorch 2.8.0a0+5228986c39 NVIDIA CUDA 12.9.0 NVIDIA cuBLAS 12.9.0.13 cuDNN 9.10.1.3 NVIDIA NCCL 2.26.5 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.10.0.31 UCX 1.18.0 GDRCopy 2.4.1 NVIDIA HPC-X 2.21 OpenMPI 4.1.7 FIL NVIDIA DALI® 1.49 ONNX Runtime 1.21.0 Intel OpenVINO 2025.0.0 DCGM 3.2.6 TensorRT-LLM version release/0.18.2 vLLM version 0.8.1 nvImageCodec 0.2.0.7	Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.12 2.57 including PyTorch 2.7.0a0+79aa17489c NVIDIA CUDA 12.9.0 NVIDIA cuBLAS 12.9.0.2 cuDNN 9.9.0.52 NVIDIA NCCL 2.26.3 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.9.0.34 UCX 1.18.0 GDRCopy 2.4.1 NVIDIA HPC-X 2.21 OpenMPI 4.1.7 FIL NVIDIA DALI® 1.49 ONNX Runtime 1.21.0 Intel OpenVINO 2025.0.0 DCGM 3.2.6 TensorRT-LLM version release/0.18.2 vLLM version 0.8.1 nvImageCodec 0.2.0.7	Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.12 2.47 including PyTorch 2.7.0a0+7c8ec84dab NVIDIA CUDA 12.8.1.012 NVIDIA cuBLAS 12.8.4.1 cuDNN 9.8.0.87 NVIDIA NCCL 2.25.1 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.9.0.34 UCX 1.18.0 GDRCopy 2.4.1 NVIDIA HPC-X 2.21 OpenMPI 4.1.7 FIL NVIDIA DALI® 1.47 ONNX Runtime 1.21.0 Intel OpenVINO 2025.0.0 DCGM 3.2.6 TensorRT-LLM version release/0.18.0 vLLM version 0.7.3 nvImageCodec 0.2.0.7	Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.12 2.47 including PyTorch 2.7.0a0+6c54963f75 TensorFlow 2.17.0 NVIDIA CUDA 12.8.0.038 NVIDIA cuBLAS 12.8.3.14 cuDNN 9.7.1.26 NVIDIA NCCL 2.25.1 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.8.0.43 UCX 1.18.0 GDRCopy 2.3 NVIDIA HPC-X 2.21 OpenMPI 4.1.7 FIL NVIDIA DALI® 1.46 ONNX Runtime 1.20.1 Intel OpenVINO 2024.05.0 DCGM 3.2.6 TensorRT-LLM version release/0.17.0 vLLM version 0.6.3.1 nvImageCodec 0.2.0.7	Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.12 2.47 including PyTorch2.6.0a0+ecf3bae40a TensorFlow 2.17.0 NVIDIA CUDA 12.8.0.038 NVIDIA cuBLAS 12.8.3.14 cuDNN 9.7.0.66 NVIDIA NCCL 2.25.1 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.8.0.43 UCX 1.18.0 GDRCopy 2.3 NVIDIA HPC-X 2.21 OpenMPI 4.1.7 FIL NVIDIA DALI® 1.45 ONNX Runtime 1.20.1 Intel OpenVINO 2024.05.0 DCGM 3.2.6 TensorRT-LLM version release/0.17.0 vLLM version 0.6.3.1 nvImageCodec 0.2.0.7
	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA
	Docker image size: 17.8 GB	Docker image size: 18.3 GB	Docker image size: 17.7 GB	Docker image size: 16.6 GB	Docker image size: 18.1 GB	Docker image size: 18 GB
TensorFlow For Jetson	-	-	-	-	2.17.0	2.17.0
PyTorch for Jetson	2.8.0a0+5228986c39	2.8.0a0+5228986c39	2.7.0a0+79aa17489c	2.7.0a0+7c8ec84dab	2.7.0a0+6c54963f75	2.6.0a0+ecf3bae40a
Triton for Jetson	25.06	25.05	25.04	2.56.0	2.54.0	2.54.0

2. 24.xx Framework Containers Support Matrix

Important:

Content that is included in <<>> brackets indicates new content from the previously published version.

Note:

The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 24.01 release of an image was released in January 2024.

24.xx container images

Container Image	24.12	24.11	24.10	24.09	24.08	24.07	24.06	24.05	24.04	24.03	24.02	24.01
DGX
DGX System	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100
Operating System	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9⁷ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9⁸ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9⁹ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹⁰ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹¹ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹² EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹³ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹⁴ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹⁵ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹⁶ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+^{https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html#fntarg_1}	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹⁷ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹⁸ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹
System Requirements
NVIDIA Driver	Release 24.12 is based on CUDA 12.6.3 which requires NVIDIA Driver release 560 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520, R530, R545 and R555 drivers, which are not forward-compatible with CUDA 12.6. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 24.11 is based on CUDA 12.6.3 which requires NVIDIA Driver release 560 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520, R530, R545 and R555 drivers, which are not forward-compatible with CUDA 12.6. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 24.10 is based on CUDA 12.6.2 which requires NVIDIA Driver release 560 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520, R530, R545 and R555 drivers, which are not forward-compatible with CUDA 12.6. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 24.09 is based on CUDA 12.6.1 which requires NVIDIA Driver release 560 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520, R530, R545 and R555 drivers, which are not forward-compatible with CUDA 12.6. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades. .	Release 24.08 is based on CUDA 12.6 which requires NVIDIA Driver release 560 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520, R530, R545 and R555 drivers, which are not forward-compatible with CUDA 12.6. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades. .	Release 24.07 is based on CUDA 12.5.1 which requires NVIDIA Driver release 555 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520 and R545 drivers, which are not forward-compatible with CUDA 12.5. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 24.06 is based on CUDA 12.4.1, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 24.05 is based on CUDA 12.4.1, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 24.04 is based on CUDA 12.4.1, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 24.03 is based on CUDA 12.4.0.41, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 24.02 is based on CUDA 12.3.2, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 24.01 is based on CUDA 12.3.2, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.
GPU Model	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™
CUDA Deep Learning Base Container Image (included in all containers)
Container OS	Ubuntu 24.04	Ubuntu 24.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04
CUDA	NVIDIA CUDA 12.6.3	NVIDIA CUDA 12.6.3	NVIDIA CUDA 12.6.2	NVIDIA CUDA 12.6.1	NVIDIA CUDA 12.6	NVIDIA CUDA 12.5.1	NVIDIA CUDA 12.5.0.23	NVIDIA CUDA 12.4.1	NVIDIA CUDA 12.4.1	NVIDIA CUDA 12.4.0.41	NVIDIA CUDA 12.3.2	NVIDIA CUDA 12.3.2
cuBLAS	NVIDIA cuBLAS 12.6.4.1	NVIDIA cuBLAS 12.6.4.1	NVIDIA cuBLAS 12.6.3.3	NVIDIA cuBLAS 12.6.3.1	NVIDIA cuBLAS 12.6.0.22	NVIDIA cuBLAS 12.5.3.2	NVIDIA cuBLAS 12.5.2.13	NVIDIA cuBLAS 12.4.5.8	NVIDIA cuBLAS 12.4.5.8	NVIDIA cuBLAS 12.4.2.65	NVIDIA cuBLAS 12.3.4.1	NVIDIA cuBLAS 12.3.4.1
cuDNN	9.6.0.74	9.5.1.17	9.5.0.50	9.4.0.58	9.3.0.75	9.2.1.18	9.1.0.70	9.1.0.70	9.1.0.70	9.0.0.306	9.0.0.306	8.9.7.29
cuTENSOR	2.0.2.5	2.0.2.5	2.0.2.5	2.0.2.5	2.0.2.5	2.0.2.4	2.0.1.2	2.0.1.2	2.0.1.2	2.0.1.2	2.0	2.0
DALI	1.44	1.43	1.42	1.41	1.40	1.39	1.38	1.37.1	1.36	1.35	1.34	1.33
NCCL	2.23.4	2.23.4	2.22.3	2.22.3	2.22.3	2.22.3	2.21.5	2.21.5	2.21.5	2.20	2.19.4	2.19.4
TensorRT	TensorRT 10.7.0.23	TensorRT 10.6.0.26	TensorRT 10.5.0.18	TensorRT 10.4.0.26	TensorRT 10.3.0.26	TensorRT 10.2.0.19	TensorRT 10.1.0.27	TensorRT 10.0.1.6	TensorRT 8.6.3	TensorRT 8.6.3	TensorRT 8.6.3	TensorRT 8.6.1.6
rdma-core	39.0	39.0	39.0	39.0	39.0	39.0	39.0	39.0	39.0	39.0	39.0	39.0
NVIDIA HPC-X	2.21 with OpenMPI 4.1.7 OpenUCX 1.18.0	2.21 with OpenMPI 4.1.7 OpenUCX 1.18.0	2.20 with OpenMPI 4.1.7 OpenUCX 1.17.0	2.20 with OpenMPI 4.1.7 OpenUCX 1.15.0	2.19 with OpenMPI 4.1.7 OpenUCX 1.15.0	2.19 with OpenMPI 4.1.7 OpenUCX 1.15.0	2.19 with OpenMPI 4.1.4+ OpenUCX 1.15.0	2.19 with OpenMPI 4.1.4+ OpenUCX 1.15.0	2.18 with OpenMPI 4.1.4+ OpenUCX 1.15.0	2.1 with OpenMPI 4.1.4+ OpenUCX 1.15.0	2.16rc4 with OpenMPI 4.1.4+ OpenUCX 1.15.0	2.16rc4 with OpenMPI 4.1.4+ OpenUCX 1.15.0
GDRcopy											2.3	2.3
Nsight Compute	2024.3.2.3	2024.3.2.3	2024.3.2.3	2024.3.1.2	2024.3.0.15	2024.2.1.2	2024.2.0.16	2024.1.1.4	2024.1.1.4	2024.1.0.13	2023.3.1.1	2023.3.1.1
Nsight Systems	2024.7.1.84	2024.6.1.90	2024.6.1.90	2024.4.2.133	2024.4.2.133	2024.4.2.133	2024.2.3.38	2024.2.1.106	2024.2.1.106	2024.2.1.38	2023.4.1.97	2023.4.1.97
NVIDIA Optimized Frameworks
DGL	-	2.4.0 (including DGL-Graphbolt, a recently released GNN dataloader library which has achieved state-of-the-art performance on NVIDIA GPUs). RAPIDS 24.10 PyTorch 2.6.0a0+df5bbc0 Apex GDRCopy 2.3 TensorBoard 2.12.0 Torch-TensorRT 2.6.0.a0 MAGMA 2.6.2 JupyterLab 4.2.5 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.12 NVSHMEM 2.10.1	-	2.4.0 (including DGL-Graphbolt, a recently released GNN dataloader library which has achieved state-of-the-art performance on NVIDIA GPUs). RAPIDS 24.08 PyTorch 2.5.0a0+b465a5843b Apex GDRCopy 2.3 TensorBoard 2.12.0 Torch-TensorRT 2.5.0.a0 MAGMA 2.6.2 JupyterLab 4.2.5 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.10 NVSHMEM 2.10.1	-	2.3.0 (including DGL-Graphbolt, a recently released GNN dataloader library which has achieved state-of-the-art performance on NVIDIA GPUs). RAPIDS 24.04 PyTorch 2.4.0a0+3bcc3cddb5 Apex GDRCopy 2.3 TensorBoard 2.12.0 Torch-TensorRT 2.4.0.a0 MAGMA 2.6.2 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.8 NVSHMEM 2.10.1	-	2.2+22aea5c (including DGL-Graphbolt, a recently released GNN dataloader library which has achieved state-of-the-art performance on NVIDIA GPUs). RAPIDS 24.04 PyTorch 2.4.0a0+07cecf4 Apex GDRCopy 2.3 TensorBoard 2.12.0 Torch-TensorRT 2.4.0.a0 MAGMA 2.6.2 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.6 NVSHMEM 2.10.1	2.1+e1f7738 (including DGL-Graphbolt, a recently released GNN dataloader library which has achieved state-of-the-art performance on NVIDIA GPUs). RAPIDS 24.02 PyTorch 2.3.0a0+6ddf5cf85e Apex GDRCopy 2.3 TensorBoard 2.12.0 Torch-TensorRT 2.30.a0 MAGMA 2.6.2 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.5 NVSHMEM 2.10.1	2.1+7c51cd16 including: PyTorch 2.3.0a0+40ec155e58 APEX MAGMA 2.6.2 TensorBoard 2.12.0 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.4	-	1.2 including: PyTorch 2.2.0a0+81ea7a4 APEX MAGMA 2.6.2 TensorBoard 2.12.0 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.2.1
	-	Multi arch support: x86, Arm SBSA	-	Multi arch support: x86, Arm SBSA	-	Multi arch support: x86, Arm SBSA	-	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	-	Multi arch support: x86, Arm SBSA
	-	Docker image size: 24.6 GB	-	Docker image size: 24.5 GB	-	Docker image size: 23.3 GB	-	Docker image size: 21.0 GB	Docker image size: 23.6 GB	Docker image size: 23.3 GB	-	Docker image size: 24.8 GB
JAX	-	-	JAX v0.4.26 including: Ubuntu 22.04.2 LTS with Python 3.10 NVIDIA cuDNN 9.5.0.50 NVIDIA NCCL 2.22.3 NVIDIA HPC-X 2.20 Nsight Compute 2024.3.2.3 Nsight Systems 2024.6.1.90 TransformerEngine 1.11	-	-	-	-	-	JAX v0.4.26 including: Ubuntu 22.04.2 LTS with Python 3.10 NVIDIA cuDNN 9.1.0.70 NVIDIA NCCL 2.21.5 NVIDIA HPC-X 2.18 Nsight Compute 2024.1.1.0 Nsight Systems 2024.2.1.106 TransformerEngine 1.5	-	-	-
	-	-	Multi arch support: x86 only	-	-	-	-	-	Multi arch support: x86 only			-
	-	-	Docker image size: 12.5GB	-	-	-	-	-	Docker image size: 10.1GB
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet	-	-	-	-	-	-	1.9.1 including: Horovod 0.28 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: Horovod 0.28 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: Horovod 0.28 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: Horovod 0.28 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: Horovod 0.28 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: Horovod 0.28 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4
	-	-	-	-	-	-	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA
	-	-	-	-	-	-	Docker image size: 12.7 GB	Docker image size: 12.6 GB	Docker image size: 12.2 GB	Docker image size: 12.1 GB	Docker image size: 12.0 GB	Docker image size: 12.1 GB
PaddlePaddle	3.0.0 beta2 including: Python 3.10 OpenMPI 4.1.7 GDRCopy 2.3 Paddle-TRT 2.4 SHARP 3.0.2	3.0.0 beta2 including: Python 3.10 OpenMPI 4.1.7 GDRCopy 2.3 Paddle-TRT 2.4 SHARP 3.0.2	2.6.1 including: Python 3.10 OpenMPI 4.1.7 GDRCopy 2.3 Paddle-TRT 2.4 SHARP 3.0.2	2.6.1 including: Python 3.10 OpenMPI 4.1.7 GDRCopy 2.3 Paddle-TRT 2.4 SHARP 3.0.2	2.6.1 including: Python 3.10 OpenMPI 4.1.7 GDRCopy 2.3 Paddle-TRT 2.4 SHARP 3.0.2	2.6.1 including: Python 3.10 OpenMPI 4.1.7 GDRCopy 2.3 Paddle-TRT 2.4 SHARP 3.0.2	2.6.0 including: Python 3.10 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 3.0.2	2.6.0 including: Python 3.10 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 3.0.2	2.6.0 including: Python 3.10 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 3.0.2	2.6.0 including: Python 3.10 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 3.0.2	2.5.2 including: Python 3.10 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 3.0.2	2.5.2 including: Python 3.10 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 2.6.0
	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only
	Docker image size: 14.1 GB	Docker image size: 14.2 GB	Docker image size: 11.8 GB	Docker image size: 11.8 GB	Docker image size: 11.7 GB	Docker image size: 11.3 GB	Docker image size: 10.0 GB	Docker image size: 9.93 GB	Docker image size: 9.58 GB	Docker image size: 9.55 GB	Docker image size: 8.94 GB	Docker image size: 9.01 GB
PyG	-	PyG 2.6.1 PyTorch2.6.0a0+df5bbc0including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.6.0a0 MAGMA 2.6.2 RAPIDS 24.10 JupyterLab 4.2.5 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.12	-	PyG 2.6.0 PyTorch2.5.0a0+b465a5843bincluding Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.5.0a0 MAGMA 2.6.2 RAPIDS 24.08 JupyterLab 4.2.5 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.10	-	PyG 2.6.0 PyTorch2.4.0a0+3bcc3cddb5including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.4.0a0 MAGMA 2.6.2 RAPIDS 24.04 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.8	-	PyG 2.6.0 PyTorch2.4.0a0+07cecf4including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.4.0a0 MAGMA 2.6.2 RAPIDS 24.04 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.6
	-	Multi arch support: x86, Arm SBSA	-	Multi arch support: x86, Arm SBSA	-	Multi arch support: x86, Arm SBSA	-	Multi arch support: x86, Arm SBSA
	-	Docker image size: 23.4 GB	-	Docker image size: 22.7 GB	-	Docker image size: 22.2 GB	-	Docker image size: 20.5 GB
PyTorch	2.6.0a0+df5bbc0including Python 3.10 APEX TensorBoard 2.16.2 Torch-TensorRT 2.6.0a0 MAGMA 2.6.2 RAPIDS 24.10 JupyterLab 4.2.5 including Jupyter-TensorBoard TensorRT Model Optimizer 0.17.1 TransformerEngine v1.13	2.6.0a0+df5bbc0including Python 3.10 APEX TensorBoard 2.16.2 Torch-TensorRT 2.6.0a0 MAGMA 2.6.2 RAPIDS 24.10 JupyterLab 4.2.5 including Jupyter-TensorBoard TensorRT Model Optimizer 0.17.1 TransformerEngine v1.12	2.5.0a0+e000cf0ad9including Python 3.10 APEX TensorBoard 2.16.2 Torch-TensorRT 2.5.0a0 MAGMA 2.6.2 RAPIDS 24.08 JupyterLab 4.2.5 including Jupyter-TensorBoard TensorRT Model Optimizer 0.17.1 TransformerEngine v1.11	2.5.0a0+b465a5843bincluding Python 3.10 APEX TensorBoard 2.16.2 Torch-TensorRT 2.5.0a0 MAGMA 2.6.2 RAPIDS 24.08 JupyterLab 4.2.5 including Jupyter-TensorBoard TensorRT Model Optimizer 0.15.1 TransformerEngine v1.10	2.5.0a0+872d972e41including Python 3.10 APEX TensorBoard 2.16.2 Torch-TensorRT 2.5.0a0 MAGMA 2.6.2 RAPIDS 24.06 JupyterLab 2.3.2 including Jupyter-TensorBoard TensorRT Model Optimizer 0.15.0 TransformerEngine v1.9	2.4.0a0+3bcc3cddb5including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.4.0a0 MAGMA 2.6.2 RAPIDS 24.04 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.8	2.4.0a0+f70bd71a48including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.4.0a0 MAGMA 2.6.2 RAPIDS 24.04 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.7	2.4.0a0+07cecf4including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.4.0a0 MAGMA 2.6.2 RAPIDS 24.04 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.6	2.3.0a0+6ddf5cf85eincluding Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.3.0a0 MAGMA 2.6.2 RAPIDS 24.02 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.5	2.3.0a0+40ec155e58including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.0.0.dev0 MAGMA 2.6.2 RAPIDS 24.02 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.4	2.3.0a0+ebedce2including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.0.0.dev0 MAGMA 2.6.2 RAPIDS 23.12 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.2.1	2.2.0a0+81ea7a4including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.0.0.dev0 MAGMA 2.6.2 RAPIDS 23.12 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine v1.2.1
	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA
	Docker image size: 21.7 GB	Docker image size: 21.8 GB	Docker image size: 21 GB	Docker image size: 21 GB	Docker image size: 20.4 GB	Docker image size: 18.32 GB	Docker image size: 19.2 GB	Docker image size: 18.8 GB	Docker image size: 20.0 GB	Docker image size: 19.8 GB	Docker image size: 22.2 GB	Docker image size: 22.0 GB
TensorFlow	2.16.1 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 24.10 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 4.2.5 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.16.1 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 24.10 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 4.2.5 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.16.1 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 24.08 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 4.2.5 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.16.1 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 24.08 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 4.2.5 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.16.1 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 24.06 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.16.1 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 24.04 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.16.1 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 24.04 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.15.0 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 24.02 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.15.0 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 24.02 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.15.0 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 24.02 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.15.0 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 23.12 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.14.0 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 23.12 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard
	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA
	Docker image size: 17.1 GB	Docker image size: 17.2 GB	Docker image size: 16.1 GB	Docker image size: 16.1 GB	Docker image size: 15.3 GB	Docker image size: 15.18 GB	Docker image size: 13.8 GB	Docker image size: 13.5 GB	Docker image size: 13.9 GB	Docker image size: 13.9 GB	Docker image size: 14.4 GB	Docker image size: 14.4 GB
TensorRT	TensorRT 10.7.0.23	TensorRT 10.6.0.26	TensorRT 10.5.0.18	TensorRT 10.4.0.26	TensorRT 10.3.0.26	TensorRT 10.2.0	TensorRT 10.1.0	TensorRT 10.0.1.6	TensorRT 8.6.3	TensorRT 8.6.3	TensorRT 8.6.3	TensorRT 8.6.1.6
	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA
	Docker image size: 9.21 GB	Docker image size: 9.3 GB	Docker image size: 9.17 GB	Docker image size: 9.13 GB	Docker image size: 9.07 GB	Docker image size: 9.60 GB	Docker image size: 7.56 GB	Docker image size: 7.51 GB	Docker image size: 7.16 GB	Docker image size: 7.15 GB	Docker image size: 7.05 GB	Docker image size: 7.46 GB
Triton Inference Server	Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.12 2.47 including PyTorch2.6.0a0+df5bbc0 TensorFlow 2.16.0 NVIDIA CUDA 12.6.3 NVIDIA cuBLAS 12.6.4.1 cuDNN 9.6.0.74 NVIDIA NCCL 2.23.4 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.6.0.26 OpenUCX 1.18.0 GDRCopy 2.3 NVIDIA HPC-X 2.21 OpenMPI 4.1.7 FIL NVIDIA DALI® 1.44 ONNX Runtime 1.20.1 Intel OpenVINO 2024.05.0 DCGM 3.2.6 TensorRT-LLM version release/0.16.0 vLLM version 0.5.5 nvImageCodec 0.2.0.7	Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.12 2.47 including PyTorch2.6.0a0+df5bbc0 TensorFlow 2.16.0 NVIDIA CUDA 12.6.3 NVIDIA cuBLAS 12.6.4.1 cuDNN 9.5.1.17 NVIDIA NCCL 2.23.4 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.6.0.26 OpenUCX 1.18.0 GDRCopy 2.3 NVIDIA HPC-X 2.21 OpenMPI 4.1.7 FIL NVIDIA DALI® 1.43 ONNX Runtime 1.19.2 Intel OpenVINO 2024.0.0 DCGM 3.2.6 TensorRT-LLM version release/0.15.0 vLLM version 0.5.5 nvImageCodec 0.2.0.7	Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.47 including PyTorch2.5.0a0+e000cf0ad9 TensorFlow 2.16.0 NVIDIA CUDA 12.6.2 NVIDIA cuBLAS 12.6.3.3 cuDNN 9.5.050. NVIDIA NCCL 2.22.3 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.5.0.18 OpenUCX 1.17.0 GDRCopy 2.3 NVIDIA HPC-X 2.20 OpenMPI 4.1.7 FIL NVIDIA DALI® 1.42 ONNX Runtime 1.19.2 Intel OpenVINO 2024.0.0 DCGM 3.2.6 TensorRT-LLM version release/0.13.0 vLLM version 0.5.3 post1 nvImageCodec 0.2.0.7	Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.47 including PyTorch2.5.0a0+b465a5843b TensorFlow 2.16.0 NVIDIA CUDA 12.6.1 NVIDIA cuBLAS 12.6.3.1 cuDNN 9.4.0.58 NVIDIA NCCL 2.22.3 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.4.0.26 OpenUCX 1.15.0 GDRCopy 2.3 NVIDIA HPC-X 2.20 OpenMPI 4.1.7 FIL NVIDIA DALI® 1.41 ONNX Runtime 1.19.2 Intel OpenVINO 2024.0.0 DCGM 3.2.6 TensorRT-LLM version release/0.13.0 vLLM version 0.5.3 post1 nvImageCodec 0.2.0.7	Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.47 including PyTorch2.5.0a0+872d972e41 TensorFlow 2.16.0 NVIDIA CUDA 12.6 NVIDIA cuBLAS 12.6.0.22 cuDNN 9.3.0.75 NVIDIA NCCL 2.22.3 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.3.0.26 OpenUCX 1.15.0 GDRCopy 2.3 NVIDIA HPC-X 2.19 OpenMPI 4.1.7 FIL NVIDIA DALI® 1.40 ONNX Runtime 1.18.1 Intel OpenVINO 2024.0.0 DCGM 3.2.6 TensorRT-LLM version release/0.12.0 vLLM version 0.5.3 post1 nvImageCodec 0.2.0.7	Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.47 including PyTorch2.4.0a0+3bcc3cddb5 TensorFlow 2.16.0 NVIDIA CUDA 12.5.1 NVIDIA cuBLAS 12.5.3.2 cuDNN 9.2.1.18 NVIDIA NCCL 2.22.3 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.2.0.19 OpenUCX 1.15.0 GDRCopy 2.3 NVIDIA HPC-X 2.19 OpenMPI 4.1.7 FIL NVIDIA DALI® 1.39 ONNX Runtime 1.18.1 Intel OpenVINO 2024.0.0 DCGM 3.2.6 TensorRT-LLM version release/0.11.0 vLLM version 0.5.0 post1 nvImageCodec 0.2.0.7	Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.47 including PyTorch 2.4.0a0+f70bf71 TensorFlow 2.15.0 NVIDIA CUDA 12.5.0.23 NVIDIA cuBLAS 12.5.2.13 cuDNN 9.1.0.70 NVIDIA NCCL 2.21.5 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.1.0.27 OpenUCX 1.15.0 GDRCopy 2.3 NVIDIA HPC-X 2.19 OpenMPI 4.1.4+ FIL NVIDIA DALI® 1.38 ONNX Runtime 1.18.0 Intel OpenVINO 2024.0.0 DCGM 3.2.6 TensorRT-LLM version release/0.10.0 vLLM version 0.4.3 nvImageCodec 0.2.0.7	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.46 including PyTorch 2.4.0a0+07cecf4 TensorFlow 2.15.0 NVIDIA CUDA 12.4.1 NVIDIA cuBLAS 12.4.5.8 cuDNN 9.1.0.70 NVIDIA NCCL 2.21.5 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 10.0.1.6 OpenUCX 1.15.0 GDRCopy 2.3 NVIDIA HPC-X 2.19 OpenMPI 4.1.4+ FIL NVIDIA DALI® 1.37 ONNX Runtime 1.18.0 Intel OpenVINO 2023.3.0 DCGM 3.2.6 TensorRT-LLM version release/0.9 vLLM version 0.4.0 post1 nvImageCodec 0.2.0.7	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.45 including PyTorch 2.3.0a0+6ddf5cf85e TensorFlow 2.15.0 NVIDIA CUDA 12.4.1 NVIDIA cuBLAS 12.4.5.8 cuDNN 9.1.0.70 NVIDIA NCCL 2.21.5 (optimized for NVIDIA NVLink®) Amazon Labs Sockeye sequence-to-sequence framework 2.3.14 (for machine translation) NVIDIA TensorRT™ 8.6.3 OpenUCX 1.15.0 GDRCopy 2.3 NVIDIA HPC-X 2.18 OpenMPI 4.1.4+ FIL NVIDIA DALI® 1.36 ONNX Runtime 1.17.3 Intel OpenVINO 2023.3.0 DCGM 3.2.6 TensorRT-LLM version release/0.9 vLLM version 0.4.0 post1 nvImageCodec 0.2.0.7	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.43 including PyTorch 2.3.0a0+40ec155e58 TensorFlow 2.15.0 Intel OpenVINO 2023.3.0 DCGM 2.4.7 FIL TensorRT-LLM release/0.8 vLLM 0.3.0	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.43 including PyTorch 2.3.0a0+ebedce2 TensorFlow 2.15.0 Intel OpenVINO 2023.3.0 DCGM 2.4.7 FIL TensorRT-LLM release/0.8 vLLM 0.3.0	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.41 including PyTorch 2.2.0a0+81ea7a4 TensorFlow 2.12.0 ONNX Runtime 1.16.3 Intel OpenVINO 2023.0.0 DCGM 2.4.7 FIL TensorRT-LLM release/0.7.1 vLLM 0.2.3
	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA
	Docker image size: 17 .7GB	Docker image size: 17 .4GB	Docker image size: 17 GB	Docker image size: 16.9 GB	Docker image size: 16.8 GB	Docker image size: 15.98 GB	Docker image size: 15.5 GB	Docker image size: 15.3 GB	Docker image size: 14.8 GB	Docker image size: 14.9 GB	Docker image size: 13.8 GB	Docker image size: 14.7 GB
TensorFlow For Jetson	2.17.0	2.17.0	2.16.1	2.16.1	2.16.1	2.16.0	2.15.0	2.15.0	2.15.0	2.15.0	2.15.0
PyTorch for Jetson	2.6.0a0+df5bbc0	2.6.0a0+df5bbc0	2.5.0a0+e000cf0ad9	2.5.0a0+b465a5843b	2.5.0a0+872d972e41	2.4.0a0+3bcc3cddb5	2.4.0a0+f70bf71	2.4.0a0+07cecf4	2.3.0a0+6ddf5cf85e	2.3.0a0+40ec155e58	2.3.0a0+ebedce2
Triton for Jetson	2.53.0	2.52.0	2.51.0	2.50.0	2.49.0	2.48.0	2.47.0	2.46.0	2.45.0	2.44.0	2.43.0

3. 23.xx Framework Containers Support Matrix

Important:

Content that is included in <<>> brackets indicates new content from the previously published version.

Note:

The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 23.01 release of an image was released in January 2023.

23.xx container images

Container Image	23.12	23.11	23.10	23.09	23.08	23.07	23.06	23.05	23.04	23.03		23.02		23.01
DGX
DGX System	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100		NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100		NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100
Operating System	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹⁹ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9²⁰ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹ EL9¹ Red Hat Enterprise Linux 8 / CentOS 8¹ (All DGX systems except DGX Station A100) EL8-20.11+¹	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9¹ EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹	4.1+²¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹	4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7¹ EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹	4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹
System Requirements
NVIDIA Driver	Release 23.12 is based on CUDA 12.3.2, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R460, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 23.11 is based on CUDA 12.3.0, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R460, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.	Release 23.10 is based on CUDA 12.2.2, which requires NVIDIA Driver release 535 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), or 525.85 (or later R525), or 535.86 (or later R535). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.2. ²²	Release 23.09 is based on CUDA 12.2.1, which requires NVIDIA Driver release 535 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), or 525.85 (or later R525), or 535.86 (or later R535). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.2. ³	Release 23.08 is based on CUDA 12.2.1, which requires NVIDIA Driver release 535 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), or 525.85 (or later R525), or 535.86 (or later R535). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.2. ³	Release 23.07 is based on CUDA 12.1.1, which requires NVIDIA Driver release 530 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), 525.85 (or later R525), or 530.30 (or later R530). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.1. ³	Release 23.06 is based on CUDA 12.1.1, which requires NVIDIA Driver release 530 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), 525.85 (or later R525), or 530.30 (or later R530). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.1. ³	Release 23.05 is based on CUDA 12.1.1, which requires NVIDIA Driver release 530 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), 525.85 (or later R525), or 530.30 (or later R530). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.1. ³	Release 23.04 is based on CUDA 12.1.0, which requires NVIDIA Driver release 530 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), 525.85 (or later R525), or 530.30 (or later R530). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.0. ³	Release 23.03 is based on CUDA 12.1.0, which requires NVIDIA Driver release 530 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), 525.85 (or later R525), or 530.30 (or later R530). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.0. ³		Release 23.02 is based on CUDA 12.0.1, which requires NVIDIA Driver release 525 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), or 525.85 (or later R525). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.0. ³		Release 23.01 is based on CUDA 12.0.1, which requires NVIDIA Driver release 525 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), or 525.85 (or later R525). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.0. ³
GPU Model	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™ NVIDIA Pascal Note: Starting with the 23.06 release, containers will no longer be tested on Pascal GPU architecture.	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™ NVIDIA Pascal	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™ NVIDIA Pascal		NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™ NVIDIA Pascal		NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™ NVIDIA Pascal
Base Container Image (included in all containers)
Container OS	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 22.04	Ubuntu 20.04	Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04
CUDA	NVIDIA CUDA 12.3.2	NVIDIA CUDA 12.3.0	NVIDIA CUDA 12.2.2	NVIDIA CUDA 12.2.1	NVIDIA CUDA 12.2.1	NVIDIA CUDA 12.1.1	NVIDIA CUDA 12.1.1	NVIDIA CUDA 12.1.1	NVIDIA CUDA 12.1.0	NVIDIA CUDA 12.1.0		NVIDIA CUDA 12.0.1		NVIDIA CUDA 12.0.1
cuBLAS	NVIDIA cuBLAS 12.3.4.1	NVIDIA cuBLAS 12.3.2.1	NVIDIA cuBLAS 12.2.5.6	NVIDIA cuBLAS 12.2.5.6	NVIDIA cuBLAS 12.2.5.1	NVIDIA cuBLAS 12.1.3.1	NVIDIA cuBLAS 12.1.3.1	NVIDIA cuBLAS 12.1.3.1	NVIDIA cuBLAS 12.1.3	cuBLAS from CUDA 12.1.0		12.0.2 from CUDA		12.0.2 from CUDA
cuDNN	8.9.7.29	8.9.6.50	8.9.5	8.9.5	8.9.4	8.9.3	8.9.2	8.9.1.23	8.9.0	8.8.1.3		8.7.0		8.7.0
cuTENSOR	1.7.0.1	1.7.0.1	1.7.0.1	1.7.0.1	1.7.0.1	1.7.0.1	1.7.0.1	1.7.0.1	1.7.0	1.6.2.3		1.6.2.3		1.6.2.3
DALI	1.32.0	1.31.0	1.30.0	1.29.0	1.28.0	1.27.0	1.26.0	1.25.0	1.24.0	1.23.0		1.22.0		1.21.0
NCCL	2.19.3	2.19.3	2.19.3	2.18.5	2.18.3	2.18.3	2.18.1	2.18.1	2.17.1	2.17.1		2.16.5		2.16.5
TensorRT	TensorRT 8.6.1.6	TensorRT 8.6.1.6	TensorRT 8.6.1.6	TensorRT 8.6.1.6	TensorRT 8.6.1.6	TensorRT 8.6.1.6	TensorRT 8.6.1.6	TensorRT 8.6.1.2	TensorRT 8.6.1	TensorRT 8.5.3		TensorRT 8.5.3		TensorRT 8.5.2.2
rdma-core	39.0	39.0	39.0	39.0	39.0	39.0	39.0	36.0	36.0	36.0		36.0		36.0
NVIDIA HPC-X	2.16 with OpenMPI 4.1.4+ OpenUCX 1.15.0	2.16 with OpenMPI 4.1.4+ OpenUCX 1.15.0	2.16 with OpenMPI 4.1.4+ OpenUCX 1.15.0	2.16 with OpenMPI 4.1.4+ OpenUCX 1.15.0	2.15 with OpenMPI 4.1.4+ OpenUCX 1.15.0	2.15 with OpenMPI 4.1.4+ OpenUCX 1.15.0	2.15 with OpenMPI 4.1.4+ OpenUCX 1.15.0	2.14 with OpenMPI 4.1.4+ OpenUCX 1.14.0	2.13 with OpenMPI 4.1.4+ OpenUCX 1.14.0	2.13 with OpenMPI 4.1.4+ OpenUCX 1.14.0		2.13 with OpenMPI 4.1.4+ OpenUCX 1.14.0		2.13 with OpenMPI 4.1.4+ OpenUCX 1.14.0
GDRcopy	2.3	2.3	2.3	2.3	2.3	2.3	2.3	2.3	2.3	2.3		2.3		2.3
Nsight Compute	2023.3.1.1	2023.3.0.12	2023.2.1.3	2023.2.1.3	2023.2.1.3	2023.1.1.4	2023.1.1.4	2023.1.1.4	2023.1.0.15	2023.1.0.15		2022.4.1.6		2022.4.1.6
Nsight Systems	2023.4.1	2023.3.1.92	2023.3.1.92	2023.3.1.92	2023.2.3.1001	2023.2.3.1001	2023.2.3.1001	2023.2	2023.1.1.127	2023.1.1.127		2022.5.1		2022.5.1
NVIDIA Optimized Frameworks
DGL	-	1.1.1 including: PyTorch 23.11 APEX MAGMA 2.6.2 TensorBoard 2.12.0 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.12	-	1.1.1 including: PyTorch 23.09 APEX MAGMA 2.6.2 TensorBoard 2.12.0 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.12	-	1.1.1 including: PyTorch 23.07 APEX MAGMA 2.6.2 TensorBoard 2.12.0 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.10.0+96ed6fc	-	-	-	-		-		-
				Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA
				Docker image size: 23.4 GB		Docker image size: 20.8 GB
JAX	-	-	Python 3.10.12 Nsight Compute 2023.2.2.3 Nsight Systems 2023.3.1.92 NVIDIA CUDA 12.2.0 NVIDIA DALI 1.30.0 in T5x containers Mellanox OFED 5.9-0.5.6.0 Transformer Engine 1.0.0.dev0+1f4c397 in Paxml container 0.13.0+511241c in T5X container	-	Python 3.10.12 Nsight Compute 2023.2.0 Nsight Systems 2023.2.0 NVIDIA CUDA 12.1.1 NVIDIA DALI 1.28.0 in Paxml and T5x containers Mellanox OFED 5.9-0.5.6.0 Transformer Engine 0.13.0.dev0+03202c3
JAX			Multi arch support: x86 only		Multi arch support: x86 only
Kaldi	-	26b9f648 Python 3.10	26b9f648 Python 3.10	26b9f648 Python 3.10	26b9f648 Python 3.10	26b9f648 Python 3.10	26b9f648 Python 3.10	26b9f648 Python 3.10	26b9f648 Python 3.8	26b9f648 Python 3.8		26b9f648 Python 3.8		26b9f648 Python 3.8
		Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only
		Docker image size: 9.13 GB	Docker image size: 9.1 GB	Docker image size: 9.16 GB	Docker image size: 9.36 GB	Docker image size: 9.14 GB	Docker image size: 9.29 GB	Docker image size: 9.19 GB	Docker image size: 10.9 GB	Docker image size: 11.1 GB		Docker image size: 11.8 GB		Docker image size: 11.1 GB
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet	1.9.1 including: Horovod 0.28 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: Horovod 0.28 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: Horovod 0.28 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: Horovod 0.28 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: Horovod 0.28 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: Horovod 0.28 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: Horovod 0.26.1 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: rdma-core 32.1 Horovod 0.26.1 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: rdma-core 32.1 Horovod 0.26.1 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: rdma-core 32.1 Horovod 0.26.1 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4	1.9.1 including: rdma-core 32.1 Horovod 0.26.1 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4		1.9.1 including: rdma-core 32.1 Horovod 0.26.1 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4
	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA
	Docker image size: 12.1 GB	Docker image size: 12.1 GB	Docker image size: 12 GB	Docker image size: 12.1 GB	Docker image size: 12.1 GB	Docker image size: 12.1 GB	Docker image size: 12.0 GB	Docker image size: 12.1 GB	Docker image size: 13.1 GB	Docker image size: 13.2 GB	Docker image size: 13.9 GB		Docker image size: 13.1 GB
PaddlePaddle	2.5.2 including: Python 3.10 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 2.6.0	2.5.2 including: Python 3.10 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 2.6.0	2.5.1 including: Python 3.10 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 2.6.0	2.5.0 including: Python 3.10 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 2.6.0	2.5.0 including: Python 3.10 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 2.6.0	2.4.1 including: Python 3.10 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 2.6.0	2.4.1 including: Python 3.10 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 2.6.0	No 23.05 release.	2.4.1 including: Python 3.8 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 2.6.0	2.4.1 including: Python 3.8 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 2.6.0		2.4.0 including: Python 3.8 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.4 SHARP 2.6.0		2.3.2 including: Python 3.8 OpenMPI 4.1.4+ GDRCopy 2.3 Paddle-TRT 2.3.2 SHARP 2.6.0
	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	Multi arch support: x86 only	-	Multi arch support: x86 only	Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only
	Docker image size: 8.98 GB	Docker image size: 8.98 GB	Docker image size: 8.94 GB	Docker image size: 8.99 GB	Docker image size: 9.02 GB	Docker image size: 8.58 GB	Docker image size: 8.59 GB	-	Docker image size: 9.47 GB	Docker image size: 9.74 GB		Docker image size: 10.5 GB		Docker image size: 9.41 GB
PyTorch	2.2.0a0+81ea7a48including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.0.0.dev0 MAGMA 2.6.2 RAPIDS 23.10 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.12+170797	2.2.0a0+6a974be including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.0.0.dev0 MAGMA 2.6.2 RAPIDS 23.10 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.12+170797	2.1.0a0+32f93b1 including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.0.0.dev0 MAGMA 2.6.2 RAPIDS 23.08 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.12+170797	2.1.0a0+32f93b1 including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.0.0.dev0 MAGMA 2.6.2 RAPIDS 23.08 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.12	2.1.0a0+29c30b1 including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 2.0.0.dev0 MAGMA 2.6.2 RAPIDS 23.06 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.11.0++3f01b4f	2.1.0a0+b5021ba including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 1.5.0.dev0 MAGMA 2.6.2 RAPIDS 23.06 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.10.0+96ed6fc	2.1.0a0+4136153 including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 1.5.0.dev0 MAGMA 2.6.2 RAPIDS 23.04 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.9	2.0.0 including Python 3.10 APEX TensorBoard 2.9.0 Torch-TensorRT 1.4.0.dev0 MAGMA 2.6.2 RAPIDS 23.04 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.8	2.1.0a0+fe05266f including Python 3.8 APEX TensorBoard 2.9.0 Torch-TensorRT 1.4.0.dev0 MAGMA 2.6.2 RAPIDS 23.02 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.7	2.0.0a0+1767026 including Python 3.8 APEX TensorBoard 2.9.0 Torch-TensorRT 1.1.0a0 MAGMA 2.6.2 RAPIDS 23.02 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.6.0		1.14.0a0+410ce96 including Python 3.8 APEX TensorBoard 2.9.0 Torch-TensorRT 1.1.0a0 MAGMA 2.6.2 RAPIDS 22.12.1 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.5.0		1.14.0a0+410ce96 including Python 3.8 APEX TensorBoard 2.9.0 Torch-TensorRT 1.1.0a0 MAGMA 2.6.2 RAPIDS 22.12.1 JupyterLab 2.3.2 including Jupyter-TensorBoard PyTorch quantization wheel v2.1.2 TransformerEngine 0.3.0
	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA
	Docker image size: 21.9 GB	Docker image size: 21.9 GB	Docker image size: 22.1 GB	Docker image size: 22.0 GB	Docker image size: 20.6 GB	Docker image size: 19.8 GB	Docker image size: 19.7 GB	Docker image size: 22 GB	Docker image size: 20.4 GB	Docker image size: 20.4 GB		Docker image size: 20.5 GB		Docker image size: 19.7 GB
TensorFlow	2.14.0 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 23.10 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.14.0 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 23.10 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.13.0 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 23.08 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.4.10 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.13.0 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 23.08 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.13.0 including Python 3.10 Horovod 0.28.1 TensorBoard 2.13.0 RAPIDS 23.06 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.12.0 including Python 3.10 Horovod 0.28.1 TensorBoard 2.12.0 RAPIDS 23.06 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.12.0 including Python 3.10 Horovod 0.28.0 TensorBoard 2.12.0 RAPIDS 23.04 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.12.0 including Python 3.10 Horovod 0.27.0 TensorBoard 2.12.0 RAPIDS 23.04 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.12.0 including Python 3.8 Horovod 0.27.0 TensorBoard 2.12.0 RAPIDS 23.02 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	2.11.0 including Python 3.8 Horovod 0.27.0 TensorBoard 2.9.0 RAPIDS 23.02 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.27.0 TensorBoard 1.15.5 RAPIDS 23.02 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.11.0 including Python 3.8 Horovod 0.26.1 TensorBoard 2.9.0 RAPIDS 22.12.0 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.26.1 TensorBoard 1.15.5 RAPIDS 22.12.0 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.11.0 including Python 3.8 Horovod 0.24.3 TensorBoard 2.9.0 RAPIDS 22.12.0 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.24.3 TensorBoard 1.15.5 RAPIDS 22.12.0 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard
	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA
	Docker image size: 14.3 GB	Docker image size: 14.1 GB	Docker image size: 14.2 GB	Docker image size: 14.2 GB	Docker image size: 14.2 GB	Docker image size: 13.9 GB	Docker image size: 14.3 GB	Docker image size: 14.2 GB	Docker image size: 15.4 GB	Docker image size: 15.9 GB	Docker image size: 16.3 GB	Docker image size: 16.6 GB	Docker image size: 17.0 GB	Docker image size: 15.9 GB	Docker image size: 16.2 GB
TensorRT	TensorRT 8.6.1.6	TensorRT 8.6.1.6	TensorRT 8.6.1.6	TensorRT 8.6.1.6	TensorRT 8.6.1.6	TensorRT 8.6.1.6	TensorRT 8.6.1.6	TensorRT 8.6.1.2	TensorRT 8.6.1	TensorRT 8.5.3		TensorRT 8.5.3		TensorRT 8.5.2.2
	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA
	Docker image size: 7.45 GB	Docker image size: 7.45 GB	Docker image size: 7.41 GB	Docker image size: 7.47 GB	Docker image size: 7.5 GB	Docker image size: 7.45 GB	Docker image size: 7.45 GB	Docker image size: 7.5 GB	Docker image size: 8.05 GB	Docker image size: 8.32 GB		Docker image size: 9.03 GB		Docker image size: 8.3 GB
Triton Inference Server	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.41 including PyTorch 2.2.0a0+81ea7a48 TensorFlow 2.12.0 ONNX Runtime 1.16.3 Intel OpenVINO 2023.0.0 DCGM 2.4.7 FIL TensorRT-LLM release/0.7.0 vLLM 0.2.3	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.40 including PyTorch 2.2.0a0+6a974be TensorFlow 2.12.0 ONNX Runtime 1.16.3 Intel OpenVINO 2023.0.0 DCGM 2.4.7 FIL TensorRT-LLM release/0.6.0 vLLM 0.2.0	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.39 including PyTorch 2.1.0a0+32f93b1 TensorFlow 2.12.0 ONNX Runtime 1.16.0 Intel OpenVINO 2023.0.0 DCGM 2.4.7 FIL TensorRT-LLM release/0.5.0 vLLM 0.2.0	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.38 including PyTorch 2.1.0a0+32f93b1 TensorFlow 2.12.0 ONNX Runtime 1.16.0 Intel OpenVINO 2023.0.0 DCGM 2.4.7 FIL	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.37 including PyTorch 2.1.0a0+29c30b1 TensorFlow 2.12.0 ONNX Runtime 1.15.1 Intel OpenVINO 2023.0.0 DCGM 2.4.7 FIL	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.36 including PyTorch 2.1.0a0+b5021ba TensorFlow 2.12.0 ONNX Runtime 1.15.0 Intel OpenVINO 2023.0.0 DCGM 2.4.7 FIL	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.35 including PyTorch 2.1.0a0+4136153 TensorFlow 2.12.0 ONNX Runtime 1.15.0 Intel OpenVINO 2022.1.0 DCGM 2.4.7 FIL	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU Python 3.10 2.34 including PyTorch 2.0.0 TensorFlow 2.12.0 ONNX Runtime 1.15.0 Intel OpenVINO 2022.1.0 DCGM 2.4.7 FIL	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.33 including PyTorch 2.1.0a0+fe05266f TensorFlow 2.12.0 ONNX Runtime 1.14.1 Intel OpenVINO 2022.1.0 DCGM 2.2.9 FIL	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.32 including PyTorch 2.0.0a0+1767026 TensorFlow 2.11.0 ONNX Runtime 1.14.1 Intel OpenVINO 2022.1.0 DCGM 2.2.9 FIL		In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.31 including PyTorch 1.14.0a0+44dac51 TensorFlow 1.15.5 TensorFlow 2.11.0 ONNX Runtime 1.13.1 Intel OpenVINO 2022.1.0 DCGM 2.2.9 FIL		In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.30 including PyTorch 1.14.0a0+44dac51 TensorFlow 1.15.5 TensorFlow 2.11.0 ONNX Runtime 1.13.1 Intel OpenVINO 2022.1.0 DCGM 2.2.9 FIL
	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA
	Docker image size: 14.7 GB	Docker image size: 14.3 GB	Docker image size: 12.6 GB	Docker image size: 12.6 GB	Docker image size: 12.4 GB	Docker image size: 12.3 GB	Docker image size: 12.3 GB	Docker image size: 12.5 GB	Docker image size: 13 GB	Docker image size: 14.7 GB		Docker image size: 15.3 GB		Docker image size: 15.3 GB
TensorFlow For Jetson	TensorFlow 2.14.0 for Jetson						TensorFlow 2.12.0 for Jetson	TensorFlow 2.12.0 for Jetson	TensorFlow 2.12.0 for Jetson	TensorFlow 1.15.5 and 2.10.1 for Jetson		TensorFlow 1.15.5 and 2.10.1 for Jetson		TensorFlow 1.15.5 and 2.10.1 for Jetson
PyTorch for Jetson							2.1.0a0+4136153 for Jetson	2.0.0 for Jetson	2.1.0a0+fe05266f for Jetson	PyTorch 2.0.0a0+1767026 for Jetson		PyTorch 1.14.0a0+44dac51 for Jetson		PyTorch 1.14.0a0+44dac51 for Jetson
TensorFlow Wheel for x86						-	-	-	-	TensorFlow 1.15.5 for x86		TensorFlow 1.15.5 for x86		TensorFlow 1.15.5 for x86
Triton for Jetson						Triton Inference Server 2.36.0 for Jetson AGX Xavier	Triton Inference Server 2.35.0 for Jetson AGX Xavier	Triton Inference Server 2.34.0 for Jetson AGX Xavier	Triton Inference Server 2.33.0 for Jetson AGX Xavier	Triton Inference Server 2.32.0 for Jetson AGX Xavier		Triton Inference Server 2.31.0 for Jetson AGX Xavier		Triton Inference Server 2.30.0 for Jetson AGX Xavier

4. 22.xx Framework Containers Support Matrix

Important:

Content that is included in <<>> brackets indicates new content from the previously published version.

Note:

The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 22.03 release of an image was released in March 2022.

22.xx container images

Container Image	22.12		22.11		22.10		22.09		22.08		22.07		22.06		22.05		22.04		22.03		22.02		22.01
DGX
DGX System	NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100		NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100		NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100		NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100 NVIDIA DGX H100		NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100		NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100		NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100		NVIDIA DGX-1™ DGX-2™ NVIDIA DGX™ A100 DGX Station DGX Station A100		DGX-1 DGX-2 NVIDIA DGX A100 DGX Station DGX Station A100		DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100		DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100		DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100
Operating System	4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		DGX OS 4.1+1 (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0+ Red Hat Enterprise Linux 7 / CentOS 72 EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 5.1 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹
System Requirements
NVIDIA Driver	Release 22.12 is based on CUDA 11.8.0, which requires NVIDIA Driver release 520 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 515.65 (or later R515). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.8. ³		Release 22.11 is based on CUDA 11.8.0, which requires NVIDIA Driver release 520 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 515.65 (or later R515). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.8. ³		Release 22.10 is based on CUDA 11.8.0, which requires NVIDIA Driver release 520 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 515.65 (or later R515). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.8. ³		Release 22.09 is based on CUDA 11.8.0, which requires NVIDIA Driver release 520 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 515.65 (or later R515). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.8. ³		Release 22.08 is based on CUDA 11.7.1, which requires NVIDIA Driver release 515 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), or 510.47 (or later R510). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.8. ³		Release 22.07 is based on CUDA 11.7 Update 1 Preview, which requires NVIDIA Driver release 515 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), or 510.47 (or later R510). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.7. ³		Release 22.06 is based on CUDA 11.7 Update 1 Preview, which requires NVIDIA Driver release 515 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), or 510.47 (or later R510). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.7. ³		Release 22.05 is based on CUDA 11.7, which requires NVIDIA Driver release 515 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), or 510.47 (or later R510). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.7. ³		Release 22.04 is based on NVIDIA CUDA® 11.6.2, which requires NVIDIA Driver release 510 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). The CUDA driver's compatibility package only supports particular drivers. ³		Release 22.03 is based on NVIDIA CUDA® 11.6.1, which requires NVIDIA Driver release 510 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). The CUDA driver's compatibility package only supports particular drivers. ³		Release 22.02 is based on NVIDIA CUDA 11.6.0, which requires NVIDIA Driver release 510 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). The CUDA driver's compatibility package only supports particular drivers. 3		Release 22.01 is based on NVIDIA CUDA 11.6.0, which requires NVIDIA Driver release 510 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). The CUDA driver's compatibility package only supports particular drivers. ³
GPU Model	NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™ NVIDIA Pascal		NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™ NVIDIA Pascal		NVIDIA Ada Lovelace Architecture NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™ NVIDIA Pascal		NVIDIA Hopper™ Architecture NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™ NVIDIA Pascal		NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™ NVIDIA Pascal		NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™ NVIDIA Pascal		NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™ NVIDIA Pascal		NVIDIA Ampere GPU Architecture NVIDIA Turing™ NVIDIA Volta™ NVIDIA Pascal		Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal
Base Container Image (included in all containers)
Container OS	Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04
CUDA	NVIDIA CUDA 11.8.0		NVIDIA CUDA 11.8.0		NVIDIA CUDA 11.8.0		NVIDIA CUDA 11.8.0		NVIDIA CUDA 11.7 Update 1		NVIDIA CUDA 11.7 Update 1 Preview		NVIDIA CUDA 11.7 Update 1 Preview		NVIDIA CUDA 11.7.0		NVIDIA CUDA 11.6.2		NVIDIA CUDA 11.6.1		NVIDIA CUDA 11.6.0		NVIDIA CUDA 11.6.0
cuBLAS	11.11.3.6		11.11.3.6		11.11.3.6		11.11.3.6		11.10.3.66		11.10.3.66		11.10.3.66		11.10.1.25		11.9.3.115		11.8.1.74		11.8.1.74		11.8.1.74
cuDNN	8.7.0 GA		8.7.0.80		8.6.0.163		8.6.0.163		8.5.0.96		8.4.1		8.4.1		8.4.0.27		8.4.0.27		8.3.3.40		8.3.2.44		8.3.2.44
cuTENSOR	1.6.1.5		1.6.1.5		1.6.1.5		1.6.1.5		1.6.0.2		1.5.0.3		1.5.0.3		1.5.0.3		1.5.0.3		1.5.0.1		1.4		1.4
DALI	1.20.0		1.18.0		1.18.0		1.17.0		1.16.0		1.15.0		1.14.0		1.13.0		1.12.0		1.11.1		1.10.0		1.9.0
NCCL	2.15.5		2.15.5		2.15.5		2.15.1		2.12.12		2.12.12		2.12.12		2.12.10		2.12.10		2.12.9		2.11.4		2.11.4
TensorRT	TensorRT 8.5.1		TensorRT 8.5.1		TensorRT 8.5.0.12		TensorRT 8.5.0.12		TensorRT 8.4.2.4		TensorRT 8.4.1		TensorRT 8.2.5		TensorRT 8.2.5		TensorRT 8.2.4.2		TensorRT 8.2.3		TensorRT 8.2.3		TensorRT 8.2.2
rdma-core	36.0		36.0		36.0		36.0		36.0		36.0		36.0		36.0		36.0		36.0		36.0		36.0
NVIDIA HPC-X	2.13 with OpenMPI 4.1.4+ OpenUCX 1.14.0		2.12.2tp1 with OpenMPI 4.1.4+ OpenUCX 1.14.0		2.12.2tp1 with OpenMPI 4.1.4+ OpenUCX 1.14.0		2.12.1a0 with OpenMPI 4.1.2rc4+ OpenUCX 1.12.0		2.10 with OpenMPI 4.1.2rc4+ OpenUCX 1.12.0		2.10 with OpenMPI 4.1.2rc4+ OpenUCX 1.12.0		2.10 with OpenMPI 4.1.2rc4+ OpenUCX 1.12.0		2.10 with OpenMPI 4.1.2rc4+ OpenUCX 1.12.0		2.10 with OpenMPI 4.1.2rc4+ OpenUCX 1.12.0		2.10 with OpenMPI 4.1.2rc4+ OpenUCX 1.12.0		2.10 with OpenMPI 4.1.2rc4+ OpenUCX 1.12.0		2.10 with OpenMPI 4.1.2rc4+ OpenUCX 1.12.0
GDRcopy	2.3		2.3		2.3		2.3		2.3		2.3		2.3		2.3		2.3		2.3		2.3		2.3
Nsight Systems	2022.4.2.1		2022.4.2.1		2022.4.2.1		2022.4.1		2022.1.3.18		2022.1.3.3		2022.1.3.3		2022.1.3.3		2022.2.1.31-5fe97ab		2021.5.2.53		2021.5.2.53		2021.5.2.53
NVIDIA Optimized Frameworks
Kaldi	26b9f648 Python 3.8		26b9f648 Python 3.8		26b9f648 Python 3.8		26b9f648 Python 3.8		26b9f648 Python 3.8		26b9f648 Python 3.8		26b9f648 Python 3.8		26b9f648 Python 3.8		26b9f648 Python 3.8		26b9f648 Python 3.8		26b9f648 Python 3.8		26b9f648 Python 3.8
	Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only
	Docker image size: 10.4 GB		Docker image size: 10.3 GB		Docker image size: 10.3 GB		Docker image size: 10.3 GB		Docker image size: 8.89 GB		Docker image size: 9.07 GB		Docker image size: 9 GB		Docker image size: 9.11 GB		Docker image size: 9.01 GB		Docker image size: 9 GB		Docker image size: 9 GB		Docker image size: 8.96 GB
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet	1.9.1 including: rdma-core 32.1 Nsight Compute 2022.3.0.22 Horovod 0.26.1 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4		1.9.1 including: rdma-core 32.1 Nsight Compute 2022.3.0.22 Horovod 0.24.2 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4		1.9.1 including: rdma-core 32.1 Nsight Compute 2022.3.0.22 Horovod 0.24.2 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4		1.9.1 including: rdma-core 32.1 Nsight Compute 2022.3.0.22 Horovod 0.24.2 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 JupyterLab 2.2.10 with updated mistune 2.0.4		1.9.1 including: rdma-core 32.1 Nsight Compute 2022.2.1.0 Horovod 0.24.2 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.2.10 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.9.1 including: rdma-core 32.1 Nsight Compute 2022.2.1.0 Horovod 0.24.2 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.2.10 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.9.1 including: rdma-core 32.1 Nsight Compute 2022.1.1.2 Horovod 0.24.2 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.2.10 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.9.0.rc6 including: rdma-core 32.1 Nsight Compute 2022.1.1.2 Horovod 0.24.2 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.2.10 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.9.0.rc6 including: rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.12 NVIDIA HPC-X 2.10 Nsight Compute 2022.1.1.2 Nsight Systems 2022.2.1.31-5fe97ab Horovod 0.24.2 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 8.2.4.2 DALI 1.12.0 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.2.10 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.9.0.rc6 including: rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.12 NVIDIA HPC-X 2.10 Nsight Compute 2022.1.1.2 Nsight Systems 2021.2.5.2.53 Horovod 0.23.0 with enhancements GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 8.2.3 DALI 1.11.1 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.2.10 JupyterLab Server 1.0.6 Jupyter-TensorBoard		Release paused		Release paused
	Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA
	Docker image size: 12 GB		Docker image size: 11.7 GB		Docker image size: 11.7 GB		Docker image size: 11.7 GB		Docker image size: 9.97 GB		Docker image size: 10.2 GB		Docker image size: 10.1 GB		Docker image size: 10.7 GB		Docker image size: 10.6 GB		Docker image size: 11.0 GB
PyTorch	1.14.0a0+410ce96 including Python 3.8 APEX Nsight Compute 2022.3.0.0 TensorBoard 2.9.0 Torch-TensorRT 1.1.0a0 MAGMA 2.6.2 RAPIDS 22.10.01 JupyterLab 2.3.2 including Jupyter-TensorBoard TransformerEngine 0.3.0		1.13.0a0+936e930 including Python 3.8 APEX Nsight Compute 2022.3.0.0 TensorBoard 2.9.0 Torch-TensorRT 1.1.0a0 MAGMA 2.6.2 RAPIDS 22.10 JupyterLab 2.3.2 including Jupyter-TensorBoard TransformerEngine 0.2.0		1.13.0a0+d0d6b1f including Python 3.8 APEX Nsight Compute 2022.3.0.22 TensorBoard 2.9.0 Torch-TensorRT 1.1.0a0 MAGMA 2.6.2 RAPIDS 22.08.01 JupyterLab 2.3.2 including Jupyter-TensorBoard TransformerEngine 0.1.0		1.13.0a0+d0d6b1f including Python 3.8 APEX Nsight Compute 2022.3.0.22 TensorBoard 2.9.0 Torch-TensorRT 1.1.0a0 MAGMA 2.6.2 RAPIDS 22.08.01 JupyterLab 2.3.2 including Jupyter-TensorBoard TransformerEngine 0.1.0		1.13.0a0+d321be6 including Python 3.8 APEX Nsight Compute 2022.2.1 TensorBoard 2.9.0 Torch-TensorRT 1.1.0a0 MAGMA 2.6.2 RAPIDS 22.06 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.13.0a0+08820cb including Python 3.8 APEX Nsight Compute 2022.2.1 TensorBoard 2.9.0 Torch-TensorRT 1.1.0a0 MAGMA 2.6.2 RAPIDS 22.06 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.13.0a0+340c412 including Python 3.8 APEX Nsight Compute 2022.2.1 TensorBoard 2.9.0 Torch-TensorRT 1.1.0a0 MAGMA 2.6.2 RAPIDS 22.04 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.12.0a0+8a1a93a including Python 3.8 APEX Nsight Compute 2022.2.0.13 TensorBoard 2.9.0 Torch-TensorRT 1.1.0a0 MAGMA 2.6.2 RAPIDS 22.04 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.12.0a0+bd13bc6 including Python 3.8 APEX Nsight Compute 2022.1.1.2 TensorBoard 2.8.0 Torch-TensorRT 1.1.0a0 MAGMA 2.5.2 RAPIDS 22.02 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.12.0a0+2c916ef including Python 3.8 APEX Nsight Compute 2022.1.1.2 TensorBoard 2.8.0 Torch-TensorRT 1.1.0a0 MAGMA 2.5.2 RAPIDS 22.02 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.11.0a0+17540c5c including Python 3.8 APEX Nsight Compute 2022.1.0.0 TensorBoard 2.8.0 Torch-TensorRT 1.1.0a0 MAGMA 2.5.2 RAPIDS 21.12 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.11.0a0+bfe5ad28 including Python 3.8 APEX Nsight Compute 2022.1.0.0 TensorBoard 2.8.0 Torch-TensorRT 1.1.0a0 MAGMA 2.5.2 RAPIDS 21.12 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard
	Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)
	Docker image size: 18.3 GB		Docker image size: 17.3 GB		Docker image size: 16.9 GB		Docker image size: 16.8 GB		Docker image size: 14.6 GB		Docker image size: 14.8 GB		Docker image size: 14.6 GB		Docker image size: 14.6 GB		Docker image size: 14.1 GB		Docker image size: 14.6 GB		Docker image size: 14.4 GB		Docker image size: 14.8 GB
TensorFlow	2.10.1 including Python 3.8 Horovod 0.24.3 TensorBoard 2.9.0 RAPIDS 22.10.01 Nsight Compute 2022.3.0.22 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.24.3 TensorBoard 1.15.5 Nsight Compute 2022.3.0.22 RAPIDS 22.10.01 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.10.0 including Python 3.8 Horovod 0.24.3 TensorBoard 2.9.0 RAPIDS 22.10 Nsight Compute 2022.3.0.22 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.24.3 TensorBoard 1.15.5 Nsight Compute 2022.3.0.22 RAPIDS 22.10 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.10.0 including Python 3.8 Horovod 0.24.3 TensorBoard 2.9.0 RAPIDS 22.08.01 Nsight Compute 2022.3.0.22 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.24.3 TensorBoard 1.15.5 Nsight Compute 2022.3.0.22 RAPIDS 22.08.01 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.9.1 including Python 3.8 Horovod 0.24.3 TensorBoard 2.9.0 RAPIDS 22.08.01 Nsight Compute 2022.3.0.22 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.24.3 TensorBoard 1.15.5 Nsight Compute 2022.3.0.22 RAPIDS 22.08.01 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.9.1 including Python 3.8 Horovod 0.24.3 TensorBoard 2.9.0 RAPIDS 22.06 Nsight Compute 2022.2.1.3 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.24.3 TensorBoard 1.15.5 Nsight Compute 2022.2.1.3 RAPIDS 22.06 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.9.1 including Python 3.8 Horovod 0.24.3 TensorBoard 2.9.0 RAPIDS 22.06 Nsight Compute 2022.2.1.3 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.24.3 TensorBoard 1.15.5 Nsight Compute 2022.2.1.3 RAPIDS 22.06 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.9.1 including Python 3.8 Horovod 0.24.3 TensorBoard 2.9.0 RAPIDS 22.04 Nsight Compute 2022.2.1.3 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.24.3 TensorBoard 1.15.5 Nsight Compute 2022.2.1.3 RAPIDS 22.04 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.8.0 including Python 3.8 Horovod 0.24.2 TensorBoard 2.8.0 RAPIDS 22.04 Nsight Compute 2022.2.0.13 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.24.2 TensorBoard 1.15.5 Nsight Compute 2022.2.0.13 RAPIDS 22.04 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.8.0 including Python 3.8 Horovod 0.24.2 TensorBoard 2.8.0 RAPIDS 22.02 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.24.2 TensorBoard 1.15.5 Nsight Compute 2022.1.0.2 RAPIDS 22.02 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.8.0 including Python 3.8 Horovod 0.23.0 TensorBoard 2.8.0 RAPIDS 22.02 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.23.0 TensorBoard 1.15.5 Nsight Compute 2022.1.0.2 RAPIDS 22.02 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.7.0 including Python 3.8 Horovod 0.23.0 TensorBoard 2.7.0 RAPIDS 21.12 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.23.0 TensorBoard 1.15.0 Nsight Compute 2022.1.0.2 RAPIDS 21.12 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.7.0 including Python 3.8 Horovod 0.23.0 TensorBoard 2.7.0 RAPIDS 21.12 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.1 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including Python 3.8 Horovod 0.23.0 TensorBoard 1.15.0 Nsight Compute 2021.3.0.13 RAPIDS 21.12 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.7 Jupyter-TensorBoard
	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA	Multi arch support: x86, Arm SBSA (beta)	Multi arch support: x86, Arm SBSA (beta)	Multi arch support: x86, Arm SBSA (beta)	Multi arch support: x86, Arm SBSA (beta)	Multi arch support: x86, Arm SBSA (beta)	Multi arch support: x86, Arm SBSA (beta)	Multi arch support: x86, Arm SBSA (beta)	Multi arch support: x86, Arm SBSA (beta)
	Docker image size: 14.3 GB	Docker image size: 14.8 GB	Docker image size: 14.4 GB	Docker image size: 15.0 GB	Docker image size: 14.4 GB	Docker image size: 14.9 GB	Docker image size: 14.1 GB	Docker image size: 14.9 GB	Docker image size: 12 GB	Docker image size: 12.8 GB	Docker image size: 12.2 GB	Docker image size: 13.0 GB	Docker image size: 12.2 GB	Docker image size: 14.4 GB	Docker image size: 12.2 GB	Docker image size: 14.4 GB	Docker image size: 13.1 GB	Docker image size: 14.4 GB	Docker image size: 13.6 GB	Docker image size: 14.9 GB	Docker image size: 13.1 GB	Docker image size: 14.5 GB	Docker image size: 13.1 GB	Docker image size: 15.1 GB
TensorRT	TensorRT 8.5.1		TensorRT 8.5.1		TensorRT 8.5.0.12		TensorRT 8.5.0.12 including: Python 3.8		TensorRT 8.4.2.4 including: Python 3.8		TensorRT 8.4.1 including: Python 3.8		TensorRT 8.2.5 including: Python 3.8		TensorRT 8.2.5 including: Python 3.8		TensorRT 8.2.4.2 including: Python 3.8		TensorRT 8.2.3 including: Python 3.8		TensorRT 8.2.2 including: Python 3.8		TensorRT 8.2.2 including: Python 3.8
	Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)
	Docker image size: 7.61 GB		Docker image size: 7.25 GB		Docker image size: 7.51 GB		Docker image size: 7.49 GB		Docker image size: 6.09 GB		Docker image size: 6.27 GB		Docker image size: 6.21 GB		Docker image size: 6.33 GB		Docker image size: 6.21 GB		Docker image size: 6.21 GB		Docker image size: 6.21 GB		Docker image size: 6.17 GB
Triton Inference Server	In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.29.0 including PyTorch 1.14.0a0+410ce96 TensorFlow 1.15.5 TensorFlow 2.10.0 ONNX Runtime 1.13.1 Intel OpenVINO 2022.1.0 DCGM 2.2.9 FIL		In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.28.0 including PyTorch 1.13.0a0+936e930 TensorFlow 1.15.5 TensorFlow 2.10.0 ONNX Runtime 1.13.1 Intel OpenVINO 2022.1.0 DCGM 2.2.9 FIL		In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.27.0 including PyTorch 1.13.0a0+d0d6b1f TensorFlow 1.15.5 TensorFlow 2.9.1 ONNX Runtime 1.13.1 Intel OpenVINO 2022.1.0 DCGM 2.2.9 FIL		In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.26.0 including PyTorch 1.13.0a0+d0d6b1f TensorFlow 1.15.5 TensorFlow 2.9.1 ONNX Runtime 1.12 Intel OpenVINO 2022.1.0 DCGM 2.2.9 FIL		In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.25.0 including PyTorch 1.13.0a0+d321be6 TensorFlow 1.15.5 TensorFlow 2.9.1 ONNX Runtime 1.12 Intel OpenVINO 2021.4 DCGM 2.2.9 FIL		In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.24.0 including PyTorch 1.13.0a0+08820cb TensorFlow 1.15.5 TensorFlow 2.9.1 ONNX Runtime 1.12 Intel OpenVINO 2021.4 DCGM 2.2.9 FIL		In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.23.0 including PyTorch 1.13.0a0+340c412 TensorFlow 1.15.5 TensorFlow 2.9.1 ONNX Runtime 1.11.0 Intel OpenVINO 2021.4 DCGM 2.2.9 FIL		In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.22.0 including PyTorch 1.12.0a0+8a1a93a TensorFlow 1.15.5 TensorFlow 2.8.0 ONNX Runtime 1.11.0 Intel OpenVINO 2021.4 DCGM 2.2.9 FIL		In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.21.0 including PyTorch 1.12.0a0+2c916ef TensorFlow 1.15.5 TensorFlow 2.8.0 ONNX Runtime 1.10.0 Intel OpenVINO 2021.4 DCGM 2.2.9 FIL		In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.20.0 including PyTorch 1.12.0a0+2c916ef TensorFlow 1.15.5 TensorFlow 2.8.0 ONNX Runtime 1.10.0 Intel OpenVINO 2021.4 DCGM 2.2.9 FIL		In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.19.0 including PyTorch 1.11.0a0+17540c5c TensorFlow 1.15.5 TensorFlow 2.7.0 ONNX Runtime 1.9.0 Intel OpenVINO 2021.2 DCGM 2.2.9 FIL		In addition to the hardware and software listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.18.0 including PyTorch 1.11.0a0+bfe5ad2 TensorFlow 1.15.5 TensorFlow 2.7.0 ONNX Runtime 1.9.0 Intel OpenVINO 2021.2 DCGM 2.2.9 FIL
	Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA		Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)
	Docker image size: 14 GB		Docker image size: 13.8 GB		Docker image size: 13.4 GB		Docker image size: 13.7 GB		Docker image size: 11.7 GB		Docker image size: 11.9 GB		Docker image size: 11 GB		Docker image size: 11 GB		Docker image size: 11.4 GB		Docker image size: 12.1 GB		Docker image size: 12.3 GB		Docker image size: 12.4 GB
PaddlePaddle	2.3.2 including: Python 3.8 OpenMPI 4.1.4+ GDRCopy 2.3 Nsight Compute 2022.3.0.22 Paddle-TRT 2.3.2 SHARP 2.6.0		2.3.2 including: Python 3.8 OpenMPI 4.1.4+ GDRCopy 2.3 Nsight Compute 2022.3.0.22 Paddle-TRT 2.3.2 SHARP 2.6.0		2.3.2 including: Python 3.8 OpenMPI 4.1.4+ GDRCopy 2.3 Nsight Compute 2022.3.0.22 Paddle-TRT 2.3.2 SHARP 2.6.0		2.3.0 including: Python 3.8 OpenMPI 4.1.2rc4+ GDRCopy 2.3 Nsight Compute 2022.3.0.22 Paddle-TRT 2.3.2 SHARP 2.6.0		2.3.0 including: Python 3.8 OpenMPI 4.1.2rc4+ GDRCopy 2.3 Nsight Compute 2022.2.1.0 Paddle-TRT 2.3.1 SHARP 2.6.0		2.3.0 including: Python 3.8 OpenMPI 4.1.2rc4+ GDRCopy 2.3 Nsight Compute 2022.2.1.0 Paddle-TRT 2.3.0 SHARP 2.6.0		2.2.2 including: Python 3.8 OpenMPI 4.1.2rc4+ GDRCopy 2.3 Nsight Compute 2022.2.0.0 Paddle-TRT 2.2.2 SHARP 2.6.0		2.2.2 including: Python 3.8 OpenMPI 4.1.2rc4+ GDRCopy 2.3 Nsight Compute 2022.2.0.0 Paddle-TRT 2.2.2 SHARP 2.6.0
	Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only
	Docker image size: 8.72 GB		Docker image size: 8.48 GB		Docker image size: 8.46 GB		Docker image size: 8.44 GB		Docker image size: 8.43 GB		Docker image size: 8.43 GB		Docker image size: 7.98 GB		Docker image size: 8.09 GB
TensorFlow For Jetson	TensorFlow 1.15.5 and 2.10.1 for Jetson		TensorFlow 1.15.5 and 2.10.0 for Jetson		TensorFlow 1.15.5 and 2.10.0 for Jetson		TensorFlow 1.15.5 and 2.9.1 for Jetson		This release was skipped.		TensorFlow 1.15.5 and 2.9.1 for Jetson		TensorFlow 1.15.5 and 2.9.1 for Jetson		TensorFlow 1.15.5 and 2.8.0 for Jetson		TensorFlow 1.15.5 and 2.8.0 for Jetson		TensorFlow 1.15.5 and 2.8.0 for Jetson		TensorFlow 1.15.5 and 2.7.0 for Jetson		TensorFlow 1.15.5 and 2.7.0 for Jetson
PyTorch for Jetson	PyTorch 1.14.0a0+410ce96 for Jetson		PyTorch 1.13.0a0+936e930 for Jetson		PyTorch 1.13.0a0+d0d6b1f for Jetson		PyTorch 1.13.0a0+d0d6b1f for Jetson		This release was skipped.		PyTorch 1.13.0a0+08820cb for Jetson		PyTorch 1.13.0a0+340c412 for Jetson		PyTorch 1.12.0a0+8a1a93a for Jetson		PyTorch 1.12.0a0+84d1cb9 for Jetson		PyTorch 1.12.0a0+2c916ef for Jetson		This release was skipped.		PyTorch 1.11.0a0+bfe5ad28 for Jetson
TensorFlow Wheel for x86	TensorFlow 1.15.5 for x86		TensorFlow 1.15.5 for x86		TensorFlow 1.15.5 for x86		TensorFlow 1.15.5 for x86		TensorFlow 1.15.5 for x86		TensorFlow 1.15.5 for x86		TensorFlow 1.15.5 for x86		TensorFlow 1.15.5 for x86		TensorFlow 1.15.5 for x86		TensorFlow 1.15.5 for x86		TensorFlow 1.15.5 for x86		TensorFlow 1.15.5 for x86
Triton for Jetson	Triton Inference Server 2.27.0 for Jetson AGX Xavier		Triton Inference Server 2.27.0 for Jetson AGX Xavier		Triton Inference Server 2.27.0 for Jetson AGX Xavier		Triton Inference Server 2.26.0 for Jetson AGX Xavier		Triton Inference Server 2.24.0 for Jetson AGX Xavier		Triton Inference Server 2.24.0 for Jetson AGX Xavier		Triton Inference Server 2.23.0 for Jetson AGX Xavier		Triton Inference Server 2.22.0 for Jetson AGX Xavier		Triton Inference Server 2.21.0 for Jetson AGX Xavier		Triton Inference Server 2.20.0 for Jetson AGX Xavier		Triton Inference Server 2.19.0 for Jetson AGX Xavier Nano TX2		Triton Inference Server 2.18.0 for Jetson AGX Xavier Nano TX2

5. 21.xx Framework Containers Support Matrix

Important:

Content that is included in <<>> brackets indicates new content from the previously published version.

Note:

The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 21.02 release of an image was released in February 2021.

21.xx container images

Container Image	21.12		21.11		21.10		21.09		21.08		21.07		21.06		21.05		21.04		21.03		21.02
DGX
DGX System	DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100		DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100		DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100		DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100		DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100		DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100		DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100		DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100		DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100		DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100		DGX-1 DGX-2 DGX A100 DGX Station DGX Station A100
Operating System	DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 5.1 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 5.1 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 5.1 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 5.1 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		DGX OS 4.1+¹(4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹		DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹
NVIDIA Driver	Release 21.12 is based on NVIDIA CUDA 11.5.0, which requires NVIDIA Driver release 495 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). The CUDA driver's compatibility package only supports particular drivers. ³		Release 21.11 is based on NVIDIA CUDA 11.5.0, which requires NVIDIA Driver release 495 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). The CUDA driver's compatibility package only supports particular drivers. ³		Release 21.10 is based on NVIDIA CUDA 11.4.2 with cuBLAS 11.6.5.2, which requires NVIDIA Driver release 470 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460). The CUDA driver's compatibility package only supports particular drivers. ³		Release 21.09 is based on NVIDIA CUDA 11.4.2, which requires NVIDIA Driver release 470 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460). The CUDA driver's compatibility package only supports particular drivers. ³		Release 21.08 is based on NVIDIA CUDA 11.4.1, which requires NVIDIA Driver release 470 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460). The CUDA driver's compatibility package only supports particular drivers. ³		Release 21.07 is based on NVIDIA CUDA 11.4.0, which requires NVIDIA Driver release 470 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460). The CUDA driver's compatibility package only supports particular drivers. ³		Release 21.06 is based on NVIDIA CUDA 11.3.1, which requires NVIDIA Driver release 465.19.01 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460). The CUDA driver's compatibility package only supports particular drivers. ³		Release 21.05 is based on NVIDIA CUDA 11.3.0, which requires NVIDIA Driver release 465.19.01 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460). The CUDA driver's compatibility package only supports particular drivers.³		Release 21.04 is based on NVIDIA CUDA 11.3.0, which requires NVIDIA Driver release 465.19.01 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460). The CUDA driver's compatibility package only supports particular drivers. ³		Release 21.03 is based on NVIDIA CUDA 11.2.1, which requires NVIDIA Driver release 460.32.03 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51(or later R450). The CUDA driver's compatibility package only supports particular drivers. ³		Release 21.02 is based on NVIDIA CUDA 11.2.0, which requires NVIDIA Driver release 460.27.04 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51(or later R450). The CUDA driver's compatibility package only supports particular drivers.³
GPU Model	NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal
Base Container Image
Container OS	Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04
CUDA	NVIDIA CUDA 11.5.0		NVIDIA CUDA 11.5.0		NVIDIA CUDA 11.4.2 with cuBLAS 11.6.5.2		11.4.2		11.4.1		11.4.0		11.3.1		11.3.0		11.3.0		11.2.1		11.2.0
cuBLAS	11.7.3.1		11.7.3.1		11.6.1.51		11.6.1.51		11.5.4		11.5.2.43		11.5.1.109		11.5.1.101		11.5.1.101		11.4.1.1026		11.3.1.68
cuDNN	8.3.1.22		8.3.0.96		8.2.4.15		8.2.4.15		8.2.2.26		8.2.2.26		8.2.1		8.2.0.51		8.2.0.41		8.1.1		8.1.0.77
NCCL	2.11.4		2.11.4		2.11.4		2.11.4		2.10.3		2.10.3		2.9.9		2.9.8		2.9.6		2.8.4		2.8.4
TensorRT	TensorRT 8.2.1.8		TensorRT 8.0.3.4		TensorRT 8.0.3.4
NVIDIA Optimized Frameworks
Kaldi	c6772ba4 Python 3.8 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.2.4		c6772ba4 Python 3.8 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.2.4		2897564e Python 3.8 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.2.4		40573bb Python 3.8 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.1.57 TensorRT 8.0.3		851a0a79 Python 3.8 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.2 NVIDIA HPC-X 2.9 Nsight Systems 2021.2.4.12 TensorRT 8.0.1.6		9bca2777 Python 3.8 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.1 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.2.4.12 TensorRT 8.0.1.6		b1786aa5 Python 3.8 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.1 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.2.1.58 TensorRT 7.2.3.4		e92970ae Python 3.8 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2020.4.3.7 TensorRT 7.2.3.4		ec554a7c Python 3.8 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2020.4.3.7 TensorRT 7.2.3.4		376281ca Python 3.8 MLNX_OFED 5.1-2.3.7 OpenMPI 4.0.5 Nsight Compute 2020.3.0.18 Nsight Systems 2020.4.3.7 TensorRT 7.2.2.3		b78fdcaf Python 3.8 MLNX_OFED 5.1-2.3.7 OpenMPI 4.0.5 Nsight Compute 2020.3.0.18 Nsight Systems 2020.4.3.7 TensorRT 7.2.2.3+cuda11.1.0.024
	Multi arch support: x86 only		Multi arch support: x86 only		Multi arch support: x86 only
	Docker image size: 8.78 GB		Docker image size: 8.69 GB		Docker image size: 9.16 GB		Docker image size: 9.12 GB		Docker image size: 8.86 GB		Docker image size: 8.77 GB		Docker image size: 8.62 GB		Docker image size: 8.43 GB		Docker image size: 8.3 GB		Docker image size: 8.62 GB		Docker image size: 8.73 GB
DIGITS	Release paused		Release paused		Release paused		6.1.1 including Python 3.8 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.1.57 TensorFlow 1.15.5 TensorRT 8.0.3		6.1.1 including Python 3.8 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.2 NVIDIA HPC-X 2.9 Nsight Systems 2021.2.4.12 TensorFlow 1.15.5 TensorRT 8.0.1.6		6.1.1 including Python 3.8 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.1 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.2.4.12 TensorFlow 1.15.5 TensorRT 8.0.1.6		6.1.1 including Python 3.8 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.1 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.2.1.58 TensorFlow 1.15.5 TensorRT 7.2.3.4		6.1.1 including Python 3.8 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2020.4.3.7 TensorFlow 1.15.5 TensorRT 7.2.3.4		6.1.1 including Python 3.8 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2020.4.3.7 TensorFlow 1.15.5 TensorRT 7.2.3.4		6.1.1 including Python 3.8 MLNX_OFED 5.1-2.3.7 OpenMPI 4.0.5 Nsight Compute 2020.3.0.18 Nsight Systems 2020.4.3.7 TensorFlow 1.15.5 TensorRT 7.2.2.3		6.1.1 including Python 3.8 MLNX_OFED 5.1-2.3.7 OpenMPI 4.0.5 Nsight Compute 2020.3.0.18 Nsight Systems 2020.4.3.7 TensorFlow 1.15.5 TensorRT 7.2.2.3+cuda11.1.0.024
DIGITS	-		-		-		Docker image size: 14.6 GB		Docker image size: 14.9 GB		Docker image size: 15 GB		Docker image size: 14.7 GB		Docker image size: 15.1 GB		Docker image size: 15.1 GB		Docker image size: 15.4 GB		Docker image size: 15.5 GB
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet	Release paused		Release paused		Release paused		1.9.0.rc6 including Sockeye 2.2.3 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Compute 2021.2.2.0 Nsight Systems 2021.3.1.57 Horovod 0.22.1 GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 8.0.3 DALI 1.5.0 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.2.9 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.9.0.rc6 including Sockeye 2.2.3 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.2 NVIDIA HPC-X 2.9 Nsight Systems 2021.2.4.12 Horovod 0.22.1 GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 8.0.1.6 DALI 1.4.0 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.2.9 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.9.0.rc3 including Sockeye 2.2.3 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.1 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.2.4.12 Horovod 0.22.1 GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 8.0.1.6 DALI 1.3.0 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.2.9 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.9.0.rc2 including Sockeye 2.2.3 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.1 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.2.1.58 Horovod 0.21.3 GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 7.2.3.4 DALI 1.2.0 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.8.0 including Sockeye 2.2.3 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.4.3.7 Horovod 0.21.3 GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 7.2.3.4 DALI 1.0.0 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.8.0 including Sockeye 2.2.3 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.4.3.7 Horovod 0.21.3 GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 7.2.3.4 DALI 1.0.0 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.8.0 including Sockeye 2.2.3 MLNX_OFED 5.1-2.3.7 OpenMPI 4.0.5 Horovod 0.21.2 Nsight Compute 2020.3.0.18 Nsight Systems 2020.4.3.7 GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 7.2.2.3 DALI 0.31 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.8.0.rc2 including Sockeye 2.2.3 MLNX_OFED 5.1-2.3.7 OpenMPI 4.0.5 Horovod 0.21.0 Nsight Compute 2020.3.0.18 Nsight Systems 2020.4.3.7 GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 7.2.2.3+cuda11.1.0.024 DALI 0.29 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard
	-		-		-		Docker image size: 11.2 GB		Docker image size: 10.9 GB		Docker image size: 10.6 GB		Docker image size: 10.4 GB		Docker image size: 10.8 GB		Docker image size: 10.7 GB		Docker image size: 11.1 GB		Docker image size: 10.8 GB
PyTorch	1.11.0a0+b6df043 including Python 3.8 APEX rdma-core 36.0 OpenMPI 4.1.2a1 OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 TensorRT 8.2.1.8 Nsight Compute 2021.3.0.13 Nsight Systems 2021.3.2.4 TensorBoard 2.7.0 Torch-TensorRT 1.1.0a0 DALI 1.8 MAGMA 2.5.2 DLProf 1.8.0 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.11.0a0+b6df043 including Python 3.8 APEX rdma-core 36.0 OpenMPI 4.1.2a1 OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 TensorRT 8.0.3.4 for x64 Linux TensorRT 8.0.2.2 for ARM SBSA Linux Nsight Compute 2021.3.0.13 Nsight Systems 2021.3.2.4 TensorBoard 2.7.0 Torch-TensorRT 1.0.0a0 DALI 1.7 MAGMA 2.5.2 DLProf 1.7.0 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.10.0a0+0aef44c including Python 3.8 APEX rdma-core 36.0 OpenMPI 4.1.2a1 OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 TensorRT 8.0.3.4 for x64 Linux TensorRT 8.0.2.2 for ARM SBSA Linux Nsight Systems 2021.3.1.57 TensorBoard 2.6.0 DALI 1.6 MAGMA 2.5.2 DLProf 1.6.0 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.10.0a0+3fd9dcf including Python 3.8 APEX rdma-core 36.0 OpenMPI 4.1.2a1 OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.1.57 TensorBoard 2.6.0 DALI 1.5 MAGMA 2.5.2 DLProf 1.5.0 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.10.0a0+3fd9dcf including Python 3.8 APEX rdma-core 36.0 OpenMPI 4.1.2a1 OpenUCX 1.11.0rc1 GDRCopy 2.2 NVIDIA HPC-X 2.9 Nsight Systems 2021.2.4.12 TensorBoard 2.6.0 TensorRT 8.0.1.6 DALI 1.4 MAGMA 2.5.2 DLProf 1.4.0 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.10.0a0+ecc3718 including Python 3.8 APEX rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.1 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.2.0 Nsight Systems 2021.2.4.12 TensorBoard 2.5.0 TensorRT 8.0.1.6 DALI 1.3 MAGMA 2.5.2 DLProf 1.3.0 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.9.0a0+c3d40fd including Python 3.8 APEX rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.1 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.2.1.58 TensorBoard 1.15 TensorRT 7.2.3.4 DALI 1.2 MAGMA 2.5.2 DLProf 1.2.0 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.9.0a0+2ecb2c7 including Python 3.8 APEX rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2020.4.3.7 TensorBoard 1.15+nv TensorRT 7.2.3.4 DALI 1.0 MAGMA 2.5.2 DLProf 1.1.0 PyProf 3.10 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard PyProf cb1099f6b		1.9.0a0+2ecb2c7 including Python 3.8 APEX rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2020.4.3.7 TensorBoard 1.15+nv TensorRT 7.2.3.4 DALI 1.0 MAGMA 2.5.2 DLProf 1.1.0 PyProf 3.10 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard PyProf cb1099f6b		1.9.0a0+df837d0 including Python 3.8 APEX MLNX_OFED 5.1-2.3.7 OpenMPI 4.0.5 Nsight Compute 2020.3.1.0 Nsight Systems 2020.4.3.7 TensorBoard 1.15+nv TensorRT 7.2.2.3 DALI 0.31 MAGMA 2.5.2 DLProf 1.0.0 PyProf r21.03 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.14 JupyterLab Server 1.0.6 Jupyter-TensorBoard PyProf cb1099f6b		1.8.0a0+52ea372 including Python 3.8 APEX MLNX_OFED 5.1-2.3.7 OpenMPI 4.0.5 Nsight Compute 2020.3.0.18 Nsight Systems 2020.4.3.7 TensorBoard 1.15+nv TensorRT 7.2.2.3+cuda11.1.0.024 DALI 0.29 MAGMA 2.5.2 DLProf 0.19.0 PyProf r21.02 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.14 JupyterLab Server 1.0.6 Jupyter-TensorBoard PyProf cb1099f6b
	Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)
	Docker image size: 14.7 GB		Docker image size: 14.5 GB		Docker image size: 13.2 GB		Docker image size: 13.1 GB		Docker image size: 12.7 GB		Docker image size: 15 GB		Docker image size: 14.5 GB		Docker image size: 14.5 GB		Docker image size: 14.3 GB		Docker image size: 14.4 GB		Docker image size: 12.9 GB
TensorFlow	2.6.2 including Python 3.8 Horovod 0.22.1 TensorRT 8.2.1.8 TensorBoard 2.6.0 DALI 1.8 DLProf 1.8.0 rdma-core 36.0 OpenMPI 4.1.2a1 OpenUCX 1.11.0rc1 GDRCopy 2.3 RAPIDS 21.10 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.2.4 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.1 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including `21.12-tf1-py3`contains Python 3.8 Horovod 0.22.1 OpenMPI 4.1.2a1 OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 TensorRT 8.2.1.8 TensorBoard 1.15.0 RAPIDS 21.10 rdma-core 36.0 DALI 1.8 DLProf 1.8.0 Nsight Compute 2021.3.0.13 Nsight Systems 2021.3.2.4 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.6.0 including Python 3.8 Horovod 0.22.1 TensorRT 8.0.3.4 for x64 Linux TensorRT 8.0.2.2 for ARM SBSA Linux TensorBoard 2.6.0 DALI 1.7 DLProf 1.7.0 rdma-core 36.0 OpenMPI 4.1.2a1 OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.2.4 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.1 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including `21.11-tf1-py3`contains Python 3.8 Horovod 0.22.1 OpenMPI 4.1.2a1 OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 TensorRT 8.0.3.4 for x64 Linux TensorRT 8.0.2.2 for ARM SBSA Linux TensorBoard 1.15.0 rdma-core 36.0 DALI 1.7 DLProf 1.7.0 Nsight Compute 2021.3.0.13 Nsight Systems 2021.3.2.4 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.6.0 including Python 3.8 Horovod 0.22.1 TensorRT 8.0.3.4 for x64 Linux TensorRT 8.0.2.2 for ARM SBSA Linux TensorBoard 2.6.0 DALI 1.6 DLProf 1.6.0 rdma-core 36.0 OpenMPI 4.1.2a1 OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.2.4 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.1 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including `21.10-tf1-py3`contains Python 3.8 Horovod 0.22.1 OpenMPI 4.1.2a1 OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 TensorRT 8.0.3.4 for x64 Linux TensorRT 8.0.2.2 for ARM SBSA Linux TensorBoard 1.15.0 rdma-core 36.0 DALI 1.6 DLProf 1.6.0 Nsight Compute 2021.2.2.1 Nsight Systems 2021.3.2.4 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.6.0 including Python 3.8 Horovod 0.22.1 TensorBoard 2.6.0 TensorRT 8.0.3 DALI 1.5 DLProf 1.5.0 rdma-core 36.0 OpenMPI 4.1.2a1 OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.1.57 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.1 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including `21.09-tf1-py3`contains Python 3.8 Horovod 0.22.1 OpenMPI 4.1.2a1 OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 TensorBoard 1.15.0 rdma-core 36.0 TensorRT 8.0.3 DALI 1.5 DLProf 1.5.0 Nsight Compute 2021.2.2.1 Nsight Systems 2021.3.1.57 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.5.0 including Python 3.8 Horovod 0.22.1 TensorBoard 2.6.0 TensorRT 8.0.1.6 DALI 1.4 DLProf 1.4.0 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.2 NVIDIA HPC-X 2.9 Nsight Systems 2021.2.4.12 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.1 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including `21.08-tf1-py3`contains Python 3.8 Horovod 0.22.1 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.2 NVIDIA HPC-X 2.9 TensorBoard 1.15.0 rdma-core 36.0 OpenSeq2Seq at commit a81babd TensorRT 8.0.1.6 DALI 1.4 DLProf 1.4.0 Nsight Systems 2021.2.4.12 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.5.0 including Python 3.8 Horovod 0.22.1 TensorBoard 2.5.0 TensorRT 8.0.1.6 DALI 1.3 DLProf 1.3.0 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.1 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.2.4.12 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.1 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including `21.07-tf1-py3`contains Python 3.8 Horovod 0.22.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 TensorBoard 1.15.0 rdma-core 32.1 OpenSeq2Seq at commit a81babd TensorRT 8.0.1.6 DALI 1.3 DLProf 1.3.0 Nsight Compute 2021.1.3.14 Nsight Systems 2021.2.4.12 Tensor Core optimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.5.0 including Python 3.8 Horovod 0.22.0 TensorBoard 2.5.0 TensorRT 7.2.3.4 DALI 1.2 DLProf 1.2.0 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.1 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.2.1.58 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.1 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including `21.06-tf1-py3`contains Python 3.8 Horovod 0.22.0 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 TensorBoard 1.15.0 rdma-core 32.1 OpenSeq2Seq at commit a81babd TensorRT 7.2.3.4 DALI 1.2 DLProf 1.2.0 Nsight Compute 2021.1.3.14 Nsight Systems 2021.2.1.58 Tensor Core optimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.4.0 including Python 3.8 Horovod 0.21.3 TensorBoard 2.4.1 TensorRT 7.2.3.4 DALI 1.0 DLProf 1.1.0 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.4.3.7 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.1 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including `21.05-tf1-py3`contains Python 3.8 Horovod 0.21.3 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 TensorBoard 1.15.0+nv21.4 rdma-core 32.1 OpenSeq2Seq at commit a81babd TensorRT 7.2.3.4 DALI 1.0 DLProf 1.1.0 Nsight Compute 2021.1.3.14 Nsight Systems 2020.4.3.7 Tensor Core optimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.4.0 including Python 3.8 Horovod 0.21.3 TensorBoard 2.4.1 TensorRT 7.2.3.4 DALI 1.0 DLProf 1.1.0 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.4.3.7 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 2.3.1 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including `21.04-tf1-py3`contains Python 3.8 Horovod 0.21.3 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 TensorBoard 1.15.0+nv21.4 rdma-core 32.1 OpenSeq2Seq at commit a81babd TensorRT 7.2.3.4 DALI 1.0 DLProf 1.1.0 Nsight Compute 2021.1.3.14 Nsight Systems 2020.4.3.7 Tensor Core optimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 2.3.1 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.4.0 including Python 3.8 Horovod 0.21.3 OpenMPI 4.0.5 TensorBoard 2.4.1 MLNX_OFED 5.1-2.3.7 TensorRT 7.2.2.3 DALI 0.31 Nsight Compute 2020.3.0.18 Nsight Systems 2020.4.3.7 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including `21.03-tf1-py3`contains Python 3.8 Horovod 0.21.3 OpenMPI 4.0.5 TensorBoard 1.15.0+nv21.03 MLNX_OFED 5.1-2.3.7 OpenSeq2Seq at commit a81babd TensorRT 7.2.2.3+cuda11.1.0.024 DALI 0.31 DLProf 1.0.0 Nsight Compute 2020.3.0.18 Nsight Systems 2020.4.3.7 Tensor Core optimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.4.0 including Python 3.8 Horovod 0.21.0 OpenMPI 4.0.5 TensorBoard 2.4.1 MLNX_OFED 5.1-2.3.7 TensorRT 7.2.2.3+cuda11.1.0.024 DALI 0.29 Nsight Compute 2020.3.0.18 Nsight Systems 2020.4.3.7 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.5 including `21.02-tf1-py3`contains Python 3.8 Horovod 0.21.0 OpenMPI 4.0.5 TensorBoard 1.15.0+nv20.11 MLNX_OFED 5.1-2.3.7 OpenSeq2Seq at commit a81babd TensorRT 7.2.2.3+cuda11.1.0.024 DALI 0.29 DLProf 0.19.0 Nsight Compute 2020.3.0.18 Nsight Systems 2020.4.3.7 Tensor Core optimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard
	Multi arch support: x86, Arm SBSA (beta)	Multi arch support: x86, Arm SBSA (beta)	Multi arch support: x86, Arm SBSA (beta)	Multi arch support: x86, Arm SBSA (beta)	Multi arch support: x86, Arm SBSA (beta)	Multi arch support: x86, Arm SBSA (beta)
	Docker image size: 12.8 GB	Docker image size: 16.9 GB	Docker image size: 12.5 GB	Docker image size: 16.5 GB	Docker image size: 10.6 GB	Docker image size: 14.5 GB	Docker image size: 11.5 GB	Docker image size: 13.6 GB	Docker image size: 11.5 GB	Docker image size: 13.9 GB	Docker image size: 11.1 GB	Docker image size: 14 GB	Docker image size: 10.8 GB	Docker image size: 13.7 GB	Docker image size: 10.8 GB	Docker image size: 14.1 GB	Docker image size: 10.6 GB	Docker image size: 14.1 GB	Docker image size: 10.9 GB	Docker image size: 14.4 GB	Docker image size: 11.1 GB	Docker image size: 14.5 GB
TensorRT	TensorRT 8.2.1.8 including: Python 3.8 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.2.4		TensorRT 8.0.3.4 including: Python 3.8 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.2.4		TensorRT 8.0.3.4 including: Python 3.8 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.2.4		TensorRT 8.0.3 including: Python 3.8 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.3 NVIDIA HPC-X 2.9 Nsight Systems 2021.3.1.57		TensorRT 8.0.1.6 including: Python 3.8 rdma-core 36.0 OpenMPI 4.1.1+ OpenUCX 1.11.0rc1 GDRCopy 2.2 NVIDIA HPC-X 2.9 Nsight Systems 2021.2.4.12		TensorRT 8.0.1.6 including: Python 3.8 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.1 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.2.0 Nsight Systems 2021.2.4.12		TensorRT 7.2.3.4 including: Python 3.8 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.1 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2021.2.1.58		TensorRT 7.2.3.4 including: Python 3.8 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2020.4.3.7		TensorRT 7.2.3.4 including: Python 3.8 rdma-core 32.1 OpenMPI 4.1.1rc1 OpenUCX 1.10.0 GDRCopy 2.2 NVIDIA HPC-X 2.8.2rc3 Nsight Compute 2021.1.3.14 Nsight Systems 2020.4.3.7		TensorRT 7.2.2.3 including: Python 3.8 MLNX_OFED 5.1-2.3.7 OpenMPI 4.0.5 Nsight Compute 2020.3.0.18 Nsight Systems 2020.4.3.7		TensorRT 7.2.2.3+cuda11.1.0.024 including: Python 3.8 MLNX_OFED 5.1-2.3.7 OpenMPI 4.0.5 Nsight Compute 2020.3.0.18 Nsight Systems 2020.4.3.7
	Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)
	Docker image size: 5.98 GB		Docker image size: 5.88 GB		Docker image size: 6.37 GB		Docker image size: 6.3 GB		Docker image size: 6.04 GB		Docker image size: 5.95 GB		Docker image size: 5.8 GB		Docker image size: 5.76 GB		Docker image size: 5.63 GB		Docker image size: 5.94 GB		Docker image size: 7.09 GB
Triton Inference Server	In addition to the hardware listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.17.0 including TensorRT 8.2.1.8 PyTorch 1.11.0a0+b6df043 TensorFlow 1.15.5 TensorFlow 2.6.2 ONNX Runtime 1.9.0 Intel OpenVINO 2021.2 DCGM 2.2.9 FIL DALI 1.8		In addition to the hardware listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.16.0 including TensorRT 8.0.3.4 for x64 Linux TensorRT 8.0.2.2 for ARM SBSA Linux PyTorch 1.11.0a0+b6df043 TensorFlow 1.15.5 TensorFlow 2.6.0 ONNX Runtime 1.9.0 Intel OpenVINO 2021.2 DCGM 2.2.9 FIL DALI 1.7		In addition to the hardware listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.15.0 including TensorRT 8.0.3.4 for x64 Linux TensorRT 8.0.2.2 for ARM SBSA Linux PyTorch 1.10.0a0+0aef44c TensorFlow 1.15.5 TensorFlow 2.6.0 ONNX Runtime 1.9.0 Intel OpenVINO 2021.2 DCGM 2.2.9 FIL DALI 1.6		In addition to the hardware listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.14.0 including TensorRT 8.0.3 PyTorch 1.10.0a0+3fd9dcf TensorFlow 1.15.5 TensorFlow 2.6.0 ONNX Runtime 1.8.1 Intel OpenVINO 2021.2 DCGM 2.2.9 FIL DALI 1.5		In addition to the hardware listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.13.0 including TensorRT 8.0.1.6 PyTorch 1.10.0a0+3fd9dcf TensorFlow 1.15.5 TensorFlow 2.5.0 ONNX Runtime 1.8.1 Intel OpenVINO 2021.2 DCGM 2.2.8 FIL DALI 1.4		In addition to the hardware listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.12.0 including TensorRT 8.0.1.6 PyTorch 1.9.0a0+c3d40fd TensorFlow 1.15.5 TensorFlow 2.5.0 ONNX Runtime 1.8.0 Intel OpenVINO 2021.2 DCGM 2.2.3 FIL DALI 1.3		In addition to the hardware listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.11.0 including TensorRT 7.2.3.4 PyTorch 1.9.0a0+c3d40fd TensorFlow 1.15.5 TensorFlow 2.5.0 ONNX Runtime 1.8.0 Intel OpenVINO 2021.2 FIL DALI 1.2		In addition to the hardware listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.10.0 including TensorRT 7.2.3.4 PyTorch 1.9.0a0+2ecb2c7 TensorFlow 1.15.5 TensorFlow 2.4.0 ONNX Runtime 1.7.1 Intel OpenVINO 2021.2		In addition to the hardware listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.9.0 including TensorRT 7.2.3.4 PyTorch 1.9.0a0+2ecb2c7 TensorFlow 1.15.5 TensorFlow 2.4.0 ONNX Runtime 1.7.1 Intel OpenVINO 2021.2 DALI 1.0		In addition to the hardware listed above, Triton Inference Server also supports: AMD x86 CPU Intel x86 CPU 2.8.0 including TensorRT 7.2.2.3 PyTorch 1.9.0a0+df837d0 TensorFlow 1.15.5 TensorFlow 2.4.0 ONNX Runtime 1.6.0 Intel OpenVINO 2021.2 DALI 0.31		2.7.0 including TensorRT 7.2.2.3+cuda11.1.0.024 PyTorch 1.8.0a0+52ea372 TensorFlow 1.15.5 TensorFlow 2.4.0 ONNX Runtime 1.6.0 DALI 0.29
	Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)		Multi arch support: x86, Arm SBSA (beta)
	Docker image size: 12.1 GB		Docker image size: 12.2 GB		Docker image size: 13.7 GB		Docker image size: 13.6 GB		Docker image size: 13.1 GB		Docker image size: 14.6 GB		Docker image size: 13.4 GB		Docker image size: 10.6 GB		Docker image size: 11.1 GB		Docker image size: 11.3 GB		Docker image size: 15.6 GB
TensorFlow For Jetson	TensorFlow 1.15.5 and 2.6.2 for Jetson		TensorFlow 1.15.5 and 2.6.0 for Jetson		TensorFlow 1.15.5 and 2.6.0 for Jetson		TensorFlow 1.15.5 and 2.6.0 for Jetson		TensorFlow 1.15.5 and 2.5.0 for Jetson		TensorFlow 1.15.5 and 2.5.0 for Jetson		TensorFlow 1.15.5 and 2.5.0 for Jetson		TensorFlow 1.15.5 and 2.4.0 for Jetson		TensorFlow 1.15.5 and 2.4.0 for Jetson		TensorFlow 1.15.5 and 2.4.0 for Jetson		TensorFlow 1.15.5 and 2.4.0 for Jetson
Triton for Jetson	Triton Inference Server 2.17.0 for Jetson AGX Xavier Nano TX2		Triton Inference Server 2.16.0 for Jetson AGX Xavier Nano TX2		Triton Inference Server 2.15.0 for Jetson AGX Xavier Nano TX2		Triton Inference Server 2.14.0 for Jetson AGX Xavier Nano TX2		Triton Inference Server 2.13.0 for Jetson AGX Xavier Nano TX2		Triton Inference Server 2.12.0 for Jetson AGX Xavier Nano TX2		Triton Inference Server 2.11.0 for Jetson AGX Xavier Nano TX2		Triton Inference Server 2.10.0 for Jetson AGX Xavier Nano TX2		Triton Inference Server 2.9.0 for Jetson AGX Xavier Nano TX2		Triton Inference Server 2.8.0 for Jetson AGX Xavier Nano TX2		Triton Inference Server 2.7.0 for Jetson

6. 20.xx Framework Containers Support Matrix

Important:

Content that is included in <<>> brackets indicates new content from the previously published version.

Note:

The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 20.06 release of an image was released in June, 2020.

20.xx container images

Container Image	20.12		20.11		20.10		20.09		20.08		20.07		20.06		20.03		20.02		20.01
DGX
DGX System	DGX-1 DGX-2 DGX A100 DGX Station		DGX-1 DGX-2 DGX A100 DGX Station		DGX-1 DGX-2 DGX A100 DGX Station		DGX-1 DGX-2 DGX A100 DGX Station		DGX-1 DGX-2 DGX A100 DGX Station		DGX-1 DGX-2 DGX A100 DGX Station		DGX-1 DGX-2 DGX Station		DGX-1 DGX-2 DGX Station		DGX-1 DGX-2 DGX Station		DGX-1 DGX-2
Operating System	DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹		DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹		DGX OS 4.1+¹ (4.6+ multi-node NCCL) 4.99.x (DGX A100) 5.0 Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+¹		DGX OS 4.1+ 4.99.x (DGX A100) Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+²³		DGX OS 4.1+ 4.99.x (DGX A100) Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+		DGX OS 4.1+ 4.99.x (DGX A100) Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+		DGX OS 4.1+¹ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+		DGX OS 4.1+¹ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+		DGX OS 4.1+¹ Red Hat Enterprise Linux 7 / CentOS 7² EL7-20.02+		DGX OS 4.1+¹
NVIDIA Certified Systems
NVIDIA Driver	Release 20.12 is based on CUDA 11.1.1, which requires NVIDIA driver release 455.23. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx, 440.30, or 450.51. The CUDA driver's compatibility package only supports particular drivers. ³		Release 20.11 is based on CUDA 11.1.0, which requires NVIDIA driver release 455.23. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx, 440.30, or 450.51. The CUDA driver's compatibility package only supports particular drivers. ³		Release 20.10 is based on CUDA 11.1.0, which requires NVIDIA driver release 455.23. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx, 440.30, or 450.51. The CUDA driver's compatibility package only supports particular drivers. ³		Release 20.09 is based on CUDA 11.0.3, which requires NVIDIA driver release 450.51. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx or 440.30. The CUDA driver's compatibility package only supports particular drivers. ³		Release 20.08 is based on CUDA 11.0.3, which requires NVIDIA driver release 450.51. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx or 440.30. The CUDA driver's compatibility package only supports particular drivers. ³		Release 20.07 is based on CUDA 11.0.194, which requires NVIDIA driver release 450.51. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx or 440.30. The CUDA driver's compatibility package only supports particular drivers. ³		Release 20.06 is based on CUDA 11.0.167, which requires NVIDIA driver release 450.36. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx or 440.30. The CUDA driver's compatibility package only supports particular drivers. ³		Release 20.03 is based on CUDA 10.2.89, which requires NVIDIA driver release 440.33.01. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+, 410, 418.xx or 440.30. The CUDA driver's compatibility package only supports particular drivers. ³		Release 20.02 is based on CUDA 10.2.89, which requires NVIDIA driver release 440.33.01. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+, 410, 418.xx or 440.30. The CUDA driver's compatibility package only supports particular drivers. ³		Release 20.01 is based on CUDA 10.2.89, which requires NVIDIA driver release 440.33.01. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+, 410, 418.xx or 440.30. The CUDA driver's compatibility package only supports particular drivers. ³
GPU Model	NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		NVIDIA Ampere GPU Architecture Turing Volta Pascal		Turing Volta Pascal		Turing Volta Pascal		Turing Volta Pascal
Base Container Image
Container OS	Ubuntu 20.04		Ubuntu 18.04		Ubuntu 18.04		Ubuntu 18.04		Ubuntu 18.04		Ubuntu 18.04		Ubuntu 18.04		Ubuntu 18.04		Ubuntu 18.04		Ubuntu 18.04
CUDA	11.1.1		11.1.0		11.1.0		11.0.3		11.0.3		11.0.194		11.0.167		10.2.89		10.2.89		10.2.89
cuBLAS	11.3.0.106		11.2.1.74		11.2.1.74		11.2.0.252		11.2.0.252		11.1.0.229		11.1.0.213		10.2.2.89		10.2.2.89		10.2.2.89
cuDNN	8.0.5		8.0.4		8.0.4		8.0.4		8.0.2		8.0.1		8.0.1		7.6.5		7.6.5		7.6.5
NCCL	2.8.3		2.8.2		2.7.8		2.7.8		2.7.8		2.7.6		2.7.5		2.5.6		2.5.6		2.5.6
NVIDIA Optimized Frameworks
Kaldi	7905fe5a Python 3.8 MLNX_OFED 5.1 OpenMPI 4.0.5 Nsight Compute 2020.2.1.8 Nsight Systems 2020.3.4.32 TensorRT 7.2.2		edf6963c Python 3.6 MLNX_OFED 5.1 OpenMPI 4.0.5 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32 TensorRT 7.2.1		49dab837 Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32 TensorRT 7.2.1		f88d5a36 Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6 TensorRT 7.1.3		f88d5a36 Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6 TensorRT 7.1.3		67db30cc Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.1.8 Nsight Systems 2020.3.2.6 TensorRT 7.1.3		2c7e78f Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.0.33 Nsight Systems 2020.2.5.8 TensorRT 7.1.2		da71f301 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 TensorRT 7.0.0		5.5 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 TensorRT 7.0.0		5.5 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorRT 7.0.0
Kaldi	Docker image size: 9.75 GB		Docker image size: 8.72 GB		Docker image size: 8.69 GB		Docker image size: 7.49 GB		Docker image size: 7.36 GB		Docker image size: 6.94 GB		Docker image size: 6.82 GB		Docker image size: 5.76 GB		Docker image size: 5.53 GB		Docker image size: 5.55 GB
NVCaffe															0.17.3 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 TensorRT 7.0.0 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.1.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard		0.17.3 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 TensorRT 7.0.0 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.1.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard		0.17.3 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorRT 7.0.0 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.1 JupyterLab 1.1.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard
NVCaffe															Docker image size: 4.82 GB		Docker image size: 4.82 GB		Docker image size: 4.85 GB
DIGITS	6.1.1 including Python 3.8 MLNX_OFED 5.1 OpenMPI 4.0.5 Nsight Compute 2020.2.1.8 Nsight Systems 2020.3.4.32 TensorFlow 1.15.4 TensorRT 7.2.2		6.1.1 including Python 3.6 MLNX_OFED 5.1 OpenMPI 4.0.5 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32 TensorFlow 1.15.4 TensorRT 7.2.1		6.1.1 including Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32 TensorFlow 1.15.4 TensorRT 7.2.1		6.1.1 including Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6 TensorFlow 1.15.3 TensorRT 7.1.3		6.1.1 including Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6 TensorFlow 1.15.3 TensorRT 7.1.3		6.1.1 including Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.1.8 Nsight Systems 2020.3.2.6 TensorFlow 1.15.3 TensorRT 7.1.3		6.1.1 including Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.0.33 Nsight Systems 2020.2.5.8 TensorFlow 1.15.2 TensorRT 7.1.2		6.1.1 including Python 3.6 NVCaffe 0.17.3 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 TensorFlow 1.15.2 TensorRT 7.0.0		6.1.1 including Python 3.6 NVCaffe 0.17.3 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 TensorFlow 1.15.2 TensorRT 7.0.0		6.1.1 including Python 3.6 NVCaffe 0.17.3 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorFlow 1.15.0 TensorRT 7.0.0
DIGITS	Docker image size: 16.1 GB		Docker image size: 15.3 GB		Docker image size: 15.2 GB		Docker image size: 13.3 GB		Docker image size: 12.9 GB		Docker image size: 12.4 GB		Docker image size with TensorFlow: 12.4 GB		Docker image size with TensorFlow: 10.5 GB Docker image size with Caffe: 5.06 GB		Docker image size with TensorFlow: 10.5 GB Docker image size with Caffe: 5.06 GB		Docker image size with TensorFlow: 9.36 GB Docker image size with Caffe: 5.01 GB
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet	1.8.0.rc0 including Sockeye 2.2.3 MLNX_OFED 5.1 OpenMPI 4.0.5 Horovod 0.20.2 Nsight Compute 2020.2.1.8 Nsight Systems 2020.3.4.32 GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 7.2.2 DALI 0.28 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.8.0.rc0 including Sockeye 2.2.3 MLNX_OFED 5.1 OpenMPI 4.0.5 Horovod 0.20.2 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32 GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 7.2.1 DALI 0.27 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.7.0 including ONNX exporter 0.1 Sockeye 2.1.16 MLNX_OFED OpenMPI 3.1.6 Horovod 0.19.1 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32 GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 7.2.1 DALI 0.26 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.7.0 including ONNX exporter 0.1 Sockeye 2.1.16 MLNX_OFED OpenMPI 3.1.6 Horovod 0.19.1 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6 GluonCV Toolkit 0.7 GluonNLP Toolkit 0.8.1 TensorRT 7.1.3 DALI 0.25.1 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.6.0 including ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.6 Horovod 0.19.1 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6 GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 7.1.3 DALI 0.24 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.6.0 including ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.6 Horovod 0.19.1 Nsight Compute 2020.1.1.8 Nsight Systems 2020.3.2.6 GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 7.1.3 DALI 0.23 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.6.0 including ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.6 Horovod 0.19.1 Nsight Compute 2020.1.0.33 Nsight Systems 2020.2.5.8 GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 7.1.2 DALI 0.22 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.6.0 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.19.0 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 DALI 0.19.0 GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 7.0.0 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.6.0.rc2 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.19.0 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 DALI 0.18.0 Beta GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 7.0.0 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.5.1 commit c98184806 from September 4, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.18.1 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 DALI 0.17.0 Beta GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 7.0.0 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.2.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard
	Docker image size: 11.8 GB		Docker image size: 10.7 GB		Docker image size: 12.7 GB		Docker image size: 11.6 GB		Docker image size: 8.68 GB		Docker image size: 8.98 GB		Docker image size: 9.16 GB		Docker image size: 6.73 GB		Docker image size: 6.59 GB		Docker image size: 6.11 GB
PyTorch	1.8.0a0+1606899 including Python 3.8 APEX MLNX_OFED 5.1 OpenMPI 4.0.5 Nsight Compute 2020.2.1.8 Nsight Systems 2020.3.4.32 TensorBoard 1.15+nv TensorRT 7.2.2 DALI 0.28 MAGMA 2.5.2 DLProf 0.18.0 PyProf r20.12 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.14 JupyterLab Server 1.0.6 Jupyter-TensorBoard PyProf cb1099f6b		1.8.0a0+17f8c32 including Python 3.6 APEX MLNX_OFED 5.1 OpenMPI 4.0.5 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32 TensorBoard 1.15+nv TensorRT 7.2.1 DALI 0.27 MAGMA 2.5.2 DLProf 0.17.0 PyProf r20.11 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.14 JupyterLab Server 1.0.6 Jupyter-TensorBoard PyProf cb1099f6b		1.7.0a0+7036e91 including Python 3.6 APEX MLNX_OFED OpenMPI 4.0.5 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32 TensorBoard 1.15+nv TensorRT 7.2.1 DALI 0.26 MAGMA 2.5.2 DLProf 0.16.0 PyProf r20.10 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.14 JupyterLab Server 1.0.6 Jupyter-TensorBoard PyProf cb1099f6b		1.7.0a0+8deb4fe including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6 TensorBoard 1.15+nv TensorRT 7.1.3 DALI 0.25.1 MAGMA 2.5.2 DLProf 0.15.0 PyProf r20.09 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.14 JupyterLab Server 1.0.6 Jupyter-TensorBoard PyProf cb1099f6b		1.7.0a0+6392713 including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6 TensorBoard 1.15+nv TensorRT 7.1.3 DALI 0.24 MAGMA 2.5.2 DLProf 0.14.0 PyProf r20.08 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.14 JupyterLab Server 1.0.6 Jupyter-TensorBoard PyProf cb1099f6b		1.6.0a0+9907a3e including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.1.8 Nsight Systems 2020.3.2.6 TensorBoard 1.15+nv TensorRT 7.1.3 DALI 0.23 MAGMA 2.5.2 DLProf 0.13.0 PyProf r20.07 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.14 JupyterLab Server 1.0.6 Jupyter-TensorBoard PyProf cb1099f6b		1.6.0a0+9907a3e including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.0.33 Nsight Systems 2020.2.5.8 TensorBoard 1.15+nv TensorRT 7.1.3 DALI 0.22 MAGMA 2.5.2 DLProf 0.12.0 PyProf r20.06 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.14 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.5.0a0+8f84ded including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 TensorBoard 2.1.0 DALI 0.19.0 TensorRT 7.0.0 MAGMA 2.5.2 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.5.0a0+3bbb36e including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 TensorBoard 2.1.0 DALI 0.18.0 Beta TensorRT 7.0.0 MAGMA 2.5.2 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.4.0a0+a5b4d78 including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorBoard 2.1.0 DALI 0.17.0 Beta TensorRT 7.0.0 MAGMA 2.5.2 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard
PyTorch	Docker image size: 14.2 GB		Docker image size: 13.2 GB		Docker image size: 12.9 GB		Docker image size: 11.1 GB		Docker image size: 12.2 GB		Docker image size: 11.9 GB		Docker image size: 11.9 GB		Docker image size: 9.41 GB		Docker image size: 9.11 GB		Docker image size: 9.12 GB
TensorFlow	2.3.1 including Python 3.8 Horovod 0.20.2 OpenMPI 4.0.5 TensorBoard 2.3.0+nv20.11 MLNX_OFED 5.1 TensorRT 7.2.2 DALI 0.28 Nsight Compute 2020.2.1.8 Nsight Systems 2020.3.4.32 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.4 including `20.12-tf1-py3`contains Python 3.8 Horovod 0.20.2 OpenMPI 4.0.5 TensorBoard 1.15.0+nv20.11 MLNX_OFED 5.1 OpenSeq2Seq at commit a81babd TensorRT 7.2.2 DALI 0.28 DLProf 0.18.0 Nsight Compute 2020.2.1.8 Nsight Systems 2020.3.4.32 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.3.1 including Python 3.6 Horovod 0.20.2 OpenMPI 4.0.5 TensorBoard 2.3.0+nv20.11 MLNX_OFED 5.1 TensorRT 7.2.1 DALI 0.27 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.4 including `20.11-tf1-py3`contains Python 3.6 Horovod 0.20.2 OpenMPI 4.0.5 TensorBoard 1.15.0+nv20.11 MLNX_OFED 5.1 OpenSeq2Seq at commit a81babd TensorRT 7.2.1 DALI 0.27 DLProf 0.17.0 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.3.1 including Python 3.6 Horovod 0.20.0 OpenMPI 3.1.6 TensorBoard 2.3.2 MLNX_OFED TensorRT 7.2.1 DALI 0.26 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.4 including `20.10-tf1-py3`contains Python 3.6 Horovod 0.20.0 OpenMPI 3.1.6 TensorBoard 1.15.0 MLNX_OFED OpenSeq2Seq at commit a81babd TensorRT 7.2.1 DALI 0.26 DLProf 0.15.0 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.3.0 including Python 3.6 Horovod 0.19.5 OpenMPI 3.1.6 TensorBoard 2.3.2 MLNX_OFED TensorRT 7.1.3 DALI 0.25.1 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.3 including `20.09-tf1-py3`contains Python 3.6 Horovod 0.19.1 OpenMPI 3.1.6 TensorBoard 2.2.2 MLNX_OFED OpenSeq2Seq at commit a81babd TensorRT 7.1.3 DALI 0.25.1 DLProf 0.15.0 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.2.0 including Python 3.6 Horovod 0.19.5 OpenMPI 3.1.6 TensorBoard 2.2.2 MLNX_OFED TensorRT 7.1.3 DALI 0.24 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.3 including `20.08-tf1-py3`contains Python 3.6 Horovod 0.19.1 OpenMPI 3.1.6 TensorBoard 2.2.2 MLNX_OFED OpenSeq2Seq at commit a81babd TensorRT 7.1.3 DALI 0.24 DLProf 0.14.0 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.2.0 including Python 3.6 Horovod 0.19.5 OpenMPI 3.1.6 TensorBoard 2.2.2 MLNX_OFED TensorRT 7.1.3 DALI 0.23 Nsight Compute 2020.1.1.8 Nsight Systems 2020.3.2.6 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.3 including `20.07-tf1-py3`contains Python 3.6 Horovod 0.19.1 OpenMPI 3.1.6 TensorBoard 2.2.2 MLNX_OFED OpenSeq2Seq at commit a81babd TensorRT 7.1.3 DALI 0.23 DLProf 0.13.0 Nsight Compute 2020.1.1.8 Nsight Systems 2020.3.2.6 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.2.0 including Python 3.6 Horovod 0.19.2 OpenMPI 3.1.6 TensorBoard 2.2.2 MLNX_OFED TensorRT 7.1.3 DALI 0.23 Nsight Compute 2020.1.0.33 Nsight Systems 2020.2.5.8 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.2 including `20.06-tf1-py3`contains Python 3.6 Horovod 0.19.1 OpenMPI 3.1.6 TensorBoard 2.2.2 MLNX_OFED OpenSeq2Seq at commit a81babd TensorRT 7.1.2 DALI 0.22 DLProf 0.12.0 Nsight Compute 2020.1.0.33 Nsight Systems 2020.2.5.8 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.1.0 including Python 3.6 Horovod 0.19.0 OpenMPI 3.1.4 TensorBoard 2.1.0 MLNX_OFED TensorRT 7.0.0 NCCL 2.6.3 DALI 0.19.0 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.2 including `20.03-tf1-py3` contains Python 3.6 Horovod 0.19.0 OpenMPI 3.1.4 TensorBoard 1.15.0+nv MLNX_OFED OpenSeq2Seq at commit a81babd TensorRT 7.0.0 NCCL 2.6.3 DALI 0.19.0 DLProf 0.10.0 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.1.0 including Python 3.6 Horovod 0.19.0 OpenMPI 3.1.4 TensorBoard 2.1.0 MLNX_OFED TensorRT 7.0.0 DALI 0.18.0 Beta Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.2 including `20.02-tf1-py3` contains Python 3.6 Horovod 0.19.0 OpenMPI 3.1.4 TensorBoard 1.15.0+nv MLNX_OFED OpenSeq2Seq at commit a81babd TensorRT 7.0.0 DALI 0.18.0 Beta DLProf 20.02 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard	2.0.0 including Python 3.6 Horovod 0.18.2 OpenMPI 3.1.4 TensorBoard 2.0.2 MLNX_OFED TensorRT 7.0.0 DALI 0.17.0 Beta Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.0 including `20.01-tf1-py2` contains Python 2.7 or `20.01-tf1-py3` contains Python 3.6 Horovod 0.18.2 OpenMPI 3.1.4 TensorBoard 1.15.0+nv MLNX_OFED OpenSeq2Seqat commit a81babd TensorRT 7.0.0 DALI 0.17.0 Beta DLProf 20.01 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.2.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard
TensorFlow	Docker image size: 12.2 GB	Docker image size: 15.2 GB	Docker image size: 11.6 GB	Docker image size: 14.4 GB	Docker image size: 11.4 GB	Docker image size: 14.3 GB	Docker image size: 9.62 GB	Docker image size: 12.4 GB	Docker image size: 11 GB	Docker image size: 11.9 GB	Docker image size: 9.3 GB	Docker image size: 11.5 GB	Docker image size: 9.45 GB	Docker image size: 11.5 GB	Docker image size: 8.05 GB	Docker image size: 9.53 GB	Docker size image: 7.42 GB	Docker size image: 9.49 GB	Docker size image: 7.15 GB	Docker image size with Python 2.7: 7.88 GB Docker image size with Python 3.6: 8.39 GB
TensorRT	7.2.2 including: Python 3.8 MLNX_OFED 5.1 OpenMPI 4.0.5 Nsight Compute 2020.2.1.8 Nsight Systems 2020.3.4.32		7.2.1 including: Python 3.6 MLNX_OFED 5.1 OpenMPI 4.0.5 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32		7.2.1 including: Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.2.0.18 Nsight Systems 2020.3.4.32		7.1.3 including: Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6		7.1.3 including: Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.2.4 Nsight Systems 2020.3.2.6		7.1.3 including: Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.1.8 Nsight Systems 2020.3.2.6		7.1.2 including: Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.0.33 Nsight Systems 2020.2.5.8		7.0.0 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1		7.0.0 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1		7.0.0 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1
TensorRT	Docker image size: 7.09 GB		Docker image size: 6.96 GB		Docker image size: 6.93 GB		Docker image size: 5.75 GB		Docker image size: 5.63 GB		Docker image size: 5.57 GB		Docker image size: 4.97 GB		Docker image size with Python 2.7: 4.05 GB Docker image size with Python 3.6: 4.08 GB		Docker image size with Python 2.7: 4.04 GB Docker image size with Python 3.6: 4.07 GB		Docker image size with Python 2.7: 4.08 GB Docker image size with Python 3.6: 4.11 GB
Triton Inference Server	2.6.0 including TensorRT 7.2.2 PyTorch 1.8.0a0+1606899 TensorFlow 1.15.4 TensorFlow 2.3.1 ONNX Runtime 1.5.3 DALI 0.28		2.5.0 including TensorRT 7.2.1 PyTorch 1.8.0a0+17f8c32 TensorFlow 1.15.4 TensorFlow 2.3.1 ONNX Runtime 1.5.3 DALI 0.27		2.4.0 including TensorRT 7.2.1 PyTorch 1.7.0a0+6392713 TensorFlow 1.15.4 TensorFlow 2.3.1 ONNX Runtime 1.4.0 DALI 0.26		2.3.0 including TensorRT 7.1.3 PyTorch 1.7.0a0+6392713 TensorFlow 1.15.3 TensorFlow 2.3.0 ONNX Runtime 1.4.0		2.3.0 including TensorRT 7.1.3 1.7.0a0+6392713 TensorFlow 1.15.3 TensorFlow 2.2.0 ONNX Runtime 1.4.0		1.15.0 and 2.1.0 and including TensorRT 7.1.3 PyTorch 1.6.0a0+9907a3e TensorFlow 1.15.3 ONNX Runtime 1.3.0		1.14.0 and 2.0.0 including TensorRT 7.1.2 PyTorch 1.6.0a0+9907a3e TensorFlow 1.15.2 ONNX Runtime 1.3.0		1.12.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 7.0.0		1.11.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 7.0.0		1.10.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 7.0.0
Triton Inference Server	Docker image size: 15.6 GB		Docker image size: 11.55 GB		Docker image size: 11.3 GB		Docker image size: 8.3 GB		Docker image size: 9.97 GB		Docker image size: 8.22 GB		Docker image size for 1.14.0: 9.73 GB	Docker image size for 2.0.0: 8.68 GB	Docker image size: 6.31 GB		Docker image size: 6.07 GB		Docker image size: 6.16 GB
TensorFlow For Jetson	TensorFlow 1.15.4 and 2.3.1 for Jetson		TensorFlow 1.15.4 and 2.3.1 for Jetson		TensorFlow 1.15.4 and 2.3.1 for Jetson		TensorFlow 1.15.3 and 2.3.0 for Jetson		TensorFlow 1.15.3 and 2.2.0 for Jetson		TensorFlow 1.15.3 and 2.2.0 for Jetson		TensorFlow 1.15.2 and 2.1.0 for Jetson		TensorFlow 1.15.2 and 2.1.0 for Jetson		TensorFlow 1.15.2 and 2.1.0 for Jetson		TensorFlow 1.15.0 and 2.0.0 for Jetson
Triton for Jetson	Triton Inference Server 2.6.0 for Jetson		Triton Inference Server 2.5.0 for Jetson		Triton Inference Server 2.4.0 for Jetson

7. 19.xx Framework Containers Support Matrix

Important:

Content that is included in <<>> brackets indicates new content from the previously published version.

Note:

The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 19.01 release of an image was released in January, 2019.

19.xx container images

	Container Image	19.12		19.11		19.10	19.09	19.08	19.07	19.06	19.05	19.04	19.03	19.02	19.01
Supported Platform	Host OS	DGX OS Server 4.1.0+ (DGX-1)²⁴ 4.1.0+ (DGX-2) DGX OS Desktop 4.1.0+ (DGX Station)¹		DGX OS Server 4.1.0+ (DGX-1)¹ 4.1.0+ (DGX-2) DGX OS Desktop 4.1.0+ (DGX Station)¹		DGX OS Server 4.1.0+ (DGX-1)¹ 4.1.0+ (DGX-2) DGX OS Desktop 4.1.0+ (DGX Station)¹	DGX OS Server 4.1.0+, 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1)¹ 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 4.0.4+ and 3.1.2+ (DGX Station)¹	DGX OS Server 4.1.0+, 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1)¹ 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 4.0.4+ and 3.1.2+ (DGX Station)¹	DGX OS Server 4.1.0+, 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1)¹ 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 4.0.4+ and 3.1.2+ (DGX Station)¹	DGX OS Server 4.1.0+, 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1)¹ 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 4.0.4+ and 3.1.2+ (DGX Station)¹	DGX OS Server 4.1.0+, 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1)¹ 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 4.0.4+ and 3.1.2+ (DGX Station)¹	DGX OS Server 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 4.0.4+ and 3.1.2+ (DGX Station)	DGX OS Server 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 4.0.4+ and 3.1.2+ (DGX Station)	DGX OS Server 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 4.0.4+ and 3.1.2+ (DGX Station)	DGX OS Server 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 4.0.4+ and 3.1.2+ (DGX Station)
Supported Platform	NVIDIA Driver	Release 19.12 is based on CUDA 10.2.89, which requires NVIDIA driver release 440.33.01. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+, 410, 418.xx or 440.30 The CUDA driver's compatibility package only supports particular drivers. ³		Release 19.11 is based on CUDA 10.2.89, which requires NVIDIA driver release 440.xx. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+, 410 or 418.xx. The CUDA driver's compatibility package only supports particular drivers. ²		Release 19.10 is based on CUDA 10.1.243, which requires NVIDIA driver release 418.xx. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.09 is based on CUDA 10.1.243, which requires NVIDIA driver release 418.xx. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.08 is based on CUDA 10.1.243, which requires NVIDIA driver release 418.87. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.07 is based on CUDA 10.1.168, which requires NVIDIA driver release 418.67. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.06 is based on CUDA 10.1.168, which requires NVIDIA driver release 418.xx. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.05 is based on CUDA 10.1 Update 1, which requires NVIDIA driver release 418.xx. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.04 is based on CUDA 10.1, which requires NVIDIA driver release 418.xx.x+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.03 is based on CUDA 10.1, which requires NVIDIA driver release 418.xx+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.02 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.01 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+. The CUDA driver's compatibility package only supports particular drivers. ²
Supported Hardware	GPU Model	Turing Volta Pascal		Turing Volta Pascal		Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal
Base Image	Container OS	Ubuntu 18.04		Ubuntu 18.04		Ubuntu 18.04	Ubuntu 18.04	Ubuntu 18.04	Ubuntu 18.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04
	CUDA	10.2.89		10.2.89		10.1.243	10.1.243	10.1.243	10.1.168	10.1.168	10.1 Update 1	10.1.105	10.1.105	10.0.130	10.0.130
	cuBLAS	10.2.2.89		10.2.2.89		10.2.1.243	10.2.1.243	10.2.1.243	10.2.0.168	10.2.0.168	10.1 Update 1	10.1.0.105	10.1.105	10.0.130	10.0.130
	cuDNN	7.6.5		7.6.5		7.6.4	7.6.3	7.6.2	7.6.1	7.6.0	7.6.0	7.5.0	7.5.0	7.4.2	7.4.2
	NCCL	2.5.6		2.5.6		2.4.8	2.4.8	2.4.8	2.4.7	2.4.7	2.4.6	2.4.6	2.4.3	2.3.7	2.3.7
NVIDIA Optimized Frameworks	Kaldi	5.5 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorRT 6.0.1		5.5 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.5.2 TensorRT 6.0.1		5.5 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1 TensorRT 6.0.1	5.5 including Python 3.5 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2 TensorRT 6.0.1	5.5 including Python 3.5 MLNX_OFED +4.0 OpenMPI 3.1.4 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9 TensorRT 5.1.5	5.5 including Python 3.5 MLNX_OFED +3.4 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30 TensorRT 5.1.5	5.5 including Python 3.5 OpenMPI 3.1.3 TensorRT 5.1.5 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94	5.5 including Python 3.5 Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94 OpenMPI 3.1.3 TensorRT 5.1.5	5.5 including Python 3.5 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8 TensorRT 5.1.2 RC	5.5 including Python 3.5 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 10.1.105 TensorRT 5.1.2 RC
	Kaldi	Docker image size: 5.49 GB		Docker image size: 5.61 GB		Docker image size: 5.63 GB	Docker image size: 5.57 GB	Docker image size: 5.57 GB	Docker image size: 5.00 GB	Docker image size: 5.11 GB	Docker image size: 5.11 GB	Docker image size: 5.01 GB	Docker image size: 5.09 GB
	NVCaffe	0.17.3 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorRT 6.0.1 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.1 JupyterLab 1.1.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard		0.17.3 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.5.2 TensorRT 6.0.1 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.1 JupyterLab 1.1.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard		0.17.3 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1 TensorRT 6.0.1 Jupyter and JupyterLab: Jupyter Client 5.3.3 Jupyter Core 4.5.0 Jupyter Notebook 6.0.1 JupyterLab 1.1.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard	0.17.3 including Python 2.7 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2 TensorRT 6.0.1 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 6.0.1 JupyterLab 1.1.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard	0.17.3 including Python 2.7 MLNX_OFED +4.0 OpenMPI 3.1.4 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9 TensorRT 5.1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 Jupyter Notebook 6.0.0 JupyterLab 1.0.5 JupyterLab Server 1.0.0 Jupyter-TensorBoard	0.17.3 including Python 2.7 MLNX_OFED +3.4 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30 TensorRT 5.1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	0.17.3 including Python 2.7 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94 TensorRT 5.1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.6 JupyterLab Server 0.2.0	0.17.3 including Python 2.7 OpenMPI 3.1.3 Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94 TensorRT 5.1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.4 JupyterLab Server 0.2.0	0.17.3 including Python 2.7 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8 TensorRT 5.1.2 RC Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.4 JupyterLab Server 0.2.0	0.17.3 including Python 2.7 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 10.1.105 TensorRT 5.1.2 RC Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.4 JupyterLab Server 0.2.0	0.17.2 including Python 2.7 OpenMPI 3.1.3 TensorRT 5.0.2 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.4 JupyterLab Server 0.2.0	0.17.2 including Python 2.7 OpenMPI 3.1.3 TensorRT 5.0.2
	NVCaffe	Docker image size: 4.81 GB		Docker image size: 5.02 GB		Docker image size: 5.1 GB	Docker image size: 5.02 GB	Docker image size: 5.02 GB	Docker image size: 4.45 GB	Docker image size: 4.33 GB	Docker image size: 4.33 GB	Docker image size: 4.29 GB	Docker image size: 4.42 GB	Docker image size: 3.56 GB	Docker image size: 3.51 GB
	DIGITS	6.1.1 including Python 3.6 NVCaffe 0.17.3 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorFlow 1.15.0 TensorRT 6.0.1		NA		6.1.1 including Python 2.7 NVCaffe 0.17.3 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1 TensorFlow 1.14.0 TensorRT 6.0.1	6.1.1 including Python 2.7 NVCaffe 0.17.3 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2 TensorFlow 1.14.0 TensorRT 6.0.1	6.1.1 including Python 2.7 NVCaffe 0.17.3 MLNX_OFED +4.0 OpenMPI 3.1.4 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9 TensorFlow 1.14.0 TensorRT 5.1.5	6.1.1 including Python 2.7 NVCaffe 0.17.3 MLNX_OFED +3.4 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30 TensorFlow 1.14.0 TensorRT 5.1.5	6.1.1 including Python 2.7 NVCaffe 0.17.3 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94 TensorFlow 1.13.1 TensorRT 5.1.5	6.1.1 including Python 2.7 NVCaffe 0.17.3 OpenMPI 3.1.3 Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94 TensorFlow 1.13.1 TensorRT 5.1.5	6.1.1 including Python 2.7 NVCaffe 0.17.3 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8 TensorFlow 1.13.1 TensorRT 5.1.2 RC	6.1.1 including Python 2.7 NVCaffe 0.17.3 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 10.1.105 TensorFlow 1.13.1 TensorRT 5.1.2 RC	6.1.1 including Python 2.7 NVCaffe 0.17.2 OpenMPI 3.1.3 TensorFlow 1.13.0-rc0 TensorRT 5.0.2	6.1.1 including Python 2.7 NVCaffe 0.17.2 OpenMPI 3.1.3 TensorFlow 1.12.0 TensorRT 5.0.2
	DIGITS	Docker image size with TensorFlow: 9.3 GB Docker image size with Caffe: 4.97 GB		NA		Docker image size with TensorFlow: 8.78 GB Docker image size with Caffe: 5.22 GB	Docker image size with TensorFlow: 7.74 GB Docker image size with Caffe: 5.13 GB	Docker image size with TensorFlow: 7.74 GB Docker image size with Caffe: 5.13 GB	Docker image size with TensorFlow: 7.14 GB Docker image size with Caffe: 4.57 GB	Docker image size with TensorFlow: 7.86 GB Docker image size with Caffe: 4.45 GB	Docker image size with TensorFlow: 7.00 GB Docker image size with Caffe: 4.45 GB	Docker image size with TensorFlow: 7.04 GB Docker image size with Caffe: 4.41 GB	Docker image size with TensorFlow: 6.92 GB Docker image size with Caffe: 4.49 GB	Docker image size with TensorFlow: 6.43 GB Docker image size with Caffe: 3.70 GB	Docker image size with TensorFlow: 5.96 GB Docker image size with Caffe: 3.66 GB
	NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet	1.5.1 commit c98184806 from September 4, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.18.1 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 DALI 0.16.0 Beta GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 6.0.1 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.2.3 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.5.1 commit c98184806 from September 4, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.18.1 Nsight Compute 2019.5.0 Nsight Systems 2019.5.2 DALI 0.15.0 Beta GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 6.0.1 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.2.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.5.1 commit c98184806 from September 4, 2019 including Python 3.6 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.18.1 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1 DALI 0.14.0 Beta GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 6.0.1 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.3 Jupyter Core 4.5.0 Jupyter Notebook 6.0.1 JupyterLab 1.1.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard	1.5.0 commit 006486af3 from August 28, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.18.1 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2 DALI 0.13.0 Beta GluonCV Toolkit 0.4 GluonNLP Toolkit 0.8.1 TensorRT 6.0.1 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 6.0.1 JupyterLab 1.1.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard	1.5.0 commit 75a9e187d from June 27, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED +4.0 OpenMPI 3.1.4 Horovod 0.16.4 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9 DALI 0.12.0 Beta GluonCV Toolkit 0.4 GluonNLP Toolkit 0.7.1 TensorRT 5.1.5 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 Jupyter Notebook 6.0.0 JupyterLab 1.0.5 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.5.0.rc2 including upstream commits up through commit 75a9e187d from June 27, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED +3.4 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30 Horovod 0.16.2 DALI 0.11.0 Beta TensorRT 5.1.5 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.0 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.4.1 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.61 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94 Horovod 0.16.2 DALI 0.10.0 Beta TensorRT 5.1.5 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.6 JupyterLab Server 0.2.0	1.4.0 commit 87c7addcd from February 12, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.61 OpenMPI 3.1.3 Horovod 0.16.1 Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94 DALI 0.9.1 Beta TensorRT 5.1.5 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.4.0 commit 87c7addcd from February 12, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.61 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8 Horovod 0.13.11 DALI 0.8.1 Beta TensorRT 5.1.2 RC Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.4.0 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.61 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 10.1.105 Horovod 0.13.11 DALI 0.7 Beta TensorRT 5.1.2 RC Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.4.0.rc0 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.61 OpenMPI 3.1.3 Horovod 0.13.11 TensorRT 5.0.2 DALI 0.6.1 Beta Tensor Core optimized example: ResNet50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.4.0.rc0 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.61 OpenMPI 3.1.3 Horovod 0.13.11 TensorRT 5.0.2 DALI 0.6 Beta Tensor Core optimized example: ResNet50 v1.5
		Docker image size: 6.05 GB		Docker image size: 6.14 GB		Docker image size: 6.2 GB	Docker image size: 5.75 GB	Docker image size: 5.75 GB	Docker image size: 5.11 GB	Docker image size: 4.99 GB	Docker image size: 4.95 GB	Docker image size: 4.9 GB	Docker image size: 4.73 GB	Docker image size: 3.83 GB	Docker image size: 3.82 GB
	PyTorch	1.4.0a0+a5b4d78 including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorBoard 2.1.0 DALI 0.16.0 Beta TensorRT 6.0.1 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.4.0a0+174e1ba including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.5.2 TensorBoard 2.0.1 DALI 0.15.0 Beta TensorRT 6.0.1 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.3.0a0+24ae9b5 including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1 TensorBoard 2.0.0 DALI 0.14.0 Beta TensorRT 6.0.1 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.3 Jupyter Core 4.5.0 Jupyter Notebook 6.0.1 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard	1.2.0 including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2 TensorBoard 1.14.0+nv DALI 0.12.0 Beta TensorRT 6.0.1 Tensor Core optimized examples: Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 6.0.1 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard	1.2.0a0 including upstream commits up through commit 9130ab38 from July 31, 2019 as well as a cherry-picked performance fix 9462ca29 including Python 3.6 APEX MLNX_OFED +4.0 OpenMPI 3.1.4 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9 TensorBoard 1.14.0+nv DALI 0.12.0 Beta TensorRT 5.1.5 Tensor Core optimized examples: Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 Jupyter Notebook 6.0.0 JupyterLab 1.0.4 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.2.0a0 including upstream commits up through commit f6aac41 from June 19, 2019 including Python 3.6 APEX MLNX_OFED +3.4 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30 TensorBoard 1.14.0+nv DALI 0.11.0 Beta TensorRT 5.1.5 Tensor Core optimized examples: BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.1.0 commit 0885dd28 from May 28, 2019 including Python 3.6 APEX OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94 DALI 0.10.0 Beta TensorRT 5.1.5 Tensor Core optimized examples: BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.6 JupyterLab Server 0.2.0	1.0.1 commit 828a6a3b from March 31, 2019 including Python 3.6 APEX OpenMPI 3.1.3 Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94 DALI 0.9.1 Beta TensorRT 5.1.5 Tensor Core optimized examples: Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.0.1 commit 9eb0f43 from March 28, 2019 including Python 3.6 APEX OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8 DALI 0.8.1 Beta TensorRT 5.1.2 RC Tensor Core optimized examples: Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.1.0a0+81e025d including Python 3.6 APEX OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 10.1.105 DALI 0.7 Beta TensorRT 5.1.2 RC Tensor Core optimized examples: Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.1.0a0+c42431b including Python 3.6 APEX OpenMPI 3.1.3 DALI 0.6.1 Beta TensorRT 5.0.2 Tensor Core optimized examples: Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.0.0 including Python 3.6 Caffe2 APEx OpenMPI 3.1.3 DALI 0.6 Beta Tensor Core optimized examples: Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 TensorRT 5.0.2
	PyTorch	Docker image size: 9.28 GB		Docker image size: 9.21 GB		Docker image size: 9.32 GB	Docker image size: 9 GB	Docker image size: 9 GB	Docker image size: 8.33 GB	Docker image size: 7.7 GB	Docker image size: 7.55 GB	Docker image size: 7.45 GB	Docker image size: 7.71 GB	Docker image size: 6.61 GB	Docker image size: 7.70 GB
	TensorFlow	2.0.0 including Python 3.6 Horovod 0.18.2 OpenMPI 3.1.4 TensorBoard 2.0.2 MLNX_OFED TensorRT 6.0.1 DALI 0.16.0 Beta Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.0 including `19.12-tf1-py2` contains Python 2.7 or `19.12-tf1-py3` contains Python 3.6 Horovod 0.18.2 OpenMPI 3.1.4 TensorBoard 1.15.0+nv MLNX_OFED OpenSeq2Seqat commit a81babd TensorRT 6.0.1 DALI 0.16.0 Beta DLProf 19.12 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.2.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard	2.0.0 including Python 3.6 Horovod 0.18.1 OpenMPI 3.1.4 TensorBoard 2.0.1 MLNX_OFED TensorRT 6.0.1 DALI 0.15.0 Beta Nsight Compute 2019.5.0 Nsight Systems 2019.5.2 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.0 including `19.11-tf1-py2` contains Python 2.7 or `19.11-tf1-py3` contains Python 3.6 Horovod 0.18.1 OpenMPI 3.1.4 TensorBoard 1.15.0+nv MLNX_OFED OpenSeq2Seqat commit a81babd TensorRT 6.0.1 DALI 0.15.0 Beta DLProf 19.11 Nsight Compute 2019.5.0 Nsight Systems 2019.5.2 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.14.0 including Python 2.7 or Python 3.6 Horovod 0.18.1 OpenMPI 3.1.4 TensorBoard 1.14.0+nv MLNX_OFED OpenSeq2Seq at commit 2e0b1d8 TensorRT 6.0.1 DALI 0.14.0 Beta DLProf 19.10 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1 Tensor Core optimized example: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.3 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.14.0 including Python 2.7 or Python 3.6 Horovod 0.18.0 OpenMPI 3.1.4 TensorBoard 1.14.0+nv MLNX_OFED OpenSeq2Seq at commit 2e0b1d8 TensorRT 6.0.1 DALI 0.13.0 Beta DLProf 19.09 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2 Tensor Core optimized example: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.14.0 including Python 2.7 or Python 3.6 Horovod 0.16.2 OpenMPI 3.1.4 TensorBoard 1.14.0+nv MLNX_OFED +4.0 OpenSeq2Seq at commit 2e0b1d8 TensorRT 5.1.5 DALI 0.12.0 Beta DLProf 19.08 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9 Tensor Core optimized example: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.14.0 including Python 2.7 or Python 3.6 Horovod 0.16.2 OpenMPI 3.1.3 TensorBoard 1.14.0+nv MLNX_OFED +3.4 OpenSeq2Seq at commit 27346d1 TensorRT 5.1.5 DALI 0.11.0 Beta DLProf 19.07 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30 Tensor Core optimized example: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.1 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.13.1 including Python 2.7 or Python 3.5 Horovod 0.16.2 OpenMPI 3.1.3 TensorBoard 1.13.1+nv MLNX_OFED 3.4 OpenSeq2Seq at commit 27346d1 TensorRT 5.1.5 DALI 0.10.0 Beta DLProf 19.06 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94 Tensor Core optimized example: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.6 JupyterLab Server 0.2.0	1.13.1 including Python 2.7 or Python 3.5 Horovod 0.16.1 OpenMPI 3.1.3 TensorBoard 1.13.1 MLNX_OFED 3.4 OpenSeq2Seq TensorRT 5.1.5 DALI 0.9.1 Beta Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94 Tensor Core optimized example: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.13.1 including Python 2.7 or Python 3.5 Horovod 0.16.1 OpenMPI 3.1.3 TensorBoard 1.13.1 MLNX_OFED 3.4 OpenSeq2Seq TensorRT 5.1.2 RC DALI 0.8.1 Beta Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8 Tensor Core optimized example: SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.13.1 including Python 2.7 or Python 3.5 Horovod 0.16.0 OpenMPI 3.1.3 TensorBoard 1.13.1 MLNX_OFED 3.4 OpenSeq2Seq TensorRT 5.1.2 RC DALI 0.7 Beta Nsight Compute 10.1.105 Nsight Systems 10.1.105 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.13.0-rc0 including Python 2.7 or Python 3.5 Horovod 0.15.1 OpenMPI 3.1.3 TensorBoard 1.12.2 MLNX_OFED 3.4 OpenSeq2Seq v8.12 TensorRT 5.0.2 DALI 0.6.1 Beta Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.12.0 including Python 2.7 or Python 3.5 Horovod 0.15.1 OpenMPI 3.1.3 TensorBoard 1.12.0 MLNX_OFED 3.4 OpenSeq2Seq v19.01 TensorRT 5.0.2 DALI 0.6 Beta
	TensorFlow	Docker size image: 7.71 GB	Docker image size with Python 2.7: 7.84 GB Docker image size with Python 3.6: 8.32 GB	Docker image size: 7.78	Docker image size with Python 2.7: 8.02 GB Docker image size with Python 3.6: 8.60 GB	Docker image size with Python 2.7: 7.81 GB Docker image size with Python 3.6: 8.38 GB	Docker image size with Python 2.7: 6.78 GB Docker image size with Python 3.6: 7.34 GB	Docker image size with Python 2.7: 6.78 GB Docker image size with Python 3.6: 7.34 GB	Docker image size with Python 2.7: 6.18 GB Docker image size with Python 3.6: 6.71 GB	Docker image size: 6.88 GB	Docker image size: 6.76 GB	Docker image size: 6.8 GB	Docker image size: 6.72 GB	Docker image size: 6.06 GB	Docker image size: 5.57 GB
	TensorRT	6.0.1 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1		6.0.1 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.5.2		6.0.1 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1	6.0.1 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2	5.1.5 including: Python 2.7 or Python 3.6 MLNX_OFED +4.0 OpenMPI 3.1.4 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9	5.1.5 including: Python 2.7 or Python 3.5 MLNX_OFED +3.4 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30	5.1.5 including: Python 2.7 or Python 3.5 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94	5.1.5 including: Python 2.7 or Python 3.5 OpenMPI 3.1.3 Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94	5.1.2 RC including: Python 2.7 or Python 3.5 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8	5.1.2 RC including: Python 2.7 or Python 3.5 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 10.1.105	5.0.2 including: Python 2.7 or Python 3.5 OpenMPI 3.1.3	5.0.2 including: Python 2.7 or Python 3.5 OpenMPI 3.1.3
	TensorRT	Docker image size Python 2.7: 4.03 GB Docker image size with Python 3.6: 4.06 GB		Docker image size Python 2.7: 3.83 GB Docker image size with Python 3.6: 4.16 GB		Docker image size: 4.2 GB	Docker image size: 4.4 GB	Docker image size: 4.4 GB	Docker image size: 3.83 GB	Docker image size: 3.83 GB	Docker image size: 3.83 GB	Docker image size: 3.79 GB	Docker image size: 3.91 GB	Docker image size: 3.01 GB	Docker image size: 3.00 GB
	TensorRT Inference Server	1.9.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 6.0.1		1.8.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 6.0.1		1.7.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 6.0.1	1.6.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 6.0.1	1.5.0 including MLNX_OFED +4.0 OpenMPI 3.1.4 TensorRT 5.1.5	1.4.0 including MLNX_OFED +3.4 OpenMPI 3.1.3 TensorRT 5.1.5	1.3.0 including OpenMPI 3.1.3 TensorRT 5.1.5	1.2.0 including OpenMPI 3.1.3 TensorRT 5.1.5	1.1.0 including OpenMPI 3.1.3 TensorRT 5.1.2 RC	1.0.0 including OpenMPI 3.1.3 TensorRT 5.1.2 RC	0.11.0 Beta including OpenMPI 3.1.3 TensorRT 5.0.2	0.10.0 Beta including OpenMPI 3.1.3 TensorRT 5.0.2
	TensorRT Inference Server	Docker image size: 6.12 GB		Docker image size: 6.15 GB		Docker image size: 8.26 GB	Docker image size: 7.73 GB	Docker image size: 7.73 GB	Docker image size: 7.15 GB	Docker image size: 7.15 GB	Docker image size: 7.02 GB	Docker image size: 5.22 GB	Docker image size: 5.33 GB	Docker image size: 4.42 GB	Docker image size: 4.17 GB
TensorFlow For Jetson		TensorFlow 1.15.0 and 2.0.0 for Jetson		TensorFlow 1.15.0 and 2.0.0 for Jetson		TensorFlow 1.14.0 for Jetson	TensorFlow 1.14.0 for Jetson	TensorFlow 1.14.0 for Jetson	TensorFlow 1.14.0 for Jetson		TensorFlow 1.13.1 for Jetson	TensorFlow 1.13.1 for Jetson	TensorFlow 1.13.1 for Jetson	TensorFlow 1.13.0-rc0 for Jetson	TensorFlow 1.12.0 for Jetson

18.xx Framework Containers Support Matrix

Note:

The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 18.01 release of an image was released in January, 2018.

18.xx container images

	Container Image	18.12	18.11	18.10	18.09	18.08	18.07	18.06	18.05	18.04	18.03	18.02	18.01
Supported Platform	Host OS	DGX OS Server 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 4.0.4+ and 3.1.2+ (DGX Station)	DGX OS Server 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 4.0.4+ and 3.1.2+ (DGX Station) DGX Software Stack for Red Hat Enterprise Linux EL7-18.11 (DGX-1)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)
Supported Platform	NVIDIA Driver	Release 18.12 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+. The CUDA driver's compatibility package only supports particular drivers (see footnote ¹).	Release 18.11 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+. The CUDA driver's compatibility package only supports particular drivers (see footnote ¹).	Release 18.10 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+. The CUDA driver's compatibility package only supports particular drivers (see footnote ¹).	Release 18.09 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+. The CUDA driver's compatibility package only supports particular drivers (see footnote ¹).	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+
Supported Hardware	GPU Model	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Volta and Pascal	Volta and Pascal	Volta and Pascal	Volta and Pascal	Volta and Pascal	Volta and Pascal	Volta and Pascal	Volta and Pascal
Base Image	Container OS	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04
	CUDA	10.0.130	10.0.130	10.0.130	10.0.130 includes: Support for DGX-2 Support for Turing Support for Jetson Xavier CUDA 10 compatibility package version 410.48²⁵	9.0.176	9.0.176	9.0.176	9.0.176	9.0.176	9.0.176	9.0.176	9.0.176
	cuBLAS	10.0.130	10.0.130	10.0.130	10.0.130	9.0.425	9.0.425	9.0.333	9.0.333	9.0.333	9.0.333	9.0.282 Patch 2 and cuBLAS 9.0.234 Patch 1	9.0.282 Patch 2
	cuDNN	7.4.1	7.4.1	7.4.0	7.3.0	7.2.1	7.1.4	7.1.4	7.1.2	7.1.1	7.1.1	7.0.5	7.0.5
	NCCL	2.3.7	2.3.7	2.3.6	2.3.4	2.2.13	2.2.13	2.2.13	2.1.15	2.1.15	2.1.2	2.1.2	2.1.2
NVIDIA Optimized Frameworks	NVCaffe	0.17.2 including Python 2.7 OpenMPI 3.1.2 TensorRT 5.0.2	0.17.1 including Python 2.7 OpenMPI 3.1.2 TensorRT 5.0.2	0.17.1 including Python 2.7 OpenMPI 3.1.2 TensorRT 5.0.0 RC	0.17.1 including Python 2.7 OpenMPI 2.0 TensorRT 5.0.0 RC	0.17.1 including Python 2.7 OpenMPI 2.0	0.17.1 and Python 2.7	0.17.0 and Python 2.7	0.17.0 and Python 2.7	0.17.0 and Python 2.7	0.16.6 and Python 2.7	0.16.5 and Python 2.7	0.16.5 and Python 2.7
	NVCaffe	Docker image size: 3.41 GB	Docker image size: 3.41 GB	Docker image size: 3.41 GB	Docker image size: 3.40 GB	Docker image size: 3.37 GB	Docker image size: 4.29 GB	0.17.0 and Python 2.7	0.17.0 and Python 2.7	0.17.0 and Python 2.7	0.16.6 and Python 2.7	0.16.5 and Python 2.7	0.16.5 and Python 2.7
	Caffe2					0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3
	Caffe2					Docker image size: 3.02 GB	Docker image size: 2.94 GB	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3
	DIGITS	6.1.1 including Python 2.7 OpenMPI 3.1.2 NVCaffe 0.17.2 TensorFlow 1.12.0 TensorRT 5.0.2	6.1.1 including Python 2.7 OpenMPI 3.1.2 NVCaffe 0.17.1 TensorFlow 1.12.0-rc2 TensorRT 5.0.2	6.1.1 including Python 2.7 OpenMPI 3.1.2 NVCaffe 0.17.1 TensorFlow 1.10.0 TensorRT 5.0.0 RC	6.1.1 including Python 2.7 NVCaffe 0.17.1 TensorFlow 1.10.0 TensorRT 5.0.0 RC	6.1.1 including Python 2.7 NVCaffe 0.17.1 TensorFlow 1.9.0 Torch7	6.1.1 including Python 2.7 NVCaffe 0.17.1 TensorFlow 1.8.0 Torch7	6.1.1 including Python 2.7 NVCaffe 0.17.0 TensorFlow 1.8.0 Torch7	6.1.1 including Python 2.7 NVCaffe 0.17.0 TensorFlow 1.7.0 Torch7	6.1.1 including Python 2.7 NVCaffe 0.17.0 TensorFlow 1.7.0 Torch7	6.1.0 including Python 2.7 NVCaffe 0.16.6 TensorFlow 1.4.0 Torch7	6.1.0 including Python 2.7 NVCaffe 0.16.5 TensorFlow 1.4.0 Torch7	6.0.0 including Python 2.7 NVCaffe 0.16.5 TensorFlow 1.4.0 Torch7
	DIGITS	TensorFlow Docker image size: 5.03 GB Caffe Docker image size: 3.56 GB	TensorFlow Docker image size: 5.03 GB Caffe Docker image size: 3.56 GB	Docker image size: 6.17 GB	Docker image size: 5.33 GB	Docker image size: 6.20 GB	Docker image size: 7.16 GB
	Microsoft Cognitive Toolkit					2.5 including Python 3.6 OpenMPI 3.0.0	2.5 including Python 3.6 OpenMPI 3.0.0	2.5 including Python 3.6 OpenMPI 3.0.0	2.5 including Python 3.6 OpenMPI 3.0.0	2.4 including Python 3.6 OpenMPI 3.0.0	2.4 including Python 3.6 OpenMPI 3.0.0	2.3.1 including Python 3.4 OpenMPI 3.0.0	2.3.1 including Python 3.4 OpenMPI 3.0.0
	Microsoft Cognitive Toolkit					Docker image size: 6.17 GB	Docker image size: 6.13 GB	2.5 including Python 3.6 OpenMPI 3.0.0	2.5 including Python 3.6 OpenMPI 3.0.0	2.4 including Python 3.6 OpenMPI 3.0.0	2.4 including Python 3.6 OpenMPI 3.0.0	2.3.1 including Python 3.4 OpenMPI 3.0.0	2.3.1 including Python 3.4 OpenMPI 3.0.0
	NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet	1.3.1 including Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.28 OpenMPI 3.1.2 Horovod 0.15.1 TensorRT 5.0.2 DALI 0.5 Beta	1.3.0 including Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.28 OpenMPI 3.1.2 Horovod 0.15.1 TensorRT 5.0.2 DALI 0.4.1 Beta	1.3.0 including Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.28 OpenMPI 3.1.2 TensorRT 5.0.0 RC DALI 0.4 Beta	1.3.0 including Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.28 TensorRT 5.0.0 RC DALI 0.2 Beta	1.2.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.28 TensorRT 4.0.1 DALI 0.1.2 Beta	1.2.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.23 TensorRT 4.0.1 DALI 0.1.1 Beta	1.2.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.22 TensorRT 4.0.1	1.1.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.13	1.1.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.1	1.1.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.17.4	1.0.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.17.0	1.0.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.16.2
		Docker image size: 3.69 GB	Docker image size: 3.69 GB	Docker image size: 3.68 GB	Docker image size: 3.58 GB	Docker image size: 4.09 GB	Docker image size: 3.93 GB
	PyTorch	0.4.1+ including Python 3.6 Caffe2 APEx OpenMPI 3.1.2 DALI 0.5 Beta Tensor Core Optimized Examples: ResNet50 v1.5 GNMT v2 TensorRT 5.0.2	0.4.1+ including Python 3.6 Caffe2 APEx OpenMPI 3.1.2 DALI 0.4.1 Beta Tensor Core Optimized Examples: ResNet50 v1.5 GNMT v2 TensorRT 5.0.2	0.4.1+ including Python 3.6 Caffe2 APEx OpenMPI 3.1.2 DALI 0.4 Beta Tensor Core Optimized Examples: ResNet50 v1.5 GNMT v2 TensorRT 5.0.0 RC	0.4.1+ including Python 3.6 Caffe2 APEx DALI 0.2 Beta Tensor Core Optimized Examples: ResNet50 v1.5 GNMT v2 TensorRT 5.0.0 RC	0.4.1 including Python 3.6 Caffe2 0.8.1 APEx DALI 0.1.2 Beta Tensor Core Optimized Examples: GNMT v2	0.4.0 including Python 3.6 Caffe2 0.8.1 APEx DALI 0.1.1 Beta	0.4.0 including Python 3.6 Caffe2 0.8.1 APEx	0.4.0 including Python 3.6 Caffe2 0.8.1 APEx	0.3.1 and Python 3.6	0.3.0 and Python 3.6	0.3.0 and Python 3.6	0.3.0 and Python 3.6
	PyTorch	Docker image size: 6.08 GB	Docker image size: 6.08 GB	Docker image size: 6.00 GB	Docker image size: 5.89 GB	Docker image size: 5.64 GB	Docker image size: 5.67 GB	0.4.0 including Python 3.6 Caffe2 0.8.1 APEx	0.4.0 including Python 3.6 Caffe2 0.8.1 APEx	0.3.1 and Python 3.6	0.3.0 and Python 3.6	0.3.0 and Python 3.6	0.3.0 and Python 3.6
	TensorFlow	1.12.0 including Python 2.7 or Python 3.5 Horovod 0.15.1 OpenMPI 3.1.2 TensorBoard 1.12.0 MLNX_OFED 3.4 OpenSeq2Seq v18.12 TensorRT 5.0.2 DALI 0.5 Beta	1.12.0-rc2 including Python 2.7 or Python 3.5 Horovod 0.15.1 OpenMPI 3.1.2 TensorBoard 1.12.0 MLNX_OFED 3.4 OpenSeq2Seq v18.11 TensorRT 5.0.2 DALI 0.4.1 Beta	1.10.0 including Python 2.7 or Python 3.5 Horovod 0.13.10 OpenMPI 3.1.2 TensorBoard 1.10.0 MLNX_OFED 3.4 OpenSeq2Seq v18.10 TensorRT 5.0.0 RC DALI 0.4 Beta	1.10.0 including Python 2.7 or Python 3.5 Horovod 0.13.10 OpenMPI 3.0.0 TensorBoard 1.10.0 MLNX_OFED 3.4 OpenSeq2Seq v18.09 TensorRT 5.0.0 RC DALI 0.2 Beta	1.9.0 including Python 2.7 or Python 3.5 Horovod 0.12.1 OpenMPI 3.0.0 TensorBoard 1.9.0 MLNX_OFED 3.4 OpenSeq2Seq v0.5 TensorRT 4.0.1 DALI 0.1.2 Beta	1.8.0 including Python 2.7 or Python 3.5 Horovod 0.12.1 OpenMPI 3.0.0 TensorBoard 1.8.0 MLNX_OFED 3.4 OpenSeq2Seq v0.4 TensorRT 4.0.1 DALI 0.1.1 Beta	1.8.0 including Python 2.7 or Python 3.5 Horovod 0.12.1 OpenMPI 3.0.0 TensorBoard 1.8.0 MLNX_OFED 3.4 OpenSeq2Seq v0.2 TensorRT 4.0.1	1.7.0 including Python 2.7 or Python 3.5 Horovod 0.12.1 OpenMPI 3.0.0 TensorBoard 1.7.0 MLNX_OFED 3.4 OpenSeq2Seq v0.2 TensorRT 3.0.4	1.7.0 including Python 2.7 or Python 3.5 Horovod 0.11.3 OpenMPI 3.0.0 TensorBoard 0.4.0-rc1 MLNX_OFED 3.4	1.4.0 including Python 2.7 or Python 3.5 Horovod 0.11.3 OpenMPI 3.0.0 TensorBoard 0.4.0-rc1	1.4.0 including Python 2.7 or Python 3.5 Horovod 0.11.2	1.4.0 including Python 2.7 or Python 3.5 Horovod 0.11.2
	TensorFlow	Docker image size: 4.64 GB	Docker image size: 4.64 GB	Docker image size: 4.57 GB	Docker image size: 3.75 GB	Docker image size: 3.40 GB	Docker image size: 3.34 GB					1.4.0 including Python 2.7 or Python 3.5 Horovod 0.11.2	1.4.0 including Python 2.7 or Python 3.5 Horovod 0.11.2
	TensorFlow For Jetson	TensorFlow 1.12.0 for Jetson	TensorFlow 1.12.0-rc2 for Jetson
	TensorRT	TensorRT 5.0.2 including: Python 2.7 or Python 3.5 OpenMPI 3.1.2	TensorRT 5.0.2 including: Python 2.7 or Python 3.5 OpenMPI 3.1.2	TensorRT 5.0.0 RC including: Python 2.7 or Python 3.5 OpenMPI 3.1.2	TensorRT 5.0.0 RC including Python 2.7 or Python 3.5	4.0.1 and Python 2.7 or Python 3.5	4.0.1 and Python 2.7 or Python 3.5	4.0.1 and Python 2.7 or Python 3.5	3.0.4 and Python 2.7	3.0.4 and Python 2.7	3.0.4 and Python 2.7	3.0.4 and Python 2.7	3.0.1 and Python 2.7
	TensorRT	Docker image size: 3.00 GB	Docker image size: 3.00 GB	Docker image size: 2.99 GB	Docker image size: 2.98 GB	Docker image size: 2.56 GB	Docker image size: 2.61 GB	4.0.1 and Python 2.7 or Python 3.5	3.0.4 and Python 2.7	3.0.4 and Python 2.7	3.0.4 and Python 2.7	3.0.4 and Python 2.7	3.0.1 and Python 2.7
	TensorRT Inference Server	0.9.0 Beta including Python 3.5 OpenMPI 3.1.2 TensorRT 5.0.2	0.8.0 Beta including Python 3.5 OpenMPI 3.1.2 TensorRT 5.0.2	0.7.0 Beta including Python 3.5 OpenMPI 3.1.2 TensorRT 5.0.0 RC	0.6.0 Beta including Python 3.5 OpenMPI 2.0 TensorRT 5.0.0 RC	0.5.0 Beta including Python 2.7 or Python 3.5 TensorRT 4.0.1	0.4.0 Beta including Python 2.7 or Python 3.5 TensorRT 4.0.1	0.3.0 Beta including Python 2.7 or Python 3.5 TensorRT 4.0.1	0.2.0 Beta including Python 2.7 or Python 3.5 TensorRT 3.0.4	0.1.0 Beta
	TensorRT Inference Server	Docker image size: 4.17 GB	Docker image size: 4.17 GB	Docker image size: 4.15 GB	Docker image size: 4.42 GB	Docker image size: 2.37 GB	Docker image size: 2.47 GB			0.1.0 Beta
	Theano					1.0.2 and Python 2.7	1.0.2 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7
	Theano					Docker image size: 3.70 GB	Docker image size: 3.74 GB	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7
	Torch					7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7
	Torch					Docker image size: 3.06 GB	Docker image size: 3 GB	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7

17.xx Framework Containers Support Matrix

Note:

The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 17.01 release of an image was released in January, 2017.

17.xx container images

	Container Image	17.12	17.11	17.10	17.09	17.07	17.06	17.05	17.04	17.03	17.02	17.01
Supported Platform	DGX OS	3.1.2+ and 2.1.1+	3.1.2+ and 2.1.1+	3.1.2+ and 2.1.1+	3.1.2+ and 2.1.1+	2.x+ and 1.x+	2.x+ and 1.x+	2.x+ and 1.x+	2.x+ and 1.x+	2.x+ and 1.x+	2.x+ and 1.x+	2.x+ and 1.x+
Supported Platform	NVIDIA Driver	384	384	384	384
Base Image	Ubuntu	16.04	16.04	16.04	16.04	16.04	16.04	16.04	16.04	16.04	14.04	14.04
	CUDA	9.0.176	9.0.176	9.0	9.0	8.0.61.2	8.0.61	8.0.61	8.0.61	8.0.61	8.0.61	8.0.54
	cuBLAS	9.0.234	9.0.234			patch 2
	cuDNN	7.0.5	7.0.4	7.0.3	7.0.2	6.0.21	6.0.21	6.0.21	6.0.20	6.0.20	6.0.13	6.0.10
	NCCL	2.1.2	2.1.2	2.0.5	2.0.5	2.0.3	1.6.1	1.6.1	1.6.1	1.6.1	1.6.1	1.6.1
NVIDIA Optimized Frameworks	NVCaffe	0.16.4	0.16.4	0.16.4	0.16.4	0.16	0.16	0.16	0.16	0.16	0.16	0.16
	Caffe2	0.8.1 and OpenMPI 1.10.3	0.8.1 and OpenMPI 1.10.3	0.8.1 and OpenMPI 1.10.3	0.8.1 and OpenMPI 1.10.3	0.7.0 and OpenMPI 1.10.3	0.7.0 and OpenMPI 1.10.3	0.5.0+ and OpenMPI 1.10.3	0.5.0+ and OpenMPI 1.10.3
	DIGITS	6.0.0 including NVCaffe 0.16.4 TensorFlow 1.4.0 Torch7	6.0.0 including NVCaffe 0.16.4 TensorFlow 1.3.0 Torch7	6.0.0 including NVCaffe 0.16.4 TensorFlow 1.3.0 Torch7	6.0.0 including NVCaffe 0.16.4 TensorFlow 1.3.0 Torch7	6.0.0 including NVCaffe 0.16 Torch7	5.0 including NVCaffe 0.16 Torch7	5.0 including NVCaffe 0.16 Torch7	5.0 including NVCaffe 0.16 Torch7	5.0 including NVCaffe 0.16 Torch7	5.0 including NVCaffe 0.16 Torch7	5.0 including NVCaffe 0.16 Torch7
	Microsoft Cognitive Toolkit	2.2 and OpenMPI 3.0.0	2.2 and OpenMPI 3.0.0	2.2	2.1	2.0	2.0	2.0.rc2	2.0.beta15.0	2.0.beta12.0	2.0.beta9.0	2.0.beta5.0
	NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet	1.0.0	0.12.0	0.11.0	0.11.0.rc3	0.10.0	0.10.0	0.9.3a+	0.9.3a+	0.9.3
	PyTorch	0.2.0	0.2.0	0.2.0	0.2.0	0.1.12	0.1.12	0.1.12	0.1.10
	TensorFlow	1.4.0	1.3.0	1.3.0	1.3.0	1.2.1	1.1.0	1.0.1	1.0.1	1.0.0	0.12.1	0.12.0
	TensorRT	3.0.1
	Theano	1.0.0rc1	1.0.0rc1	0.10beta3	0.10beta1	0.9.0	0.9.0	0.9.0	0.9.0	0.9.0rc3	0.8.0	0.8.0
	Torch	7	7	7	7	7	7	7	7	7	7	7

16.xx Framework Containers Support Matrix

Note:

The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 16.12 release of an image was released in December, 2016.

16.xx container images

	Container Image	16.12
Supported Platform	DGX OS	2.x+ and 1.x+
Supported Platform	NVIDIA Driver
Base Image	Ubuntu	14.04
	CUDA	8.0.54
	cuBLAS
	cuDNN	6.0.5
	NCCL	1.6.1
NVIDIA Optimized Frameworks	NVCaffe	0.16
	Caffe2
	DIGITS	5.0 including NVCaffe 0.16 Torch7
	Microsoft Cognitive Toolkit	2.0.beta5.0
	NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet
	PyTorch
	TensorFlow	0.12.0
	TensorRT
	Theano	0.8.0
	Torch	7

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⁵ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

⁶ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

⁷ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

⁸ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

⁹ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

¹⁰ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

¹¹ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

¹² NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

¹³ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

¹⁴ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

¹⁵ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

¹⁶ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

¹⁷ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

¹⁸ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

¹⁹ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

²⁰ NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

²¹ Starting with version 20.10, the Framework Containers require at least NVIDIA OFED driver version 4.9 installed on a system for NCCL multi-node applications (the NVIDIA OFED driver 4.9 LTS is included in DGX OS 4.6). You might see a message if you are running a Framework Container on a system that does not have the required NVIDIA OFED driver version installed. This message can be ignored if you do not plan to use NCCL for multi-node applications. (4.6+ multi-node NCCL)

²² For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

²³ Versions 20.09 and earlier have Mellanox OFED 4.6 Mellanox verbs libraries and are able to work with the kernel mode drivers from Mellanox OFED 4.7 or earlier (for multinode).

²⁴ We will stop supporting DGX OS 3.x in September 2019. Ensure you upgrade your DGX systems to DGX OS Server version 4.1 or greater or DGX OS Desktop version 4.1 or greater to obtain security and feature updates.

²⁵ The compatibility package ensures that Linux drivers R384 are compatible with Tesla GPUs.