Frameworks Support Matrix

NVIDIA Docs Hub NVIDIA Optimized Frameworks NVIDIA Optimized Frameworks 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. 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

Table 1. Software stack packaged with the 23.xx container images
Container Image	23.08	23.07	23.06	23.05	23.04	23.03		23.02		23.01
DGX
DGX System	NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100	NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100	NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100	NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100	NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100	NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100		NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100		NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100
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+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+¹	4.99.x (DGX A100) 5.0+ 6.0 Red Hat Enterprise Linux 9 / CentOS 9³ EL7-20.02+¹ Red Hat Enterprise Linux 8 / CentOS 8² (All DGX systems except DGX Station A100) EL8-20.11+¹	4.1+⁴ (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 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
System Requirements
NVIDIA Driver	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. ⁵	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 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 20.04	Ubuntu 20.04		Ubuntu 20.04		Ubuntu 20.04
CUDA	<<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 12.2.5.1>>	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	<<8.9.4>>	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	1.6.2.3		1.6.2.3		1.6.2.3
DALI	<<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	2.18.3	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 8.6.1.2	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	36.0	36.0	36.0		36.0		36.0
NVIDIA HPC-X	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
Nsight Compute	<<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	2023.2.3.1001	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 23.07 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
		Docker image size: 20.8 GB
JAX	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
Kaldi	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
	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 0.26.1 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
	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	<<2.5.0>> 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
	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	<<2.1.0a0+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
	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	<<2.13.0>> 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
	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 8.6.1.2	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
	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 <<2.37>> 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
	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 2.12.0 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

2. 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

Table 2. Software stack packaged with the 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	NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100		NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100		NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100		NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100		NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100		NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100		NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100		NVDIA DGX-1™ DGX-2™ NVDIA DGX™ A100 DGX Station DGX Station A100		DGX-1 DGX-2 NVDIA 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

3. 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

Table 3. Software stack packaged with the 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

4. 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

Table 4. Software stack packaged with the 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+⁶	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