Frameworks Support Matrix :: NVIDIA Deep Learning Frameworks Documentation

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. 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 1. Software stack packaged with the 20.xx container images
	Container Image	20.08		20.07		20.06		20.03		20.02		20.01
Supported Platform	Host OS	DGX OS Server <<4.5.x>>, 4.4.x, 4.3.x, and 4.1.0+ (DGX-1)¹ <<4.5.x>>, 4.4.x, 4.3.x, and 4.1.0+ (DGX-2) 4.99.x (DGX A100) DGX EL7-20.02 for RHEL7.x DGX OS Desktop <<4.5.x>>, 4.4.x, 4.3.x, and 4.1.0+ (DGX Station)¹ DGX EL7-20.02 for RHEL7.x		DGX OS Server 4.4.x, 4.3.x, and 4.1.0+ (DGX-1)² 4.4.x, 4.3.x, and 4.1.0+ (DGX-2) 4.99.x (DGX A100) DGX EL7-20.02 for RHEL7.x DGX OS Desktop 4.4.x, 4.3.x, and 4.1.0+ (DGX Station)¹ DGX EL7-20.02 for RHEL7.x		DGX OS Server 4.4.x, 4.3.x, and 4.1.0+ (DGX-1)³ 4.4.x, 4.3.x, and 4.1.0+ (DGX-2) 4.99.x (DGX A100) DGX EL7-20.02 for RHEL7.x DGX OS Desktop 4.4.x, 4.3.x, and 4.1.0+ (DGX Station)¹ DGX EL7-20.02 for RHEL7.x		DGX OS Server 4.4.x, 4.3.x, and 4.1.0+ (DGX-1)⁴ 4.4.x, 4.3.x, and 4.1.0+ (DGX-2) DGX EL7-20.02 for RHEL7.x DGX OS Desktop 4.4.x, 4.3.x, and 4.1.0+ (DGX Station)¹ DGX EL7-20.02 for RHEL7.x		DGX OS Server 4.4.x, 4.3.x, and 4.1.0+ (DGX-1)¹ 4.4.x, 4.3.x, and 4.1.0+ (DGX-2) DGX EL7-20.02 for RHEL7.x DGX OS Desktop 4.4.x, 4.3.x, and 4.1.0+ (DGX Station)¹ DGX EL7-20.02 for RHEL7.x		DGX OS Server 4.3.x, 4.1.0+ (DGX-1)¹ 4.3.x, 4.1.0+ (DGX-2) DGX OS Desktop 4.3.x, 4.1.0+ (DGX Station)¹
Supported Platform	NVIDIA Driver	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. ⁵		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. ⁶		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. ⁷		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. ⁸		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. ²		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. ²
Supported Hardware	GPU Model	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 Image	Container OS	Ubuntu 18.04		Ubuntu 18.04		Ubuntu 18.04		Ubuntu 18.04		Ubuntu 18.04		Ubuntu 18.04
	CUDA	<<11.0.3>>		11.0.194		11.0.167		10.2.89		10.2.89		10.2.89
	cuBLAS	<<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	<<8.0.2>>		8.0.1		8.0.1		7.6.5		7.6.5		7.6.5
	NCCL	<<2.7.8>>		2.7.6		2.7.5		2.5.6		2.5.6		2.5.6
NVIDIA Optimized Frameworks	Kaldi	<<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: 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 3.6 MLNX_OFED OpenMPI 3.1.6 <<Nsight Compute 2020.1.2.4>> Nsight Systems 2020.3.2.6 TensorFlow 1.15.3 TensorRT 7.1.3		6.1.1 including Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.1.8 Nsight Systems 2020.3.2.6 TensorFlow 1.15.3 TensorRT 7.1.3		6.1.1 including Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.0.33 Nsight Systems 2020.2.5.8 TensorFlow 1.15.2 TensorRT 7.1.2		6.1.1 including Python 3.6 NVCaffe 0.17.3 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 TensorFlow 1.15.2 TensorRT 7.0.0		6.1.1 including Python 3.6 NVCaffe 0.17.3 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 TensorFlow 1.15.2 TensorRT 7.0.0		6.1.1 including Python 3.6 NVCaffe 0.17.3 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorFlow 1.15.0 TensorRT 7.0.0
	DIGITS	Docker image size: 12.9 GB		Docker image size: 12.4 GB		Docker image size with TensorFlow: 12.4 GB		Docker image size with TensorFlow: 10.5 GB Docker image size with Caffe: 5.06 GB		Docker image size with TensorFlow: 10.5 GB Docker image size with Caffe: 5.06 GB		Docker image size with TensorFlow: 9.36 GB Docker image size with Caffe: 5.01 GB
	NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet	1.6.0 including ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.6 Horovod 0.19.1 <<Nsight Compute 2020.1.2.4>> Nsight Systems 2020.3.2.6 GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 7.1.3 <<DALI 0.24>> Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.6.0 including ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.6 Horovod 0.19.1 Nsight Compute 2020.1.1.8 Nsight Systems 2020.3.2.6 GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 7.1.3 DALI 0.23 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.6.0 including ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.6 Horovod 0.19.1 Nsight Compute 2020.1.0.33 Nsight Systems 2020.2.5.8 GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 7.1.2 DALI 0.22 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.6.0 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.19.0 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 DALI 0.19.0 GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 7.0.0 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.6.0.rc2 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.19.0 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 DALI 0.18.0 Beta GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 7.0.0 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.6 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.5.1 commit c98184806 from September 4, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.18.1 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 DALI 0.17.0 Beta GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 7.0.0 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.2.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard
		Docker image size: 8.68 GB		Docker image size: 8.98 GB		Docker image size: 9.16 GB		Docker image size: 6.73 GB		Docker image size: 6.59 GB		Docker image size: 6.11 GB
	PyTorch	<<1.7.0a0+6392713>> including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.6 <<Nsight Compute 2020.1.2.4>> Nsight Systems 2020.3.2.6 TensorBoard 1.15+nv TensorRT 7.1.3 <<DALI 0.24>> MAGMA 2.5.2 <<DLProf 0.14.0>> <<PyProf r20.08>> Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.14 JupyterLab Server 1.0.6 Jupyter-TensorBoard PyProf cb1099f6b		1.6.0a0+9907a3e including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.1.8 Nsight Systems 2020.3.2.6 TensorBoard 1.15+nv TensorRT 7.1.3 DALI 0.23 MAGMA 2.5.2 DLProf 0.13.0 PyProf r20.07 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.14 JupyterLab Server 1.0.6 Jupyter-TensorBoard PyProf cb1099f6b		1.6.0a0+9907a3e including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.0.33 Nsight Systems 2020.2.5.8 TensorBoard 1.15+nv TensorRT 7.1.3 DALI 0.22 MAGMA 2.5.2 DLProf 0.12.0 PyProf r20.06 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.14 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.5.0a0+8f84ded including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 TensorBoard 2.1.0 DALI 0.19.0 TensorRT 7.0.0 MAGMA 2.5.2 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.5.0a0+3bbb36e including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 TensorBoard 2.1.0 DALI 0.18.0 Beta TensorRT 7.0.0 MAGMA 2.5.2 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.4.0a0+a5b4d78 including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorBoard 2.1.0 DALI 0.17.0 Beta TensorRT 7.0.0 MAGMA 2.5.2 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard
	PyTorch	Docker image size: 12.2 GB		Docker image size: 11.9 GB		Docker image size: 11.9 GB		Docker image size: 9.41 GB		Docker image size: 9.11 GB		Docker image size: 9.12 GB
	TensorFlow	2.2.0 including Python 3.6 Horovod 0.19.5 OpenMPI 3.1.6 TensorBoard 2.2.2 MLNX_OFED TensorRT 7.1.3 <<DALI 0.24>> <<Nsight Compute 2020.1.2.4>> Nsight Systems 2020.3.2.6 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.3 including `20.08-tf1-py3`contains Python 3.6 Horovod 0.19.1 OpenMPI 3.1.6 TensorBoard 2.2.2 MLNX_OFED OpenSeq2Seq at commit a81babd TensorRT 7.1.3 <<DALI 0.24>> <<DLProf 0.14.0>> <<Nsight Compute 2020.1.2.4>> Nsight Systems 2020.3.2.6 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.2.0 including Python 3.6 Horovod 0.19.5 OpenMPI 3.1.6 TensorBoard 2.2.2 MLNX_OFED TensorRT 7.1.3 DALI 0.23 Nsight Compute 2020.1.1.8 Nsight Systems 2020.3.2.6 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.3 including `20.07-tf1-py3`contains Python 3.6 Horovod 0.19.1 OpenMPI 3.1.6 TensorBoard 2.2.2 MLNX_OFED OpenSeq2Seq at commit a81babd TensorRT 7.1.3 DALI 0.23 DLProf 0.13.0 Nsight Compute 2020.1.1.8 Nsight Systems 2020.3.2.6 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.2.0 including Python 3.6 Horovod 0.19.2 OpenMPI 3.1.6 TensorBoard 2.2.2 MLNX_OFED TensorRT 7.1.3 DALI 0.23 Nsight Compute 2020.1.0.33 Nsight Systems 2020.2.5.8 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.2 including `20.06-tf1-py3`contains Python 3.6 Horovod 0.19.1 OpenMPI 3.1.6 TensorBoard 2.2.2 MLNX_OFED OpenSeq2Seq at commit a81babd TensorRT 7.1.2 DALI 0.22 DLProf 0.12.0 Nsight Compute 2020.1.0.33 Nsight Systems 2020.2.5.8 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.1.0 including Python 3.6 Horovod 0.19.0 OpenMPI 3.1.4 TensorBoard 2.1.0 MLNX_OFED TensorRT 7.0.0 NCCL 2.6.3 DALI 0.19.0 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.2 including `20.03-tf1-py3` contains Python 3.6 Horovod 0.19.0 OpenMPI 3.1.4 TensorBoard 1.15.0+nv MLNX_OFED OpenSeq2Seq at commit a81babd TensorRT 7.0.0 NCCL 2.6.3 DALI 0.19.0 DLProf 0.10.0 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 6.0.0 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.7 Jupyter-TensorBoard	2.1.0 including Python 3.6 Horovod 0.19.0 OpenMPI 3.1.4 TensorBoard 2.1.0 MLNX_OFED TensorRT 7.0.0 DALI 0.18.0 Beta Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.2 including `20.02-tf1-py3` contains Python 3.6 Horovod 0.19.0 OpenMPI 3.1.4 TensorBoard 1.15.0+nv MLNX_OFED OpenSeq2Seq at commit a81babd TensorRT 7.0.0 DALI 0.18.0 Beta DLProf 20.02 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.3 JupyterLab 1.2.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard	2.0.0 including Python 3.6 Horovod 0.18.2 OpenMPI 3.1.4 TensorBoard 2.0.2 MLNX_OFED TensorRT 7.0.0 DALI 0.17.0 Beta Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.0 including `20.01-tf1-py2` contains Python 2.7 or `20.01-tf1-py3` contains Python 3.6 Horovod 0.18.2 OpenMPI 3.1.4 TensorBoard 1.15.0+nv MLNX_OFED OpenSeq2Seqat commit a81babd TensorRT 7.0.0 DALI 0.17.0 Beta DLProf 20.01 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.2.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard
	TensorFlow	Docker image size: 11 GB	Docker image size: 11.9 GB	Docker image size: 9.3 GB	Docker image size: 11.5 GB	Docker image size: 9.45 GB	Docker image size: 11.5 GB	Docker image size: 8.05 GB	Docker image size: 9.53 GB	Docker size image: 7.42 GB	Docker size image: 9.49 GB	Docker size image: 7.15 GB	Docker image size with Python 2.7: 7.88 GB Docker image size with Python 3.6: 8.39 GB
	TensorRT	7.1.3 including: Python 3.6 MLNX_OFED OpenMPI 3.1.6 <<Nsight Compute 2020.1.2.4>> Nsight Systems 2020.3.2.6		7.1.3 including: Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.1.8 Nsight Systems 2020.3.2.6		7.1.2 including: Python 3.6 MLNX_OFED OpenMPI 3.1.6 Nsight Compute 2020.1.0.33 Nsight Systems 2020.2.5.8		7.0.0 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1		7.0.0 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2020.1.1		7.0.0 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1
	TensorRT	Docker image size: 5.63 GB		Docker image size: 5.57 GB		Docker image size: 4.97 GB		Docker image size with Python 2.7: 4.05 GB Docker image size with Python 3.6: 4.08 GB		Docker image size with Python 2.7: 4.04 GB Docker image size with Python 3.6: 4.07 GB		Docker image size with Python 2.7: 4.08 GB Docker image size with Python 3.6: 4.11 GB
	Triton Inference Server	<<2.2.0>> including TensorRT 7.1.3 <<1.7.0a0+6392713>> TensorFlow 1.15.3 <<TensorFlow 2.2.0>> <<ONNX Runtime 1.4.0>>		1.15.0 and 2.1.0 and including TensorRT 7.1.3 PyTorch 1.6.0a0+9907a3e TensorFlow 1.15.3 ONNX Runtime 1.3.0		1.14.0 and 2.0.0 including TensorRT 7.1.2 PyTorch 1.6.0a0+9907a3e TensorFlow 1.15.2 ONNX Runtime 1.3.0		1.12.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 7.0.0		1.11.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 7.0.0		1.10.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 7.0.0
	Triton Inference Server	Docker image size: 9.97 GB		Docker image size: 8.22 GB		Docker image size for 1.14.0: 9.73 GB	Docker image size for 2.0.0: 8.68 GB	Docker image size: 6.31 GB		Docker image size: 6.07 GB		Docker image size: 6.16 GB
TensorFlow For Jetson		TensorFlow 1.15.3 and 2.2.0 for Jetson		TensorFlow 1.15.3 and 2.2.0 for Jetson		TensorFlow 1.15.2 and 2.1.0 for Jetson		TensorFlow 1.15.2 and 2.1.0 for Jetson		TensorFlow 1.15.2 and 2.1.0 for Jetson		TensorFlow 1.15.0 and 2.0.0 for Jetson

2. 19.xx Framework Containers Support Matrix

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

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

19.xx container images

Table 2. Software stack packaged with the 19.xx container images
	Container Image	19.12		19.11		19.10	19.09	19.08	19.07	19.06	19.05	19.04	19.03	19.02	19.01
Supported Platform	Host OS	DGX OS Server 4.1.0+ (DGX-1)⁹ 4.1.0+ (DGX-2) DGX OS Desktop 4.1.0+ (DGX Station)¹		DGX OS Server 4.1.0+ (DGX-1)¹ 4.1.0+ (DGX-2) DGX OS Desktop 4.1.0+ (DGX Station)¹		DGX OS Server 4.1.0+ (DGX-1)¹ 4.1.0+ (DGX-2) DGX OS Desktop 4.1.0+ (DGX Station)¹	DGX OS Server 4.1.0+, 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1)¹ 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 4.0.4+ and 3.1.2+ (DGX Station)¹	DGX OS Server 4.1.0+, 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1)¹ 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 4.0.4+ and 3.1.2+ (DGX Station)¹	DGX OS Server 4.1.0+, 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1)¹ 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 4.0.4+ and 3.1.2+ (DGX Station)¹	DGX OS Server 4.1.0+, 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1)¹ 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 4.0.4+ and 3.1.2+ (DGX Station)¹	DGX OS Server 4.1.0+, 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1)¹ 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 4.0.4+ and 3.1.2+ (DGX Station)¹	DGX OS Server 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 4.0.4+ and 3.1.2+ (DGX Station)	DGX OS Server 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 4.0.4+ and 3.1.2+ (DGX Station)	DGX OS Server 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 4.0.4+ and 3.1.2+ (DGX Station)	DGX OS Server 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 4.0.4+ and 3.1.2+ (DGX Station)
Supported Platform	NVIDIA Driver	Release 19.12 is based on CUDA 10.2.89, which requires NVIDIA driver release 440.33.01. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+, 410, 418.xx or 440.30 The CUDA driver's compatibility package only supports particular drivers. ¹⁰		Release 19.11 is based on CUDA 10.2.89, which requires NVIDIA driver release 440.xx. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+, 410 or 418.xx. The CUDA driver's compatibility package only supports particular drivers. ²		Release 19.10 is based on CUDA 10.1.243, which requires NVIDIA driver release 418.xx. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.09 is based on CUDA 10.1.243, which requires NVIDIA driver release 418.xx. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.08 is based on CUDA 10.1.243, which requires NVIDIA driver release 418.87. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.07 is based on CUDA 10.1.168, which requires NVIDIA driver release 418.67. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.06 is based on CUDA 10.1.168, which requires NVIDIA driver release 418.xx. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.05 is based on CUDA 10.1 Update 1, which requires NVIDIA driver release 418.xx. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.04 is based on CUDA 10.1, which requires NVIDIA driver release 418.xx.x+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.03 is based on CUDA 10.1, which requires NVIDIA driver release 418.xx+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.02 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+. The CUDA driver's compatibility package only supports particular drivers. ²	Release 19.01 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+. The CUDA driver's compatibility package only supports particular drivers. ²
Supported Hardware	GPU Model	Turing Volta Pascal		Turing Volta Pascal		Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal
Base Image	Container OS	Ubuntu 18.04		Ubuntu 18.04		Ubuntu 18.04	Ubuntu 18.04	Ubuntu 18.04	Ubuntu 18.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04
	CUDA	10.2.89		10.2.89		10.1.243	10.1.243	10.1.243	10.1.168	10.1.168	10.1 Update 1	10.1.105	10.1.105	10.0.130	10.0.130
	cuBLAS	10.2.2.89		10.2.2.89		10.2.1.243	10.2.1.243	10.2.1.243	10.2.0.168	10.2.0.168	10.1 Update 1	10.1.0.105	10.1.105	10.0.130	10.0.130
	cuDNN	7.6.5		7.6.5		7.6.4	7.6.3	7.6.2	7.6.1	7.6.0	7.6.0	7.5.0	7.5.0	7.4.2	7.4.2
	NCCL	2.5.6		2.5.6		2.4.8	2.4.8	2.4.8	2.4.7	2.4.7	2.4.6	2.4.6	2.4.3	2.3.7	2.3.7
NVIDIA Optimized Frameworks	Kaldi	5.5 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorRT 6.0.1		5.5 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.5.2 TensorRT 6.0.1		5.5 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1 TensorRT 6.0.1	5.5 including Python 3.5 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2 TensorRT 6.0.1	5.5 including Python 3.5 MLNX_OFED +4.0 OpenMPI 3.1.4 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9 TensorRT 5.1.5	5.5 including Python 3.5 MLNX_OFED +3.4 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30 TensorRT 5.1.5	5.5 including Python 3.5 OpenMPI 3.1.3 TensorRT 5.1.5 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94	5.5 including Python 3.5 Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94 OpenMPI 3.1.3 TensorRT 5.1.5	5.5 including Python 3.5 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8 TensorRT 5.1.2 RC	5.5 including Python 3.5 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 10.1.105 TensorRT 5.1.2 RC
	Kaldi	Docker image size: 5.49 GB		Docker image size: 5.61 GB		Docker image size: 5.63 GB	Docker image size: 5.57 GB	Docker image size: 5.57 GB	Docker image size: 5.00 GB	Docker image size: 5.11 GB	Docker image size: 5.11 GB	Docker image size: 5.01 GB	Docker image size: 5.09 GB
	NVCaffe	0.17.3 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorRT 6.0.1 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.1 JupyterLab 1.1.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard		0.17.3 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.5.2 TensorRT 6.0.1 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.1 JupyterLab 1.1.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard		0.17.3 including Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1 TensorRT 6.0.1 Jupyter and JupyterLab: Jupyter Client 5.3.3 Jupyter Core 4.5.0 Jupyter Notebook 6.0.1 JupyterLab 1.1.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard	0.17.3 including Python 2.7 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2 TensorRT 6.0.1 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 6.0.1 JupyterLab 1.1.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard	0.17.3 including Python 2.7 MLNX_OFED +4.0 OpenMPI 3.1.4 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9 TensorRT 5.1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 Jupyter Notebook 6.0.0 JupyterLab 1.0.5 JupyterLab Server 1.0.0 Jupyter-TensorBoard	0.17.3 including Python 2.7 MLNX_OFED +3.4 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30 TensorRT 5.1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	0.17.3 including Python 2.7 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94 TensorRT 5.1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.6 JupyterLab Server 0.2.0	0.17.3 including Python 2.7 OpenMPI 3.1.3 Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94 TensorRT 5.1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.4 JupyterLab Server 0.2.0	0.17.3 including Python 2.7 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8 TensorRT 5.1.2 RC Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.4 JupyterLab Server 0.2.0	0.17.3 including Python 2.7 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 10.1.105 TensorRT 5.1.2 RC Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.4 JupyterLab Server 0.2.0	0.17.2 including Python 2.7 OpenMPI 3.1.3 TensorRT 5.0.2 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.4 JupyterLab Server 0.2.0	0.17.2 including Python 2.7 OpenMPI 3.1.3 TensorRT 5.0.2
	NVCaffe	Docker image size: 4.81 GB		Docker image size: 5.02 GB		Docker image size: 5.1 GB	Docker image size: 5.02 GB	Docker image size: 5.02 GB	Docker image size: 4.45 GB	Docker image size: 4.33 GB	Docker image size: 4.33 GB	Docker image size: 4.29 GB	Docker image size: 4.42 GB	Docker image size: 3.56 GB	Docker image size: 3.51 GB
	DIGITS	6.1.1 including Python 3.6 NVCaffe 0.17.3 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorFlow 1.15.0 TensorRT 6.0.1		NA		6.1.1 including Python 2.7 NVCaffe 0.17.3 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1 TensorFlow 1.14.0 TensorRT 6.0.1	6.1.1 including Python 2.7 NVCaffe 0.17.3 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2 TensorFlow 1.14.0 TensorRT 6.0.1	6.1.1 including Python 2.7 NVCaffe 0.17.3 MLNX_OFED +4.0 OpenMPI 3.1.4 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9 TensorFlow 1.14.0 TensorRT 5.1.5	6.1.1 including Python 2.7 NVCaffe 0.17.3 MLNX_OFED +3.4 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30 TensorFlow 1.14.0 TensorRT 5.1.5	6.1.1 including Python 2.7 NVCaffe 0.17.3 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94 TensorFlow 1.13.1 TensorRT 5.1.5	6.1.1 including Python 2.7 NVCaffe 0.17.3 OpenMPI 3.1.3 Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94 TensorFlow 1.13.1 TensorRT 5.1.5	6.1.1 including Python 2.7 NVCaffe 0.17.3 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8 TensorFlow 1.13.1 TensorRT 5.1.2 RC	6.1.1 including Python 2.7 NVCaffe 0.17.3 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 10.1.105 TensorFlow 1.13.1 TensorRT 5.1.2 RC	6.1.1 including Python 2.7 NVCaffe 0.17.2 OpenMPI 3.1.3 TensorFlow 1.13.0-rc0 TensorRT 5.0.2	6.1.1 including Python 2.7 NVCaffe 0.17.2 OpenMPI 3.1.3 TensorFlow 1.12.0 TensorRT 5.0.2
	DIGITS	Docker image size with TensorFlow: 9.3 GB Docker image size with Caffe: 4.97 GB		NA		Docker image size with TensorFlow: 8.78 GB Docker image size with Caffe: 5.22 GB	Docker image size with TensorFlow: 7.74 GB Docker image size with Caffe: 5.13 GB	Docker image size with TensorFlow: 7.74 GB Docker image size with Caffe: 5.13 GB	Docker image size with TensorFlow: 7.14 GB Docker image size with Caffe: 4.57 GB	Docker image size with TensorFlow: 7.86 GB Docker image size with Caffe: 4.45 GB	Docker image size with TensorFlow: 7.00 GB Docker image size with Caffe: 4.45 GB	Docker image size with TensorFlow: 7.04 GB Docker image size with Caffe: 4.41 GB	Docker image size with TensorFlow: 6.92 GB Docker image size with Caffe: 4.49 GB	Docker image size with TensorFlow: 6.43 GB Docker image size with Caffe: 3.70 GB	Docker image size with TensorFlow: 5.96 GB Docker image size with Caffe: 3.66 GB
	NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet	1.5.1 commit c98184806 from September 4, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.18.1 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 DALI 0.16.0 Beta GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 6.0.1 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.2.3 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.5.1 commit c98184806 from September 4, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.18.1 Nsight Compute 2019.5.0 Nsight Systems 2019.5.2 DALI 0.15.0 Beta GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 6.0.1 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.2.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.5.1 commit c98184806 from September 4, 2019 including Python 3.6 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.18.1 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1 DALI 0.14.0 Beta GluonCV Toolkit 0.5 GluonNLP Toolkit 0.8.1 TensorRT 6.0.1 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.3 Jupyter Core 4.5.0 Jupyter Notebook 6.0.1 JupyterLab 1.1.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard	1.5.0 commit 006486af3 from August 28, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED OpenMPI 3.1.4 Horovod 0.18.1 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2 DALI 0.13.0 Beta GluonCV Toolkit 0.4 GluonNLP Toolkit 0.8.1 TensorRT 6.0.1 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 6.0.1 JupyterLab 1.1.1 JupyterLab Server 1.0.6 Jupyter-TensorBoard	1.5.0 commit 75a9e187d from June 27, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED +4.0 OpenMPI 3.1.4 Horovod 0.16.4 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9 DALI 0.12.0 Beta GluonCV Toolkit 0.4 GluonNLP Toolkit 0.7.1 TensorRT 5.1.5 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 Jupyter Notebook 6.0.0 JupyterLab 1.0.5 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.5.0.rc2 including upstream commits up through commit 75a9e187d from June 27, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.99 MLNX_OFED +3.4 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30 Horovod 0.16.2 DALI 0.11.0 Beta TensorRT 5.1.5 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.0 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.4.1 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.61 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94 Horovod 0.16.2 DALI 0.10.0 Beta TensorRT 5.1.5 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.6 JupyterLab Server 0.2.0	1.4.0 commit 87c7addcd from February 12, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.61 OpenMPI 3.1.3 Horovod 0.16.1 Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94 DALI 0.9.1 Beta TensorRT 5.1.5 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.4.0 commit 87c7addcd from February 12, 2019 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.61 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8 Horovod 0.13.11 DALI 0.8.1 Beta TensorRT 5.1.2 RC Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.4.0 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.61 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 10.1.105 Horovod 0.13.11 DALI 0.7 Beta TensorRT 5.1.2 RC Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.4.0.rc0 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.61 OpenMPI 3.1.3 Horovod 0.13.11 TensorRT 5.0.2 DALI 0.6.1 Beta Tensor Core optimized example: ResNet50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.4.0.rc0 including Python 3.5 ONNX exporter 0.1 Sockeye 1.18.61 OpenMPI 3.1.3 Horovod 0.13.11 TensorRT 5.0.2 DALI 0.6 Beta Tensor Core optimized example: ResNet50 v1.5
		Docker image size: 6.05 GB		Docker image size: 6.14 GB		Docker image size: 6.2 GB	Docker image size: 5.75 GB	Docker image size: 5.75 GB	Docker image size: 5.11 GB	Docker image size: 4.99 GB	Docker image size: 4.95 GB	Docker image size: 4.9 GB	Docker image size: 4.73 GB	Docker image size: 3.83 GB	Docker image size: 3.82 GB
	PyTorch	1.4.0a0+a5b4d78 including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 TensorBoard 2.1.0 DALI 0.16.0 Beta TensorRT 6.0.1 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.4.0a0+174e1ba including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.5.2 TensorBoard 2.0.1 DALI 0.15.0 Beta TensorRT 6.0.1 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext TransformerXL Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard		1.3.0a0+24ae9b5 including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1 TensorBoard 2.0.0 DALI 0.14.0 Beta TensorRT 6.0.1 Tensor Core optimized examples: ResNeXt101-32x4d SE-ResNext Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.3 Jupyter Core 4.5.0 Jupyter Notebook 6.0.1 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard	1.2.0 including Python 3.6 APEX MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2 TensorBoard 1.14.0+nv DALI 0.12.0 Beta TensorRT 6.0.1 Tensor Core optimized examples: Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 6.0.1 JupyterLab 1.0.4 JupyterLab Server 1.0.6 Jupyter-TensorBoard	1.2.0a0 including upstream commits up through commit 9130ab38 from July 31, 2019 as well as a cherry-picked performance fix 9462ca29 including Python 3.6 APEX MLNX_OFED +4.0 OpenMPI 3.1.4 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9 TensorBoard 1.14.0+nv DALI 0.12.0 Beta TensorRT 5.1.5 Tensor Core optimized examples: Jasper BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 Jupyter Notebook 6.0.0 JupyterLab 1.0.4 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.2.0a0 including upstream commits up through commit f6aac41 from June 19, 2019 including Python 3.6 APEX MLNX_OFED +3.4 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30 TensorBoard 1.14.0+nv DALI 0.11.0 Beta TensorRT 5.1.5 Tensor Core optimized examples: BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.1.0 commit 0885dd28 from May 28, 2019 including Python 3.6 APEX OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94 DALI 0.10.0 Beta TensorRT 5.1.5 Tensor Core optimized examples: BERT Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.6 JupyterLab Server 0.2.0	1.0.1 commit 828a6a3b from March 31, 2019 including Python 3.6 APEX OpenMPI 3.1.3 Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94 DALI 0.9.1 Beta TensorRT 5.1.5 Tensor Core optimized examples: Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.0.1 commit 9eb0f43 from March 28, 2019 including Python 3.6 APEX OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8 DALI 0.8.1 Beta TensorRT 5.1.2 RC Tensor Core optimized examples: Mask R-CNN Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.1.0a0+81e025d including Python 3.6 APEX OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 10.1.105 DALI 0.7 Beta TensorRT 5.1.2 RC Tensor Core optimized examples: Tacotron 2 and WaveGlow v1.1 SSD300 v1.1 Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.1.0a0+c42431b including Python 3.6 APEX OpenMPI 3.1.3 DALI 0.6.1 Beta TensorRT 5.0.2 Tensor Core optimized examples: Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.0.0 including Python 3.6 Caffe2 APEx OpenMPI 3.1.3 DALI 0.6 Beta Tensor Core optimized examples: Neural Collaborative Filtering (NCF) Transformer ResNet50 v1.5 GNMT v2 TensorRT 5.0.2
	PyTorch	Docker image size: 9.28 GB		Docker image size: 9.21 GB		Docker image size: 9.32 GB	Docker image size: 9 GB	Docker image size: 9 GB	Docker image size: 8.33 GB	Docker image size: 7.7 GB	Docker image size: 7.55 GB	Docker image size: 7.45 GB	Docker image size: 7.71 GB	Docker image size: 6.61 GB	Docker image size: 7.70 GB
	TensorFlow	2.0.0 including Python 3.6 Horovod 0.18.2 OpenMPI 3.1.4 TensorBoard 2.0.2 MLNX_OFED TensorRT 6.0.1 DALI 0.16.0 Beta Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.0 including `19.12-tf1-py2` contains Python 2.7 or `19.12-tf1-py3` contains Python 3.6 Horovod 0.18.2 OpenMPI 3.1.4 TensorBoard 1.15.0+nv MLNX_OFED OpenSeq2Seqat commit a81babd TensorRT 6.0.1 DALI 0.16.0 Beta DLProf 19.12 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 6.0.2 JupyterLab 1.2.2 JupyterLab Server 1.0.6 Jupyter-TensorBoard	2.0.0 including Python 3.6 Horovod 0.18.1 OpenMPI 3.1.4 TensorBoard 2.0.1 MLNX_OFED TensorRT 6.0.1 DALI 0.15.0 Beta Nsight Compute 2019.5.0 Nsight Systems 2019.5.2 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.15.0 including `19.11-tf1-py2` contains Python 2.7 or `19.11-tf1-py3` contains Python 3.6 Horovod 0.18.1 OpenMPI 3.1.4 TensorBoard 1.15.0+nv MLNX_OFED OpenSeq2Seqat commit a81babd TensorRT 6.0.1 DALI 0.15.0 Beta DLProf 19.11 Nsight Compute 2019.5.0 Nsight Systems 2019.5.2 Tensor Coreoptimized examples: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.4 Jupyter Core 4.6.1 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.14.0 including Python 2.7 or Python 3.6 Horovod 0.18.1 OpenMPI 3.1.4 TensorBoard 1.14.0+nv MLNX_OFED OpenSeq2Seq at commit 2e0b1d8 TensorRT 6.0.1 DALI 0.14.0 Beta DLProf 19.10 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1 Tensor Core optimized example: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.3 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.14.0 including Python 2.7 or Python 3.6 Horovod 0.18.0 OpenMPI 3.1.4 TensorBoard 1.14.0+nv MLNX_OFED OpenSeq2Seq at commit 2e0b1d8 TensorRT 6.0.1 DALI 0.13.0 Beta DLProf 19.09 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2 Tensor Core optimized example: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.14.0 including Python 2.7 or Python 3.6 Horovod 0.16.2 OpenMPI 3.1.4 TensorBoard 1.14.0+nv MLNX_OFED +4.0 OpenSeq2Seq at commit 2e0b1d8 TensorRT 5.1.5 DALI 0.12.0 Beta DLProf 19.08 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9 Tensor Core optimized example: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.2 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.14.0 including Python 2.7 or Python 3.6 Horovod 0.16.2 OpenMPI 3.1.3 TensorBoard 1.14.0+nv MLNX_OFED +3.4 OpenSeq2Seq at commit 27346d1 TensorRT 5.1.5 DALI 0.11.0 Beta DLProf 19.07 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30 Tensor Core optimized example: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.3.1 Jupyter Core 4.5.0 Jupyter Notebook 5.7.8 JupyterLab 1.0.1 JupyterLab Server 1.0.0 Jupyter-TensorBoard	1.13.1 including Python 2.7 or Python 3.5 Horovod 0.16.2 OpenMPI 3.1.3 TensorBoard 1.13.1+nv MLNX_OFED 3.4 OpenSeq2Seq at commit 27346d1 TensorRT 5.1.5 DALI 0.10.0 Beta DLProf 19.06 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94 Tensor Core optimized example: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 Jupyter Notebook 5.7.8 JupyterLab 0.35.6 JupyterLab Server 0.2.0	1.13.1 including Python 2.7 or Python 3.5 Horovod 0.16.1 OpenMPI 3.1.3 TensorBoard 1.13.1 MLNX_OFED 3.4 OpenSeq2Seq TensorRT 5.1.5 DALI 0.9.1 Beta Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94 Tensor Core optimized example: U-Net Medical SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.13.1 including Python 2.7 or Python 3.5 Horovod 0.16.1 OpenMPI 3.1.3 TensorBoard 1.13.1 MLNX_OFED 3.4 OpenSeq2Seq TensorRT 5.1.2 RC DALI 0.8.1 Beta Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8 Tensor Core optimized example: SSD320 v1.2 Neural Collaborative Filtering (NCF) BERT U-Net Industrial Defect Segmentation GNMT v2 ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.13.1 including Python 2.7 or Python 3.5 Horovod 0.16.0 OpenMPI 3.1.3 TensorBoard 1.13.1 MLNX_OFED 3.4 OpenSeq2Seq TensorRT 5.1.2 RC DALI 0.7 Beta Nsight Compute 10.1.105 Nsight Systems 10.1.105 Tensor Core optimized example: ResNet-50 v1.5 Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.13.0-rc0 including Python 2.7 or Python 3.5 Horovod 0.15.1 OpenMPI 3.1.3 TensorBoard 1.12.2 MLNX_OFED 3.4 OpenSeq2Seq v8.12 TensorRT 5.0.2 DALI 0.6.1 Beta Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	1.12.0 including Python 2.7 or Python 3.5 Horovod 0.15.1 OpenMPI 3.1.3 TensorBoard 1.12.0 MLNX_OFED 3.4 OpenSeq2Seq v19.01 TensorRT 5.0.2 DALI 0.6 Beta
	TensorFlow	Docker size image: 7.71 GB	Docker image size with Python 2.7: 7.84 GB Docker image size with Python 3.6: 8.32 GB	Docker image size: 7.78	Docker image size with Python 2.7: 8.02 GB Docker image size with Python 3.6: 8.60 GB	Docker image size with Python 2.7: 7.81 GB Docker image size with Python 3.6: 8.38 GB	Docker image size with Python 2.7: 6.78 GB Docker image size with Python 3.6: 7.34 GB	Docker image size with Python 2.7: 6.78 GB Docker image size with Python 3.6: 7.34 GB	Docker image size with Python 2.7: 6.18 GB Docker image size with Python 3.6: 6.71 GB	Docker image size: 6.88 GB	Docker image size: 6.76 GB	Docker image size: 6.8 GB	Docker image size: 6.72 GB	Docker image size: 6.06 GB	Docker image size: 5.57 GB
	TensorRT	6.0.1 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.6.1		6.0.1 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.5.0 Nsight Systems 2019.5.2		6.0.1 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.5.1	6.0.1 including: Python 2.7 or Python 3.6 MLNX_OFED OpenMPI 3.1.4 Nsight Compute 2019.4.0 Nsight Systems 2019.4.2	5.1.5 including: Python 2.7 or Python 3.6 MLNX_OFED +4.0 OpenMPI 3.1.4 Nsight Compute 10.1.168 Nsight Systems 2019.3.7.9	5.1.5 including: Python 2.7 or Python 3.5 MLNX_OFED +3.4 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.6.30	5.1.5 including: Python 2.7 or Python 3.5 OpenMPI 3.1.3 Nsight Compute 10.1.168 Nsight Systems 2019.3.1.94	5.1.5 including: Python 2.7 or Python 3.5 OpenMPI 3.1.3 Nsight Compute 10.1.163 Nsight Systems 2019.3.1.94	5.1.2 RC including: Python 2.7 or Python 3.5 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 2019.3.1.8	5.1.2 RC including: Python 2.7 or Python 3.5 OpenMPI 3.1.3 Nsight Compute 10.1.105 Nsight Systems 10.1.105	5.0.2 including: Python 2.7 or Python 3.5 OpenMPI 3.1.3	5.0.2 including: Python 2.7 or Python 3.5 OpenMPI 3.1.3
	TensorRT	Docker image size Python 2.7: 4.03 GB Docker image size with Python 3.6: 4.06 GB		Docker image size Python 2.7: 3.83 GB Docker image size with Python 3.6: 4.16 GB		Docker image size: 4.2 GB	Docker image size: 4.4 GB	Docker image size: 4.4 GB	Docker image size: 3.83 GB	Docker image size: 3.83 GB	Docker image size: 3.83 GB	Docker image size: 3.79 GB	Docker image size: 3.91 GB	Docker image size: 3.01 GB	Docker image size: 3.00 GB
	TensorRT Inference Server	1.9.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 6.0.1		1.8.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 6.0.1		1.7.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 6.0.1	1.6.0 including MLNX_OFED OpenMPI 3.1.4 TensorRT 6.0.1	1.5.0 including MLNX_OFED +4.0 OpenMPI 3.1.4 TensorRT 5.1.5	1.4.0 including MLNX_OFED +3.4 OpenMPI 3.1.3 TensorRT 5.1.5	1.3.0 including OpenMPI 3.1.3 TensorRT 5.1.5	1.2.0 including OpenMPI 3.1.3 TensorRT 5.1.5	1.1.0 including OpenMPI 3.1.3 TensorRT 5.1.2 RC	1.0.0 including OpenMPI 3.1.3 TensorRT 5.1.2 RC	0.11.0 Beta including OpenMPI 3.1.3 TensorRT 5.0.2	0.10.0 Beta including OpenMPI 3.1.3 TensorRT 5.0.2
	TensorRT Inference Server	Docker image size: 6.12 GB		Docker image size: 6.15 GB		Docker image size: 8.26 GB	Docker image size: 7.73 GB	Docker image size: 7.73 GB	Docker image size: 7.15 GB	Docker image size: 7.15 GB	Docker image size: 7.02 GB	Docker image size: 5.22 GB	Docker image size: 5.33 GB	Docker image size: 4.42 GB	Docker image size: 4.17 GB
TensorFlow For Jetson		TensorFlow 1.15.0 and 2.0.0 for Jetson		TensorFlow 1.15.0 and 2.0.0 for Jetson		TensorFlow 1.14.0 for Jetson	TensorFlow 1.14.0 for Jetson	TensorFlow 1.14.0 for Jetson	TensorFlow 1.14.0 for Jetson		TensorFlow 1.13.1 for Jetson	TensorFlow 1.13.1 for Jetson	TensorFlow 1.13.1 for Jetson	TensorFlow 1.13.0-rc0 for Jetson	TensorFlow 1.12.0 for Jetson

18.xx Framework Containers Support Matrix

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

18.xx container images

Table 3. Software stack packaged with the 18.xx container images
	Container Image	18.12	18.11	18.10	18.09	18.08	18.07	18.06	18.05	18.04	18.03	18.02	18.01
Supported Platform	Host OS	DGX OS Server 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 4.0.4+ and 3.1.2+ (DGX Station)	DGX OS Server 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 4.0.4+ and 3.1.2+ (DGX Station) DGX Software Stack for Red Hat Enterprise Linux EL7-18.11 (DGX-1)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)	DGX OS Server 3.1.2+ and 2.1.1+ (DGX-1) 4.0.1+ (DGX-2) DGX OS Desktop 3.1.2+ (DGX Station)
Supported Platform	NVIDIA Driver	Release 18.12 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+. The CUDA driver's compatibility package only supports particular drivers (see footnote ¹).	Release 18.11 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+. The CUDA driver's compatibility package only supports particular drivers (see footnote ¹).	Release 18.10 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+. The CUDA driver's compatibility package only supports particular drivers (see footnote ¹).	Release 18.09 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+. The CUDA driver's compatibility package only supports particular drivers (see footnote ¹).	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+	Ubuntu 16.04 NVIDIA Driver 384.xx+
Supported Hardware	GPU Model	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Volta and Pascal	Volta and Pascal	Volta and Pascal	Volta and Pascal	Volta and Pascal	Volta and Pascal	Volta and Pascal	Volta and Pascal
Base Image	Container OS	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04
	CUDA	10.0.130	10.0.130	10.0.130	10.0.130 includes: Support for DGX-2 Support for Turing Support for Jetson Xavier CUDA 10 compatibility package version 410.48¹¹	9.0.176	9.0.176	9.0.176	9.0.176	9.0.176	9.0.176	9.0.176	9.0.176
	cuBLAS	10.0.130	10.0.130	10.0.130	10.0.130	9.0.425	9.0.425	9.0.333	9.0.333	9.0.333	9.0.333	9.0.282 Patch 2 and cuBLAS 9.0.234 Patch 1	9.0.282 Patch 2
	cuDNN	7.4.1	7.4.1	7.4.0	7.3.0	7.2.1	7.1.4	7.1.4	7.1.2	7.1.1	7.1.1	7.0.5	7.0.5
	NCCL	2.3.7	2.3.7	2.3.6	2.3.4	2.2.13	2.2.13	2.2.13	2.1.15	2.1.15	2.1.2	2.1.2	2.1.2
NVIDIA Optimized Frameworks	NVCaffe	0.17.2 including Python 2.7 OpenMPI 3.1.2 TensorRT 5.0.2	0.17.1 including Python 2.7 OpenMPI 3.1.2 TensorRT 5.0.2	0.17.1 including Python 2.7 OpenMPI 3.1.2 TensorRT 5.0.0 RC	0.17.1 including Python 2.7 OpenMPI 2.0 TensorRT 5.0.0 RC	0.17.1 including Python 2.7 OpenMPI 2.0	0.17.1 and Python 2.7	0.17.0 and Python 2.7	0.17.0 and Python 2.7	0.17.0 and Python 2.7	0.16.6 and Python 2.7	0.16.5 and Python 2.7	0.16.5 and Python 2.7
	NVCaffe	Docker image size: 3.41 GB	Docker image size: 3.41 GB	Docker image size: 3.41 GB	Docker image size: 3.40 GB	Docker image size: 3.37 GB	Docker image size: 4.29 GB	0.17.0 and Python 2.7	0.17.0 and Python 2.7	0.17.0 and Python 2.7	0.16.6 and Python 2.7	0.16.5 and Python 2.7	0.16.5 and Python 2.7
	Caffe2					0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3
	Caffe2					Docker image size: 3.02 GB	Docker image size: 2.94 GB	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3	0.8.1 including Python 2.7 or Python 3.5 OpenMPI 1.10.3
	DIGITS	6.1.1 including Python 2.7 OpenMPI 3.1.2 NVCaffe 0.17.2 TensorFlow 1.12.0 TensorRT 5.0.2	6.1.1 including Python 2.7 OpenMPI 3.1.2 NVCaffe 0.17.1 TensorFlow 1.12.0-rc2 TensorRT 5.0.2	6.1.1 including Python 2.7 OpenMPI 3.1.2 NVCaffe 0.17.1 TensorFlow 1.10.0 TensorRT 5.0.0 RC	6.1.1 including Python 2.7 NVCaffe 0.17.1 TensorFlow 1.10.0 TensorRT 5.0.0 RC	6.1.1 including Python 2.7 NVCaffe 0.17.1 TensorFlow 1.9.0 Torch7	6.1.1 including Python 2.7 NVCaffe 0.17.1 TensorFlow 1.8.0 Torch7	6.1.1 including Python 2.7 NVCaffe 0.17.0 TensorFlow 1.8.0 Torch7	6.1.1 including Python 2.7 NVCaffe 0.17.0 TensorFlow 1.7.0 Torch7	6.1.1 including Python 2.7 NVCaffe 0.17.0 TensorFlow 1.7.0 Torch7	6.1.0 including Python 2.7 NVCaffe 0.16.6 TensorFlow 1.4.0 Torch7	6.1.0 including Python 2.7 NVCaffe 0.16.5 TensorFlow 1.4.0 Torch7	6.0.0 including Python 2.7 NVCaffe 0.16.5 TensorFlow 1.4.0 Torch7
	DIGITS	TensorFlow Docker image size: 5.03 GB Caffe Docker image size: 3.56 GB	TensorFlow Docker image size: 5.03 GB Caffe Docker image size: 3.56 GB	Docker image size: 6.17 GB	Docker image size: 5.33 GB	Docker image size: 6.20 GB	Docker image size: 7.16 GB
	Microsoft Cognitive Toolkit					2.5 including Python 3.6 OpenMPI 3.0.0	2.5 including Python 3.6 OpenMPI 3.0.0	2.5 including Python 3.6 OpenMPI 3.0.0	2.5 including Python 3.6 OpenMPI 3.0.0	2.4 including Python 3.6 OpenMPI 3.0.0	2.4 including Python 3.6 OpenMPI 3.0.0	2.3.1 including Python 3.4 OpenMPI 3.0.0	2.3.1 including Python 3.4 OpenMPI 3.0.0
	Microsoft Cognitive Toolkit					Docker image size: 6.17 GB	Docker image size: 6.13 GB	2.5 including Python 3.6 OpenMPI 3.0.0	2.5 including Python 3.6 OpenMPI 3.0.0	2.4 including Python 3.6 OpenMPI 3.0.0	2.4 including Python 3.6 OpenMPI 3.0.0	2.3.1 including Python 3.4 OpenMPI 3.0.0	2.3.1 including Python 3.4 OpenMPI 3.0.0
	NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet	1.3.1 including Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.28 OpenMPI 3.1.2 Horovod 0.15.1 TensorRT 5.0.2 DALI 0.5 Beta	1.3.0 including Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.28 OpenMPI 3.1.2 Horovod 0.15.1 TensorRT 5.0.2 DALI 0.4.1 Beta	1.3.0 including Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.28 OpenMPI 3.1.2 TensorRT 5.0.0 RC DALI 0.4 Beta	1.3.0 including Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.28 TensorRT 5.0.0 RC DALI 0.2 Beta	1.2.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.28 TensorRT 4.0.1 DALI 0.1.2 Beta	1.2.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.23 TensorRT 4.0.1 DALI 0.1.1 Beta	1.2.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.22 TensorRT 4.0.1	1.1.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.13	1.1.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.18.1	1.1.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.17.4	1.0.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.17.0	1.0.0 including Python 2.7 or Python 3.5 ONNX exporter 0.1 Amazon Labs Sockeye sequence-to-sequence framework 1.16.2
		Docker image size: 3.69 GB	Docker image size: 3.69 GB	Docker image size: 3.68 GB	Docker image size: 3.58 GB	Docker image size: 4.09 GB	Docker image size: 3.93 GB
	PyTorch	0.4.1+ including Python 3.6 Caffe2 APEx OpenMPI 3.1.2 DALI 0.5 Beta Tensor Core Optimized Examples: ResNet50 v1.5 GNMT v2 TensorRT 5.0.2	0.4.1+ including Python 3.6 Caffe2 APEx OpenMPI 3.1.2 DALI 0.4.1 Beta Tensor Core Optimized Examples: ResNet50 v1.5 GNMT v2 TensorRT 5.0.2	0.4.1+ including Python 3.6 Caffe2 APEx OpenMPI 3.1.2 DALI 0.4 Beta Tensor Core Optimized Examples: ResNet50 v1.5 GNMT v2 TensorRT 5.0.0 RC	0.4.1+ including Python 3.6 Caffe2 APEx DALI 0.2 Beta Tensor Core Optimized Examples: ResNet50 v1.5 GNMT v2 TensorRT 5.0.0 RC	0.4.1 including Python 3.6 Caffe2 0.8.1 APEx DALI 0.1.2 Beta Tensor Core Optimized Examples: GNMT v2	0.4.0 including Python 3.6 Caffe2 0.8.1 APEx DALI 0.1.1 Beta	0.4.0 including Python 3.6 Caffe2 0.8.1 APEx	0.4.0 including Python 3.6 Caffe2 0.8.1 APEx	0.3.1 and Python 3.6	0.3.0 and Python 3.6	0.3.0 and Python 3.6	0.3.0 and Python 3.6
	PyTorch	Docker image size: 6.08 GB	Docker image size: 6.08 GB	Docker image size: 6.00 GB	Docker image size: 5.89 GB	Docker image size: 5.64 GB	Docker image size: 5.67 GB	0.4.0 including Python 3.6 Caffe2 0.8.1 APEx	0.4.0 including Python 3.6 Caffe2 0.8.1 APEx	0.3.1 and Python 3.6	0.3.0 and Python 3.6	0.3.0 and Python 3.6	0.3.0 and Python 3.6
	TensorFlow	1.12.0 including Python 2.7 or Python 3.5 Horovod 0.15.1 OpenMPI 3.1.2 TensorBoard 1.12.0 MLNX_OFED 3.4 OpenSeq2Seq v18.12 TensorRT 5.0.2 DALI 0.5 Beta	1.12.0-rc2 including Python 2.7 or Python 3.5 Horovod 0.15.1 OpenMPI 3.1.2 TensorBoard 1.12.0 MLNX_OFED 3.4 OpenSeq2Seq v18.11 TensorRT 5.0.2 DALI 0.4.1 Beta	1.10.0 including Python 2.7 or Python 3.5 Horovod 0.13.10 OpenMPI 3.1.2 TensorBoard 1.10.0 MLNX_OFED 3.4 OpenSeq2Seq v18.10 TensorRT 5.0.0 RC DALI 0.4 Beta	1.10.0 including Python 2.7 or Python 3.5 Horovod 0.13.10 OpenMPI 3.0.0 TensorBoard 1.10.0 MLNX_OFED 3.4 OpenSeq2Seq v18.09 TensorRT 5.0.0 RC DALI 0.2 Beta	1.9.0 including Python 2.7 or Python 3.5 Horovod 0.12.1 OpenMPI 3.0.0 TensorBoard 1.9.0 MLNX_OFED 3.4 OpenSeq2Seq v0.5 TensorRT 4.0.1 DALI 0.1.2 Beta	1.8.0 including Python 2.7 or Python 3.5 Horovod 0.12.1 OpenMPI 3.0.0 TensorBoard 1.8.0 MLNX_OFED 3.4 OpenSeq2Seq v0.4 TensorRT 4.0.1 DALI 0.1.1 Beta	1.8.0 including Python 2.7 or Python 3.5 Horovod 0.12.1 OpenMPI 3.0.0 TensorBoard 1.8.0 MLNX_OFED 3.4 OpenSeq2Seq v0.2 TensorRT 4.0.1	1.7.0 including Python 2.7 or Python 3.5 Horovod 0.12.1 OpenMPI 3.0.0 TensorBoard 1.7.0 MLNX_OFED 3.4 OpenSeq2Seq v0.2 TensorRT 3.0.4	1.7.0 including Python 2.7 or Python 3.5 Horovod 0.11.3 OpenMPI 3.0.0 TensorBoard 0.4.0-rc1 MLNX_OFED 3.4	1.4.0 including Python 2.7 or Python 3.5 Horovod 0.11.3 OpenMPI 3.0.0 TensorBoard 0.4.0-rc1	1.4.0 including Python 2.7 or Python 3.5 Horovod 0.11.2	1.4.0 including Python 2.7 or Python 3.5 Horovod 0.11.2
	TensorFlow	Docker image size: 4.64 GB	Docker image size: 4.64 GB	Docker image size: 4.57 GB	Docker image size: 3.75 GB	Docker image size: 3.40 GB	Docker image size: 3.34 GB					1.4.0 including Python 2.7 or Python 3.5 Horovod 0.11.2	1.4.0 including Python 2.7 or Python 3.5 Horovod 0.11.2
	TensorFlow For Jetson	TensorFlow 1.12.0 for Jetson	TensorFlow 1.12.0-rc2 for Jetson
	TensorRT	TensorRT 5.0.2 including: Python 2.7 or Python 3.5 OpenMPI 3.1.2	TensorRT 5.0.2 including: Python 2.7 or Python 3.5 OpenMPI 3.1.2	TensorRT 5.0.0 RC including: Python 2.7 or Python 3.5 OpenMPI 3.1.2	TensorRT 5.0.0 RC including Python 2.7 or Python 3.5	4.0.1 and Python 2.7 or Python 3.5	4.0.1 and Python 2.7 or Python 3.5	4.0.1 and Python 2.7 or Python 3.5	3.0.4 and Python 2.7	3.0.4 and Python 2.7	3.0.4 and Python 2.7	3.0.4 and Python 2.7	3.0.1 and Python 2.7
	TensorRT	Docker image size: 3.00 GB	Docker image size: 3.00 GB	Docker image size: 2.99 GB	Docker image size: 2.98 GB	Docker image size: 2.56 GB	Docker image size: 2.61 GB	4.0.1 and Python 2.7 or Python 3.5	3.0.4 and Python 2.7	3.0.4 and Python 2.7	3.0.4 and Python 2.7	3.0.4 and Python 2.7	3.0.1 and Python 2.7
	TensorRT Inference Server	0.9.0 Beta including Python 3.5 OpenMPI 3.1.2 TensorRT 5.0.2	0.8.0 Beta including Python 3.5 OpenMPI 3.1.2 TensorRT 5.0.2	0.7.0 Beta including Python 3.5 OpenMPI 3.1.2 TensorRT 5.0.0 RC	0.6.0 Beta including Python 3.5 OpenMPI 2.0 TensorRT 5.0.0 RC	0.5.0 Beta including Python 2.7 or Python 3.5 TensorRT 4.0.1	0.4.0 Beta including Python 2.7 or Python 3.5 TensorRT 4.0.1	0.3.0 Beta including Python 2.7 or Python 3.5 TensorRT 4.0.1	0.2.0 Beta including Python 2.7 or Python 3.5 TensorRT 3.0.4	0.1.0 Beta
	TensorRT Inference Server	Docker image size: 4.17 GB	Docker image size: 4.17 GB	Docker image size: 4.15 GB	Docker image size: 4.42 GB	Docker image size: 2.37 GB	Docker image size: 2.47 GB			0.1.0 Beta
	Theano					1.0.2 and Python 2.7	1.0.2 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7
	Theano					Docker image size: 3.70 GB	Docker image size: 3.74 GB	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7	1.0.1 and Python 2.7
	Torch					7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7
	Torch					Docker image size: 3.06 GB	Docker image size: 3 GB	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7	7 and Python 2.7

17.xx Framework Containers Support Matrix

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

17.xx container images

Table 4. Software stack packaged with the 17.xx container images
	Container Image	17.12	17.11	17.10	17.09	17.07	17.06	17.05	17.04	17.03	17.02	17.01
Supported Platform	DGX OS	3.1.2+ and 2.1.1+	3.1.2+ and 2.1.1+	3.1.2+ and 2.1.1+	3.1.2+ and 2.1.1+	2.x+ and 1.x+	2.x+ and 1.x+	2.x+ and 1.x+	2.x+ and 1.x+	2.x+ and 1.x+	2.x+ and 1.x+	2.x+ and 1.x+
Supported Platform	NVIDIA Driver	384	384	384	384
Base Image	Ubuntu	16.04	16.04	16.04	16.04	16.04	16.04	16.04	16.04	16.04	14.04	14.04
	CUDA	9.0.176	9.0.176	9.0	9.0	8.0.61.2	8.0.61	8.0.61	8.0.61	8.0.61	8.0.61	8.0.54
	cuBLAS	9.0.234	9.0.234			patch 2
	cuDNN	7.0.5	7.0.4	7.0.3	7.0.2	6.0.21	6.0.21	6.0.21	6.0.20	6.0.20	6.0.13	6.0.10
	NCCL	2.1.2	2.1.2	2.0.5	2.0.5	2.0.3	1.6.1	1.6.1	1.6.1	1.6.1	1.6.1	1.6.1
NVIDIA Optimized Frameworks	NVCaffe	0.16.4	0.16.4	0.16.4	0.16.4	0.16	0.16	0.16	0.16	0.16	0.16	0.16
	Caffe2	0.8.1 and OpenMPI 1.10.3	0.8.1 and OpenMPI 1.10.3	0.8.1 and OpenMPI 1.10.3	0.8.1 and OpenMPI 1.10.3	0.7.0 and OpenMPI 1.10.3	0.7.0 and OpenMPI 1.10.3	0.5.0+ and OpenMPI 1.10.3	0.5.0+ and OpenMPI 1.10.3
	DIGITS	6.0.0 including NVCaffe 0.16.4 TensorFlow 1.4.0 Torch7	6.0.0 including NVCaffe 0.16.4 TensorFlow 1.3.0 Torch7	6.0.0 including NVCaffe 0.16.4 TensorFlow 1.3.0 Torch7	6.0.0 including NVCaffe 0.16.4 TensorFlow 1.3.0 Torch7	6.0.0 including NVCaffe 0.16 Torch7	5.0 including NVCaffe 0.16 Torch7	5.0 including NVCaffe 0.16 Torch7	5.0 including NVCaffe 0.16 Torch7	5.0 including NVCaffe 0.16 Torch7	5.0 including NVCaffe 0.16 Torch7	5.0 including NVCaffe 0.16 Torch7
	Microsoft Cognitive Toolkit	2.2 and OpenMPI 3.0.0	2.2 and OpenMPI 3.0.0	2.2	2.1	2.0	2.0	2.0.rc2	2.0.beta15.0	2.0.beta12.0	2.0.beta9.0	2.0.beta5.0
	NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet	1.0.0	0.12.0	0.11.0	0.11.0.rc3	0.10.0	0.10.0	0.9.3a+	0.9.3a+	0.9.3
	PyTorch	0.2.0	0.2.0	0.2.0	0.2.0	0.1.12	0.1.12	0.1.12	0.1.10
	TensorFlow	1.4.0	1.3.0	1.3.0	1.3.0	1.2.1	1.1.0	1.0.1	1.0.1	1.0.0	0.12.1	0.12.0
	TensorRT	3.0.1
	Theano	1.0.0rc1	1.0.0rc1	0.10beta3	0.10beta1	0.9.0	0.9.0	0.9.0	0.9.0	0.9.0rc3	0.8.0	0.8.0
	Torch	7	7	7	7	7	7	7	7	7	7	7

16.xx Framework Containers Support Matrix

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

16.xx container images

Table 5. Software stack packaged with the 16.xx container images
	Container Image	16.12
Supported Platform	DGX OS	2.x+ and 1.x+
Supported Platform	NVIDIA Driver
Base Image	Ubuntu	14.04
	CUDA	8.0.54
	cuBLAS
	cuDNN	6.0.5
	NCCL	1.6.1
NVIDIA Optimized Frameworks	NVCaffe	0.16
	Caffe2
	DIGITS	5.0 including NVCaffe 0.16 Torch7
	Microsoft Cognitive Toolkit	2.0.beta5.0
	NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet
	PyTorch
	TensorFlow	0.12.0
	TensorRT
	Theano	0.8.0
	Torch	7

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¹ Ensure you upgrade your DGX systems to DGX OS Server version 4.1 or greater or DGX OS Desktop version 4.1 or greater to obtain security and feature updates.

² Ensure you upgrade your DGX systems to DGX OS Server version 4.1 or greater or DGX OS Desktop version 4.1 or greater to obtain security and feature updates.

³ Ensure you upgrade your DGX systems to DGX OS Server version 4.1 or greater or DGX OS Desktop version 4.1 or greater to obtain security and feature updates.

⁴ Ensure you upgrade your DGX systems to DGX OS Server version 4.1 or greater or DGX OS Desktop version 4.1 or greater to obtain security and feature updates.

⁵ For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

⁶ For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

⁷ For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

⁸ For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

⁹ We will stop supporting DGX OS 3.x in September 2019. Ensure you upgrade your DGX systems to DGX OS Server version 4.1 or greater or DGX OS Desktop version 4.1 or greater to obtain security and feature updates.

¹⁰ For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

¹¹ The compatibility package ensures that Linux drivers R384 are compatible with Tesla GPUs.