Frameworks Support Matrix :: 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. 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, 2018.

19.xx container images

Table 1. Software stack packaged with the 19.xx container images
	Container Image	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+, 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)² 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 4.0.4+ and 3.1.2+ (DGX Station)	DGX OS Server 4.1.0+, 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 4.0.4+ and 3.1.2+ (DGX Station)	DGX OS Server 4.1.0+, 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 4.0.4+ and 3.1.2+ (DGX Station)	DGX OS Server 4.1.0+, 4.0.4+, 3.1.2+ and 2.1.1+ (DGX-1) 4.1.0+, 4.0.1+ (DGX-2) DGX OS Desktop 4.1.0+, 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)	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 19.09 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. ³	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. ⁴	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. ⁵	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
Base Image	Container OS	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	10.1.243	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	10.2.1.243	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	<<7.6.3>>	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	2.4.8	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 3.5 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: 5.57 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 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: 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 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: 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
	MXNet		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
	MXNet		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	<<1.2.0>> 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 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		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 6.0.0 JupyterLab 1.0.4 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 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	<<TensorRT 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>>	TensorRT 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	TensorRT 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	TensorRT 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	TensorRT 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	TensorRT 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	TensorRT 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	TensorRT 5.0.2 including: Python 2.7 or Python 3.5 OpenMPI 3.1.3	TensorRT 5.0.2 including: Python 2.7 or Python 3.5 OpenMPI 3.1.3
	TensorRT	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		1.5.0 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: 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 1.14.0 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
TensorFlow For TensorRT (TF-TRT)			TensorFlow 1.14.0 with TensorRT 5.1.5 using the 19.08 container	TensorFlow 1.14.0 with TensorRT 5.1.5 using the 19.07 container	TensorFlow 1.13.1 with TensorRT 5.1.5 using the 19.06 container	TensorFlow 1.13.1 with TensorRT 5.1.5 using the 19.05 container	TensorFlow 1.13.1 with TensorRT 5.1.2 RC using the 19.04 container	TensorFlow 1.13.1 with TensorRT 5.1.2 RC using the 19.03 container	TensorFlow 1.13.0-rc0 with TensorRT 5.0.2 using the 19.02 container	TensorFlow 1.12.0 with TensorRT 5.0.2 using the 19.01 container

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 2. 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 410.48⁶	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
	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
	MXNet	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 3. 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
	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 4. 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
	MXNet
	PyTorch
	TensorFlow	0.12.0
	TensorRT
	Theano	0.8.0
	Torch	7

Notices

Notice

_{THE INFORMATION IN THIS GUIDE AND ALL OTHER INFORMATION CONTAINED IN NVIDIA
DOCUMENTATION REFERENCED IN THIS GUIDE IS PROVIDED “AS IS.” NVIDIA MAKES NO
WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE
INFORMATION FOR THE PRODUCT, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF
NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE.
Notwithstanding any damages that customer might incur for any reason whatsoever,
NVIDIA’s aggregate and cumulative liability towards customer for the product
described in this guide shall be limited in accordance with the NVIDIA terms and
conditions of sale for the product.}

_{THE NVIDIA PRODUCT DESCRIBED IN THIS GUIDE IS NOT FAULT TOLERANT AND IS NOT
DESIGNED, MANUFACTURED OR INTENDED FOR USE IN CONNECTION WITH THE DESIGN,
CONSTRUCTION, MAINTENANCE, AND/OR OPERATION OF ANY SYSTEM WHERE THE USE OR A
FAILURE OF SUCH SYSTEM COULD RESULT IN A SITUATION THAT THREATENS THE SAFETY OF
HUMAN LIFE OR SEVERE PHYSICAL HARM OR PROPERTY DAMAGE (INCLUDING, FOR EXAMPLE,
USE IN CONNECTION WITH ANY NUCLEAR, AVIONICS, LIFE SUPPORT OR OTHER LIFE
CRITICAL APPLICATION). NVIDIA EXPRESSLY DISCLAIMS ANY EXPRESS OR IMPLIED
WARRANTY OF FITNESS FOR SUCH HIGH RISK USES. NVIDIA SHALL NOT BE LIABLE TO
CUSTOMER OR ANY THIRD PARTY, IN WHOLE OR IN PART, FOR ANY CLAIMS OR DAMAGES
ARISING FROM SUCH HIGH RISK USES.}

_{NVIDIA makes no representation or warranty that the product described in this
guide will be suitable for any specified use without further testing or
modification. Testing of all parameters of each product is not necessarily
performed by NVIDIA. It is customer’s sole responsibility to ensure the product
is suitable and fit for the application planned by customer and to do the
necessary testing for the application in order to avoid a default of the
application or the product. Weaknesses in customer’s product designs may affect
the quality and reliability of the NVIDIA product and may result in additional
or different conditions and/or requirements beyond those contained in this
guide. NVIDIA does not accept any liability related to any default, damage,
costs or problem which may be based on or attributable to: (i) the use of the
NVIDIA product in any manner that is contrary to this guide, or (ii) customer
product designs.}

_{Other than the right for customer to use the information in this guide with the
product, no other license, either expressed or implied, is hereby granted by
NVIDIA under this guide. Reproduction of information in this guide is
permissible only if reproduction is approved by NVIDIA in writing, is reproduced
without alteration, and is accompanied by all associated conditions,
limitations, and notices.}

Trademarks

_{NVIDIA, the NVIDIA logo, and cuBLAS, CUDA, cuDNN, DALI, DIGITS, DGX, DGX-1,
DGX-2, DGX Station, DLProf, Jetson, Kepler, Maxwell, NCCL, Nsight Compute,
Nsight Systems, NvCaffe, PerfWorks, Pascal, SDK Manager, Tegra, TensorRT,
TensorRT Inference Server, Tesla, TF-TRT, and Volta are trademarks and/or
registered trademarks of NVIDIA Corporation in the U.S. and other countries.
Other company and product names may be trademarks of the respective companies
with which they are associated.}

Copyright

¹ 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.

² 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.

³ For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

⁴ For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

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

⁶ The compatibility package ensures that Linux drivers R384 are compatible with Tesla GPUs.