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	19.04	19.03	19.02	19.01
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 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 19.04 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 (see footnote for more information). ¹	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 (see footnote ¹).	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 (see footnote ¹).	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 (see footnote ¹).
Supported Hardware	GPU Model	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal	Turing Volta Pascal
Base Image	Container OS	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04	Ubuntu 16.04
	CUDA	10.1.105	10.1.105	10.0.130	10.0.130
	cuBLAS	<<10.1.0.105>>	10.1.105	10.0.130	10.0.130
	cuDNN	7.5.0	7.5.0	7.4.2	7.4.2
	NCCL	<<2.4.6>>	2.4.3	2.3.7	2.3.7
NVIDIA Optimized Frameworks	Kaldi	5.5 including Python 3.5 OpenMPI 3.1.3 TensorRT 5.1.2 RC	5.5 including Python 3.5 OpenMPI 3.1.3 TensorRT 5.1.2 RC
	Kaldi	Docker image size: 5.01 GB	Docker image size: 5.09 GB
	NVCaffe	0.17.3 including Python 2.7 OpenMPI 3.1.3 TensorRT 5.1.2 RC Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 JupyterLab 0.35.4 JupyterLab Server 0.2.0	0.17.3 including Python 2.7 OpenMPI 3.1.3 TensorRT 5.1.2 RC Jupyter and JupyterLab: Jupyter Client 5.2.4 Jupyter Core 4.4.0 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 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: 4.29 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 0.17.3 OpenMPI 3.1.3 TensorFlow 1.13.1 TensorRT 5.1.2 RC	6.1.1 including Python 2.7 0.17.3 OpenMPI 3.1.3 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	TensorFlow Docker image size: 7.04 GB Caffe Docker image size: 4.41 GB	TensorFlow Docker image size: 6.92 GB Caffe Docker image size: 4.49 GB	TensorFlow Docker image size: 6.43 GB Caffe Docker image size: 3.70 GB	TensorFlow Docker image size: 5.96 GB Caffe Docker image size: 3.66 GB
	MXNet	<<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 0.13.11 <<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 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: 4.9 GB	Docker image size: 4.73 GB	Docker image size: 3.83 GB	Docker image size: 3.82 GB
	PyTorch	<<1.0.1>><<commit 828a6a3b from March 31, 2019>> including Python 3.6 APEX OpenMPI 3.1.3 <<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 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: 7.45 GB	Docker image size: 7.71 GB	Docker image size: 6.61 GB	Docker image size: 7.70 GB
	TensorFlow	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>> 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 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: 6.8 GB	Docker image size: 6.72 GB	Docker image size: 6.06 GB	Docker image size: 5.57 GB
	TensorRT	TensorRT 5.1.2 RC including: Python 2.7 or Python 3.5 OpenMPI 3.1.3	TensorRT 5.1.2 RC 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 5.0.2 including: Python 2.7 or Python 3.5 OpenMPI 3.1.3
	TensorRT	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	<<1.1.0>> including Python 3.5 OpenMPI 3.1.3 TensorRT 5.1.2 RC	1.0.0 including Python 3.5 OpenMPI 3.1.3 TensorRT 5.1.2 RC	0.11.0 Beta including Python 3.5 OpenMPI 3.1.3 TensorRT 5.0.2	0.10.0 Beta including Python 3.5 OpenMPI 3.1.3 TensorRT 5.0.2
	TensorRT Inference Server	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 1.13.1 for Jetson AGX Xavier TensorFlow 1.9 for Jetson TX2	TensorFlow 1.13.1 for Jetson AGX Xavier TensorFlow 1.9 for Jetson TX2	TensorFlow 1.13.0-rc0 for Jetson AGX Xavier TensorFlow 1.9 for Jetson TX2	TensorFlow 1.12.0 for Jetson AGX Xavier TensorFlow 1.9 for Jetson TX2
TensorFlow For TensorRT (TF-TRT)		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 410.48²	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 1.10.0 on JetPack 4.1 Developer Early Access for Jetson AGX Xavier	TensorFlow 1.10.0 on JetPack 4.1 Developer Early Access for Jetson AGX Xavier	TensorFlow 1.9.0 on JetPack 3.2 for Jetson TX2
	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

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¹ For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

² The compatibility package ensures that Linux drivers R384 are compatible with Tesla GPUs.