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.

2. 21.xx Framework Containers Support Matrix

Important: Content that is included in <<>> brackets indicates new content from the previously published version.
Note: The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 21.02 release of an image was released in February 2021.

21.xx container images

Table 1. Software stack packaged with the 21.xx container images
Container Image 21.07 21.06 21.05 21.04 21.03 21.02
DGX
DGX System
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
Operating System DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+1
Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)
  • EL8-20.11+1
DGX OS
  • 4.1+1(4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+1
Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)
  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+1
Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)
  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+1
Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)
  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+1
Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)
  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+1
Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)
  • EL8-20.11+1
NVIDIA Driver

Release 21.07 is based on NVIDIA CUDA 11.4.0, which requires NVIDIA Driver release 470 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460).

The CUDA driver's compatibility package only supports particular drivers. 3

Release 21.06 is based on NVIDIA CUDA 11.3.1, which requires NVIDIA Driver release 465.19.01 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460).

The CUDA driver's compatibility package only supports particular drivers. 3

Release 21.05 is based on NVIDIA CUDA 11.3.0, which requires NVIDIA Driver release 465.19.01 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460).

The CUDA driver's compatibility package only supports particular drivers.3

Release 21.04 is based on NVIDIA CUDA 11.3.0, which requires NVIDIA Driver release 465.19.01 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460).

The CUDA driver's compatibility package only supports particular drivers. 3

Release 21.03 is based on NVIDIA CUDA 11.2.1, which requires NVIDIA Driver release 460.32.03 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51(or later R450).

The CUDA driver's compatibility package only supports particular drivers. 3

Release 21.02 is based on NVIDIA CUDA 11.2.0, which requires NVIDIA Driver release 460.27.04 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51(or later R450).

The CUDA driver's compatibility package only supports particular drivers.3

GPU Model
Base Container Image
Container OS Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04
CUDA <<11.4.0>> 11.3.1 11.3.0 11.3.0 11.2.1 11.2.0
cuBLAS <<11.5.2.43>> 11.5.1.109 11.5.1.101 11.5.1.101 11.4.1.1026 11.3.1.68
cuDNN <<8.2.2.26>> 8.2.1 8.2.0.51 8.2.0.41 8.1.1 8.1.0.77
NCCL <<2.10.3>> 2.9.9 2.9.8 2.9.6 2.8.4 2.8.4
NVIDIA Optimized Frameworks
Kaldi
Docker image size: 8.77 GB Docker image size: 8.62 GB Docker image size: 8.43 GB Docker image size: 8.3 GB Docker image size: 8.62 GB Docker image size: 8.73 GB
DIGITS 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including
Docker image size: 15 GB Docker image size: 14.7 GB Docker image size: 15.1 GB Docker image size: 15.1 GB Docker image size: 15.4 GB Docker image size: 15.5 GB
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet <<1.9.0.rc3>> including 1.9.0.rc2 including 1.8.0 including 1.8.0 including 1.8.0 including 1.8.0.rc2 including
Docker image size: 10.6 GB Docker image size: 10.4 GB Docker image size: 10.8 GB Docker image size: 10.7 GB Docker image size: 11.1 GB Docker image size: 10.8 GB
PyTorch <<1.10.0a0+ecc3718>> including 1.9.0a0+c3d40fd including 1.9.0a0+2ecb2c7 including 1.9.0a0+2ecb2c7 including 1.9.0a0+df837d0 including 1.8.0a0+52ea372 including
Docker image size: 21.2 GB Docker image size: 14.5 GB Docker image size: 14.5 GB Docker image size: 14.3 GB Docker image size: 14.4 GB Docker image size: 12.9 GB
TensorFlow 2.5.0 including 1.15.5 including 2.5.0 including 1.15.5 including 2.4.0 including 1.15.5 including 2.4.0 including 1.15.5 including 2.4.0 including 1.15.5 including 2.4.0 including 1.15.5 including
Docker image size: 11.1 GB Docker image size: 14 GB Docker image size: 10.8 GB Docker image size: 13.7 GB Docker image size: 10.8 GB Docker image size: 14.1 GB Docker image size: 10.6 GB Docker image size: 14.1 GB Docker image size: 10.9 GB Docker image size: 14.4 GB Docker image size: 11.1 GB Docker image size: 14.5 GB
TensorRT <<TensorRT 8.0.1.6>> including: TensorRT 7.2.3.4 including: TensorRT 7.2.3.4 including: TensorRT 7.2.3.4 including: TensorRT 7.2.2.3 including: TensorRT 7.2.2.3+cuda11.1.0.024 including:
Docker image size: 5.95 GB Docker image size: 5.8 GB Docker image size: 5.76 GB Docker image size: 5.63 GB Docker image size: 5.94 GB Docker image size: 7.09 GB
Triton Inference Server
In addition to the hardware listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU
<<2.12.0>> including
In addition to the hardware listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU
2.11.0 including
In addition to the hardware listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU
2.10.0 including
In addition to the hardware listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU
2.9.0 including
In addition to the hardware listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU
2.8.0 including
2.7.0 including
Docker image size: 14.6 GB Docker image size: 13.4 GB Docker image size: 10.6 GB Docker image size: 11.1 GB Docker image size: 11.3 GB Docker image size: 15.6 GB
TensorFlow For Jetson TensorFlow 1.15.5 and 2.5.0 for Jetson TensorFlow 1.15.5 and 2.5.0 for Jetson TensorFlow 1.15.5 and 2.4.0 for Jetson TensorFlow 1.15.5 and 2.4.0 for Jetson TensorFlow 1.15.5 and 2.4.0 for Jetson TensorFlow 1.15.5 and 2.4.0 for Jetson
Triton for Jetson <<Triton Inference Server 2.12.0 for Jetson>> Triton Inference Server 2.11.0 for Jetson Triton Inference Server 2.10.0 for Jetson Triton Inference Server 2.9.0 for Jetson Triton Inference Server 2.8.0 for Jetson Triton Inference Server 2.7.0 for Jetson

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 2. Software stack packaged with the 20.xx container images
Container Image 20.12 20.11 20.10 20.09 20.08 20.07 20.06 20.03 20.02 20.01
DGX
DGX System
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX-1
  • DGX-2
  • DGX Station
  • DGX-1
  • DGX-2
  • DGX Station
  • DGX-1
  • DGX-2
  • DGX Station
  • DGX-1
  • DGX-2
Operating System DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+1
DGX OS
  • 4.1+
  • 4.99.x (DGX A100)
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+4
DGX OS
  • 4.1+
  • 4.99.x (DGX A100)
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+
DGX OS
  • 4.1+
  • 4.99.x (DGX A100)
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+
DGX OS
  • 4.1+1
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+
DGX OS
  • 4.1+1
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+
DGX OS
  • 4.1+1
Red Hat Enterprise Linux 7 / CentOS 72
  • EL7-20.02+
DGX OS
  • 4.1+1
NVIDIA Certified Systems
NVIDIA Driver

Release 20.12 is based on CUDA 11.1.1, which requires NVIDIA driver release 455.23.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx, 440.30, or 450.51.

The CUDA driver's compatibility package only supports particular drivers. 3

Release 20.11 is based on CUDA 11.1.0, which requires NVIDIA driver release 455.23.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx, 440.30, or 450.51.

The CUDA driver's compatibility package only supports particular drivers. 3

Release 20.10 is based on CUDA 11.1.0, which requires NVIDIA driver release 455.23.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx, 440.30, or 450.51.

The CUDA driver's compatibility package only supports particular drivers. 3

Release 20.09 is based on CUDA 11.0.3, which requires NVIDIA driver release 450.51.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx or 440.30.

The CUDA driver's compatibility package only supports particular drivers. 3

Release 20.08 is based on CUDA 11.0.3, which requires NVIDIA driver release 450.51.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx or 440.30.

The CUDA driver's compatibility package only supports particular drivers. 3

Release 20.07 is based on CUDA 11.0.194, which requires NVIDIA driver release 450.51.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx or 440.30.

The CUDA driver's compatibility package only supports particular drivers. 3

Release 20.06 is based on CUDA 11.0.167, which requires NVIDIA driver release 450.36.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx or 440.30.

The CUDA driver's compatibility package only supports particular drivers. 3

Release 20.03 is based on CUDA 10.2.89, which requires NVIDIA driver release 440.33.01.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+, 410, 418.xx or 440.30.

The CUDA driver's compatibility package only supports particular drivers. 3

Release 20.02 is based on CUDA 10.2.89, which requires NVIDIA driver release 440.33.01.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+, 410, 418.xx or 440.30.

The CUDA driver's compatibility package only supports particular drivers. 3

Release 20.01 is based on CUDA 10.2.89, which requires NVIDIA driver release 440.33.01.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+, 410, 418.xx or 440.30.

The CUDA driver's compatibility package only supports particular drivers. 3

GPU Model
Base Container Image
Container OS Ubuntu 20.04 Ubuntu 18.04 Ubuntu 18.04 Ubuntu 18.04 Ubuntu 18.04 Ubuntu 18.04 Ubuntu 18.04 Ubuntu 18.04 Ubuntu 18.04 Ubuntu 18.04
CUDA 11.1.1 11.1.0 11.1.0 11.0.3 11.0.3 11.0.194 11.0.167 10.2.89 10.2.89 10.2.89
cuBLAS 11.3.0.106 11.2.1.74 11.2.1.74 11.2.0.252 11.2.0.252 11.1.0.229 11.1.0.213 10.2.2.89 10.2.2.89 10.2.2.89
cuDNN 8.0.5 8.0.4 8.0.4 8.0.4 8.0.2 8.0.1 8.0.1 7.6.5 7.6.5 7.6.5
NCCL 2.8.3 2.8.2 2.7.8 2.7.8 2.7.8 2.7.6 2.7.5 2.5.6 2.5.6 2.5.6
NVIDIA Optimized Frameworks
Kaldi da71f301 including 5.5 including 5.5 including
Docker image size: 9.75 GB Docker image size: 8.72 GB Docker image size: 8.69 GB Docker image size: 7.49 GB Docker image size: 7.36 GB Docker image size: 6.94 GB Docker image size: 6.82 GB Docker image size: 5.76 GB Docker image size: 5.53 GB Docker image size: 5.55 GB
NVCaffe               0.17.3 including 0.17.3 including 0.17.3 including
              Docker image size: 4.82 GB Docker image size: 4.82 GB Docker image size: 4.85 GB
DIGITS 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including
Docker image size: 16.1 GB Docker image size: 15.3 GB Docker image size: 15.2 GB Docker image size: 13.3 GB Docker image size: 12.9 GB Docker image size: 12.4 GB Docker image size with TensorFlow: 12.4 GB
  • Docker image size with TensorFlow: 10.5 GB
  • Docker image size with Caffe: 5.06 GB
  • Docker image size with TensorFlow: 10.5 GB
  • Docker image size with Caffe: 5.06 GB
  • Docker image size with TensorFlow: 9.36 GB
  • Docker image size with Caffe: 5.01 GB
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet 1.8.0.rc0 including 1.8.0.rc0 including 1.7.0 including 1.7.0 including 1.6.0 including 1.6.0 including 1.6.0 including 1.6.0 including 1.6.0.rc2 including 1.5.1commit c98184806 from September 4, 2019 including
Docker image size: 11.8 GB Docker image size: 10.7 GB Docker image size: 12.7 GB Docker image size: 11.6 GB Docker image size: 8.68 GB Docker image size: 8.98 GB Docker image size: 9.16 GB Docker image size: 6.73 GB Docker image size: 6.59 GB Docker image size: 6.11 GB
PyTorch 1.8.0a0+1606899 including 1.8.0a0+17f8c32 including 1.7.0a0+7036e91 including 1.7.0a0+8deb4fe including 1.7.0a0+6392713 including 1.6.0a0+9907a3e including 1.6.0a0+9907a3e including 1.5.0a0+8f84ded including 1.5.0a0+3bbb36e including 1.4.0a0+a5b4d78 including
Docker image size: 14.2 GB Docker image size: 13.2 GB Docker image size: 12.9 GB Docker image size: 11.1 GB Docker image size: 12.2 GB Docker image size: 11.9 GB Docker image size: 11.9 GB Docker image size: 9.41 GB Docker image size: 9.11 GB Docker image size: 9.12 GB
TensorFlow 2.3.1 including 1.15.4 including 2.3.1 including 1.15.4 including 2.3.1 including 1.15.4 including 2.3.0 including 1.15.3 including 2.2.0 including 1.15.3 including 2.2.0 including 1.15.3 including 2.2.0 including 1.15.2 including 2.1.0 including 1.15.2 including 2.1.0 including 1.15.2 including 2.0.0 including 1.15.0 including
Docker image size: 12.2 GB Docker image size: 15.2 GB Docker image size: 11.6 GB Docker image size: 14.4 GB Docker image size: 11.4 GB Docker image size: 14.3 GB Docker image size: 9.62 GB Docker image size: 12.4 GB Docker image size: 11 GB Docker image size: 11.9 GB Docker image size: 9.3 GB Docker image size: 11.5 GB Docker image size: 9.45 GB Docker image size: 11.5 GB Docker image size: 8.05 GB Docker image size: 9.53 GB Docker size image: 7.42 GB Docker size image: 9.49 GB Docker size image: 7.15 GB
  • Docker image size with Python 2.7: 7.88 GB
  • Docker image size with Python 3.6: 8.39 GB
TensorRT 7.2.2 including: 7.2.1 including: 7.2.1 including: 7.1.3 including: 7.1.3 including: 7.1.3 including: 7.1.2 including: 7.0.0 including: 7.0.0 including: 7.0.0 including:
Docker image size: 7.09 GB Docker image size: 6.96 GB Docker image size: 6.93 GB Docker image size: 5.75 GB Docker image size: 5.63 GB Docker image size: 5.57 GB Docker image size: 4.97 GB
  • Docker image size with Python 2.7: 4.05 GB
  • Docker image size with Python 3.6: 4.08 GB
  • Docker image size with Python 2.7: 4.04 GB
  • Docker image size with Python 3.6: 4.07 GB
  • Docker image size with Python 2.7: 4.08 GB
  • Docker image size with Python 3.6: 4.11 GB
Triton Inference Server 2.6.0 including 2.5.0 including 2.4.0 including 2.3.0 including 2.3.0 including 1.15.0 and 2.1.0 and including 1.14.0 and 2.0.0 including 1.12.0 including 1.11.0 including 1.10.0 including
Docker image size: 15.6 GB Docker image size: 11.55 GB Docker image size: 11.3 GB Docker image size: 8.3 GB Docker image size: 9.97 GB Docker image size: 8.22 GB

Docker image size for 1.14.0: 9.73 GB

Docker image size for 2.0.0: 8.68 GB

Docker image size: 6.31 GB Docker image size: 6.07 GB Docker image size: 6.16 GB
TensorFlow For Jetson TensorFlow 1.15.4 and 2.3.1 for Jetson TensorFlow 1.15.4 and 2.3.1 for Jetson TensorFlow 1.15.4 and 2.3.1 for Jetson TensorFlow 1.15.3 and 2.3.0 for Jetson TensorFlow 1.15.3 and 2.2.0 for Jetson TensorFlow 1.15.3 and 2.2.0 for Jetson TensorFlow 1.15.2 and 2.1.0 for Jetson TensorFlow 1.15.2 and 2.1.0 for Jetson TensorFlow 1.15.2 and 2.1.0 for Jetson TensorFlow 1.15.0 and 2.0.0 for Jetson
Triton for Jetson Triton Inference Server 2.6.0 for Jetson Triton Inference Server 2.5.0 for Jetson Triton Inference Server 2.4.0 for Jetson              

3. 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 3. 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)5
  • 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)
NVIDIA Driver

Release 19.12 is based on CUDA 10.2.89, which requires NVIDIA driver release 440.33.01.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+, 410, 418.xx or 440.30

The CUDA driver's compatibility package only supports particular drivers. 3

Release 19.11 is based on CUDA 10.2.89, which requires NVIDIA driver release 440.xx.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+, 410 or 418.xx.

The CUDA driver's compatibility package only supports particular drivers. 2

Release 19.10 is based on CUDA 10.1.243, which requires NVIDIA driver release 418.xx.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+ or 410.

The CUDA driver's compatibility package only supports particular drivers. 2

Release 19.09 is based on CUDA 10.1.243, which requires NVIDIA driver release 418.xx.

However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+ or 410.

The CUDA driver's compatibility package only supports particular drivers. 2

Release 19.08 is based on CUDA 10.1.243, which requires NVIDIA driver release 418.87.

However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410.

The CUDA driver's compatibility package only supports particular drivers. 2

Release 19.07 is based on CUDA 10.1.168, which requires NVIDIA driver release 418.67.

However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410.

The CUDA driver's compatibility package only supports particular drivers. 2

Release 19.06 is based on CUDA 10.1.168, which requires NVIDIA driver release 418.xx.

However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410.

The CUDA driver's compatibility package only supports particular drivers. 2

Release 19.05 is based on CUDA 10.1 Update 1, which requires NVIDIA driver release 418.xx.

However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410.

The CUDA driver's compatibility package only supports particular drivers. 2

Release 19.04 is based on CUDA 10.1, which requires NVIDIA driver release 418.xx.x+.

However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410.

The CUDA driver's compatibility package only supports particular drivers. 2

Release 19.03 is based on CUDA 10.1, which requires NVIDIA driver release 418.xx+.

However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410.

The CUDA driver's compatibility package only supports particular drivers. 2

Release 19.02 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+.

However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+.

The CUDA driver's compatibility package only supports particular drivers. 2

Release 19.01 is based on CUDA 10.0, which requires NVIDIA driver release 410.72+.

However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+.

The CUDA driver's compatibility package only supports particular drivers. 2

Supported Hardware GPU Model
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 5.5 including 5.5 including 5.5 including 5.5 including 5.5 including 5.5 including 5.5 including 5.5 including 5.5 including    
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 0.17.3 including 0.17.3 including 0.17.3 including 0.17.3 including 0.17.3 including 0.17.3 including 0.17.3 including 0.17.3 including 0.17.3 including 0.17.2 including 0.17.2 including
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 NA 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including 6.1.1 including
  • 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 1.5.1 commit c98184806 from September 4, 2019 including 1.5.1 commit c98184806 from September 4, 2019 including 1.5.0commit 006486af3 from August 28, 2019 including 1.5.0commit 75a9e187d from June 27, 2019 including 1.5.0.rc2 including upstream commits up through commit 75a9e187d from June 27, 2019 including 1.4.1 including 1.4.0 commit 87c7addcd from February 12, 2019 including 1.4.0 commit 87c7addcd from February 12, 2019 including 1.4.0 including 1.4.0.rc0 including 1.4.0.rc0 including
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 1.4.0a0+174e1ba including 1.3.0a0+24ae9b5 including 1.2.0 including 1.2.0a0 including upstream commits up through commit 9130ab38 from July 31, 2019 as well as a cherry-picked performance fix 9462ca29 including 1.2.0a0including upstream commits up through commit f6aac41 from June 19, 2019 including 1.1.0commit 0885dd28 from May 28, 2019 including 1.0.1commit 828a6a3b from March 31, 2019 including 1.0.1commit 9eb0f43 from March 28, 2019 including 1.1.0a0+81e025d including 1.1.0a0+c42431b including 1.0.0 including
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 1.15.0 including