Frameworks Support Matrix

Abstract

This support matrix is for NVIDIA® optimized frameworks. The matrix provides a single view into the supported software and specific versions that come packaged with the frameworks based on the container image.


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 24.01 release of an image was released in January 2024.

24.xx container images

Table 1. Software stack packaged with the 24.xx container images
Container Image 24.02 24.01
DGX
DGX System
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
Operating System
  • 4.99.x (DGX A100)
  • 5.0+
  • 6.0

Red Hat Enterprise Linux 9 / CentOS 91

  • EL91

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.99.x (DGX A100)
  • 5.0+
  • 6.0

Red Hat Enterprise Linux 9 / CentOS 92

  • EL91

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
System Requirements
NVIDIA Driver

Release 24.02 is based on CUDA 12.3.2, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

Release 24.01 is based on CUDA 12.3.2, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

GPU Model
Base Container Image (included in all containers)
Container OS Ubuntu 22.04 Ubuntu 22.04
CUDA NVIDIA CUDA 12.3.2 NVIDIA CUDA 12.3.2
cuBLAS NVIDIA cuBLAS 12.3.4.1 NVIDIA cuBLAS 12.3.4.1
cuDNN <<9.0.0.306>> 8.9.7.29
cuTENSOR 2.0 2.0
DALI <<1.34>> 1.33
NCCL 2.19.4 2.19.4
TensorRT <<TensorRT 8.6.3>> TensorRT 8.6.1.6
rdma-core 39.0 39.0
NVIDIA HPC-X 2.16rc4 with 2.16rc4 with
GDRcopy 2.3 2.3
Nsight Compute 2023.3.1.1 2023.3.1.1
Nsight Systems 2023.4.1.97 2023.4.1.97
NVIDIA Optimized Frameworks
DGL - 1.2 including:
- Multi arch support: x86, Arm SBSA
- Docker image size: 24.8 GB
JAX - -
  -
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet 1.9.1 including: 1.9.1 including:
Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA
Docker image size: 12.0 GB Docker image size: 12.1 GB
PaddlePaddle 2.5.2 including: 2.5.2 including:
Multi arch support: x86 only Multi arch support: x86 only
Docker image size: 8.94 GB Docker image size: 9.01 GB
PyTorch 2.3.0a0+ebedce2including 2.2.0a0+81ea7a4including
Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA
Docker image size: 22.2 GB Docker image size: 22.0 GB
TensorFlow 2.15.0 including 2.14.0 including
Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA
Docker image size: 14.4 GB Docker image size: 14.4 GB
TensorRT TensorRT 8.6.3 TensorRT 8.6.1.6
Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA
Docker image size: 7.05 GB Docker image size: 7.46 GB
Triton Inference Server In addition to the hardware and software listed above, Triton Inference Server also supports:

2.43 including

In addition to the hardware and software listed above, Triton Inference Server also supports:

2.41 including

Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA
Docker image size: 13.8 GB Docker image size: 14.7 GB
TensorFlow For Jetson 2.15.0  
PyTorch for Jetson 2.3.0a0+ebedce2  
Triton for Jetson 2.43.0  


Important:

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

Note:

The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 23.01 release of an image was released in January 2023.

23.xx container images

Table 2. Software stack packaged with the 23.xx container images
Container Image 23.12 23.11 23.10 23.09 23.08 23.07 23.06 23.05 23.04 23.03 23.02 23.01
DGX
DGX System
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
Operating System
  • 4.99.x (DGX A100)
  • 5.0+
  • 6.0

Red Hat Enterprise Linux 9 / CentOS 93

  • EL91

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.99.x (DGX A100)
  • 5.0+
  • 6.0

Red Hat Enterprise Linux 9 / CentOS 94

  • EL91

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.99.x (DGX A100)
  • 5.0+
  • 6.0

Red Hat Enterprise Linux 9 / CentOS 91

  • EL91

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.99.x (DGX A100)
  • 5.0+
  • 6.0

Red Hat Enterprise Linux 9 / CentOS 91

  • EL91

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.99.x (DGX A100)
  • 5.0+
  • 6.0

Red Hat Enterprise Linux 9 / CentOS 91

  • EL91

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.99.x (DGX A100)
  • 5.0+
  • 6.0

Red Hat Enterprise Linux 9 / CentOS 91

  • EL91

Red Hat Enterprise Linux 8 / CentOS 81 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.99.x (DGX A100)
  • 5.0+
  • 6.0

Red Hat Enterprise Linux 9 / CentOS 91

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+5 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+
  • 6.0

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 71

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
System Requirements
NVIDIA Driver

Release 23.12 is based on CUDA 12.3.2, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R460, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

Release 23.11 is based on CUDA 12.3.0, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R460, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

Release 23.10 is based on CUDA 12.2.2, which requires NVIDIA Driver release 535 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), or 525.85 (or later R525), or 535.86 (or later R535).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.2. 6

Release 23.09 is based on CUDA 12.2.1, which requires NVIDIA Driver release 535 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), or 525.85 (or later R525), or 535.86 (or later R535).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.2. 3

Release 23.08 is based on CUDA 12.2.1, which requires NVIDIA Driver release 535 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), or 525.85 (or later R525), or 535.86 (or later R535).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.2. 3

Release 23.07 is based on CUDA 12.1.1, which requires NVIDIA Driver release 530 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), 525.85 (or later R525), or 530.30 (or later R530).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.1. 3

Release 23.06 is based on CUDA 12.1.1, which requires NVIDIA Driver release 530 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), 525.85 (or later R525), or 530.30 (or later R530).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.1. 3

Release 23.05 is based on CUDA 12.1.1, which requires NVIDIA Driver release 530 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), 525.85 (or later R525), or 530.30 (or later R530).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.1. 3

Release 23.04 is based on CUDA 12.1.0, which requires NVIDIA Driver release 530 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), 525.85 (or later R525), or 530.30 (or later R530).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.0. 3

Release 23.03 is based on CUDA 12.1.0, which requires NVIDIA Driver release 530 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), 525.85 (or later R525), or 530.30 (or later R530).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.0. 3

Release 23.02 is based on CUDA 12.0.1, which requires NVIDIA Driver release 525 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), or 525.85 (or later R525).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.0. 3

Release 23.01 is based on CUDA 12.0.1, which requires NVIDIA Driver release 525 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), or 525.85 (or later R525).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 12.0. 3

GPU Model
Base Container Image (included in all containers)
Container OS Ubuntu 22.04 Ubuntu 22.04 Ubuntu 22.04 Ubuntu 22.04 Ubuntu 22.04 Ubuntu 22.04 Ubuntu 22.04 Ubuntu 22.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04
CUDA NVIDIA CUDA 12.3.2 NVIDIA CUDA 12.3.0 NVIDIA CUDA 12.2.2 NVIDIA CUDA 12.2.1 NVIDIA CUDA 12.2.1 NVIDIA CUDA 12.1.1 NVIDIA CUDA 12.1.1 NVIDIA CUDA 12.1.1 NVIDIA CUDA 12.1.0 NVIDIA CUDA 12.1.0 NVIDIA CUDA 12.0.1 NVIDIA CUDA 12.0.1
cuBLAS NVIDIA cuBLAS 12.3.4.1 NVIDIA cuBLAS 12.3.2.1 NVIDIA cuBLAS 12.2.5.6 NVIDIA cuBLAS 12.2.5.6 NVIDIA cuBLAS 12.2.5.1 NVIDIA cuBLAS 12.1.3.1 NVIDIA cuBLAS 12.1.3.1 NVIDIA cuBLAS 12.1.3.1 NVIDIA cuBLAS 12.1.3 cuBLAS from CUDA 12.1.0 12.0.2 from CUDA 12.0.2 from CUDA
cuDNN 8.9.7.29 8.9.6.50 8.9.5 8.9.5 8.9.4 8.9.3 8.9.2 8.9.1.23 8.9.0 8.8.1.3 8.7.0 8.7.0
cuTENSOR 1.7.0.1 1.7.0.1 1.7.0.1 1.7.0.1 1.7.0.1 1.7.0.1 1.7.0.1 1.7.0.1 1.7.0 1.6.2.3 1.6.2.3 1.6.2.3
DALI 1.32.0 1.31.0 1.30.0 1.29.0 1.28.0 1.27.0 1.26.0 1.25.0 1.24.0 1.23.0 1.22.0 1.21.0
NCCL 2.19.3 2.19.3 2.19.3 2.18.5 2.18.3 2.18.3 2.18.1 2.18.1 2.17.1 2.17.1 2.16.5 2.16.5
TensorRT TensorRT 8.6.1.6 TensorRT 8.6.1.6 TensorRT 8.6.1.6 TensorRT 8.6.1.6 TensorRT 8.6.1.6 TensorRT 8.6.1.6 TensorRT 8.6.1.6 TensorRT 8.6.1.2 TensorRT 8.6.1 TensorRT 8.5.3 TensorRT 8.5.3 TensorRT 8.5.2.2
rdma-core 39.0 39.0 39.0 39.0 39.0 39.0 39.0 36.0 36.0 36.0 36.0 36.0
NVIDIA HPC-X 2.16 with 2.16 with 2.16 with 2.16 with 2.15 with 2.15 with 2.15 with 2.14 with 2.13 with 2.13 with 2.13 with 2.13 with
GDRcopy 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3
Nsight Compute 2023.3.1.1 2023.3.0.12 2023.2.1.3 2023.2.1.3 2023.2.1.3 2023.1.1.4 2023.1.1.4 2023.1.1.4 2023.1.0.15 2023.1.0.15 2022.4.1.6 2022.4.1.6
Nsight Systems 2023.4.1 2023.3.1.92 2023.3.1.92 2023.3.1.92 2023.2.3.1001 2023.2.3.1001 2023.2.3.1001 2023.2 2023.1.1.127 2023.1.1.127 2022.5.1 2022.5.1
NVIDIA Optimized Frameworks
DGL - 1.1.1 including: - 1.1.1 including: - 1.1.1 including: - - - - - -
      Multi arch support: x86, Arm SBSA   Multi arch support: x86, Arm SBSA            
      Docker image size: 23.4 GB   Docker image size: 20.8 GB            
JAX - - -              
    Multi arch support: x86 only   Multi arch support: x86 only              
Kaldi -
  Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only
  Docker image size: 9.13 GB Docker image size: 9.1 GB Docker image size: 9.16 GB Docker image size: 9.36 GB Docker image size: 9.14 GB Docker image size: 9.29 GB Docker image size: 9.19 GB Docker image size: 10.9 GB Docker image size: 11.1 GB Docker image size: 11.8 GB Docker image size: 11.1 GB
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.1 including:
Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA
Docker image size: 12.1 GB Docker image size: 12.1 GB Docker image size: 12 GB Docker image size: 12.1 GB Docker image size: 12.1 GB Docker image size: 12.1 GB Docker image size: 12.0 GB Docker image size: 12.1 GB Docker image size: 13.1 GB Docker image size: 13.2 GB Docker image size: 13.9 GB Docker image size: 13.1 GB
PaddlePaddle 2.5.2 including: 2.5.2 including: 2.5.1 including: 2.5.0 including: 2.5.0 including: 2.4.1 including: 2.4.1 including: No 23.05 release. 2.4.1 including: 2.4.1 including: 2.4.0 including: 2.3.2 including:
Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only - Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only
Docker image size: 8.98 GB Docker image size: 8.98 GB Docker image size: 8.94 GB Docker image size: 8.99 GB Docker image size: 9.02 GB Docker image size: 8.58 GB Docker image size: 8.59 GB - Docker image size: 9.47 GB Docker image size: 9.74 GB Docker image size: 10.5 GB Docker image size: 9.41 GB
PyTorch 2.2.0a0+81ea7a48including 2.2.0a0+6a974be including 2.1.0a0+32f93b1 including 2.1.0a0+32f93b1 including 2.1.0a0+29c30b1 including 2.1.0a0+b5021ba including 2.1.0a0+4136153 including 2.0.0 including 2.1.0a0+fe05266f including 2.0.0a0+1767026 including 1.14.0a0+410ce96 including 1.14.0a0+410ce96 including
Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA
Docker image size: 21.9 GB Docker image size: 21.9 GB Docker image size: 22.1 GB Docker image size: 22.0 GB Docker image size: 20.6 GB Docker image size: 19.8 GB Docker image size: 19.7 GB Docker image size: 22 GB Docker image size: 20.4 GB Docker image size: 20.4 GB Docker image size: 20.5 GB Docker image size: 19.7 GB
TensorFlow 2.14.0 including 2.13.0 including 2.13.0 including 2.13.0 including 2.13.0 including 2.12.0 including 2.12.0 including 2.12.0 including 2.12.0 including 2.11.0 including 1.15.5 including 2.11.0 including 1.15.5 including 2.11.0 including 1.15.5 including
Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA
Docker image size: 14.3 GB Docker image size: 14.1 GB Docker image size: 14.2 GB Docker image size: 14.2 GB Docker image size: 14.2 GB Docker image size: 13.9 GB Docker image size: 14.3 GB Docker image size: 14.2 GB Docker image size: 15.4 GB Docker image size: 15.9 GB Docker image size: 16.3 GB Docker image size: 16.6 GB Docker image size: 17.0 GB Docker image size: 15.9 GB Docker image size: 16.2 GB
TensorRT TensorRT 8.6.1.6 TensorRT 8.6.1.6 TensorRT 8.6.1.6 TensorRT 8.6.1.6 TensorRT 8.6.1.6 TensorRT 8.6.1.6 TensorRT 8.6.1.6 TensorRT 8.6.1.2 TensorRT 8.6.1 TensorRT 8.5.3 TensorRT 8.5.3 TensorRT 8.5.2.2
Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA
Docker image size: 7.45 GB Docker image size: 7.45 GB Docker image size: 7.41 GB Docker image size: 7.47 GB Docker image size: 7.5 GB Docker image size: 7.45 GB Docker image size: 7.45 GB Docker image size: 7.5 GB Docker image size: 8.05 GB Docker image size: 8.32 GB Docker image size: 9.03 GB Docker image size: 8.3 GB
Triton Inference Server In addition to the hardware and software listed above, Triton Inference Server also supports:

2.41 including

In addition to the hardware and software listed above, Triton Inference Server also supports:

2.40 including

In addition to the hardware and software listed above, Triton Inference Server also supports:

2.39 including

In addition to the hardware and software listed above, Triton Inference Server also supports:

2.38 including

In addition to the hardware and software listed above, Triton Inference Server also supports:

2.37 including

In addition to the hardware and software listed above, Triton Inference Server also supports:

2.36 including

In addition to the hardware and software listed above, Triton Inference Server also supports:

2.35 including

In addition to the hardware and software listed above, Triton Inference Server also supports:

2.34 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.33 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.32 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.31 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.30 including

Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA
Docker image size: 14.7 GB Docker image size: 14.3 GB Docker image size: 12.6 GB Docker image size: 12.6 GB Docker image size: 12.4 GB Docker image size: 12.3 GB Docker image size: 12.3 GB Docker image size: 12.5 GB Docker image size: 13 GB Docker image size: 14.7 GB Docker image size: 15.3 GB Docker image size: 15.3 GB
TensorFlow For Jetson TensorFlow 2.14.0 for Jetson           TensorFlow 2.12.0 for Jetson TensorFlow 2.12.0 for Jetson TensorFlow 2.12.0 for Jetson TensorFlow 1.15.5 and 2.10.1 for Jetson TensorFlow 1.15.5 and 2.10.1 for Jetson TensorFlow 1.15.5 and 2.10.1 for Jetson
PyTorch for Jetson             2.1.0a0+4136153 for Jetson 2.0.0 for Jetson 2.1.0a0+fe05266f for Jetson PyTorch 2.0.0a0+1767026 for Jetson PyTorch 1.14.0a0+44dac51 for Jetson PyTorch 1.14.0a0+44dac51 for Jetson
TensorFlow Wheel for x86           - - - - TensorFlow 1.15.5 for x86 TensorFlow 1.15.5 for x86 TensorFlow 1.15.5 for x86
Triton for Jetson           Triton Inference Server 2.36.0 for Jetson Triton Inference Server 2.35.0 for Jetson Triton Inference Server 2.34.0 for Jetson Triton Inference Server 2.33.0 for Jetson Triton Inference Server 2.32.0 for Jetson Triton Inference Server 2.31.0 for Jetson Triton Inference Server 2.30.0 for Jetson


Important:

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

Note:

The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 22.03 release of an image was released in March 2022.

22.xx container images

Table 3. Software stack packaged with the 22.xx container images
Container Image 22.12 22.11 22.10 22.09 22.08 22.07 22.06 22.05 22.04 22.03 22.02 22.01
DGX
DGX System
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX H100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVIDIA DGX-1™
  • DGX-2™
  • NVIDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • NVIDIA DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
Operating System
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0+

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
  • 5.1

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
System Requirements
NVIDIA Driver

Release 22.12 is based on CUDA 11.8.0, which requires NVIDIA Driver release 520 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 515.65 (or later R515).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.8. 3

Release 22.11 is based on CUDA 11.8.0, which requires NVIDIA Driver release 520 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 515.65 (or later R515).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.8. 3

Release 22.10 is based on CUDA 11.8.0, which requires NVIDIA Driver release 520 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 515.65 (or later R515).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.8. 3

Release 22.09 is based on CUDA 11.8.0, which requires NVIDIA Driver release 520 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 515.65 (or later R515).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.8. 3

Release 22.08 is based on CUDA 11.7.1, which requires NVIDIA Driver release 515 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), or 510.47 (or later R510).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.8. 3

Release 22.07 is based on CUDA 11.7 Update 1 Preview, which requires NVIDIA Driver release 515 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), or 510.47 (or later R510).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.7. 3

Release 22.06 is based on CUDA 11.7 Update 1 Preview, which requires NVIDIA Driver release 515 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), or 510.47 (or later R510).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.7. 3

Release 22.05 is based on CUDA 11.7, which requires NVIDIA Driver release 515 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), or 510.47 (or later R510).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, and R460 drivers, which are not forward-compatible with CUDA 11.7. 3

Release 22.04 is based on NVIDIA CUDA® 11.6.2, which requires NVIDIA Driver release 510 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). The CUDA driver's compatibility package only supports particular drivers. 3 Release 22.03 is based on NVIDIA CUDA® 11.6.1, which requires NVIDIA Driver release 510 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). The CUDA driver's compatibility package only supports particular drivers. 3 Release 22.02 is based on NVIDIA CUDA 11.6.0, which requires NVIDIA Driver release 510 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). The CUDA driver's compatibility package only supports particular drivers. 3 Release 22.01 is based on NVIDIA CUDA 11.6.0, which requires NVIDIA Driver release 510 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). The CUDA driver's compatibility package only supports particular drivers. 3
GPU Model
Base Container Image (included in all containers)
Container OS Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04
CUDA NVIDIA CUDA 11.8.0 NVIDIA CUDA 11.8.0 NVIDIA CUDA 11.8.0 NVIDIA CUDA 11.8.0 NVIDIA CUDA 11.7 Update 1 NVIDIA CUDA 11.7 Update 1 Preview NVIDIA CUDA 11.7 Update 1 Preview NVIDIA CUDA 11.7.0 NVIDIA CUDA 11.6.2 NVIDIA CUDA 11.6.1 NVIDIA CUDA 11.6.0 NVIDIA CUDA 11.6.0
cuBLAS 11.11.3.6 11.11.3.6 11.11.3.6 11.11.3.6 11.10.3.66 11.10.3.66 11.10.3.66 11.10.1.25 11.9.3.115 11.8.1.74 11.8.1.74 11.8.1.74
cuDNN 8.7.0 GA 8.7.0.80 8.6.0.163 8.6.0.163 8.5.0.96 8.4.1 8.4.1 8.4.0.27 8.4.0.27 8.3.3.40 8.3.2.44 8.3.2.44
cuTENSOR 1.6.1.5 1.6.1.5 1.6.1.5 1.6.1.5 1.6.0.2 1.5.0.3 1.5.0.3 1.5.0.3 1.5.0.3 1.5.0.1 1.4 1.4
DALI 1.20.0 1.18.0 1.18.0 1.17.0 1.16.0 1.15.0 1.14.0 1.13.0 1.12.0 1.11.1 1.10.0 1.9.0
NCCL 2.15.5 2.15.5 2.15.5 2.15.1 2.12.12 2.12.12 2.12.12 2.12.10 2.12.10 2.12.9 2.11.4 2.11.4
TensorRT TensorRT 8.5.1 TensorRT 8.5.1 TensorRT 8.5.0.12 TensorRT 8.5.0.12 TensorRT 8.4.2.4 TensorRT 8.4.1 TensorRT 8.2.5 TensorRT 8.2.5 TensorRT 8.2.4.2 TensorRT 8.2.3 TensorRT 8.2.3 TensorRT 8.2.2
rdma-core 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0
NVIDIA HPC-X 2.13 with 2.12.2tp1 with 2.12.2tp1 with 2.12.1a0 with 2.10 with 2.10 with 2.10 with 2.10 with 2.10 with 2.10 with 2.10 with 2.10 with
GDRcopy 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3
Nsight Systems 2022.4.2.1 2022.4.2.1 2022.4.2.1 2022.4.1 2022.1.3.18 2022.1.3.3 2022.1.3.3 2022.1.3.3 2022.2.1.31-5fe97ab 2021.5.2.53 2021.5.2.53 2021.5.2.53
NVIDIA Optimized Frameworks
Kaldi
Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only
Docker image size: 10.4 GB Docker image size: 10.3 GB Docker image size: 10.3 GB Docker image size: 10.3 GB Docker image size: 8.89 GB Docker image size: 9.07 GB Docker image size: 9 GB Docker image size: 9.11 GB Docker image size: 9.01 GB Docker image size: 9 GB Docker image size: 9 GB Docker image size: 8.96 GB
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.1 including: 1.9.0.rc6 including: 1.9.0.rc6 including: 1.9.0.rc6 including: Release paused Release paused
Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA    
Docker image size: 12 GB Docker image size: 11.7 GB Docker image size: 11.7 GB Docker image size: 11.7 GB Docker image size: 9.97 GB Docker image size: 10.2 GB Docker image size: 10.1 GB Docker image size: 10.7 GB Docker image size: 10.6 GB Docker image size: 11.0 GB    
PyTorch 1.14.0a0+410ce96 including 1.13.0a0+936e930 including 1.13.0a0+d0d6b1f including 1.13.0a0+d0d6b1f including 1.13.0a0+d321be6 including 1.13.0a0+08820cb including 1.13.0a0+340c412 including 1.12.0a0+8a1a93a including 1.12.0a0+bd13bc6 including 1.12.0a0+2c916ef including 1.11.0a0+17540c5c including 1.11.0a0+bfe5ad28 including
Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta)
Docker image size: 18.3 GB Docker image size: 17.3 GB Docker image size: 16.9 GB Docker image size: 16.8 GB Docker image size: 14.6 GB Docker image size: 14.8 GB Docker image size: 14.6 GB Docker image size: 14.6 GB Docker image size: 14.1 GB Docker image size: 14.6 GB Docker image size: 14.4 GB Docker image size: 14.8 GB
TensorFlow 2.10.1 including 1.15.5 including 2.10.0 including 1.15.5 including 2.10.0 including 1.15.5 including 2.9.1 including 1.15.5 including 2.9.1 including 1.15.5 including 2.9.1 including 1.15.5 including 2.9.1 including 1.15.5 including 2.8.0 including 1.15.5 including 2.8.0 including 1.15.5 including 2.8.0 including 1.15.5 including 2.7.0 including 1.15.5 including 2.7.0 including 1.15.5 including
Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta)
Docker image size: 14.3 GB Docker image size: 14.8 GB Docker image size: 14.4 GB Docker image size: 15.0 GB Docker image size: 14.4 GB Docker image size: 14.9 GB Docker image size: 14.1 GB Docker image size: 14.9 GB Docker image size: 12 GB Docker image size: 12.8 GB Docker image size: 12.2 GB Docker image size: 13.0 GB Docker image size: 12.2 GB Docker image size: 14.4 GB Docker image size: 12.2 GB Docker image size: 14.4 GB Docker image size: 13.1 GB Docker image size: 14.4 GB Docker image size: 13.6 GB Docker image size: 14.9 GB Docker image size: 13.1 GB Docker image size: 14.5 GB Docker image size: 13.1 GB Docker image size: 15.1 GB
TensorRT TensorRT 8.5.1 TensorRT 8.5.1 TensorRT 8.5.0.12 TensorRT 8.5.0.12 including: TensorRT 8.4.2.4 including: TensorRT 8.4.1 including: TensorRT 8.2.5 including: TensorRT 8.2.5 including: TensorRT 8.2.4.2 including: TensorRT 8.2.3 including: TensorRT 8.2.2 including: TensorRT 8.2.2 including:
Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta)
Docker image size: 7.61 GB Docker image size: 7.25 GB Docker image size: 7.51 GB Docker image size: 7.49 GB Docker image size: 6.09 GB Docker image size: 6.27 GB Docker image size: 6.21 GB Docker image size: 6.33 GB Docker image size: 6.21 GB Docker image size: 6.21 GB Docker image size: 6.21 GB Docker image size: 6.17 GB
Triton Inference Server In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.29.0 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.28.0 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.27.0 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.26.0 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.25.0 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.24.0 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.23.0 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.22.0 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.21.0 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.20.0 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.19.0 including

In addition to the hardware and software listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.18.0 including

Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta)
Docker image size: 14 GB Docker image size: 13.8 GB Docker image size: 13.4 GB Docker image size: 13.7 GB Docker image size: 11.7 GB Docker image size: 11.9 GB Docker image size: 11 GB Docker image size: 11 GB Docker image size: 11.4 GB Docker image size: 12.1 GB Docker image size: 12.3 GB Docker image size: 12.4 GB

PaddlePaddle

2.3.2 including: 2.3.2 including: 2.3.2 including: 2.3.0 including: 2.3.0 including: 2.3.0 including: 2.2.2 including: 2.2.2 including:        
Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only        
Docker image size: 8.72 GB Docker image size: 8.48 GB Docker image size: 8.46 GB Docker image size: 8.44 GB Docker image size: 8.43 GB Docker image size: 8.43 GB Docker image size: 7.98 GB Docker image size: 8.09 GB        
TensorFlow For Jetson TensorFlow 1.15.5 and 2.10.1 for Jetson TensorFlow 1.15.5 and 2.10.0 for Jetson TensorFlow 1.15.5 and 2.10.0 for Jetson TensorFlow 1.15.5 and 2.9.1 for Jetson This release was skipped. TensorFlow 1.15.5 and 2.9.1 for Jetson TensorFlow 1.15.5 and 2.9.1 for Jetson TensorFlow 1.15.5 and 2.8.0 for Jetson TensorFlow 1.15.5 and 2.8.0 for Jetson TensorFlow 1.15.5 and 2.8.0 for Jetson TensorFlow 1.15.5 and 2.7.0 for Jetson TensorFlow 1.15.5 and 2.7.0 for Jetson
PyTorch for Jetson PyTorch 1.14.0a0+410ce96 for Jetson PyTorch 1.13.0a0+936e930 for Jetson PyTorch 1.13.0a0+d0d6b1f for Jetson PyTorch 1.13.0a0+d0d6b1f for Jetson This release was skipped. PyTorch 1.13.0a0+08820cb for Jetson PyTorch 1.13.0a0+340c412 for Jetson PyTorch 1.12.0a0+8a1a93a for Jetson PyTorch 1.12.0a0+84d1cb9 for Jetson PyTorch 1.12.0a0+2c916ef for Jetson This release was skipped. PyTorch 1.11.0a0+bfe5ad28 for Jetson
TensorFlow Wheel for x86 TensorFlow 1.15.5 for x86 TensorFlow 1.15.5 for x86 TensorFlow 1.15.5 for x86 TensorFlow 1.15.5 for x86 TensorFlow 1.15.5 for x86 TensorFlow 1.15.5 for x86 TensorFlow 1.15.5 for x86 TensorFlow 1.15.5 for x86 TensorFlow 1.15.5 for x86 TensorFlow 1.15.5 for x86 TensorFlow 1.15.5 for x86 TensorFlow 1.15.5 for x86
Triton for Jetson Triton Inference Server 2.27.0 for Jetson Triton Inference Server 2.27.0 for Jetson Triton Inference Server 2.27.0 for Jetson Triton Inference Server 2.26.0 for Jetson Triton Inference Server 2.24.0 for Jetson Triton Inference Server 2.24.0 for Jetson Triton Inference Server 2.23.0 for Jetson Triton Inference Server 2.22.0 for Jetson Triton Inference Server 2.21.0 for Jetson Triton Inference Server 2.20.0 for Jetson Triton Inference Server 2.19.0 for Jetson Triton Inference Server 2.18.0 for Jetson


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 4. Software stack packaged with the 21.xx container images
Container Image 21.12 21.11 21.10 21.09 21.08 21.07 21.06 21.05 21.04 21.03 21.02
DGX
DGX System
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • DGX A100
  • DGX Station
  • DGX Station A100
Operating System DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
  • 5.1

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
  • 5.1

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
  • 5.1

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0
  • 5.1

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
DGX OS
  • 4.1+1(4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
DGX OS
  • 4.1+1 (4.6+ multi-node NCCL)
  • 4.99.x (DGX A100)
  • 5.0

Red Hat Enterprise Linux 7 / CentOS 72

  • EL7-20.02+1

Red Hat Enterprise Linux 8 / CentOS 82 (All DGX systems except DGX Station A100)

  • EL8-20.11+1
NVIDIA Driver Release 21.12 is based on NVIDIA CUDA 11.5.0, which requires NVIDIA Driver release 495 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). The CUDA driver's compatibility package only supports particular drivers. 3 Release 21.11 is based on NVIDIA CUDA 11.5.0, which requires NVIDIA Driver release 495 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). The CUDA driver's compatibility package only supports particular drivers. 3 Release 21.10 is based on NVIDIA CUDA 11.4.2 with cuBLAS 11.6.5.2, which requires NVIDIA Driver release 470 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460).

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

Release 21.09 is based on NVIDIA CUDA 11.4.2, which requires NVIDIA Driver release 470 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460).

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

Release 21.08 is based on NVIDIA CUDA 11.4.1, which requires NVIDIA Driver release 470 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460).

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

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

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

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

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

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

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

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

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

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

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

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

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

GPU Model
Base Container Image
Container OS Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04 Ubuntu 20.04
CUDA NVIDIA CUDA 11.5.0 NVIDIA CUDA 11.5.0 NVIDIA CUDA 11.4.2 with cuBLAS 11.6.5.2 11.4.2 11.4.1 11.4.0 11.3.1 11.3.0 11.3.0 11.2.1 11.2.0
cuBLAS 11.7.3.1 11.7.3.1 11.6.1.51 11.6.1.51 11.5.4 11.5.2.43 11.5.1.109 11.5.1.101 11.5.1.101 11.4.1.1026 11.3.1.68
cuDNN 8.3.1.22 8.3.0.96 8.2.4.15 8.2.4.15 8.2.2.26 8.2.2.26 8.2.1 8.2.0.51 8.2.0.41 8.1.1 8.1.0.77
NCCL 2.11.4 2.11.4 2.11.4 2.11.4 2.10.3 2.10.3 2.9.9 2.9.8 2.9.6 2.8.4 2.8.4
TensorRT TensorRT 8.2.1.8 TensorRT 8.0.3.4

TensorRT 8.0.3.4

               
NVIDIA Optimized Frameworks
Kaldi
Multi arch support: x86 only Multi arch support: x86 only Multi arch support: x86 only                
Docker image size: 8.78 GB Docker image size: 8.69 GB Docker image size: 9.16 GB Docker image size: 9.12 GB Docker image size: 8.86 GB Docker image size: 8.77 GB Docker image size: 8.62 GB Docker image size: 8.43 GB Docker image size: 8.3 GB Docker image size: 8.62 GB Docker image size: 8.73 GB
DIGITS Release paused Release paused Release paused 6.1.1 including 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: 14.6 GB Docker image size: 14.9 GB Docker image size: 15 GB Docker image size: 14.7 GB Docker image size: 15.1 GB Docker image size: 15.1 GB Docker image size: 15.4 GB Docker image size: 15.5 GB
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet Release paused Release paused Release paused 1.9.0.rc6 including 1.9.0.rc6 including 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: 11.2 GB Docker image size: 10.9 GB Docker image size: 10.6 GB Docker image size: 10.4 GB Docker image size: 10.8 GB Docker image size: 10.7 GB Docker image size: 11.1 GB Docker image size: 10.8 GB
PyTorch 1.11.0a0+b6df043 including 1.11.0a0+b6df043 including 1.10.0a0+0aef44c including 1.10.0a0+3fd9dcf including 1.10.0a0+3fd9dcf including 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
Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta)                
Docker image size: 14.7 GB Docker image size: 14.5 GB Docker image size: 13.2 GB Docker image size: 13.1 GB Docker image size: 12.7 GB Docker image size: 15 GB Docker image size: 14.5 GB Docker image size: 14.5 GB Docker image size: 14.3 GB Docker image size: 14.4 GB Docker image size: 12.9 GB
TensorFlow 2.6.2 including 1.15.5 including 2.6.0 including 1.15.5 including 2.6.0 including 1.15.5 including 2.6.0 including 1.15.5 including 2.5.0 including 1.15.5 including 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
Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta)                                
Docker image size: 12.8 GB Docker image size: 16.9 GB Docker image size: 12.5 GB Docker image size: 16.5 GB Docker image size: 10.6 GB Docker image size: 14.5 GB Docker image size: 11.5 GB Docker image size: 13.6 GB Docker image size: 11.5 GB Docker image size: 13.9 GB Docker image size: 11.1 GB Docker image size: 14 GB Docker image size: 10.8 GB Docker image size: 13.7 GB Docker image size: 10.8 GB Docker image size: 14.1 GB Docker image size: 10.6 GB Docker image size: 14.1 GB Docker image size: 10.9 GB Docker image size: 14.4 GB Docker image size: 11.1 GB Docker image size: 14.5 GB
TensorRT TensorRT 8.2.1.8 including: TensorRT 8.0.3.4 including: TensorRT 8.0.3.4 including: TensorRT 8.0.3 including: TensorRT 8.0.1.6 including: 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:
Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta)                
Docker image size: 5.98 GB Docker image size: 5.88 GB Docker image size: 6.37 GB Docker image size: 6.3 GB Docker image size: 6.04 GB Docker image size: 5.95 GB Docker image size: 5.8 GB Docker image size: 5.76 GB Docker image size: 5.63 GB Docker image size: 5.94 GB Docker image size: 7.09 GB
Triton Inference Server In addition to the hardware listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.17.0 including

In addition to the hardware listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.16.0 including

In addition to the hardware listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.15.0 including

In addition to the hardware listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.14.0 including

In addition to the hardware listed above, Triton Inference Server also supports:
  • AMD x86 CPU
  • Intel x86 CPU

2.13.0 including

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
Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta) Multi arch support: x86, Arm SBSA (beta)                
Docker image size: 12.1 GB Docker image size: 12.2 GB Docker image size: 13.7 GB Docker image size: 13.6 GB Docker image size: 13.1 GB Docker image size: 14.6 GB Docker image size: 13.4 GB Docker image size: 10.6 GB Docker image size: 11.1 GB Docker image size: 11.3 GB Docker image size: 15.6 GB
TensorFlow For Jetson TensorFlow 1.15.5 and 2.6.2 for Jetson TensorFlow 1.15.5 and 2.6.0 for Jetson TensorFlow 1.15.5 and 2.6.0 for Jetson TensorFlow 1.15.5 and 2.6.0 for Jetson TensorFlow 1.15.5 and 2.5.0 for Jetson TensorFlow 1.15.5 and 2.5.0 for Jetson TensorFlow 1.15.5 and 2.5.0 for Jetson TensorFlow 1.15.5 and 2.4.0 for Jetson TensorFlow 1.15.5 and 2.4.0 for Jetson TensorFlow 1.15.5 and 2.4.0 for Jetson TensorFlow 1.15.5 and 2.4.0 for Jetson
Triton for Jetson Triton Inference Server 2.17.0 for Jetson Triton Inference Server 2.16.0 for Jetson Triton Inference Server 2.15.0 for Jetson Triton Inference Server 2.14.0 for Jetson Triton Inference Server 2.13.0 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


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 5. 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+7
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              


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 6. 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)8
  • 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 2.0.0 including 1.15.0 including 1.14.0 including 1.14.0 including 1.14.0 including 1.14.0 including 1.13.1 including 1.13.1 including 1.13.1 including 1.13.1 including 1.13.0-rc0 including 1.12.0 including
Docker size image: 7.71 GB
  • Docker image size with Python 2.7: 7.84 GB
  • Docker image size with Python 3.6: 8.32 GB
Docker image size: 7.78
  • Docker image size with Python 2.7: 8.02 GB
  • Docker image size with Python 3.6: 8.60 GB
  • Docker image size with Python 2.7: 7.81 GB
  • Docker image size with Python 3.6: 8.38 GB
  • Docker image size with Python 2.7: 6.78 GB
  • Docker image size with Python 3.6: 7.34 GB
  • Docker image size with Python 2.7: 6.78 GB
  • Docker image size with Python 3.6: 7.34 GB
  • Docker image size with Python 2.7: 6.18 GB
  • Docker image size with Python 3.6: 6.71 GB
Docker image size: 6.88 GB Docker image size: 6.76 GB Docker image size: 6.8 GB Docker image size: 6.72 GB Docker image size: 6.06 GB Docker image size: 5.57 GB
TensorRT 6.0.1 including: 6.0.1 including: 6.0.1 including: 6.0.1 including: 5.1.5 including: 5.1.5 including: 5.1.5 including: 5.1.5 including: 5.1.2 RC including: 5.1.2 RC including: 5.0.2 including: 5.0.2 including:
  • Docker image size Python 2.7: 4.03 GB
  • Docker image size with Python 3.6: 4.06 GB
  • Docker image size Python 2.7: 3.83 GB
  • Docker image size with Python 3.6: 4.16 GB
Docker image size: 4.2 GB Docker image size: 4.4 GB Docker image size: 4.4 GB Docker image size: 3.83 GB Docker image size: 3.83 GB Docker image size: 3.83 GB Docker image size: 3.79 GB Docker image size: 3.91 GB Docker image size: 3.01 GB Docker image size: 3.00 GB
TensorRT Inference Server 1.9.0 including 1.8.0 including 1.7.0 including 1.6.0 including 1.5.0 including 1.4.0 including 1.3.0 including 1.2.0 including 1.1.0 including 1.0.0 including 0.11.0 Beta including 0.10.0 Beta including
Docker image size: 6.12 GB Docker image size: 6.15 GB Docker image size: 8.26 GB Docker image size: 7.73 GB Docker image size: 7.73 GB Docker image size: 7.15 GB Docker image size: 7.15 GB Docker image size: 7.02 GB Docker image size: 5.22 GB Docker image size: 5.33 GB Docker image size: 4.42 GB Docker image size: 4.17 GB
TensorFlow For Jetson TensorFlow 1.15.0 and 2.0.0 for Jetson TensorFlow 1.15.0 and 2.0.0 for Jetson TensorFlow 1.14.0 for Jetson TensorFlow 1.14.0 for Jetson TensorFlow 1.14.0 for Jetson TensorFlow 1.14.0 for Jetson   TensorFlow 1.13.1 for Jetson TensorFlow 1.13.1 for Jetson TensorFlow 1.13.1 for Jetson TensorFlow 1.13.0-rc0 for Jetson TensorFlow 1.12.0 for Jetson


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 7. 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)
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 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 410.489
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 0.17.1 including 0.17.1 including 0.17.1 including 0.17.1 including 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
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
Caffe2         0.8.1 including 0.8.1 including 0.8.1 including 0.8.1 including 0.8.1 including 0.8.1 including 0.8.1 including 0.8.1 including
        Docker image size: 3.02 GB Docker image size: 2.94 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 6.1.0 including 6.1.0 including 6.0.0 including
  • 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 2.5 including 2.5 including 2.5 including 2.4 including 2.4 including 2.3.1 including 2.3.1 including
        Docker image size: 6.17 GB Docker image size: 6.13 GB
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet 1.3.1 including 1.3.0 including 1.3.0 including 1.3.0 including 1.2.0 including 1.2.0 including 1.2.0 including 1.1.0 including 1.1.0 including 1.1.0 including 1.0.0 including 1.0.0 including
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 0.4.1+ including 0.4.1+ including 0.4.1+ including 0.4.1 including 0.4.0 including 0.4.0 including 0.4.0 including 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
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
TensorFlow 1.12.0 including 1.12.0-rc2 including 1.10.0 including 1.10.0 including 1.9.0 including 1.8.0 including 1.8.0 including 1.7.0 including 1.7.0 including 1.4.0 including 1.4.0 including 1.4.0 including
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
TensorFlow For Jetson TensorFlow 1.12.0 for Jetson TensorFlow 1.12.0-rc2 for Jetson                    
TensorRT TensorRT 5.0.2 including: TensorRT 5.0.2 including: TensorRT 5.0.0 RC including: 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
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
TensorRT Inference Server 0.9.0 Beta including 0.8.0 Beta including 0.7.0 Beta including 0.6.0 Beta including 0.5.0 Beta including 0.4.0 Beta including 0.3.0 Beta including 0.2.0 Beta including 0.1.0 Beta      
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
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
        Docker image size: 3.70 GB Docker image size: 3.74 GB
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
        Docker image size: 3.06 GB Docker image size: 3 GB


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 8. 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+
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 6.0.0 including 6.0.0 including 6.0.0 including 6.0.0 including 5.0 including 5.0 including 5.0 including 5.0 including 5.0 including 5.0 including
Microsoft Cognitive Toolkit 2.2 and OpenMPI 3.0.0 2.2 and OpenMPI 3.0.0 2.2 2.1 2.0 2.0 2.0.rc2 2.0.beta15.0 2.0.beta12.0 2.0.beta9.0 2.0.beta5.0
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet 1.0.0 0.12.0 0.11.0 0.11.0.rc3 0.10.0 0.10.0 0.9.3a+ 0.9.3a+ 0.9.3    
PyTorch 0.2.0 0.2.0 0.2.0 0.2.0 0.1.12 0.1.12 0.1.12 0.1.10      
TensorFlow 1.4.0 1.3.0 1.3.0 1.3.0 1.2.1 1.1.0 1.0.1 1.0.1 1.0.0 0.12.1 0.12.0
TensorRT 3.0.1                    
Theano 1.0.0rc1 1.0.0rc1 0.10beta3 0.10beta1 0.9.0 0.9.0 0.9.0 0.9.0 0.9.0rc3 0.8.0 0.8.0
Torch 7 7 7 7 7 7 7 7 7 7 7


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 9. Software stack packaged with the 16.xx container images
  Container Image 16.12
Supported Platform DGX OS 2.x+ and 1.x+
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
Microsoft Cognitive Toolkit 2.0.beta5.0
NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet  
PyTorch  
TensorFlow 0.12.0
TensorRT  
Theano 0.8.0
Torch 7

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1 NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

2 NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

3 NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

4 NVIDIA acknowledges the wide use of CentOS and understands that it is a community-developed derivative of the NVIDIA supported Red Hat Enterprise Linux. Support for CentOS is available directly from the CentOS community. NVIDIA tests the Frameworks Containers primarily on Red Hat Linux but ensures that all NVIDIA provided software runs on tested CentOS versions and will try to identify and correct issues related to NVIDIA provided software.

5 Starting with version 20.10, the Framework Containers require at least NVIDIA OFED driver version 4.9 installed on a system for NCCL multi-node applications (the NVIDIA OFED driver 4.9 LTS is included in DGX OS 4.6). You might see a message if you are running a Framework Container on a system that does not have the required NVIDIA OFED driver version installed. This message can be ignored if you do not plan to use NCCL for multi-node applications. (4.6+ multi-node NCCL)

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

7 Versions 20.09 and earlier have Mellanox OFED 4.6 Mellanox verbs libraries and are able to work with the kernel mode drivers from Mellanox OFED 4.7 or earlier (for multinode).

8 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.

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

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