Abstract

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

1. 22.xx Framework Containers Support Matrix

Important: Content that is included in <<>> brackets indicates new content from the previously published version.
Note: The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 22.03 release of an image was released in March 2022.

22.xx container images

Table 1. Software stack packaged with the 22.xx container images
Container Image 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
  • NVDIA DGX-1™
  • DGX-2™
  • NVDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVDIA DGX-1™
  • DGX-2™
  • NVDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVDIA DGX-1™
  • DGX-2™
  • NVDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVDIA DGX-1™
  • DGX-2™
  • NVDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVDIA DGX-1™
  • DGX-2™
  • NVDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVDIA DGX-1™
  • DGX-2™
  • NVDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • NVDIA DGX-1™
  • DGX-2™
  • NVDIA DGX™ A100
  • DGX Station
  • DGX Station A100
  • DGX-1
  • DGX-2
  • NVDIA 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
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 22.11 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. 4

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
CUDA 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 11.10.3.66 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 8.6.0.163 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 1.6.0.2 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 1.18.0 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 2.15.1 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 8.5.0.12 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
NVIDIA HPC-X 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
Nsight Systems 2022.4.2.1 2022.4.2.1 2022.4.2.1 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
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: 1.9.0.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    
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 <<1.13.0a0+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 (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: 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 2.10.0 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 (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: 14.4 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 8.5.0.12 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 (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: 7.25 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
<<2.28.0>> 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 (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: 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:
2.3.0 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        
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 2.10.0 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 1.13.0a0+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
Triton 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

2. 21.xx Framework Containers Support Matrix

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

21.xx container images

Table 2. 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

3. 20.xx Framework Containers Support Matrix

Important: Content that is included in <<>> brackets indicates new content from the previously published version.
Note: The deep learning framework container packages follow a naming convention that is based on the year and month of the image release. For example, the 20.06 release of an image was released in June, 2020.

20.xx container images

Table 3. 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+5
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