TensorFlow Release 21.10

TensorFlow Release 21.10 (PDF)

The NVIDIA container image of TensorFlow, release 21.10, is available on NGC.

Contents of the TensorFlow container

This container image includes the complete source of the NVIDIA version of TensorFlow in /opt/tensorflow. It is pre-built and installed as a system Python module.

To achieve optimum TensorFlow performance, for image based training, the container includes a sample script that demonstrates efficient training of convolutional neural networks (CNNs). The sample script may need to be modified to fit your application. The container also includes the following:

Driver Requirements

Release 21.10 is based on NVIDIA CUDA 11.4.2 with cuBLAS, 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. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades and NVIDIA CUDA and Drivers Support.

GPU Requirements

Release 21.10 supports CUDA compute capability 6.0 and higher. This corresponds to GPUs in the NVIDIA Pascal, Volta, Turing, and Ampere Architecture GPU families. Specifically, for a list of GPUs that this compute capability corresponds to, see CUDA GPUs. For additional support details, see Deep Learning Frameworks Support Matrix.

Key Features and Enhancements

This TensorFlow release includes the following key features and enhancements.

  • TensorFlow container images version 21.10 are based on Tensorflow 1.15.5 and 2.6.0.
  • Improved handling of exp ops in XLA.
  • Enabled pointwise row vectorization for small rows in XLA.
  • Integrated latest TF-TRT features for dynamic shape support.
  • Gemm+bias+relu cublasLt based epilogue fusion in XLA. This feature can be enabled by setting the environment variable TF_USE_CUBLASLT=1.


  • Starting with the 21.10 release, a beta version of the TensorFlow 1 and 2 containers is available for the Arm SBSA platform. Pulling the Docker image nvcr.io/nvidia/tensorflow:21.10-tf2-py3 on an Arm SBSA machine will automatically fetch the Arm-specific image.
  • The TensorCore example models are no longer provided in the core container (previously shipped in /workspace/nvidia-examples). Instead they can be obtained from Github or the NVIDIA GPU Cloud (NGC). Some python packages, included in previous containers to support these example models, have also been removed. Depending on their specific use cases, users may need to add some packages that were previously pre-installed.
  • Support for SLURM PMI2 is deprecated and will be removed after the 21.12 release. PMIX is supported by the container, but is not supported by default in SLURM. Users depending on SLURM integration may need to configure SLURM for PMIX in the base OS as appropriate to their OS distribution (for Ubuntu 20.04, the required package is slurm-wlm-basic-plugins).
  • The nvtx-plugins utility package pre-installed in previous releases has been removed. Users depending on nvtx-plugins can install it using `pip install nvtx-plugins`.

NVIDIA TensorFlow Container Versions

The following table shows what versions of Ubuntu, CUDA, TensorFlow, and TensorRT are supported in each of the NVIDIA containers for TensorFlow. For older container versions, refer to the Frameworks Support Matrix.

Container Version Ubuntu CUDA Toolkit TensorFlow TensorRT
21.10 20.04 NVIDIA CUDA 11.4.2 with cuBLAS



TensorRT for x64 Linux

TensorRT for Arm SBSA Linux

21.09 NVIDIA CUDA 11.4.2



TensorRT 8.0.3
21.08 NVIDIA CUDA 11.4.1



21.07 NVIDIA CUDA 11.4.0
21.06 NVIDIA CUDA 11.3.1



21.05 NVIDIA CUDA 11.3.0



21.03 NVIDIA CUDA 11.2.1 TensorRT
21.02 NVIDIA CUDA 11.2.0 TensorRT
20.12 NVIDIA CUDA 11.1.1



TensorRT 7.2.2


NVIDIA CUDA 11.1.0 TensorRT 7.2.1
20.09 NVIDIA CUDA 11.0.3



TensorRT 7.1.3



20.07 NVIDIA CUDA 11.0.194
20.06 NVIDIA CUDA 11.0.167



TensorRT 7.1.2






TensorRT 7.0.0






TensorRT 6.0.1
19.10 NVIDIA CUDA 10.1.243 1.14.0
19.08 TensorRT 5.1.5

Tensor Core Examples

The tensor core examples provided in GitHub focus on achieving the best performance and convergence by using the latest deep learning example networks and model scripts for training. Each example model trains with mixed precision Tensor Cores on Volta, therefore you can get results much faster than training without Tensor Cores. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

Known Issues


If you encounter functional or performance issues when XLA is enabled, please refer to the XLA Best Practices document. It offers pointers on how to diagnose symptoms and possibly address them.

  • For TensorFlow 1.15, TF-TRT inference throughput may regress for certain models by up to 37% compared to the 21.06-tf1 release. This will be fixed in a future release.
  • The OpenSeq2Seq toolkit has been removed from the TensorFlow 1.x container.
  • There is a known issue in TensorRT 8.0 regarding accuracy for a certain case of int8 inferencing on A40 and similar GPUs. The version of TF-TRT in TF2 includes a feature that works around this issue, but TF1 does not include that feature and may experience the accuracy drop for a small subset of model/data type/batch size combinations on A40. This will be fixed in the next version of TensorRT.
© Copyright 2023, NVIDIA. Last updated on Jul 26, 2023.