TensorFlow Release 20.08

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The NVIDIA container image of TensorFlow, release 20.08, 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 20.08 is based on NVIDIA CUDA 11.0.3, which requires NVIDIA Driver release 450 or later. 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. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

GPU Requirements

Release 20.08 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.


  • Python 2.7 is no longer supported in this TensorFlow container release.
  • The TF_ENABLE_AUTO_MIXED_PRECISION environment variables are no longer supported in the tf2 container because it is not possible to automatically enable loss scaling in many cases in the tf 2.x API. Instead tf.train.experimental.enable_mixed_precision_graph_rewrite() should be used to enable AMP.
  • Deep learning framework containers 19.11 and later include experimental support for Singularity v3.0.

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.

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. This container includes the following tensor core examples.

Known Issues

  • The memory required to train MaskRCNN with a given batch size has increased from 20.07 to 20.08. As a result, the batch size may need to be decreased.
  • There are several known performance regressions compared to 20.07. UNet Medical and Industrial on V100 and A100 GPUs can be up to 20% slower. VGG can be up to 95% slower on A100 and 15% slower on Turing GPUs. Googlenet can be up to 20% slower on V100. And ResNet50 inferencing can be up to 30% slower on A100 and Turing GPUs.
  • An out-of-memory condition can occur in TensorFlow (TF1) 20.08 for some models (such as ResNet-50, and ResNext) when Horovod and XLA are both in use. In XLA, we added an optimization that skips compiling a cluster the very first time it is executed, which can help avoid unnecessary recompilations for models with dynamic shapes. On the other hand, for models like ResNet-50, the preferred compilation strategy is to aggressively compile clusters, as compiled clusters are executed many times. Per the “XLA Best Practices” section of the TensorFlow User Guide, running XLA with the following environment variable opts in to that strategy: TF_XLA_FLAGS=--tf_xla_enable_lazy_compilation=false

  • There is a known performance regression of 15% compared to the 20.03 release when training the JoC V-Net Medical models with small batch size and fp32 data type on Turing GPUs. This will be addressed in a future release.
  • There is a known performance regression of up to 60% when running inference using TF-TRT for SSD models with small batch size. This will be addressed in a future release.
  • There is a known performance regression of up to 30% when training SSD models with fp32 data type on T4 GPUs. This will be addressed in a future release.
  • There is a known issue where attempting to convert some models using TF-TRT produces an error “Failed to import metagraph”. This issue is still under investigation and will be resolved in a future release.
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