TensorFlow Release 21.02

The NVIDIA container image of TensorFlow, release 21.02, 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 21.02 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. 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 21.02 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.

Announcements

  • 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

Note: 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.
  • A regression (only observed with NVIDIA Ampere GPU architecture) in CUDNN’s fused Convolution+Bias+Activation implementation can cause performance regressions of up to 24% in some models such as UNet Medical. This will be fixed in a future CUDNN release.
  • Some image-based inference workloads see a regression of up to 50% for the smallest batch sizes. This is due to regressions in cuDNN 8 which will be addressed in a future release.
  • A few models see performance regressions compared to the 20.08 release. Training WideAndDeep sees regressions of up to 30% on A100. In FP32 the TF1 Unet Industrial and Bert fine tuning training regress from 10-20%. Also the TF2 Unet Medical and MaskRCNN models regress by about 20% in some cases. These regressions will be addressed in a future release.
  • 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. ResNet50 inferencing can be up to 30% slower on A100 and Turing GPUs.
  • 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.
  • Training the UNET3D models with a batch size of 1 can result in OOM (Out-Of-Memory) in the TensorFlow 1 container.