TensorFlow Release 22.01

The NVIDIA container image of TensorFlow, release 22.01, 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 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. 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 22.01 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 22.01 are based on Tensorflow 1.15.5 and 2.7.0.
  • Fixed circular dependency in monolithic builds to address github issue 21.
  • TF-TRT respects enable_tensor_float_32_execution Python API.
  • TF-TRT supports Structured Sparsity on NVIDIA Ampere architecture GPUs. This can be enabled by passing enable_sparse_compute=True to TrtGraphConverterV2.

Announcements

  • DLProf v1.8, which was included in the 21.12 container, was the last release of DLProf. Starting with the 22.01 container, DLProf is no longer included. It can still be manually installed via a pip wheel on the nvidia-pyindex.
  • Starting with the 21.10 release, a beta version of the TensorFlow 1 and 2 containers is available for the ARM SBSA platform. For example, pulling the Docker image nvcr.io/nvidia/tensorflow:22.01-tf2-py3 on an ARM SBSA machine will automatically fetch the ARM-specific image.
  • Support for SLURM PMI2 has been removed from the 22.01 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).

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

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.
  • 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.
  • A CUDNN performance regression can cause slowdowns of up to 15% in certain ResNet models. This will be fixed in a future release.
  • There is a known performance regression affecting UNet Medical 3D model training by up to 23%. This will be addressed in a future release.
  • TF-TRT native segment fallback has a known issue causing a crash. This will occur when using TF-TRT to convert a model with a subgraph that is converted to TensorRT but fails to build. Instead of falling back to native TensorFlow TF-TRT will crash. Using export TF_TRT_OP_DENYLIST="ProblematicOp" can help to prevent conversion of an OP causing a native segment fallback.
  • Debugging with TF_CPP_MIN_VLOG_LEVEL=3 can result in a segmentation while auto-tuning convolution algorithms. This will be fixed in the 22.02 release.