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TensorFlow Release 19.03

The NVIDIA container image of TensorFlow, release 19.03, is available on NGC.

Contents of the TensorFlow container

This container image contains the complete source of the version of NVIDIA 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 19.03 is based on CUDA 10.1, which requires NVIDIA Driver release 418.xx+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. 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 19.03 supports CUDA compute capability 6.0 and higher. This corresponds to GPUs in the Pascal, Volta, and Turing 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.

Accelerating Inference In TensorFlow With TensorRT (TF-TRT)

For step-by-step instructions on how to use TF-TRT, see Accelerating Inference In TensorFlow With TensorRT User Guide.

Key Features And Enhancements

  • Integrated TensorRT 5.1.2 RC into TensorFlow. See the TensorRT 5.1.2 RC Release Notes for a full list of new features.

  • Improved examples at GitHub: TF-TRT, including README files, build scripts, benchmark mode, ResNet models from TensorFlow official model zoo, etc...

Announcements

TensorRT 3.x is not longer supported, therefore, models that were accelerated using TensorRT 3.x will no longer run. If you have a production model that was accelerated with TensorRT 3.x, you will need to convert your model with TensorRT 5.x or later again.

For more information, see the Note in Serializing A Model In C++ or Serializing A Model In Python.

Automatic Mixed Precision (AMP)

Automatic mixed precision converts certain float32 operations to operate in float16 which can run much faster on Tensor Cores. Automatic mixed precision is built on two components:

  • a loss scaling optimizer
  • graph rewriter

For models already using a tf.Optimizer() for both compute_gradients() and apply_gradients() operations, automatic mixed precision can be enabled by defining the following environment variable before calling the usual float32 training script:

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export TF_ENABLE_AUTO_MIXED_PRECISION=1


Models implementing their own optimizers can use the graph rewriter on its own (while implementing loss scaling manually) with the following environment variable:

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export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=1


For more information about how to access and enable Automatic mixed precision for TensorFlow, see Automatic Mixed Precision Training In TensorFlow from the TensorFlow User Guide, along with Training With Mixed Precision.

Tensor Core Examples

These examples focus on achieving the best performance and convergence from NVIDIA Volta Tensor Cores by using the latest deep learning example networks 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.

  • An implementation of the ResNet-50 v1.5 model. The ResNet-50 v1.5 model is a modified version of the original ResNet-50 v1 model. The difference between v1 and v1.5 is in the bottleneck blocks which requires downsampling, for example, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. The following features were implemented in this model; data-parallel multi-GPU training with Horovod, Tensor Cores (mixed precision) training, and static loss scaling for tensor cores (mixed precision) training.

Known Issues

  • There is a known performance regression with TensorFlow 1.13.1 for some networks when run with small batch sizes. As a workaround, increase the batch size.
  • The AMP preview implementation is not compatible with Distributed Strategies. We recommend using Horovod for parallel training with AMP.
  • If using or upgrading to a 3-part-version driver, for example, a driver that takes the format of xxx.yy.zz, you will receive a Failed to detect NVIDIA driver version. message. This is due to a known bug in the entry point script's parsing of 3-part driver versions. This message is non-fatal and can be ignored. This will be fixed in the 19.04 release.
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