TensorFlow Release 18.06

The NVIDIA container image of TensorFlow, release 18.06, is available.

Contents of TensorFlow

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 18.06 is based on CUDA 9, which requires NVIDIA Driver release 384.xx.

Key Features and Enhancements

This TensorFlow release includes the following key features and enhancements.
  • TensorFlow container image version 18.06 is based on TensorFlow 1.8.0.
  • Updated scripts and README in nvidia-examples/cnn/ to use cleaner implementation with high-level TensorFlow APIs including Datasets, Layers, and Estimators. Multi-GPU support in these scripts is now provided exclusively using Horovod/MPI.
  • Fixed incorrect network definition in resnet18 and resnet34 models in nvidia-examples/cnn/.
  • Updated scripts and README in nvidia-examples/build_imagenet_data/ to improve usability and ensure that the dataset is correctly downloaded and resized.
  • Added support for TensorRT 4 features to TensorFlow-TensorRT integration.
  • Includes integration with TensorRT 4.0.1
  • Optimized CPU bilinear image resize kernel to improve performance of input pipeline.
  • Ubuntu 16.04 with May 2018 updates

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

  • Added TensorRT 4.0 API support with extended layer support. This support includes the FullyConnected layer and BatchedMatMul op.

  • Resource management added, where memory allocation is uniformly managed by TensorFlow.

  • Bug fixes and better error handling in conversion.

Limitations

  • TensorRT conversion relies on static shape inference, where the frozen graph should provide explicit dimension on all ranks other than the first batch dimension.

  • Batchsize for converted TensorRT engines are fixed at conversion time. Inference can only run with batchsize smaller than the specified number.

  • Current supported models are limited to CNNs. Object detection models and RNNs are not yet supported.

Known Issues

  • Input tensors are required to have rank 4 for quantization mode (INT8 precision).

Announcements

Starting with the next major version of CUDA release, we will no longer provide updated Python 2 containers and will only update Python 3 containers.

Known Issues

There are no known issues in this release.