TensorFlow Release 18.08

The NVIDIA container image of TensorFlow, release 18.08, 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 18.08 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 18.08 is based on TensorFlow 1.9.0.
  • Latest version of cuDNN 7.2.1.
  • Latest version of DALI 0.1.2 Beta.
  • Latest version of TensorBoard 1.9.0.
  • Added experimental support for float16 data type in Horovod, allowing functions such as all_reduce to accept tensors in float16 precision. (This functionality is not yet integrated into multi-GPU training examples).
  • Ubuntu 16.04 with July 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
  • TensorRT conversion has been integrated into optimization pass. The tensorflow/contrib/tensorrt/test/test_tftrt.py script has an example showing the use of optimization pass.

  • 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.

  • Current optimization pass does not support INT8 yet.

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


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

  • The DALI integrated ResNet-50 samples in the 18.08 NGC TensorFlow container has lower than expected accuracy and performance results. We are working to address the issue in the next release.

  • There is a known performance regression in the inference benchmarks for ResNet-50. We haven't seen this regression in the inference benchmarks for VGG or training benchmarks for any network. The cause of the regression is still under investigation.