TensorFlow Release 18.11

The NVIDIA container image of TensorFlow, release 18.11, 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.

Driver Requirements

Release 18.11 is based on CUDA 10, which requires NVIDIA Driver release 410.xx. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384. For more information, see CUDA Compatibility and Upgrades.

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
  • Added support for dilated convolution.

  • Fixed a bug in the Identity op.

  • Fixed a bug in the Relu6 op.

  • Support added to allow empty const tensor.

  • Added object detection example to nvidia-examples/inference.

Known Issues
  • In the TF-TRT API, the minimum_segment_size argument default value is 3. In the image classification examples under nvidia-examples/inference, we define a command line argument for minimum_segment_size which has its own default value. In 18.10, the default value was 7 and in 18.11 we changed it to 2. Smaller values for this argument would cause to convert more TensorFlow nodes to TensorRT which typically should improve the performance, however, we have observed cases where the performance gets worse. In particular, Resnet-50 with smaller batch sizes gets slower with minimum_segment_size=2 comparing to minimum_segment_size=7.

Announcements

Support for accelerating TensorFlow with TensorRT 3.x will be removed in a future release (likely TensorFlow 1.13). The generated plan files are not portable across platforms or TensorRT versions. Plans are specific to the exact GPU model they were built on (in addition to the platforms and the TensorRT version) and must be retargeted to the specific GPU in case you want to run them on a different GPU. 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 4.x or later again.

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

Known Issues

OpenSeq2Seq is only supported in the Python 3 container.