TensorFlow Release 18.09

The NVIDIA container image of TensorFlow, release 18.09, 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 18.09 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.
  • TensorFlow container image version 18.09 is based on TensorFlow 1.10.0.
  • Latest version of cuDNN 7.3.0.
  • Latest version of CUDA 10.0.130 which includes support for DGX-2, Turing, and Jetson Xavier.
  • Latest version of cuBLAS 10.0.130.
  • Latest version of NCCL 2.3.4.
  • Latest version of TensorRT 5.0.0 RC.
  • Latest version of TensorBoard 1.10.0.
  • Latest version of DALI 0.2 Beta
  • Added support for CUDNN float32 Tensor Op Math mode, which enables float32 models to use Tensor Cores on supported hardware, at the cost of reduced precision. This is disabled by default, but can be enabled by setting the environment variables TF_ENABLE_CUDNN_TENSOR_OP_MATH_FP32=1 (for convolutions) or TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32=1 (for RNNs that use the cudnn_rnn op). This feature is currently considered experimental.
  • Renamed the existing environment variable TF_ENABLE_TENSOR_OP_MATH_FP32 to TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32.
    Note: When using any of the TF_ENABLE_*_TENSOR_OP_MATH_FP32 environment variables, it is recommended that models also use loss scaling to avoid numerical issues during training. For more information about loss scaling, see Training With Mixed Precision.
  • Enhanced tf.contrib.layers.layer_norm by adding a use_fused_batch_norm parameter that improves performance. This parameter is disabled by default, but can be enabled by setting it to True.
  • Ubuntu 16.04 with August 2018 updates

Accelerating Inference In TensorFlow With TensorRT

For step-by-step instructions on how to use TensorRT with the TensorFlow framework, see Accelerating Inference In TensorFlow With TensorRT User Guide. To view the key features, software enhancements and improvements, and known issues, see the Release Notes.

Known Issues

  • OpenSeq2Seq is only supported in the Python 3 container.
  • The build_imagenet_data scripts have a missing dependency on the axel application. This can be resolved by issuing the following command:
    apt-get update &&
    apt-get install axel