TensorFlow Release 18.04

The NVIDIA container image of TensorFlow, release 18.04, 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:

Ubuntu 16.04

Note:

Container image 18.04-py2 contains Python 2.7; 18.04-py3 contains Python 3.5.
NVIDIA CUDA 9.0.176 (see Errata section and 2.1) including CUDA^® Basic Linear Algebra Subroutines library™ (cuBLAS) 9.0.333 (see section 2.3.1)
NVIDIA CUDA^® Deep Neural Network library™ (cuDNN) 7.1.1
NCCL 2.1.15 (optimized for NVLink™ )
Horovod™ 0.11.3
OpenMPI™ 3.0.0
TensorBoard 0.4.0-rc1
MLNX_OFED 3.4

Driver Requirements

Release 18.04 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 18.04 is based on TensorFlow 1.7.0.
Added the Mellanox user-space InfiniBand driver to the container.
Latest version of MLNX_OFED 3.4
Added support for TensorRT integration in TensorFlow. For functionality details, see TensorRT Integration Speeds Up TensorFlow Inference and the example in the nvidia-examples/tftrt directory.
Improved nvidia_examples/nvcnn.py and nvcnn_hvd.py to ensure ResNet-50 model converges correctly out of the box. See Changelog at the top of nvidia_examples/nvcnn.py for more details.
Enabled Tensor Op math for cuDNN-based RNNs in FP16 precision. This is enabled by default, but can be disabled by setting the environment variable TF_DISABLE_CUDNN_RNN_TENSOR_OP_MATH=1.
Includes integration with TensorRT 3.0.4
Latest version of NCCL 2.1.15
Ubuntu 16.04 with March 2018 updates

Announcements

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

Known Issues

There is a degraded performance for graph construction time of grouped convolutions. For more information, see Support for depthwise convolution by groups.