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.
- Ubuntu 16.04
- NVIDIA CUDA 9.0.176 (see Errata section and 2.1) including CUDA® Basic Linear Algebra Subroutines library™ (cuBLAS) 9.0.425
- NVIDIA CUDA® Deep Neural Network library™ (cuDNN) 7.2.1
- NCCL 2.2.13 (optimized for NVLink™ )
- Horovod™ 0.12.1
- OpenMPI™ 3.0.0
- TensorBoard 1.9.0
- MLNX_OFED 3.4
- OpenSeq2Seq v0.5 at commit 83e96551.
- TensorRT 4.0.1
- DALI 0.1.2 Beta
Driver Requirements
Release 18.08 is based on CUDA 9, which requires NVIDIA Driver release 384.xx.
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)
- Key Features And Enhancements
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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.
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- Limitations
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TensorRT conversion relies on static shape inference, where the frozen graph should provide explicit dimension on all ranks other than the first batch dimension.
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Batchsize for converted TensorRT engines are fixed at conversion time. Inference can only run with batchsize smaller than the specified number.
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Current supported models are limited to CNNs. Object detection models and RNNs are not yet supported.
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Current optimization pass does not support INT8 yet.
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- Known Issues
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Input tensors are required to have rank 4 for quantization mode (INT8 precision).
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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
- 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.