TensorFlow Release 19.03
The NVIDIA container image of TensorFlow, release 19.03, is available on NGC.
Contents of the TensorFlow container
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 10.1.105 including cuBLAS 10.1.105
- NVIDIA cuDNN 7.5.0
- NVIDIA NCCL 2.4.3 (optimized for NVLinkâ„¢ )
- Horovod 0.16.0
- OpenMPI 3.1.3
- TensorBoard 1.13.1
- MLNX_OFED 3.4
- OpenSeq2Seq at commit 6e8835f
- TensorRT 5.1.2
- DALI 0.7 Beta
- Nsight Compute 10.1.105
- Nsight Systems 10.1.105
- Tensor Core optimized example:
- Jupyter and JupyterLab:
Driver Requirements
Release 19.03 is based on CUDA 10.1, which requires NVIDIA Driver release 418.xx+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.
GPU Requirements
Release 19.03 supports CUDA compute capability 6.0 and higher. This corresponds to GPUs in the Pascal, Volta, and Turing families. Specifically, for a list of GPUs that this compute capability corresponds to, see CUDA GPUs. For additional support details, see Deep Learning Frameworks Support Matrix.
Key Features and Enhancements
- TensorFlow container image version 19.03 is based on TensorFlow 1.13.1.
- Latest version of NVIDIA CUDA 10.1.105 including cuBLAS 10.1.105
- Latest version of NVIDIA cuDNN 7.5.0
- Latest version of NVIDIA NCCL 2.4.3
- Latest version of DALI 0.7 Beta
- Latest version of TensorRT 5.1.2
- Latest version of Horovod 0.16.0
- Latest version of TensorBoard 1.13.1
- Added the ResNet-50 v1.5Tensor Core example
- Added Nsight Compute 10.1.105 and Nsight Systems 10.1.105 software
- Added support for TensorFlow Automatic Mixed Precision (TF-AMP); see below for more information.
- Ubuntu 16.04 with February 2019 updates
Accelerating Inference In TensorFlow With TensorRT (TF-TRT)
- Key Features And Enhancements
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Integrated TensorRT 5.1.2 RC into TensorFlow. See the TensorRT 5.1.2 RC Release Notes for a full list of new features.
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Improved examples at GitHub: TF-TRT, including README files, build scripts, benchmark mode, ResNet models from TensorFlow official model zoo, etc...
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Announcements
TensorRT 3.x is not longer supported, 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 5.x or later again.
For more information, see the Note in Serializing A Model In C++ or Serializing A Model In Python.
Automatic Mixed Precision (AMP)
- a loss scaling optimizer
- graph rewriter
export TF_ENABLE_AUTO_MIXED_PRECISION=1
export TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE=1
For more information about how to access and enable Automatic mixed precision for TensorFlow, see Automatic Mixed Precision Training In TensorFlow from the TensorFlow User Guide, along with Training With Mixed Precision.
Tensor Core Examples
These examples focus on achieving the best performance and convergence from NVIDIA Volta Tensor Cores by using the latest deep learning example networks for training.
- An implementation of the ResNet-50 v1.5 model. The ResNet-50 v1.5 model is a modified version of the original ResNet-50 v1 model. The difference between v1 and v1.5 is in the bottleneck blocks which requires downsampling, for example, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. The following features were implemented in this model; data-parallel multi-GPU training with Horovod, Tensor Cores (mixed precision) training, and static loss scaling for tensor cores (mixed precision) training.
Known Issues
- There is a known performance regression with TensorFlow 1.13.1 for some networks when run with small batch sizes. As a workaround, increase the batch size.
- The AMP preview implementation is not compatible with Distributed Strategies. We recommend using Horovod for parallel training with AMP.
- If using or upgrading to a 3-part-version driver, for example, a driver that takes the format of xxx.yy.zz, you will receive a Failed to detect NVIDIA driver version. message. This is due to a known bug in the entry point script's parsing of 3-part driver versions. This message is non-fatal and can be ignored. This will be fixed in the 19.04 release.