TensorFlow Release 19.10

The NVIDIA container image of TensorFlow, release 19.10, 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.

Driver Requirements

Release 19.10 is based on NVIDIA CUDA 10.1.243, which requires NVIDIA Driver release 418.xx. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+ or 410. 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 19.10 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

This TensorFlow release includes the following key features and enhancements.

Announcements

We will stop support for Python 2.7 in a future TensorFlow container release.

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.
Deprecated Features
  • The old API of TF-TRT is deprecated. It still works in TensorFlow 1.14 and 1.15, however, it is removed in TensorFlow 2.0. The old API is a Python function named create_inference_graph which is now replaced by the Python class TrtGraphConverter with a number of methods. Refer to TF-TRT User Guide for more information about the API and how to use it.

Known Issues
  • TensorRT INT8 calibration algorithm (see the TF-TRT User Guide for more information about how to use INT8) is very slow for certain models such as NASNet and Inception. We are working on optimizing the calibration algorithm in TensorRT.

  • The pip package of TensorFlow 1.14 released by Google is missing TensorRT. This will be fixed in the next release of TensorFlow by Google. In the meantime, you can use the more recent versions of TensorFlow pip packages released by Google (1.15 and 2.0) or the NVIDIA container for TensorFlow.

  • The accuracy of Faster RCNN with the backbone ResNet-50 using TensorRT6.0 INT8 calibration is lower than expected. We are investigating the issue.

  • The following sentence that appears in the log of TensorRT 6.0 can be safely ignored. This will be removed in the future releases of TensorRT.
    Calling isShapeTensor before the entire network is constructed may result in an inaccurate result.

Automatic Mixed Precision (AMP)

Automatic mixed precision converts certain float32 operations to operate in float16 which can run much faster on Tensor Cores. Automatic mixed precision is built on two components:
  • a loss scaling optimizer
  • graph rewriter

For models already using an optimizer from tf.train or tf.keras.optimizers for both compute_gradients() and apply_gradients() operations (for example, by calling optimizer.minimize() or model.fit(), automatic mixed precision can be enabled by wrapping the optimizer with tf.train.experimental.enable_mixed_precision_graph_rewrite().

For more information on this function, see the TensorFlow documentation here.

For backward compatibility with previous container releases, AMP can also be enabled for tf.train optimizers by defining the following environment variable:
export TF_ENABLE_AUTO_MIXED_PRECISION=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

The tensor core examples provided in GitHub focus on achieving the best performance and convergence by using the latest deep learning example networks and model scripts for training.

Each example model trains with mixed precision Tensor Cores on Volta, therefore you can get results much faster than training without tensor cores. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time. This container includes the following tensor core examples.

Known Issues

  • There are known issues regarding TF-TRT INT8 accuracy issues. See the Accelerating Inference In TensorFlow With TensorRT (TF-TRT) section above for more information.
  • There is a known performance regression in TensorFlow 1.14.0 affecting a variety of models. Affected models include GNMT, SSD, and NCF. Performance regressions can be as high as 20% compared to TensorFlow 1.13.1 in the 19.06 release.
  • For BERT Large training with the 19.08 release on Tesla V100 boards with 16 GB memory, performance with batch size 3 per GPU is lower than expected; batch size 2 per GPU may be a better choice for this model on these GPUs with the 19.08 release. 32 GB GPUs are not affected.
  • TensorBoard has a bug in its IPv6 support which can result in the following error: Tensorboard could not bind to unsupported address family ::. To workaround this error, pass the --host <IP> flag when starting TensorBoard.
  • Automatic Mixed Precision (AMP) does not support the Keras LearningRateScheduler in the 19.08 release. A fix will be included in a future release.
  • A known issue in TensorFlow results in the error Cannot take the length of Shape with unknown rank when training variable sized images with the Keras model.fit API. Details are provided here and a fix will be available in a future release.
  • Support for CUDNN float32 Tensor Op Math mode first introduced in the 18.09 release is now deprecated in favor of Automatic Mixed Precision. It is scheduled to be removed after the 19.11 release.
  • There is a known issue when your NVIDIA driver release is older than 418.xx in the 19.10 release, the Nsight Systems profiling tool (for example, the nsys) might cause CUDA runtime API error. A fix will be included in a future release.