TensorFlow Release 20.07

TensorFlow Release 20.07 (PDF)

The NVIDIA container image of TensorFlow, release 20.07, is available on NGC.

Contents of the TensorFlow container

This container image includes the complete source of the NVIDIA version of 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:

Driver Requirements

Release 20.07 is based on NVIDIA CUDA 11.0.194, which requires NVIDIA Driver release 450 or later. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx or 440.30. 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 20.07 supports CUDA compute capability 6.0 and higher. This corresponds to GPUs in the NVIDIA Pascal, Volta, Turing, and Ampere Architecture GPU 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.


  • Python 2.7 is no longer supported in this TensorFlow container release.
  • The TF_ENABLE_AUTO_MIXED_PRECISION environment variables are no longer supported in the tf2 container because it is not possible to automatically enable loss scaling in many cases in the tf 2.x API. Instead tf.train.experimental.enable_mixed_precision_graph_rewrite() should be used to enable AMP.
  • Deep learning framework containers 19.11 and later include experimental support for Singularity v3.0.

NVIDIA TensorFlow Container Versions

The following table shows what versions of Ubuntu, CUDA, TensorFlow, and TensorRT are supported in each of the NVIDIA containers for TensorFlow. For older container versions, refer to the Frameworks Support Matrix.

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.


TF1 TF-TRT is infrequently updated. In order to benefit from the latest performance improvements, optimizations and features such as implicit batch mode and dynamic shape support, we recommend using TF2.

Known Issues
  • We have observed a regression in the performance of certain TF-TRT benchmarks in TensorFlow 1.15 including image classification models with precision INT8. We are still investigating this. Since 19.11 comes with a new version of TensorFlow (1.15), which includes a lot of changes in the TensorFlow backend, it’s very possible that the regression is caused by a change in the TensorFlow backend.

  • CUDA 10.2 and NCCL 2.5.x libraries require slightly more device memory than previous releases. As a result, some models that ran previously may exhaust device memory.

  • The accuracy of Faster RCNN with the backbone ResNet-50 using TensorRT6.0 INT8 calibration is lower than expected. This will be fixed in future releases of TensorRT.

  • The following warning is issued when the method build() from the API is not called. This warning can be ignored.

    OP_REQUIRES failed at trt_engine_resource_ops.cc:183 : Not found: Container TF-TRT does not exist. (Could not find resource: TF-TRT/TRTEngineOp_...

  • The following warning is issued because internally TensorFlow calls the TensorRT optimizer for certain objects unnecessarily. This warning can be ignored.

    TensorRTOptimizer is probably called on funcdef! This optimizer must *NOT* be called on function objects.

  • We have seen failures when using INT8 calibration (post-training) within the same process that does FP32/FP16 conversion. We recommend using separate processes for different precisions until this issue gets resolved.

  • We have seen failures when calling the TensorRT optimizer on models that are already optimized by TensorRT. This issue will be fixed in a future release.

  • In case you import nets from models/slim, you might see the following error:

    AttributeError: module 'tensorflow_core.contrib' has no attribute 'tensorrt'

    Changing the order of imports can fix the issue. Therefore, import TensorRT before importing nets as follows:


    import tensorflow.contrib.tensorrt as trt import nets.nets_factory

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 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 is a known performance regression of 10 to 30% compared to the 20.03 release when training the JoC V-Net Medical and U-Net Industrial models with small batch size on V100. This will be addressed in a future release.

  • An out-of-memory condition can occur in TensorFlow (TF1) 20.07 for some models (such as ResNet-50, and ResNext) when Horovod and XLA are both in use. In XLA, we added an optimization that skips compiling a cluster the very first time it is executed, which can help avoid unnecessary recompilations for models with dynamic shapes. On the other hand, for models like ResNet-50, the preferred compilation strategy is to aggressively compile clusters, as compiled clusters are executed many times. Per the "XLA Best Practices" section of the TensorFlow User Guide, running XLA with the following environment variable opts in to that strategy: TF_XLA_FLAGS=--tf_xla_enable_lazy_compilation=false

  • There is a known performance regression of 15% compared to the 20.03 release when training the JoC V-Net Medical models with small batch size and fp32 data type on Turing GPUs. This will be addressed in a future release.
  • There is a known performance regression of up to 60% when running inference using TF-TRT for SSD models with small batch size. This will be addressed in a future release.
  • There is a known performance regression of up to 30% when training SSD models with fp32 data type on T4 GPUs. This will be addressed in a future release.
  • There is a known issue where attempting to convert some models using TF-TRT produces an error "Failed to import metagraph". This issue is still under investigation and will be resolved in a future release.
© Copyright 2024, NVIDIA. Last updated on Apr 5, 2024.