TensorFlow Wheel Platform

NVIDIA Optimized Frameworks (Latest Release)

This TensorFlow Wheel release is intended for use on the NVIDIA Ampere Architecture GPU, NVIDIA Turing Architecture GPUs, NVIDIA Volta Architecture GPUs, and NVIDIA Pascal Architecture GPU.

The NVIDIA container image release for TensorFlow Wheel 21.01 has been canceled. The next release will be the 21.02 release which is expected to be released at the end of February.

Dependencies of NVIDIA TensorFlow Wheel

This installation of the NVIDIA TensorFlow Wheel will include several other components from NVIDIA.

Table 1. TensorFlow Wheel compatibility with NVIDIA components
NVIDIA Product Version
NVIDIA CUDA cuBLAS nvidia-cublas 11.3.0.106
NVIDIA CUDA CUPTI nvidia-cublas-cupti 11.1.105
NVIDIA CUDA NVCC nvidia-cuda-nvcc 11.1.105
NVIDIA CUDA NVRTC nvidia-cuda-nvrtc 11.*
NVIDIA CUDA Runtime nvidia-cuda-runtime 11.1.74
NVIDIA CUDA cuDNN nvidia-cudnn 8.0.5.43
NVIDIA CUDA cuFFT nvidia-cufft 10.3.0.105
NVIDIA CUDA cuRAND nvidia-curand 10.2.2.105
NVIDIA CUDA cuSOLVER nvidia-cusolver 11.0.1.105
NVIDIA CUDA cuSPARSE nvidia-cusparse 11.3.0.10
NVIDIA DALI TensorFlow Plugin for CUDA 11.0 nvidia-dali-nvtf-plugin 0.28.0+nv20.12
NVIDIA DALI for CUDA 11.0 nvidia-dali-cuda110 0.28.0
NVIDIA DLprof binary installation nvidia-dlprof 0.18.0
NVIDIA Nsight Systems is a system-wide performance analysis tool designed to visualize an application’s algorithms, help you identify the largest opportunities to optimize, and tune to scale efficiently across any quantity or size of CPUs and GPUs; from large server to our smallest SoC nvidia-nsys-cli 2020.4.1.117
Distributed training framework for TensorFlow, Keras, and PyTorch nvidia-horovod 0.20.2+nv20.12
NVIDIA CUDA NCCL nvidia-nccl 2.8.3
DLprof TensorBoard plugin nvidia-tensorboard-plugin-dlprof 0.10
TensorBoard lets you watch Tensors Flow nvidia-tensorboard 1.15.0+nv20.12
NVIDIA TensorFlow nvidia-tensorflow 1.15.3
NVIDIA TensorRT, a high-performance deep learning inference library nvidia-tensorrt 7.2.2.1

Driver Requirements

Release 20.12 is based on NVIDIA CUDA 11.1.0, which requires NVIDIA Driver release 455 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, 440.xx, or 450.xx. 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.

Software Requirements

The 20.12 release of TensorFlow Wheel requires the following software to be installed:

Key Features and Enhancements

This TensorFlow Wheel release includes the following key features and enhancements.

NVIDIA TensorFlow Wheel Versions

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

  • In certain cases running on Pascal GPUs may result in out-of-memory errors which may present as apparent job hangs. This can be worked around by exporting the following environment variable:

    Copy
    Copied!
                

    TF_DEVICE_MIN_SYS_MEMORY_IN_MB=550

  • A regression in cuDNN’s fused Convolution+Bias+Activation implementation can cause performance regressions of up to 24% in some models such as UNet Medical. This will be fixed in a future cuDNN release.

  • Some image-based inference workloads see a regression of up to 50% for the smallest batch sizes. This is due to regressions in cuDNN 8.0.4, which will be addressed in a future release.

  • A few models see performance regressions compared to the 20.08 release. Training WideAndDeep sees regressions of up to 30% on A100. In FP32 the TF1 Unet Industrial and Bert fine tuning training regress from 10-20%. Also the TF2 Unet Medical and MaskRCNN models regress by about 20% in some cases. These regressions will be addressed in a future release.

  • There are several known performance regressions compared to 20.07. UNet Medical and Industrial on V100 and A100 GPUs can be up to 20% slower. VGG can be up to 95% slower on A100 and 15% slower on Turing GPUs. Googlenet can be up to 20% slower on V100. And ResNet50 inferencing can be up to 30% slower on A100 and Turing GPUs.

  • An out-of-memory condition can occur in TensorFlow (TF1) 20.08 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:

    Copy
    Copied!
                

    TF_XLA_FLAGS=--tf_xla_enable_lazy_compilation=false

  • 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.

  • There is a known issue of OOM (Out-Of-Memory) when training the UNET3D models when batch size = 1 in TensorFlow (TF1) container.

Dependencies of NVIDIA TensorFlow Wheel

This installation of the NVIDIA TensorFlow Wheel will include several other components from NVIDIA.

Table 2. TensorFlow Wheel compatibility with NVIDIA components
NVIDIA Product Version
NVIDIA CUDA cuBLAS nvidia-cublas 11.2.1.74
NVIDIA CUDA CUPTI nvidia-cublas-cupti 11.1.69
NVIDIA CUDA NVCC nvidia-cuda-nvcc 11.1.74
NVIDIA CUDA NVRTC nvidia-cuda-nvrtc 11.*
NVIDIA CUDA Runtime nvidia-cuda-runtime 11.1.74
NVIDIA CUDA cuDNN nvidia-cudnn 8.0.4.30
NVIDIA CUDA cuFFT nvidia-cufft 10.3.0.74
NVIDIA CUDA cuRAND nvidia-curand 10.2.2.74
NVIDIA CUDA cuSOLVER nvidia-cusolver 11.0.0.74
NVIDIA CUDA cuSPARSE nvidia-cusparse 11.2.0.275
NVIDIA DALI TensorFlow Plugin for CUDA 11.0 nvidia-dali-nvtf-plugin 0.27.0+nv20.11
NVIDIA DALI for CUDA 11.0 nvidia-dali-cuda110 0.27.0
NVIDIA DALI TensorFlow Plugin for CUDA 11.0 nvidia-dali-nvtf-plugin 0.27.0+nv20.11
NVIDIA DLprof binary installation nvidia-dlprof 0.17.0
NVIDIA Nsight Systems is a system-wide performance analysis tool designed to visualize an application’s algorithms, help you identify the largest opportunities to optimize, and tune to scale efficiently across any quantity or size of CPUs and GPUs; from large server to our smallest SoC nvidia-nsys-cli 2020.4.1.117
Distributed training framework for TensorFlow, Keras, and PyTorch nvidia-horovod 0.20.2+nv20.11
NVIDIA CUDA NCCL nvidia-nccl 2.8.2
DLprof TensorBoard plugin nvidia-tensorboard-plugin-dlprof 0.9
TensorBoard lets you watch Tensors Flow nvidia-tensorboard 1.15.0+nv20.11
NVIDIA TensorFlow nvidia-tensorflow 1.15.3
NVIDIA TensorRT, a high-performance deep learning inference library nvidia-tensorrt 7.2.1.6

Driver Requirements

Release 20.11 is based on NVIDIA CUDA 11.1.0, which requires NVIDIA Driver release 455 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, 440.xx, or 450.xx. 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.

Software Requirements

The 20.11 release of TensorFlow Wheel requires the following software to be installed:

Key Features and Enhancements

This TensorFlow Wheel release includes the following key features and enhancements.

  • TensorFlow Wheel version 20.11 is based on 1.15.4 and 2.3.1.
  • CUDA 11.1.0
  • cuDNN 8.0.4
  • TensorRT 7.2.1
  • DALI 0.27
  • DLProf 0.17.0

NVIDIA TensorFlow Wheel Versions

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

  • In certain cases running on Pascal GPUs may result in out-of-memory errors which may present as apparent job hangs. This can be worked around by exporting the following environment variable:

    Copy
    Copied!
                

    TF_DEVICE_MIN_SYS_MEMORY_IN_MB=550

  • Some image-based inference workloads see a regression of up to 50% for the smallest batch sizes. This is due to regressions in cuDNN 8.0.4, which will be addressed in a future release.

  • A few models see performance regressions compared to the 20.08 release. Training WideAndDeep sees regressions of up to 30% on A100. In FP32 the TF1 Unet Industrial and Bert fine tuning training regress from 10-20%. Also the TF2 Unet Medical and MaskRCNN models regress by about 20% in some cases. These regressions will be addressed in a future release.

  • There are several known performance regressions compared to 20.07. UNet Medical and Industrial on V100 and A100 GPUs can be up to 20% slower. VGG can be up to 95% slower on A100 and 15% slower on Turing GPUs. Googlenet can be up to 20% slower on V100. And ResNet50 inferencing can be up to 30% slower on A100 and Turing GPUs.

  • An out-of-memory condition can occur in TensorFlow (TF1) 20.08 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:

    Copy
    Copied!
                

    TF_XLA_FLAGS=--tf_xla_enable_lazy_compilation=false

  • 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.

Dependencies of NVIDIA TensorFlow Wheel

This installation of the NVIDIA TensorFlow Wheel will include several other components from NVIDIA.

Table 3. TensorFlow Wheel compatibility with NVIDIA components
NVIDIA Product Version
NVIDIA CUDA cuBLAS nvidia-cublas 11.2.1.74
NVIDIA CUDA CUPTI nvidia-cublas-cupti 11.1.69
NVIDIA CUDA NVCC nvidia-cuda-nvcc 11.1.74
NVIDIA CUDA NVRTC nvidia-cuda-nvrtc 11.*
NVIDIA CUDA Runtime nvidia-cuda-runtime 11.1.74
NVIDIA CUDA cuDNN nvidia-cudnn 8.0.4.30
NVIDIA CUDA cuFFT nvidia-cufft 10.3.0.74
NVIDIA CUDA cuRAND nvidia-curand 10.2.2.74
NVIDIA CUDA cuSOLVER nvidia-cusolver 11.0.0.74
NVIDIA CUDA cuSPARSE nvidia-cusparse 11.2.0.275
NVIDIA DALI TensorFlow Plugin for CUDA 11.0 nvidia-dali-nvtf-plugin 0.26.0+nv20.10
NVIDIA DALI for CUDA 11.0 nvidia-dali-cuda110 0.26.0
NVIDIA DALI TensorFlow Plugin for CUDA 11.0 nvidia-dali-nvtf-plugin 0.25.1+nv20.09
NVIDIA DLprof binary installation nvidia-dlprof 0.16.0
NVIDIA Nsight Systems is a system-wide performance analysis tool designed to visualize an application’s algorithms, help you identify the largest opportunities to optimize, and tune to scale efficiently across any quantity or size of CPUs and GPUs; from large server to our smallest SoC nvidia-nsys-cli 2020.4.1.117
Distributed training framework for TensorFlow, Keras, and PyTorch nvidia-horovod 0.20.0+nv20.10
NVIDIA CUDA NCCL nvidia-nccl 2.7.8
DLprof TensorBoard plugin nvidia-tensorboard-plugin-dlprof 0.8
TensorBoard lets you watch Tensors Flow nvidia-tensorboard 1.15.0+nv20.10
NVIDIA TensorFlow nvidia-tensorflow 1.15.3
NVIDIA TensorRT, a high-performance deep learning inference library nvidia-tensorrt 7.2.1.4

Driver Requirements

Release 20.10 is based on NVIDIA CUDA 11.1.0, which requires NVIDIA Driver release 455 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.xx. 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.

Software Requirements

The 20.10 release of TensorFlow Wheel requires the following software to be installed:

Key Features and Enhancements

This TensorFlow Wheel release includes the following key features and enhancements.

  • TensorFlow Wheel version 20.10 is based on 1.15.4 and 2.3.1.
  • CUDA 11.1.0
  • cuDNN 8.0.4
  • TensorRT 7.2.1
  • DALI 0.26
  • DLProf 0.16.0

NVIDIA TensorFlow Wheel Versions

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

  • Some image-based inference workloads see a regression of up to 50% for the smallest batch sizes. This is due to regressions in cuDNN 8.0.4, which will be addressed in a future release.
  • A few models see performance regressions compared to the 20.08 release. Training WideAndDeep sees regressions of up to 30% on A100. In FP32 the TF1 Unet Industrial and Bert fine tuning training regress from 10-20%. Also the TF2 Unet Medical and MaskRCNN models regress by about 20% in some cases. These regressions will be addressed in a future release.
  • There are several known performance regressions compared to 20.07. UNet Medical and Industrial on V100 and A100 GPUs can be up to 20% slower. VGG can be up to 95% slower on A100 and 15% slower on Turing GPUs. Googlenet can be up to 20% slower on V100. And ResNet50 inferencing can be up to 30% slower on A100 and Turing GPUs.
  • An out-of-memory condition can occur in TensorFlow (TF1) 20.08 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:

    Copy
    Copied!
                

    TF_XLA_FLAGS=--tf_xla_enable_lazy_compilation=false

  • 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 and 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.

Dependencies of NVIDIA TensorFlow Wheel

This installation of the NVIDIA TensorFlow Wheel will include several other components from NVIDIA.

Table 4. TensorFlow Wheel compatibility with NVIDIA components
NVIDIA Product Version
NVIDIA CUDA cuBLAS nvidia-cublas 11.2.0.252
NVIDIA CUDA CUPTI nvidia-cublas-cupti 11.0.221
NVIDIA CUDA NVCC nvidia-cuda-nvcc 11.0.221
NVIDIA CUDA NVRTC nvidia-cuda-nvrtc 11.0.*
NVIDIA CUDA Runtime nvidia-cuda-runtime 11.0.221
NVIDIA CUDA cuDNN nvidia-cudnn 8.0.4.12
NVIDIA CUDA cuFFT nvidia-cufft 10.2.1.245
NVIDIA CUDA cuRAND nvidia-curand 10.2.1.245
NVIDIA CUDA cuSOLVER nvidia-cusolver 10.6.0.245
NVIDIA CUDA cuSPARSE nvidia-cusparse 11.1.1.245
NVIDIA DALI for CUDA 11.0 nvidia-dali 0.25.1
NVIDIA DALI TensorFlow Plugin for CUDA 11.0 nvidia-dali-nvtf-plugin 0.25.1+nv20.09
Distributed training framework for TensorFlow, Keras, and PyTorch nvidia-horovod 0.19.2+nv20.09
NVIDIA CUDA NCCL nvidia-nccl 2.7.8
TensorBoard lets you watch Tensors Flow nvidia-tensorboard 1.15.0+nv20.09
NVIDIA TensorFlow nvidia-tensorflow 1.15.3
NVIDIA TensorRT, a high-performance deep learning inference library nvidia-tensorrt 7.1.3.4

Driver Requirements

Release 20.09 is based on NVIDIA CUDA 11.0.3, 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.xx. 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.

Software Requirements

The 20.09 release of TensorFlow Wheel requires the following software to be installed:

Key Features and Enhancements

This TensorFlow Wheel release includes the following key features and enhancements.

  • TensorFlow Wheel version 20.09 is based on 1.15.3.
  • NVIDIA cuDNN 8.0.4
  • DALI 0.25

NVIDIA TensorFlow Wheel Versions

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 several known performance regressions compared to 20.07. UNet Medical and Industrial on V100 and A100 GPUs can be up to 20% slower. VGG can be up to 95% slower on A100 and 15% slower on Turing GPUs. Googlenet can be up to 20% slower on V100. And ResNet50 inferencing can be up to 30% slower on A100 and Turing GPUs.
  • An out-of-memory condition can occur in TensorFlow (TF1) 20.08 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:

    Copy
    Copied!
                

    TF_XLA_FLAGS=--tf_xla_enable_lazy_compilation=false

  • 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 and 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.

Dependencies of NVIDIA TensorFlow Wheel

This installation of the NVIDIA TensorFlow Wheel will include several other components from NVIDIA.

Table 5. TensorFlow Wheel compatibility with NVIDIA components
NVIDIA Product Version
nvidia-cublas 11.2.0.252
nvidia-cublas-cupti 11.0.221
nvidia-cuda-nvcc 11.0.221
nvidia-cuda-nvrtc 11.0.*
nvidia-cuda-runtime 11.0.221
nvidia-cudnn 8.0.2.39
nvidia-cufft 10.2.1.245
nvidia-curand 10.2.1.245
nvidia-cusolver 10.6.0.245
nvidia-cusparse 11.1.1.245
nvidia-dali 0.24
nvidia-dali-tf-plugin 0.24
nvidia-horovod 0.19.5
nvidia-nccl 2.7.8
nvidia-tensorflow 1.15.3
nvidia-tensorrt 7.1.3.4
nvidia-cuda-nvrtc 11.0

Driver Requirements

Release 20.08 is based on NVIDIA CUDA 11.0.3, 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.

Software Requirements

The 20.08 release of TensorFlow Wheel requires the following software to be installed:

Key Features and Enhancements

This TensorFlow Wheel release includes the following key features and enhancements.

  • TensorFlow Wheel version 20.08 is based on 1.15.3.
  • Ubuntu 18.04 with July 2020 updates

NVIDIA TensorFlow Wheel Versions

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

  • The memory required to train MaskRCNN with a given batch size has increased from 20.07 to 20.08. As a result, the batch size may need to be decreased.

  • There are several known performance regressions compared to 20.07. UNet Medical and Industrial on V100 and A100 GPUs can be up to 20% slower. VGG can be up to 95% slower on A100 and 15% slower on Turing GPUs. Googlenet can be up to 20% slower on V100. And ResNet50 inferencing can be up to 30% slower on A100 and Turing GPUs.
  • An out-of-memory condition can occur in TensorFlow (TF1) 20.08 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:

    Copy
    Copied!
                

    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.

Dependencies of NVIDIA TensorFlow Wheel

This installation of the NVIDIA TensorFlow Wheel will include several other components from NVIDIA.

Table 6. TensorFlow Wheel compatibility with NVIDIA components
NVIDIA Product Version
nvidia-cublas 11.1.0
nvidia-cublas-cupti 11.0.194
nvidia-cuda-nvcc 11.0.194
nvidia-cuda-nvrtc 11.0.194
nvidia-cuda-runtime 11.0.194
nvidia-cudnn 8.0.1
nvidia-cufft 10.2.0.218
nvidia-curand 10.2.1.218
nvidia-cusolver 10.5.0.218
nvidia-cusparse 11.1.0.218
nvidia-dali 0.23
nvidia-dali-tf-plugin 0.23
nvidia-horovod 0.19.5
nvidia-nccl 2.7.6
nvidia-tensorflow 1.15.3
nvidia-tensorrt 7.1.3

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.

Software Requirements

The 20.07 release of TensorFlow Wheel requires the following software to be installed:

Key Features and Enhancements

This TensorFlow Wheel release includes the following key features and enhancements.

  • TensorFlow Wheel version 20.07 is based on 1.15.3.
  • Improved XLA to avoid excessive recompilations
  • Enhancements for Automatic Mixed Precision with einsum, 3D Convolutions, and list operations
  • Improved 3D Convolutions to support NDHWC format
  • Default TF32 support

NVIDIA TensorFlow Wheel Versions

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.
  • There is a known performance regression of 5-15% on the VAE-CF model when using the pip wheel compared to the corresponding NGC Docker container. This will be addressed in a future release.

Dependencies of NVIDIA TensorFlow Wheel

This installation of the NVIDIA TensorFlow Wheel will include several other components from NVIDIA.

Table 7. TensorFlow Wheel compatibility with NVIDIA components
NVIDIA Product Version
nvidia-cublas 11.1.0.213
nvidia-cublas-cupti 11.0.167
nvidia-cuda-nvcc 11.0.167
nvidia-cuda-nvrtc 11.0.167
nvidia-cuda-runtime 11.0.167
nvidia-cudnn 8.0.1.13
nvidia-cufft 10.1.3.191
nvidia-curand 10.2.0.191
nvidia-cusolver 10.4.0.191
nvidia-cusparse 11.0.0.191
nvidia-dali 0.22.0
nvidia-dali-tf-plugin 0.22.0
nvidia-horovod 0.19.1
nvidia-nccl 2.7.5
nvidia-tensorflow 1.15.2 + nv20.06
nvidia-tensorrt 7.1.2.8

Driver Requirements

Release 20.06 is based on NVIDIA CUDA 11.0.167, which requires NVIDIA Driver release 450.36. 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.

Software Requirements

The 20.06 release of TensorFlow Wheel requires the following software to be installed:

Key Features and Enhancements

This TensorFlow Wheel release includes the following key features and enhancements.

  • TensorFlow Wheel version 20.06 is based on TensorFlow 1.15.2.
  • Integrated latest NVIDIA Deep Learning SDK to support NVIDIA A100 using CUDA 11 and cuDNN 8
  • Improved NVTX annotations for XLA clusters for use with DLProf
  • Improved XLA to avoid excessive recompilations
  • Enhancements for Automatic Mixed Precision with einsum, 3D Convolutions, and list operations
  • Improved 3D Convolutions to support NDHWC format
  • Default TF32 support
  • Ubuntu 18.04 with May 2020 updates

NVIDIA TensorFlow Wheel Versions

20.06 is the first release of the TensorFlow Wheel.

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 A100 and Volta architectures, therefore you can get results much faster than training without Tensor Cores. These models are tested against each NVIDIA Optimized Frameworks monthly container release to ensure consistent accuracy and performance over time.

Known Issues

  • An out-of-memory condition can occur in TensorFlow (TF1) 20.06 for some models (such as ResNet-50, and ResNext) when Horovod and XLA are both in use. In XLA in TensorFlow 20.05, 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 Guide, running XLA with the following environment variable opts in to that strategy:

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    TF_XLA_FLAGS=--tf_xla_enable_lazy_compilation=false

  • TensorFlow Wheel 20.06 NCF will not work until CuPy is updated to support CUDA 11.

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

© Copyright 2024, NVIDIA. Last updated on Dec 15, 2020.