TensorFlow Release 22.02
The NVIDIA container image of TensorFlow, release 22.02, 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:
- Ubuntu 20.04
22.02-tf2-py3contains Python 3.8
- NVIDIA CUDA 11.6.0
- cuBLAS 184.108.40.206
- cuTensor 220.127.116.11
- NVIDIA cuDNN 8.3.2
- NVIDIA NCCL 2.11.4 (optimized for NVLink™)
- RAPIDS 21.10 (Only these libraries are included: cudf, xgboost, rmm, cuml, and cugraph)
- rdma-core 36.0
- NVIDIA HPC-X 2.10
- OpenMPI 4.1.2rc4+
- OpenUCX 1.12.0
- GDRCopy 2.3
- Nsight Compute 2022.1.0.12
- Nsight Systems 2021.5.2.53
- TensorRT 8.2.3
- TensorFlow-TensorRT (TF-TRT)
- SHARP 2.5
- DALI 1.10.0
22.02-tf1-py3includes version 1.15.5
22.02-tf2-py3includes version TensorBoard 2.7.0
- Horovod 0.23.0
- XLA-Lite (TF2 only)
- JupyterLab 2.3.2 including Jupyter-TensorBoard
Release 22.02 is based on NVIDIA CUDA 11.6.0, which requires NVIDIA Driver release 510 or later. However, if you are running on a Data Center GPU (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), 460.27 (or later R460), or 470.57 (or later R470). 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 and NVIDIA CUDA and Drivers Support.
Release 22.02 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.
- TensorFlow container images version 22.02 are based on Tensorflow 1.15.5 and 2.7.0.
- For TF2 added
CudnnMHAKeras op to expose CUDNN’s optimized multi-head attention implementation.
- Fixed segmentation fault when VLOG logging was enabled in TF1.
- Updated TF-TRT with latest upstream changes.
- Fixed bug in TF2 where CUDNN’s fused batched norm grad kernels could be called when
training = false.
- Extended autotuning over CUDNN fallback engines. This change may increase the execution time of the first few iterations, but can result in substantially better engines being chosen during later iterations.
- DLProf v1.8, which was included in the 21.12 container, was the last release of DLProf. Starting with the 22.01 container, DLProf is no longer included. It can still be manually installed via a pip wheel on the nvidia-pyindex.
- Starting with the 21.10 release, a beta version of the TensorFlow 1 and 2 containers is available for the Arm SBSA platform. For example, pulling the Docker image
nvcr.io/nvidia/tensorflow:22.02-tf2-py3on an Arm SBSA machine will automatically fetch the Arm-specific image.
- Support for SLURM PMI2 has been removed from the 22.01 release. PMIX is supported by the container, but is not supported by default in SLURM. Users depending on SLURM integration may need to configure SLURM for PMIX in the base OS as appropriate to their OS distribution (for Ubuntu 20.04, the required package is
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.
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.
- U-Net Medical model. The U-Net model is a convolutional neural network for 2D image segmentation. This repository contains a U-Net implementation as described in the paper U-Net: Convolutional Networks for Biomedical Image Segmentation, without any alteration. This model script is available on GitHub as well as NVIDIA GPU Cloud (NGC).
- SSD320 v1.2 model. The SSD320 v1.2 model is based on the SSD: Single Shot MultiBox Detector paper, which describes an SSD as “a method for detecting objects in images using a single deep neural network”. Our implementation is based on the existing model from the TensorFlow models repository. This model script is available on GitHub as well as NVIDIA GPU Cloud (NGC).
- Neural Collaborative Filtering (NCF) model. The NCF model is a neural network that provides collaborative filtering based on implicit feedback, specifically, it provides product recommendations based on user and item interactions. The training data for this model should contain a sequence of user ID, item ID pairs indicating that the specified user has interacted with, for example, was given a rating to or clicked on, the specified item. This model script is available on GitHub as well as NVIDIA GPU Cloud (NGC).
- BERT model. BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding paper. NVIDIA's BERT is an optimized version of Google's official implementation, leveraging mixed precision arithmetic and Tensor Cores on V100 GPUS for faster training times while maintaining target accuracy. This model script is available on GitHub as well as NVIDIA GPU Cloud (NGC).
- U-Net Industrial Defect Segmentation model. This U-Net model is adapted from the original version of the U-Net model which is a convolutional auto-encoder for 2D image segmentation. U-Net was first introduced by Olaf Ronneberger, Philip Fischer, and Thomas Brox in the paper: U-Net: Convolutional Networks for Biomedical Image Segmentation. This work proposes a modified version of U-Net, called TinyUNet which performs efficiently and with very high accuracy on the industrial anomaly dataset DAGM2007. This model script is available on GitHub as well as NVIDIA GPU Cloud (NGC).
- GNMT v2 model. The GNMT v2 model is similar to the one discussed in the Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation paper. The most important difference between the two models is in the attention mechanism. In our model, the output from the first LSTM layer of the decoder goes into the attention module, then the re-weighted context is concatenated with inputs to all subsequent LSTM layers in the decoder at the current timestep. This model script is available on GitHub as well as NVIDIA GPU Cloud (NGC).
- 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 require 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. This model script is available on GitHub as well as NVIDIA GPU Cloud (NGC).
If you encounter functional or performance issues when XLA is enabled, please refer to the XLA Best Practices document. It offers pointers on how to diagnose symptoms and possibly address them.
- For TensorFlow 1.15, TF-TRT inference throughput may regress for certain models by up to 37% compared to the 21.06-tf1 release. This will be fixed in a future release.
- A CUDNN performance regression can cause slowdowns of up to 15% in certain ResNet models. This will be fixed in a future release.
- There is a known performance regression affecting UNet Medical 3D model training by up to 23%. This will be addressed in a future release.
- TF-TRT native segment fallback has a known issue causing a crash. This will occur when using TF-TRT to convert a model with a subgraph that is converted to TensorRT but fails to build. Instead of falling back to native TensorFlow TF-TRT will crash. Using
export TF_TRT_OP_DENYLIST="ProblematicOp"can help to prevent conversion of an OP causing a native segment fallback.
- There is a known issue affecting aarch64 libgomp that may cause `cannot allocate memory in static TLS block` errors in some cases. A workaround is to