This NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet, container release is intended for use on the NVIDIA Ampere Architecture A100 GPU, as well as on previous generation GPUs like V100 and T4, and with the latest CUDA 11 and cuDNN 8 libraries. The container image is available on NGC.
Contents of the Optimized Deep Learning Framework container
This container image contains the complete source of the NVIDIA Optimized Deep Learning Framework, which is based upon Apache MXNet version 1.9.0.rc6. It is pre-built and installed to the Python path. The container also includes the following:
- Ubuntu 20.04 including Python 3.8
- NVIDIA CUDA 11.4.2
- cuBLAS 126.96.36.199
- NVIDIA cuDNN 188.8.131.52
- NVIDIA NCCL 2.11.4 (optimized for NVLink™ )
- Amazon Labs Sockeye sequence-to-sequence framework 2.2.3 (for machine translation)
- Horovod 0.22.1, with enhancements
- rdma-core 36.0
- OpenMPI 4.1.1+
- OpenUCX 1.11.0rc1
- GDRCopy 2.3
- NVIDIA HPC-X 2.9
- Nsight Systems 2021.3.1.57
- TensorRT 8.0.3
- GluonCV Toolkit 0.7
- GluonNLP Toolkit 0.8.1
- DALI 1.5.0
- Jupyter and JupyterLab:
Release 21.09 is based on NVIDIA CUDA 11.4.2, which requires NVIDIA Driver release 470 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460). 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 21.09 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 Optimized Deep Learning Framework release includes the following key features and enhancements.
- NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet container image version 21.09 is based on 1.9.0.rc6. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF).
- As of the previous (21.08) release, the implementation of the Convolution and Deconvolution operators is based on a direct use of the cuDNN v8 API, rather than the cuDNN v7 legacy API. This approach can provide enhanced performance by autotuning over an expanded set of conv/deconv ‘engine configs’ as compared to the restricted set of ‘algo numbers’ in prior releases. Users should note that in this 21.09 release:
- The time spent in autotuning may be longer than seen in 21.08 and earlier releases.
- The potential issue of the 21.08 release, where a model’s GPU global memory use may have increased due to the new autotuning process and selections, has been corrected.
- After release 21.09, future releases of the NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet container will be paused for approximately 6 months.
- Deep learning framework containers 19.11 and later include experimental support for Singularity v3.0.
- The TensorCore example models are no longer provided in the core container (previously shipped in /workspace/nvidia-examples). Instead they can be obtained from Github or the NVIDIA GPU Cloud (NGC). Some python packages, included in previous containers to support these example models, have also been removed. Depending on their specific use cases, users may need to add some packages that were previously pre-installed.
NVIDIA Optimized Deep Learning Framework Container Versions
The following table shows what versions of Ubuntu, CUDA, Apache MXNet, and TensorRT are supported in each of the NVIDIA containers for the Optimized Deep Learning Framework. For older container versions, refer to the Frameworks Support Matrix.
Tensor Core Examples
The tensor core examples provided in GitHub and NVIDIA GPU Cloud (NGC) focus on achieving the best performance and convergence from NVIDIA Volta tensor cores by using the latest deep learning example networks and model scripts for training. Each example model trains with mixed precision Tensor Cores on Volta and Turing, 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.
- The ResNet50 v1.5 model is a slightly modified version of the original ResNet50 v1 model that trains to a greater accuracy. This model script is available on GitHub as well as NVIDIA GPU Cloud (NGC).
Automatic Mixed Precision (AMP)
Training deep learning networks is a very computationally intensive task. Novel model architectures tend to have an increasing number of layers and parameters, which slows down training. Fortunately, new generations of training hardware as well as software optimizations make training these new models a feasible task.
Most of the hardware and software training optimization opportunities involve exploiting lower precision like FP16 in order to utilize the Tensor Cores available on new Volta and Turing GPUs. While training in FP16 showed great success in image classification tasks, other more complicated neural networks typically stayed in FP32 due to difficulties in applying the FP16 training guidelines that are needed to ensure proper model training.
That is where AMP (Automatic Mixed Precision) comes into play—it automatically applies the guidelines of FP16 training, using FP16 precision where it provides the most benefit, while conservatively keeping in full FP32 precision operations unsafe to do in FP16.
The NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet AMP tutorial, located in
/opt/mxnet/nvidia-examples/AMP/AMP_tutorial.md inside this container, shows how to get started with mixed precision training using AMP for Apache MXNet, using by example the SSD network from GluonCV.
For more information about AMP, see the Training With Mixed Precision Guide.
- The default setting of the environment variable
MXNET_GPU_COPY_NTHREADS=1in the container may not be optimal for all networks. Networks with a high ratio of parameters and computation, like AlexNet, may achieve greater multi-GPU training speeds with the setting
MXNET_GPU_COPY_NTHREADS=2. Users are encouraged to try this setting for their own use case.