rocAL Introduction

rocAL Introduction#

Today’s deep learning applications require loading and pre-processing data efficiently to achieve high processing throughput. This requires creating efficient processing pipelines fully utilizing the underlying hardware capabilities. Some examples are load and decode data, do a variety of augmentations, color-format conversions, etc. Deep learning frameworks require supporting multiple data formats and augmentations to adapt to a variety of data-sets and models.

AMD ROCm Augmentation Library (rocAL) is designed to efficiently do such processing pipelines from both images and video as well as from a variety of storage formats.
These pipelines are programmable by the user using both C++ and Python APIs.

Key Components of rocAL#

  • Full processing pipeline support for data_loading, meta-data loading, augmentations, and data-format conversions for training and inference.

  • Being able to do processing on CPU or Radeon GPU (with OpenCL or HIP backend)

  • Ease of integration with framework plugins in Python

  • Support variety of augmentation operations through AMD’s Radeon Performance Primitives (RPP).

  • All available public and open-sourced under ROCm.

Prerequisites#

Refer to the rocAL to follow and install pre-requisites.

Build instructions#

Follow the build instructions in rocAL

rocAL Python#

  • rocAL Python package has been created using Pybind11 which enables data transfer between rocAL C++ API and Python API.

  • Module imports are made similar to other data loaders like NVidia’s DALI.

  • rali_pybind package has both PyTorch and TensorFlow framework support.

  • Various reader format support including FileReader, COCOReader, and TFRecordReader.

  • example folder contains sample implementations for each reader variation as well as sample training script for PyTorch

  • rocAL is integrated into MLPerf Resnet-50 Pytorch classification example on the ImageNet dataset.

rocAL Python API#

amd.rali.ops#

  • Contains the image augmentations & file read and decode operations which are linked to rocAL C++ API

  • All ops (listed below) are supported for the single input image and batched inputs.

Image Augmentation

Reader and Decoder

Geometric Ops

ColorTwist

File Reader

CropMirrorNormalize

Brightness

ImageDecoder

Resize

Gamma Correction

ImageDecoderRandomCrop

ResizeCrop

Snow

COCOReader

WarpAffine

Rain

TFRecordReader

FishEye

Blur

LensCorrection

Jitter

Rotate

Hue

Saturation

Fog

Contrast

Vignette

SNPNoise

Pixelate

Blend

amd.rali.pipeline#

  • Contains Pipeline class which has all the data needed to build and run the rocAL graph.

  • Contains support for context/graph creation, verify and run the graph.

  • Has data transfer functions to exchange data between frameworks and rocAL

  • define_graph functionality has been implemented to add nodes to build a pipeline graph.

amd.rali.types#

rali.types are enums exported from C++ API to python. Some examples include CPU, GPU, FLOAT, FLOAT16, RGB, GRAY, etc…

amd.rali.plugin.pytorch#

  • Contains RaliGenericIterator for Pytorch.

  • RaliClassificationIterator class implements iterator for image classification and return images with corresponding labels.

  • From the above classes, any hybrid iterator pipeline can be created by adding augmentations.

  • see example PyTorch Simple Example. Requires PyTorch.

installing rocAL python plugin (Python 3.6)#

  • Build and install RPP

  • Build and install MIVisionX which installs rocAL c++ lib

  • Go to rali_pybind folder

  • sudo ./run.sh

Steps to run MLPerf Resnet50 classification training with rocAL on a system with MI50 and ROCm#

  • Step 1: Ensure you have downloaded ILSVRC2012_img_val.tar (6.3GB) and ILSVRC2012_img_train.tar (138 GB) files and unzip into train and val folders

  • Step 2: Build MIVisionX Pytorch docker

  • Step 3: Install rocAL python_pybind plugin as described above

  • Step 4: Clone MLPerf branch and checkout mlperf-v1.1-rocal branch

sudo docker pull rocm/pytorch:rocm3.3_ubuntu16.04_py3.6_pytorch

  • Step 3: Install RPP on the docker

  • Step 4: Install MIVisionX on the docker

  • Step 5: Install rocAL python_pybind plugin

  • Step 6: Clone MLPerf branch and checkout mlperf-rali branch

git clone -b mlperf-rali https://github.com/rrawther/MLPerf-mGPU

  • Step 7: Modify SMC_RN50_FP32_50E_1GPU_MI50_16GB.sh to reflect correct path for imagenet directory

  • Step 8: Run SMC_RN50_FP32_50E_1GPU_MI50_16GB.sh

sh ./SMC_RN50_FP32_50E_1GPU_MI50_16GB.sh

Steps to run MLPerf training on rali_pytorch docker#

  • Step 1: Ensure you have downloaded ILSVRC2012_img_val.tar (6.3GB) and ILSVRC2012_img_train.tar (138 GB) files and unzip into the train and Val folders

  • Step 2: Pull and run MIVisionX rali_pytorch docker. The docker already installed with pre-built packages for rocAL

  • Step 3: Clone MLPerf branch and checkout mlperf-rali branch

git clone -b mlperf-rali https://github.com/rrawther/MLPerf-mGPU

  • Step 4: Modify SMC_RN50_FP32_50E_1GPU_MI50_16GB.sh to reflect correct path for imagenet directory

  • Step 5: Run SMC_RN50_FP32_50E_1GPU_MI50_16GB.sh

sh ./SMC_RN50_FP32_50E_1GPU_MI50_16GB.sh

MIVisionX Pytorch Docker#

sudo docker pull mivisionx/pytorch-ubuntu-16.04

  • Step 2: Run the docker image

sudo docker run -it --device=/dev/kfd --device=/dev/dri --cap-add=SYS_RAWIO --device=/dev/mem --group-add video --network host mivisionx/pytorch-ubuntu-16.04

  • Optional: Map localhost directory on the docker image

    • option to map the localhost directory with imagenet dataset folder to be accessed on the docker image.

    • usage: -v {LOCAL_HOST_DIRECTORY_PATH}:{DOCKER_DIRECTORY_PATH}

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