test-mass-forker-org-1 / pytorch-ort

Accelerate PyTorch models with ONNX Runtime

Accelerate PyTorch models with ONNX Runtime

ONNX Runtime for PyTorch accelerates PyTorch model training using ONNX Runtime.

It is available via the torch-ort python package.

This repository contains the source code for the package as well as instructions for running the package and samples demonstrating how to do so.

Pre-requisites

You need a machine with at least one NVIDIA or AMD GPU to run ONNX Runtime for PyTorch.

You can install and run torch-ort in your local environment, or with Docker.

Run in a Python environment

Default dependencies

By default, torch-ort depends on PyTorch 1.8.1, ONNX Runtime 1.8 and CUDA 10.2.

1. Install CUDA 10.2

2. Install CuDNN 7.6

3. Install torch-ort and dependencies

   • pip install ninja
   • pip install torch-ort

Explicitly install for NVIDIA CUDA 10.2

1. Install CUDA 10.2

2. Install CuDNN 7.6

3. Install torch-ort and dependencies

   • pip install ninja
   • pip install torch==1.8.1
   • pip install --pre onnxruntime-training -f https://onnxruntimepackages.z14.web.core.windows.net/onnxruntime_stable_cu102.html
   • (or pip install --pre onnxruntime-training -f https://onnxruntimepackages.z14.web.core.windows.net/onnxruntime_nightly_cu102.html to use nightly build)
   • pip install torch-ort

Explicitly install for NVIDIA CUDA 11.1

1. Install CUDA 11.1

2. Install CuDNN 8.0

3. Install torch-ort and dependencies

   • pip install ninja
   • pip install torch==1.8.1
   • pip install --pre onnxruntime-training -f https://onnxruntimepackages.z14.web.core.windows.net/onnxruntime_stable_cu111.html
   • (or pip install --pre onnxruntime-training -f https://onnxruntimepackages.z14.web.core.windows.net/onnxruntime_nightly_cu111.html to use nightly build)
   • pip install torch-ort

Explicitly install for AMD ROCm 4.2

1. Install ROCm 4.2 base package (instructions)

2. Install ROCm 4.2 libraries (instructions)

3. Install ROCm 4.2 RCCL (instructions)

4. Install torch-ort and dependencies

   • pip install ninja
   • pip install --pre torch -f https://download.pytorch.org/whl/nightly/rocm4.2/torch_nightly.html
   • pip install --pre onnxruntime-training -f https://onnxruntimepackages.z14.web.core.windows.net/onnxruntime_stable_rocm42.html
   • (or pip install --pre onnxruntime-training -f https://onnxruntimepackages.z14.web.core.windows.net/onnxruntime_nightly_rocm42.html to use nightly build)
   • pip install torch-ort

Use torch-ort from nightly build

to use torch-ort from nightly build, replace

• pip install torch-ort

with

• pip install -U --pre torch-ort -f https://onnxruntimepackages.z14.web.core.windows.net/torch_ort_nightly.html

Run using Docker

On NVIDIA CUDA 11.1

The docker directory contains dockerfiles for the NVIDIA CUDA 11.1 configuration.

• docker/Dockerfile.ort-cu111-cudnn8-devel-ubuntu18.04

1. Build the docker image

   docker build -f Dockerfile.ort-cu111-cudnn8-devel-ubuntu18.04 -t ort.cu111 .

2. Run the docker container using the image you have just built

   docker run -it --gpus all --name my-experiments ort.cu111:latest /bin/bash

On AMD Rocm 4.2

The docker directory contains dockerfiles for the NVIDIA CUDA 11.1 configuration.

• docker/Dockerfile.ort-rocm4.2-pytorch1.8.1-ubuntu18.04

1. Build the docker image

   docker build -f Dockerfile.ort-rocm4.2-pytorch1.8.1-ubuntu18.04 -t ort.rocm42 .

2. Run the docker container using the image you have just built

   docker run -it --rm \
     --privileged \
     --device=/dev/kfd \
     --device=/dev/dri \
     --group-add video \
     --cap-add=SYS_PTRACE \
     --security-opt seccomp=unconfined \
     --name my-experiments \
     ort.rocm42:latest /bin/bash
   

Test your installation

1. Clone this repo

• git clone [email protected]:pytorch/ort.git

2. Install extra dependencies

• pip install wget pandas sklearn transformers

3. Run the training script

• python ./ort/tests/bert_for_sequence_classification.py

Add ONNX Runtime for PyTorch to your PyTorch training script

from torch_ort import ORTModule
model = ORTModule(model)

# PyTorch training script follows

License

This project has an MIT license, as found in the LICENSE file.