Install torch-ort

Pre-requisites

You need a machine with at least one NVIDIA or AMD GPU to install torch-ort 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://download.onnxruntime.ai/onnxruntime_nightly_cu102.html

   • 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://download.onnxruntime.ai/onnxruntime_nightly_cu111.html

   • 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://download.onnxruntime.ai/onnxruntime_nightly_rocm42.html

   • pip install torch-ort

Run using Docker

The docker directory contains dockerfiles for building the environment for ONNX Runtime for PyTorch.

NVIDIA GPUs

Ensure that the nvidia-container-toolkit is installed.

CUDA 10.2

1. Build the docker image

   docker build -f Dockerfile.ort-cu102-cudnn7-devel-ubuntu18.04 -t ort.cu102 .

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

   docker run -it --gpus all --name my-experiments ort.cu102:latest /bin/bash

CUDA 11.1

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

AMD GPUs

ROCm 4.2

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

Once you have created your environment, either using Python or docker, execute the following steps to validate that your installation is correct.

1. Download a training script

   wget https://raw.githubusercontent.com/pytorch/ort/main/tests/bert_for_sequence_classification.py

2. Install extra dependencies

   pip install wget pandas sklearn transformers

3. Run the training script

   python ./ort/tests/bert_for_sequence_classification.py

4. Expected warnings

There are some warnings that are expected.