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test gpu ci for distributed #1481

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test gpu ci for distributed

test gpu ci for distributed #1481

Workflow file for this run

.github/workflows/unittest_ci.yml at c0c6dca
# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
name: Unit Test CI
on:
# push:
# paths-ignore:
# - "docs/*"
# - "third_party/*"
# - .gitignore
# - "*.md"
pull_request:
# paths-ignore:
# - "docs/*"
# - "third_party/*"
# - .gitignore
# - "*.md"
workflow_dispatch:
jobs:
# build on cpu hosts and upload to GHA
build_on_cpu:
runs-on: ${{ matrix.os }}
timeout-minutes: 30
strategy:
matrix:
include:
- os: linux.2xlarge
# ideally we run on 3.9 and 3.10 as well, however we are limited in resources.
python-version: 3.8
python-tag: "py38"
cuda-tag: "cu118"
steps:
# Checkout the repository to the GitHub Actions runner
- name: Check ldd --version
run: ldd --version
- name: Checkout
uses: actions/checkout@v2
- name: Update pip
run: |
sudo yum update -y
sudo yum -y install git python3-pip
sudo pip3 install --upgrade pip
- name: Setup conda
run: |
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
bash ~/miniconda.sh -b -p $HOME/miniconda -u
- name: setup Path
run: |
echo "/home/ec2-user/miniconda/bin" >> $GITHUB_PATH
echo "CONDA=/home/ec2-user/miniconda" >> $GITHUB_PATH
- name: create conda env
run: |
conda create --name build_binary python=${{ matrix.python-version }}
conda info
- name: check python version no Conda
run: |
python --version
- name: check python version
run: |
conda run -n build_binary python --version
- name: Install C/C++ compilers
run: |
sudo yum install -y gcc gcc-c++
- name: Install PyTorch and CUDA
shell: bash
run: |
conda install -n build_binary -y pytorch pytorch-cuda=11.8 -c pytorch-nightly -c nvidia
- name: Install Dependencies
shell: bash
run: |
conda run -n build_binary python -m pip install -r requirements.txt
- name: Test Installation of dependencies
run: |
conda run -n build_binary python -c "import torch.distributed"
echo "torch.distributed succeeded"
conda run -n build_binary python -c "import skbuild"
echo "skbuild succeeded"
conda run -n build_binary python -c "import numpy"
echo "numpy succeeded"
# for the conda run with quotes, we have to use "\" and double quotes
# here is the issue: https://github.com/conda/conda/issues/10972
- name: Build TorchRec Binary
run: |
export CU_VERSION=${{ matrix.cuda-tag }}
export CHANNEL="nightly"
conda run -n build_binary \
python setup.py bdist_wheel \
--python-tag=${{ matrix.python-tag }}
- name: Upload wheel as GHA artifact
uses: actions/upload-artifact@v2
with:
name: torchrec_${{ matrix.python-version }}_${{ matrix.cuda-tag }}.whl
path: dist/*.whl
# download from GHA, test on gpu
test_on_gpu:
runs-on: ${{ matrix.os }}
timeout-minutes: 30
strategy:
matrix:
os: [linux.4xlarge.nvidia.gpu]
python-version: [3.8]
cuda-tag: ["cu118"]
needs: build_on_cpu
# the glibc version should match the version of the one we used to build the binary
# for this case, it's 2.26
steps:
- name: Check ldd --version
# Run unit tests
run: ldd --version
- name: check cpu info
shell: bash
run: |
cat /proc/cpuinfo
- name: check distribution info
shell: bash
run: |
cat /proc/version
- name: Display EC2 information
shell: bash
run: |
set -euo pipefail
function get_ec2_metadata() {
# Pulled from instance metadata endpoint for EC2
# see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
category=$1
curl -fsSL "http://169.254.169.254/latest/meta-data/${category}"
}
echo "ami-id: $(get_ec2_metadata ami-id)"
echo "instance-id: $(get_ec2_metadata instance-id)"
echo "instance-type: $(get_ec2_metadata instance-type)"
- name: check gpu info
shell: bash
run: |
sudo yum install lshw -y
sudo lshw -C display
# Checkout the repository to the GitHub Actions runner
- name: Checkout
uses: actions/checkout@v2
- name: Update pip
run: |
sudo yum update -y
sudo yum -y install git python3-pip
sudo pip3 install --upgrade pip
- name: Setup conda
run: |
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
bash ~/miniconda.sh -b -p $HOME/miniconda
- name: setup Path
run: |
echo "/home/ec2-user/miniconda/bin" >> $GITHUB_PATH
echo "CONDA=/home/ec2-user/miniconda" >> $GITHUB_PATH
- name: create conda env
run: |
conda create --name build_binary python=${{ matrix.python-version }}
conda info
- name: check python version no Conda
run: |
python --version
- name: check python version
run: |
conda run -n build_binary python --version
- name: Install C/C++ compilers
run: |
sudo yum install -y gcc gcc-c++
- name: Install PyTorch and CUDA
shell: bash
run: |
conda run -n build_binary \
python -m pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu118
- name: Test torch installation
shell: bash
run: |
conda run -n build_binary \
python -c "import torch"
- name: Test cuda
shell: bash
run: |
conda run -n build_binary \
python -c "import torch; print(torch.cuda.is_available()); print(torch.cuda.device_count())"
nvidia-smi
# download wheel from GHA
- name: Download wheel
uses: actions/download-artifact@v2
with:
name: torchrec_${{ matrix.python-version }}_${{ matrix.cuda-tag }}.whl
- name: Display structure of downloaded files
run: ls -R
- name: Install TorchRec GPU
run: |
rm -r dist || true
conda run -n build_binary python -m pip install *.whl
- name: Install Dependencies
shell: bash
run: |
conda run -n build_binary python -m pip install -r requirements.txt
- name: Test fbgemm installation
shell: bash
run: |
conda run -n build_binary \
python -c "import fbgemm_gpu"
- name: Test torchrec installation
shell: bash
run: |
conda run -n build_binary \
python -c "import torchrec"
- name: Test with pytest
run: |
conda run -n build_binary \
python -m pip install pytest
conda run -n build_binary \
python -m pytest torchrec/distributed -v -s -W ignore::pytest.PytestCollectionWarning --continue-on-collection-errors