Website and Github update #166

Workflow file for this run

	name: CI_GPU

	on:
	push:
	branches:
	- main
	pull_request:
	branches:
	- main

	jobs:
	# Temporarily disable this test since there is no server including multiple GPUs.
	# unittest_multi_gpu:
	# runs-on: 4-core-ubuntu-gpu-t4
	# steps:
	# - name: Checkout
	# uses: actions/checkout@v2

	# - name: Display Python version
	# run: python3 -c "import sys; print(sys.version)"

	# - name: Set up Python
	# uses: actions/setup-python@v2
	# with:
	# python-version: '3.x'

	# - name: Install dependencies
	# run: \|
	# python -m pip install --upgrade pip
	# ./scripts/install_via_pip.sh -c

	# - name: Run multi-GPU unit tests
	# run: \|
	# python3 -m unittest opacus.tests.multigpu_gradcheck.GradientComputationTest.test_gradient_correct


	integrationtest_py39_torch_release_cuda:
	runs-on: 4-core-ubuntu-gpu-t4
	steps:
	- name: Checkout
	uses: actions/checkout@v2

	- name: Set up Python
	uses: actions/setup-python@v2
	with:
	python-version: '3.9'

	- name: Install dependencies
	run: \|
	python3 -m pip install --upgrade pip
	pip install pytest coverage coveralls
	./scripts/install_via_pip.sh -c

	# Cuda dependency has already been installed when installing PyTorch, so no need to re-install it.
	# https://discuss.pytorch.org/t/should-i-install-the-extra-cudatoolkit-and-cudnn/194528
	# Cuda installation guide: https://medium.com/@milistu/how-to-install-cuda-cudnn-7e4a00ae4f44
	# - name: Install CUDA toolkit and cuDNN
	# run: \|
	# sudo apt-get update
	# sudo apt-get install -y --no-install-recommends \
	# cuda-toolkit-11-1 \
	# libcudnn8=8.1.1.33-1+cuda11.1 \
	# libcudnn8-dev=8.1.1.33-1+cuda11.1

	- name: Run MNIST integration test (CUDA)
	run: \|
	nvidia-smi
	mkdir -p runs/mnist/data
	mkdir -p runs/mnist/test-reports
	python -c "import torch; exit(0) if torch.cuda.is_available() else exit(1)"
	python examples/mnist.py --lr 0.25 --sigma 0.7 -c 1.5 --batch-size 64 --epochs 1 --data-root runs/mnist/data --n-runs 1 --device cuda
	python -c "import torch; accuracy = torch.load('run_results_mnist_0.25_0.7_1.5_64_1.pt'); exit(0) if (accuracy[0]>0.78 and accuracy[0]<0.95) else exit(1)"

	- name: Store MNIST test results
	uses: actions/upload-artifact@v4
	with:
	name: mnist-gpu-reports
	path: runs/mnist/test-reports

	- name: Run CIFAR10 integration test (CUDA)
	run: \|
	mkdir -p runs/cifar10/data
	mkdir -p runs/cifar10/logs
	mkdir -p runs/cifar10/test-reports
	pip install tensorboard
	python examples/cifar10.py --lr 0.1 --sigma 1.5 -c 10 --batch-size 2000 --epochs 10 --data-root runs/cifar10/data --log-dir runs/cifar10/logs --device cuda
	python -c "import torch; model = torch.load('model_best.pth.tar'); exit(0) if (model['best_acc1']>0.4 and model['best_acc1']<0.49) else exit(1)"
	python examples/cifar10.py --lr 0.1 --sigma 1.5 -c 10 --batch-size 2000 --epochs 10 --data-root runs/cifar10/data --log-dir runs/cifar10/logs --device cuda --grad_sample_mode no_op
	python -c "import torch; model = torch.load('model_best.pth.tar'); exit(0) if (model['best_acc1']>0.4 and model['best_acc1']<0.49) else exit(1)"

	- name: Store CIFAR10 test results
	uses: actions/upload-artifact@v4
	with:
	name: cifar10-gpu-reports
	path: runs/cifar10/test-reports

	# To save resouces, there is no need to run all the tests.
	# - name: Run IMDb integration test (CUDA)
	# run: \|
	# mkdir -p runs/imdb/data
	# mkdir -p runs/imdb/test-reports
	# pip install --user datasets transformers
	# python examples/imdb.py --lr 0.02 --sigma 1.0 -c 1.0 --batch-size 64 --max-sequence-length 256 --epochs 2 --data-root runs/imdb/data --device cuda
	# python -c "import torch; accuracy = torch.load('run_results_imdb_classification.pt'); exit(0) if (accuracy>0.54 and accuracy<0.66) else exit(1)"

	# - name: Store IMDb test results
	# uses: actions/upload-artifact@v4
	# with:
	# name: imdb-gpu-reports
	# path: runs/imdb/test-reports

	# - name: Run charlstm integration test (CUDA)
	# run: \|
	# mkdir -p runs/charlstm/data
	# wget https://download.pytorch.org/tutorial/data.zip -O runs/charlstm/data/data.zip
	# unzip runs/charlstm/data/data.zip -d runs/charlstm/data
	# rm runs/charlstm/data/data.zip
	# mkdir -p runs/charlstm/test-reports
	# pip install scikit-learn
	# python examples/char-lstm-classification.py --epochs=20 --learning-rate=2.0 --hidden-size=128 --delta=8e-5 --batch-size 400 --n-layers=1 --sigma=1.0 --max-per-sample-grad-norm=1.5 --data-root="runs/charlstm/data/data/names/" --device cuda --test-every 5
	# python -c "import torch; accuracy = torch.load('run_results_chr_lstm_classification.pt'); exit(0) if (accuracy>0.60 and accuracy<0.80) else exit(1)"

	# - name: Store test results
	# uses: actions/upload-artifact@v4
	# with:
	# name: charlstm-gpu-reports
	# path: runs/charlstm/test-reports

	# We will have new benchmarks for Ghost Clipping.
	# micro_benchmarks_py39_torch_release_cuda:
	# runs-on: ubuntu-latest
	# needs: [integrationtest_py39_torch_release_cuda]
	# container:
	# # https://hub.docker.com/r/nvidia/cuda
	# image: nvidia/cuda:12.3.1-base-ubuntu22.04
	# options: --gpus all
	# env:
	# TZ: 'UTC'
	# steps:
	# - name: Checkout
	# uses: actions/checkout@v2

	# - name: Set up Python
	# uses: actions/setup-python@v2
	# with:
	# python-version: 3.9

	# - name: Install dependencies
	# run: \|
	# python -m pip install --upgrade pip
	# pip install pytest coverage coveralls
	# ./scripts/install_via_pip.sh

	# - name: Install CUDA toolkit and cuDNN
	# run: \|
	# apt-get update
	# apt-get install -y --no-install-recommends \
	# cuda-toolkit-11-1 \
	# libcudnn8=8.1.1.33-1+cuda11.1 \
	# libcudnn8-dev=8.1.1.33-1+cuda11.1

	# - name: Run benchmark integration tests (CUDA)
	# run: \|
	# mkdir -p benchmarks/results/raw
	# python benchmarks/run_benchmarks.py --batch_size 16 --layers "groupnorm instancenorm layernorm" --config_file ./benchmarks/config.json --root ./benchmarks/results/raw/ --cont
	# IFS=$' ';layers=("groupnorm" "instancenorm" "layernorm"); rm -rf /tmp/report_layers; mkdir -p /tmp/report_layers; IFS=$'\n'; files=`( echo "${layers[]}" ) \| sed 's/./.\/benchmarks\/results\/raw\/&*/'`
	# cp -v ${files[@]} /tmp/report_layers
	# report_id=`IFS=$'-'; echo "${layers[*]}"`
	# python benchmarks/generate_report.py --path-to-results /tmp/report_layers --save-path benchmarks/results/report-${report_id}.csv --format csv
	# python benchmarks/generate_report.py --path-to-results /tmp/report_layers --save-path benchmarks/results/report-${report_id}.pkl --format pkl
	# python benchmarks/check_threshold.py --report-path "./benchmarks/results/report-"$report_id".pkl" --metric runtime --threshold 3.0 --column "hooks/baseline"
	# python benchmarks/check_threshold.py --report-path "./benchmarks/results/report-"$report_id".pkl" --metric memory --threshold 1.6 --column "hooks/baseline"

	# - name: Store artifacts
	# uses: actions/upload-artifact@v2
	# with:
	# name: benchmarks-reports
	# path: benchmarks/results/

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Website and Github update #166

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Website and Github update #166

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