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.azure-pipelines/gpu-tests.yml

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displayName: 'Image info & NVIDIA'
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- bash: |
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export GIT_TERMINAL_PROMPT=1
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#sudo apt-get install -y cmake
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# python -m pip install "pip==20.1"
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pip install --requirement requirements.txt
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python -c "fname = 'requirements/extra.txt' ; lines = [line for line in open(fname).readlines() if 'horovod' not in line] ; open(fname, 'w').writelines(lines)"
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pip install --requirement ./requirements/devel.txt --upgrade-strategy only-if-needed
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- script: |
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set -e
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python setup.py install --user
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rm -rf pytorch_lightning
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pip list
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python -m pytest pl_examples -v --maxfail=2 --durations=0
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bash pl_examples/run_examples-args.sh --trainer.gpus 1 --trainer.max_epochs 1 --data.batch_size 64 --trainer.limit_train_batches 5 --trainer.limit_val_batches 3
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bash pl_examples/run_ddp-examples.sh --trainer.max_epochs 1 --data.batch_size 32 --trainer.limit_train_batches 2 --trainer.limit_val_batches 2
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# cd pl_examples/basic_examples
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# bash submit_ddp_job.sh
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# bash submit_ddp2_job.sh
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env:
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PL_USE_MOCKED_MNIST: "1"
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displayName: 'Examples'

.github/workflows/ci_pkg-install.yml

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fail-fast: false
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# max-parallel: 6
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matrix:
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# PyTorch 1.5 is failing on Win and bolts requires torchvision>=0.5
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os: [ubuntu-20.04, macOS-10.15 , windows-2019] #
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os: [ubuntu-20.04, macOS-10.15, windows-2019]
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python-version: [3.6, 3.9]
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steps:

pl_examples/basic_examples/README.md

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1. Log into the jumphost node of your SLURM-managed cluster.
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2. Create a conda environment with Lightning and a GPU PyTorch version.
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3. Choose a script to submit
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#### DDP
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Submit this job to run with DistributedDataParallel (2 nodes, 2 gpus each)
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```bash
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sbatch submit_ddp_job.sh YourEnv
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```
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#### DDP2
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Submit this job to run with a different implementation of DistributedDataParallel.
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In this version, each node acts like DataParallel but syncs across nodes like DDP.
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```bash
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sbatch submit_ddp2_job.sh YourEnv
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```

pl_examples/basic_examples/submit_ddp2_job.sh

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pl_examples/basic_examples/submit_ddp_job.sh

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