"Added notebooks that are colab accessible, they should both be runnable from within colab. " #28
Workflow file for this run
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| name: Tests | |
| on: | |
| - push | |
| - pull_request | |
| jobs: | |
| # Installs the conda environment and trains METL | |
| train: | |
| name: Test METL training | |
| runs-on: ${{ matrix.os }} | |
| strategy: | |
| fail-fast: false | |
| matrix: | |
| # Could also test on the beta M1 macOS runner | |
| # https://docs.github.com/en/actions/using-github-hosted-runners/about-github-hosted-runners/about-github-hosted-runners#standard-github-hosted-runners-for-public-repositories | |
| os: | |
| - macos-latest | |
| - ubuntu-latest | |
| - windows-latest | |
| steps: | |
| - name: Checkout repository | |
| uses: actions/checkout@v4 | |
| # Can set up package caching later conda-incubator/setup-miniconda | |
| - name: Install conda environment | |
| uses: conda-incubator/setup-miniconda@v2 | |
| with: | |
| activate-environment: metl | |
| environment-file: environment.yml | |
| auto-activate-base: false | |
| miniforge-variant: Mambaforge | |
| miniforge-version: 'latest' | |
| use-mamba: true | |
| # Installs latest commit from main branch | |
| - name: Install metl package from metl-pretrained repo | |
| shell: bash --login {0} | |
| run: pip install git+https://github.com/gitter-lab/metl-pretrained | |
| # Log conda environment contents | |
| - name: Log conda environment | |
| shell: bash --login {0} | |
| run: conda list | |
| # Pretrain source model on GFP Rosetta dataset | |
| - name: Pretrain source METL model | |
| shell: bash --login {0} | |
| run: python code/train_source_model.py @args/pretrain_avgfp_local.txt --max_epochs 5 --limit_train_batches 5 --limit_val_batches 5 --limit_test_batches 5 | |
| # Finetune target model on GFP DMS dataset | |
| - name: Finetune target METL model | |
| shell: bash --login {0} | |
| run: python code/train_target_model.py @args/finetune_avgfp_local.txt --enable_progress_bar false --enable_simple_progress_messages --max_epochs 50 --unfreeze_backbone_at_epoch 25 | |
| # Load target model checkpoint and run inference on example variants | |
| - name: Load and test target METL model | |
| shell: bash --login {0} | |
| # Log directory name is different on every run | |
| run: | | |
| cp=output/training_logs/*/checkpoints/epoch=49-step=50.ckpt | |
| python code/convert_ckpt.py --ckpt_path $cp | |
| cp=output/training_logs/*/checkpoints/*.pt | |
| python code/tests.py --ckpt_path $cp --variants E3K,G102S_T36P,S203T,K207R_V10A,D19G,F25S,E113V --dataset avgfp |