Era5 autoencoder#68
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Stephen Haddad (stevehadd) wants to merge 9 commits into
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Pull request overview
Adds an ERA5/WeatherBench gridded-data tutorial that prepares ERA5 data into Zarr, trains a convolutional autoencoder in PyTorch, and provides SLURM submission helpers plus environment specs.
Changes:
- Added an ERA5 Zarr data-prep CLI and an autoencoder training/evaluation CLI.
- Added JASMIN/SLURM run + submit scripts and a README describing the workflow.
- Updated/added PyTorch environment requirement files for the tutorial stack.
Reviewed changes
Copilot reviewed 10 out of 13 changed files in this pull request and generated 13 comments.
Show a summary per file
| File | Description |
|---|---|
| ml_examples/era5_autoencoder/train_era5_autoencoder.py | Training/evaluation CLI with WeatherBench Zarr Dataset, autoencoder model, and training loop. |
| ml_examples/era5_autoencoder/era5_autoencoder_data_prep.py | CLI to compute stats and write original/normalised ERA5 data to Zarr. |
| ml_examples/era5_autoencoder/ERA5_data_prep.ipynb | Notebook walkthrough for the ERA5 data-prep workflow. |
| ml_examples/era5_autoencoder/README.md | Tutorial documentation and execution instructions (direct + SLURM). |
| ml_examples/era5_autoencoder/config.json | Platform path configuration used by tutorial scripts. |
| ml_examples/era5_autoencoder/run_era5_data_prep.sh | SLURM batch script intended to run the data-prep pipeline. |
| ml_examples/era5_autoencoder/submit_era5_data_prep.sh | Convenience wrapper to submit the data-prep batch job. |
| ml_examples/era5_autoencoder/run_era5_ae_train.sh | SLURM batch script intended to stage data and run training on GPU. |
| ml_examples/era5_autoencoder/submit_era5_ae.sh | Convenience wrapper to submit the training batch job. |
| environments/requirements_pytorch.yml | Conda environment spec updates for the tutorial dependencies. |
| environments/requirements_pytorch.txt | Pip requirements file for the tutorial dependencies. |
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| def __str__(self): | ||
| return str(self._wb_ds) | ||
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| def __repr_html__(self): | ||
| return self._wb_ds.__rept_html__() |
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| epoch_val_loss = 0.0 | ||
| for batch_ix_val, X_batch_val in enumerate(val_loader): | ||
| predictions_val = ae_model.forward(X_batch_val.to(device)) | ||
| loss_batch_val = loss_function(predictions_val, X_batch_val.to(device)) | ||
| epoch_val_loss += loss_batch_val.to('cpu').item() | ||
| epoch_val_loss /= len(val_loader) |
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| print(epoch_train_loss) | ||
| print(epoch_val_loss) | ||
| epoch_duration_minutes = (datetime.datetime.now() - epoch_start_dt) // 60 |
| mlflow.log_artifact(cp_path) | ||
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| train_duration_minutes = (datetime.datetime.now() - train_start_dt) // 60 |
| epoch_duration_minutes = (datetime.datetime.now() - epoch_start_dt) // 60 | ||
| print(f'epoch train loop time {epoch_duration_minutes} minutes') | ||
| if checkpoint_dir is not None: | ||
| cp_fname = f'era5_autoencoder_checkoint_{epoch_num:03d}.pth' |
| wb_zarr_norm_dir.mkdir(parents=True) | ||
| era5_norm_ds = xarray.merge(var_norm_ds_list).chunk({'time':240}) | ||
| era5_norm_ds.to_zarr(wb_zarr_norm_dir) | ||
| print(f'zarr of original data written to {wb_zarr_norm_dir}') |
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| cd ~/prog/ai4c_hackathon/ | ||
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| python src/ai4c_hack/era5_autoencoder_data_prep.py --start-year 1980 --end-year 2016 --data-out-dir /gws/ssde/j25a/mmh_storage/ai4c_data/weatherbench/mlready --config notebooks/config.json |
| export STD_OUT_PATH=/gws/nopw/j04/mohc_shared/users/shaddad/log/era5_data_prep_log_$(date '+%Y%m%d%H%M').out | ||
| export STD_ERR_PATH=/gws/nopw/j04/mohc_shared/users/shaddad/log/era5_data_prep_log_$(date '+%Y%m%d%H%M').err | ||
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| sbatch -o $STD_OUT_PATH -e $STD_ERR_PATH util/run_era5_data_prep.sh |
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| conda activate ${CONDA_ENV} | ||
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| python ERA5_autoencoder.py --config-path config.json --model-out-dir /gws/ssde/j25a/mmh_storage/user/shaddad/experiments/era5_autoencoder --batch-size=${BATCH_SIZE} --num-epochs ${NUM_EPOCHS} --learning-rate ${LEARNING_RATE} --data-dir $DATA_CACHE_DIR # --mlflow-url "http://localhost" --mlflow-port ${MLFLOW_PORT} |
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| export WEATHERBENCH_NORM_DIR=/gws/ssde/j25a/mmh_storage/ai4c_data/weatherbench/mlready/norm/ | ||
| export DATA_CACHE_DIR=/tmp/era5_autoencoder/ | ||
| mkdir $DATA_CACHE_DIR |
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Here is the tutorial on the gridded data which I will be running.
See the readme for more details of individual files, primarily about the difference in training and evaluating models with gridded data compared to tabular data.