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spherical-dyffusion

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[NeurIPS 24] Probablistic Emulation of a Global Climate Model with Spherical DYffusion

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# Probabilistic Emulation of a Global Climate Model with Spherical DYffusion (NeurIPS 2024, Spotlight)

<h3> ✨Official implementation of our <a href="https://openreview.net/forum?id=Ib2iHIJRTh">Spherical DYffusion</a> paper✨ </h3>

[//]: # ([![Watch the video]&#40;https://img.youtube.com/vi/Hac_xGsJ1qY/hqdefault.jpg&#41;]&#40;https://youtu.be/Hac_xGsJ1qY&#41;)

## | Environment Setup

We recommend installing in a virtual environment from PyPi or Conda. Then, run:

python3 -m pip install .[dev]
    python3 -m pip install --no-deps nvidia-modulus@git+https://github.com/ai2cm/modulus.git@94f62e1ce2083640829ec12d80b00619c40a47f8

Alternatively, use the provided [environment/install_dependencies.sh](environment/install_dependencies.sh) script.

Note that for some compute setups you may want to install pytorch first for proper GPU support.
For more details about installing [PyTorch](https://pytorch.org/get-started/locally/), please refer to their official documentation.

## | Dataset

The final
training and validation data can be downloaded from Google Cloud Storage following the instructions
of the ACE paper at https://zenodo.org/records/10791087. The data are licensed under Creative
Commons Attribution 4.0 International.

The dataset statistics that you'll need to run the experiments are saved in the [data_statistics](data_statistics) directory.

## | Checkpoints

Model weights are available at [https://huggingface.co/salv47/spherical-dyffusion](https://huggingface.co/salv47/spherical-dyffusion/tree/main).

## | Running experiments

### Inference

Firstly, download the validation data as instructed in the [Dataset](#dataset) section.

Secondly, use the `run_inference.py` script with a corresponding configuration file.
The configurations files used for our paper can be found in the [src/configs/inference](src/configs/inference) directory.
That is, you can run inference with the following command:

python run_inference.py <path-to-inference-config>.yaml

The available inference configurations are:
- [ckpts_from_huggingface_debug.yaml](src/configs/inference/ckpts_from_huggingface_debug.yaml): Short inference meant for debugging with checkpoints downloaded from Hugging Face.
- [ckpts_from_huggingface_10years.yaml](src/configs/inference/ckpts_from_huggingface_10years.yaml): 10-year-long inference with checkpoints downloaded from Hugging Face.

To use these configs, **you need to correctly specify the `dataset.data_path` parameter in the configuration file to point to the downloaded validation data.**

You may also need to specify the `overrides.datamodule_config.data_dir_stats` to point to the [data statistics](data_statistics) directory.
### Training

We use [Hydra](https://hydra.cc/) for configuration management and [PyTorch Lightning](https://www.pytorchlightning.ai/) for training.
We recommend familiarizing yourself with these tools before running training experiments.

The basic configuration files used for our paper can be found in the [src/configs](src/configs) directory.
To use them, please first specify the correct data path's in the [src/configs/datamodule/fv3gfs_prescriptive_only.yaml](src/configs/datamodule/fv3gfs_prescriptive_only.yaml) config file.
Specifically, set ``datamodule.data_dir=<path-to-dataset>`` and ``datamodule.data_dir_stats=<path-to-data_statistics>`` appropriately.

To run training, you can use the `run.py` script with a corresponding experiment configuration file.
For example, to train Spherical DYffusion with the default configuration, first train the interpolator with:

python run.py experiment=fv3gfs_interpolation

After that, train the forecaster with:

python run.py experiment=fv3gfs_dyffusion diffusion.interpolator_run_id=<interpolator_run_id>

where `<interpolator_run_id>` is the Weights & Biases run ID of the interpolator experiment.
To train the basic UNet-based DYffusion, you would additionally append `model=unet` to both commands.

### Tips & Tricks

<details>
    <summary>Memory Considerations and OOM Errors</summary>

To control memory usage and avoid OOM errors, you can adjust the training batch size and evaluation batch size:

**For training**, you can adjust the `datamodule.batch_size_per_gpu` parameter. 
Note that this will automatically adjust `trainer.accumulate_grad_batches` to keep the effective batch size (set by `datamodule.batch_size`) constant (so it need to be divisible by `datamodule.batch_size_per_gpu`).

**For evaluation** or OOMs during validation, you can adjust the `datamodule.eval_batch_size` parameter. 
Note that the effective validation-time batch size is `datamodule.eval_batch_size * module.num_predictions`. Be mindful of that when choosing `eval_batch_size`. You can control how many ensemble members to run in memory 
at once with `module.num_predictions_in_memory`.

Besides those main knobs, you may turn on mixed precision training with `trainer.precision=16` to reduce memory usage and
may also adjust the `datamodule.num_workers` parameter to control the number of data loading processes.
</details>

<details>
    <summary>Wandb Integration</summary>

We use [Weights & Biases](https://wandb.ai/) for logging and checkpointing.
Please set your wandb username/entity with one of the following options:
- Edit the [src/configs/local/default.yaml](src/configs/local/default.yaml) file (recommended, local for you only).
- Edit the [src/configs/logger/wandb.yaml](src/configs/logger/wandb.yaml) file.
- as a command line argument (e.g. `python run.py logger.wandb.entity=my_username`).
</details>

<details>
    <summary>Checkpointing</summary>

By default, checkpoints are saved locally in the `<work_dir>/checkpoints` directory in the root of the repository, 
which you can control with the `work_dir=<path>` argument.

When using the wandb logger (default), checkpoints may be saved to wandb (`logger.wandb.save_to_wandb`) or S3 storage (`logger.wandb.save_to_s3_bucket`).
Set these to `False` to disable saving them to wandb or S3.
If disabling both (only save checkpoints locally), make sure to set `logger.wandb.save_best_ckpt=False logger.wandb.save_last_ckpt=False`.
You can set these preferences in your [local config](src/configs/local/default.yaml) file
(see [src/configs/local/example_local_config.yaml](src/configs/local/example_local_config.yaml) for an example).
</details>

<details>
    <summary>Debugging</summary>

For minimal data and model size, you can use the following:

python run.py ++model.debug_mode=True ++datamodule.debug_mode=True

Note that the model and datamodule need to support to appropriately handle the debug mode.
</details>

<details>
    <summary>Code Quality</summary>

Code quality is automatically checked when pushing to the repository.
However, it is recommended that you also run the checks locally with `make quality`.

To automatically fix some issues (as much as possible), run:

make style
</details>

<details>
    <summary>hydra.errors.InstantiationException</summary>

The ``hydra.errors.InstantiationException`` itself is not very informative, 
so you need to look at the preceding exception(s) (i.e. scroll up) to see what went wrong.
</details>

<details>
    <summary>Local Configurations</summary>

You can use a local config file that, defines the local data dir, working dir etc., by putting a ``default.yaml`` config 
in the [src/configs/local/](src/configs/local) subdirectory. Hydra searches for & uses by default the file configs/local/default.yaml, if it exists.
You may take inspiration from the [example_local_config.yaml](src/configs/local/example_local_config.yaml) file.
</details>

## | Citation

@inproceedings{cachay2024spherical,
        title={Probablistic Emulation of a Global Climate Model with Spherical {DY}ffusion},
        author={Salva R{\"u}hling Cachay and Brian Henn and Oliver Watt-Meyer and Christopher S. Bretherton and Rose Yu},
        booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
        year={2024},
        url={https://openreview.net/forum?id=Ib2iHIJRTh}
    }

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