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201 changes: 201 additions & 0 deletions LICENSE
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102 changes: 29 additions & 73 deletions README.md
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Overview
--------

rslearn_projects contains Ai2-specific tooling for managing remote sensing projects
built on top of rslearn, as well as project-specific code and configuration files.
rslearn_projects contains the training datasets, model weights, and corresponding code
for machine learning applications built on top of
[rslearn](https://github.com/allenai/rslearn/) at Ai2.


Tooling
-------

The additional tooling comes into play when training and deploying models. This is an
outline of the steps the tooling takes care of when training models:

1. User runs e.g. `python -m rslp.launch_beaker --config_path path/to/config.yaml`.
2. Launcher uploads the code to a canonical path on Google Cloud Storage (GCS), based
on the project ID and experiment ID specified in `config.yaml`.
3. Launcher then starts a job, in this case on Beaker, to train the model.
4. `rslp.docker_entrypoint` is the entrypoint for the job, and starts by downloading
the code. The image contains a copy of the code too, but it is overwritten with the
latest code from the user's codebase.
5. It then saves W&B run ID to GCS. It also configures rslearn to write checkpoints to
a canonical folder on GCS.
6. If the job is pre-empted and resumes, it will automatically load the latest
checkpoint and W&B run ID from GCS. It will also load these in calls to `model test`
or `model predict`.
The model weights and code are released under Apache License 2.0 while the annotations
are released under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/).


Setup
-----

rslp expects an environment variable specifying the GCS bucket to write prepared
rslearn datasets, model checkpoints, etc. The easiest way is to create a `.env` file.

RSLP_PREFIX=gs://rslearn-eai
RSLP_WEKA_PREFIX=weka://dfive-default/rslearn-eai

You will also need to setup GCP credentials that have access to this bucket.
Install rslearn:

Training additionally depends on credentials for W&B. If you train directly using
`rslp.rslearn_main`, then you will need to setup these credentials. If you use a
launcher like `rslp.launch_beaker`, then it isn't needed since the credentials are
already configured as secrets on the platform, but you would need to setup your Beaker
or other platform credentials to be able to launch the jobs.
git clone https://github.com/allenai/rslearn.git
cd rslearn
pip install .[extra]

TODO: update GCP/W&B to use service accounts.
Install requirements:

Currently, until https://github.com/allenai/rslearn/issues/33 is resolved, model config
files use S3-compatable API to access GCS rather than GCS directly. Therefore, you need
to set up environment variables to provide the appropriate credentials:

S3_ACCESS_KEY_ID=GOOG...
S3_SECRET_ACCESS_KEY=...

You can create these credentials at
https://console.cloud.google.com/storage/settings;tab=interoperability?hl=en&project=skylight-proto-1
under "Access keys for your user account".


Usage
-----
cd ..
git clone https://github.com/allenai/rslearn_projects.git
cd rslearn_projects
pip install -r requirements.txt

Create an environment for rslearn and setup with rslearn_projects requirements:
rslearn_projects includes tooling that expects model checkpoints and auxiliary files to
be stored in an `RSLP_PREFIX` directory. Create a file `.env` to set the `RSLP_PREFIX`
environment variable:

conda create -n rslearn python=3.12
conda activate rslearn
pip install -r rslearn/requirements.txt -r rslearn/extra_requirements.txt
pip install -r rslearn_projects/requirements.txt
mkdir project_data
echo "RSLP_PREFIX=project_data/" > .env

For development it is easier to use PYTHONPATH or install rslearn and rslearn_projects
in editable mode, e.g.:

export PYTHONPATH=.:/path/to/rslearn/rslearn

Execute a data processing pipeline:

python -m rslp.main maldives_ecosystem_mapping data --dp_config.workers 32

Launch training on Beaker:

python -m rslp.main maldives_ecosystem_mapping train_maxar

Manually train locally:

python -m rslp.rslearn_main model fit --config_path data/maldives_ecosystem_mapping/config.yaml


Projects
--------
Applications
------------

- [Forest Loss Driver](rslp/forest_loss_driver/README.md)
- [Sentinel-2 Vessel Detection](docs/sentinel2_vessels.md)
- [Sentinel-2 Vessel Attribute Prediction](docs/sentinel2_vessel_attribute.md)
- [Landsat Vessel Detection](docs/landsat_vessels.md)
- [Satlas: Solar Farm Segmentation](docs/satlas_solar_farm.md)
- [Satlas: Wind Turbine Detection](docs/satlas_wind_turbine.md)
- [Satlas: Marine Infrastructure Detection](docs/satlas_marine_infra.md)
- [Forest Loss Driver Classification](docs/forest_loss_driver.md)
- [Maldives Ecosystem Mapping](docs/maldives_ecosystem_mapping.md)
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