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README: more tidy up

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@ -48,7 +48,7 @@ More fully:
3. Obtain a heightmap (or *Digital Elevation Model*, as it's sometimes known) from the Ordnance Survey (can't remember the link, please PR to add this) 3. Obtain a heightmap (or *Digital Elevation Model*, as it's sometimes known) from the Ordnance Survey (can't remember the link, please PR to add this)
4. Use [`terrain50-cli`](https://www.npmjs.com/package/terrain50-cli) to slice the the output from steps #2 and #3 to be exactly the same size [TODO: Preprocess to extract just a single river basin from the data] 4. Use [`terrain50-cli`](https://www.npmjs.com/package/terrain50-cli) to slice the the output from steps #2 and #3 to be exactly the same size [TODO: Preprocess to extract just a single river basin from the data]
5. Push through [HAIL-CAESAR](https://github.com/sbrl/HAIL-CAESAR) (this fork has the ability to handle streams of .asc files rather than each time step having it's own filename) 5. Push through [HAIL-CAESAR](https://github.com/sbrl/HAIL-CAESAR) (this fork has the ability to handle streams of .asc files rather than each time step having it's own filename)
6. Use `rainfallwrangler` in this repository (finally!) to convert the output to .json.gz then .tfrecord files 6. Use `rainfallwrangler` in this repository (finally!) to convert the rainfall, heightmap, and water depth data to a .json.gz dataset, and then to a set of .tfrecord files the model can read and understand
7. Train a DeepLabV3+ prediction model 7. Train a DeepLabV3+ prediction model
Only steps #6 and #7 actually use code in this repository. Steps #2 and #4 involve the use of modular [`npm`](https://npmjs.org/) packages. Only steps #6 and #7 actually use code in this repository. Steps #2 and #4 involve the use of modular [`npm`](https://npmjs.org/) packages.
@ -104,6 +104,8 @@ This is done using the `rainfallwrangler` tool in the eponymous directory in thi
◄────────── Timesteps ─────────────► ◄────────── Timesteps ─────────────►
``` ```
This is also the point at which the compression of the rainfall history the DeepLabV3+ model sees is done - i.e. compressing multiple timesteps with `max()` to save VRAM.
Note to self: 150.12 hashes/sec on i7-4770 4c8t, ???.?? hashes/sec on Viper compute Note to self: 150.12 hashes/sec on i7-4770 4c8t, ???.?? hashes/sec on Viper compute
After double checking, rainfallwrangler does NOT mess with the ordering of the data. After double checking, rainfallwrangler does NOT mess with the ordering of the data.
@ -126,11 +128,11 @@ The model should work with any recent version of Tensorflow. See the [version ta
With requirements installed, we can train a model. The general form this is done is like so: With requirements installed, we can train a model. The general form this is done is like so:
```bash ```bash
cd aimodel cd aimodel;
[ENVIRONMENT_VARIABLES_HERE] src/deeplabv3_plus_test_rainfall.py [ENVIRONMENT_VARIABLES_HERE] src/deeplabv3_plus_test_rainfall.py
``` ```
This model has mainly been tested and trained on the [University of Hull's Viper HPC](), which runs [Slurm](). As such, a Slurm job file is available in [`aimodel/slurm-TEST-deeplabv3p-rainfall.job`](./aimodel/slurm-TEST-deeplabv3p-rainfall.job), which wraps the aforementioned script. This model has mainly been tested and trained on the [University of Hull's Viper HPC](https://hpc.wordpress.hull.ac.uk/home/), which runs [Slurm](https://slurm.schedmd.com/). As such, a Slurm job file is available in [`aimodel/slurm-TEST-deeplabv3p-rainfall.job`](./aimodel/slurm-TEST-deeplabv3p-rainfall.job), which wraps the aforementioned script.
The following environment variables are supported: The following environment variables are supported:
@ -166,7 +168,8 @@ ARGS | Optional. Any additional arguments to pass to the python program.
**Making predictions:** Set `PATH_CHECKPOINT` to point to a checkpoint file to make predictions with an existing model that you trained earlier instead of training a new one. Data is pulled from the given dataset, same as during training. The first `PREDICT_COUNT` items in the dataset are picked to make a prediction. **Making predictions:** Set `PATH_CHECKPOINT` to point to a checkpoint file to make predictions with an existing model that you trained earlier instead of training a new one. Data is pulled from the given dataset, same as during training. The first `PREDICT_COUNT` items in the dataset are picked to make a prediction.
> [!NOTE] The dataset pipeline is naturally non-deterministic with respect to the order in which samples are read. Ensuring the ordering of samples is not mangled is only possible when making predictions, and requires a number of environment variables to be set: > [!NOTE]
> The dataset pipeline is naturally non-deterministic with respect to the order in which samples are read. Ensuring the ordering of samples is not mangled is only possible when making predictions, and requires a number of environment variables to be set:
> >
> - **`PREDICT_AS_ONE`:** Set to any value to disable the training / validation split > - **`PREDICT_AS_ONE`:** Set to any value to disable the training / validation split
> - **`PARALLEL_READS`:** Set to `0` to reading input files sequentially. > - **`PARALLEL_READS`:** Set to `0` to reading input files sequentially.
@ -174,6 +177,7 @@ ARGS | Optional. Any additional arguments to pass to the python program.
## Contributing ## Contributing
Contributions are very welcome - both issues and pull requests! Please mention in any pull requests that you release your work under the AGPL-3 (see below). Contributions are very welcome - both issues and pull requests! Please mention in any pull requests that you release your work under the AGPL-3 (see below).
We acknowledge and thank the [VIPER high-performance computing facility](https://hpc.wordpress.hull.ac.uk/home/) of the [University of Hull](https://hull.ac.uk/) and its support team, without whom this project would not have been possible.
## License ## License
All the code in this repository is released under the GNU Affero General Public License 3.0 unless otherwise specified. The full license text is included in the [`LICENSE.md` file](./LICENSE.md) in this repository. GNU [have a great summary of the licence](https://www.gnu.org/licenses/#AGPL) which I strongly recommend reading before using this software. All the code in this repository is released under the GNU Affero General Public License 3.0 unless otherwise specified. The full license text is included in the [`LICENSE.md` file](./LICENSE.md) in this repository. GNU [have a great summary of the licence](https://www.gnu.org/licenses/#AGPL) which I strongly recommend reading before using this software.