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updated readme
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README.md
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1. Download dependencies: `conda env create --file environment.yml`
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2. Open environment: `conda activate dancer-net`
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3. Start the demo application: `python app.py`
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1. Download dependencies: `conda env create --file environment.yml`
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2. Open environment: `conda activate dancer-net`
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3. Start the demo application: `python app.py`
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## Training
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You can update and train models with the `train.py` script. The specific logic for training each model can be found in training functions located in the [models folder](./models/). You can customize and parameterize these training loops by directing the training script towards a custom [yaml config file](./models/config/).
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```bash
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# Train a model using a custom configuration
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python train.py --config models/config/train_local.yaml
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```
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The training loops output the weights into either the `models/weights` or `lightning_logs` directories depending on the training script. You can then reference these pretrained weights for inference.
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### Model Configuration
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The YAML configuration files for training are located in [`models/config`](./models/config/). They specify the training environment, data, architecture, and hyperparameters of the model.
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## Testing
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See tests in the `tests` folder. Use Pytest to run the tests.
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```bash
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pytest
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```
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