capibara-ent 1.0.9

A flexible multimodal AI library

CapibaraENT CLI

CapibaraENT is a command-line tool for training, evaluating, and deploying Capibara-based language models, optimized for TPUs and featuring hyperparameter optimization.

Features

• Training and evaluation of Capibara models
• Built-in TPU support
• Hyperparameter optimization
• Model deployment
• Performance measurement
• Docker container execution
• Model deserialization from JSON
• Integration with Weights & Biases for experiment tracking

Requirements

• Python 3.7+
• PyTorch 1.8+
• PyTorch/XLA
• JAX (for TPU optimization)
• Weights & Biases
• Docker (optional, for container execution)

Installation

1. Clone this repository:

   git clone https://github.com/your-username/capibaraent-cli.git
   cd capibaraent-cli
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up Weights & Biases:

   wandb login
   

Usage

The CapibaraENT CLI offers various options for working with Capibara models:

python capibaraent_cli.py [options]

Available options:

• --log-level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
• --train: Train the model
• --evaluate: Evaluate the model
• --optimize: Perform hyperparameter optimization
• --use-docker: Run the model inside Docker
• --deserialize-model: Deserialize the model from JSON
• --deploy: Deploy the model
• --measure-performance: Measure the model's performance
• --model: Path to the model JSON file (for deserialization)

Usage Examples

1. Train a model:

   python capibaraent_cli.py --train
   

2. Evaluate a model:

   python capibaraent_cli.py --evaluate
   

3. Perform hyperparameter optimization:

   python optimize_hyperparameters.py
   

4. Deploy a model:

   python capibaraent_cli.py --deploy
   

5. Measure model performance:

   python capibaraent_cli.py --measure-performance
   

6. Run a model in Docker:

   python capibaraent_cli.py --use-docker
   

7. Deserialize and run a model from JSON:

   python capibaraent_cli.py --deserialize-model --model model.json
   

Configuration

Model configuration is handled through environment variables and the .env file. Key configuration parameters include:

• CAPIBARA_LEARNING_RATE
• CAPIBARA_BATCH_SIZE
• CAPIBARA_MAX_LENGTH
• CAPIBARA_USE_TPU
• WANDB_PROJECT
• WANDB_ENTITY

For a full list of configuration options, refer to the .env.example file.

Hyperparameter Optimization

To perform hyperparameter optimization:

1. Ensure your Weights & Biases project is set up.

2. Run the optimization script:

   python optimize_hyperparameters.py
   

3. View the results in your Weights & Biases dashboard.

Development

To contribute to the project:

1. Fork the repository
2. Create a new branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Marco Durán - [e-mail]

Project Link: https://github.com/anachroni-io/capibaraent-cli