capibara-ent 1.2.1

A flexible multimodal AI library for advanced contextual understanding and deployment.

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 (optional)
• Integration with Weights & Biases for experiment tracking
• New layers and sub-models: Support for the latest modeling layers and advanced sub-models.

Requirements

• Python 3.7+
• JAX (for TPU optimization)
• TensorFlow
• Weights & Biases
• Docker (optional, for container execution)

Installation

1. Clone this repository:

   git clone https://github.com/anachroni-io/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 (optional, commented)
• --deploy: Deploy the model
• --measure-performance: Measure the model's performance
• --model: Path to the model YAML file (for deserialization)
• --new-layer: (optional) Activate new modeling layers
• --sub-model: (optional) Specify sub-models to use

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 (optional, if Docker is set up):

   python capibaraent_cli.py --use-docker
   

Configuration

Model configuration is handled through environment variables and YAML files. Key configuration parameters include:

• CAPIBARA_LEARNING_RATE
• CAPIBARA_BATCH_SIZE
• CAPIBARA_MAX_LENGTH
• CAPIBARA_USE_TPU
• WANDB_PROJECT
• WANDB_ENTITY
• CAPIBARA_NEW_LAYER (new layer)
• CAPIBARA_SUB_MODEL (sub-model)

Example .env file

CAPIBARA_LEARNING_RATE=0.001
CAPIBARA_BATCH_SIZE=32
CAPIBARA_MAX_LENGTH=512
CAPIBARA_USE_TPU=True
WANDB_PROJECT=my_project
WANDB_ENTITY=my_entity
CAPIBARA_NEW_LAYER=True
CAPIBARA_SUB_MODEL=my_sub_model

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