# 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:
```bash
git clone https://github.com/anachroni-io/capibaraent-cli.git
cd capibaraent-cli
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Set up Weights & Biases:
```bash
wandb login
```
## Usage
The CapibaraENT CLI offers various options for working with Capibara models:
```bash
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:
```bash
python capibaraent_cli.py --train
```
2. Evaluate a model:
```bash
python capibaraent_cli.py --evaluate
```
3. Perform hyperparameter optimization:
```bash
python optimize_hyperparameters.py
```
4. Deploy a model:
```bash
python capibaraent_cli.py --deploy
```
5. Measure model performance:
```bash
python capibaraent_cli.py --measure-performance
```
6. Run a model in Docker (optional, if Docker is set up):
```bash
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
```env
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:
```bash
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 - <marco@anachroni.co>
Project Link: [https://github.com/anachroni-io/capibaraent-cli](https://github.com/anachroni-io/capibaraent-cli)
Raw data
{
"_id": null,
"home_page": "https://github.com/anacronic-io/capibaraGPT-v2",
"name": "capibara-ent",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "ai nlp multimodal machine-learning deep-learning language-models ethics tpu training deployment",
"author": "Marco Dur\u00e1n",
"author_email": "marco@anachroni.com",
"download_url": "https://files.pythonhosted.org/packages/a5/58/ebad67149362da809553c0d7beb8e31fa750b991f6a200d1f56d13629a74/capibara_ent-1.2.1.tar.gz",
"platform": null,
"description": "# CapibaraENT CLI\n\n\n\nCapibaraENT is a command-line tool for training, evaluating, and deploying Capibara-based language models, optimized for TPUs and featuring hyperparameter optimization.\n\n## Features\n\n- Training and evaluation of Capibara models\n- Built-in TPU support\n- Hyperparameter optimization\n- Model deployment\n- Performance measurement\n- Docker container execution (optional)\n- Integration with Weights & Biases for experiment tracking\n- **New layers and sub-models**: Support for the latest modeling layers and advanced sub-models.\n\n## Requirements\n\n- Python 3.7+\n- JAX (for TPU optimization)\n- TensorFlow\n- Weights & Biases\n- Docker (optional, for container execution)\n\n## Installation\n\n1. Clone this repository:\n\n ```bash\n git clone https://github.com/anachroni-io/capibaraent-cli.git\n cd capibaraent-cli\n ```\n\n2. Install dependencies:\n\n ```bash\n pip install -r requirements.txt\n ```\n\n3. Set up Weights & Biases:\n\n ```bash\n wandb login\n ```\n\n## Usage\n\nThe CapibaraENT CLI offers various options for working with Capibara models:\n\n```bash\npython capibaraent_cli.py [options]\n```\n\n### Available options\n\n- `--log-level`: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)\n- `--train`: Train the model\n- `--evaluate`: Evaluate the model\n- `--optimize`: Perform hyperparameter optimization\n- `--use-docker`: Run the model inside Docker (optional, commented)\n- `--deploy`: Deploy the model\n- `--measure-performance`: Measure the model's performance\n- `--model`: Path to the model YAML file (for deserialization)\n- `--new-layer`: (optional) Activate new modeling layers\n- `--sub-model`: (optional) Specify sub-models to use\n\n### Usage Examples\n\n1. Train a model:\n\n ```bash\n python capibaraent_cli.py --train\n ```\n\n2. Evaluate a model:\n\n ```bash\n python capibaraent_cli.py --evaluate\n ```\n\n3. Perform hyperparameter optimization:\n\n ```bash\n python optimize_hyperparameters.py\n ```\n\n4. Deploy a model:\n\n ```bash\n python capibaraent_cli.py --deploy\n ```\n\n5. Measure model performance:\n\n ```bash\n python capibaraent_cli.py --measure-performance\n ```\n\n6. Run a model in Docker (optional, if Docker is set up):\n\n ```bash\n python capibaraent_cli.py --use-docker\n ```\n\n## Configuration\n\nModel configuration is handled through environment variables and YAML files. Key configuration parameters include:\n\n- `CAPIBARA_LEARNING_RATE`\n- `CAPIBARA_BATCH_SIZE`\n- `CAPIBARA_MAX_LENGTH`\n- `CAPIBARA_USE_TPU`\n- `WANDB_PROJECT`\n- `WANDB_ENTITY`\n- `CAPIBARA_NEW_LAYER` (new layer)\n- `CAPIBARA_SUB_MODEL` (sub-model)\n\n### Example `.env` file\n\n```env\nCAPIBARA_LEARNING_RATE=0.001\nCAPIBARA_BATCH_SIZE=32\nCAPIBARA_MAX_LENGTH=512\nCAPIBARA_USE_TPU=True\nWANDB_PROJECT=my_project\nWANDB_ENTITY=my_entity\nCAPIBARA_NEW_LAYER=True\nCAPIBARA_SUB_MODEL=my_sub_model\n```\n\nFor a full list of configuration options, refer to the `.env.example` file.\n\n## Hyperparameter Optimization\n\nTo perform hyperparameter optimization:\n\n1. Ensure your Weights & Biases project is set up.\n2. Run the optimization script:\n\n ```bash\n python optimize_hyperparameters.py\n ```\n\n3. View the results in your Weights & Biases dashboard.\n\n## Development\n\nTo contribute to the project:\n\n1. Fork the repository\n2. Create a new branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add some amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## License\n\nDistributed under the MIT License. See `LICENSE` for more information.\n\n## Contact\n\nMarco Dur\u00e1n - <marco@anachroni.co>\n\nProject Link: [https://github.com/anachroni-io/capibaraent-cli](https://github.com/anachroni-io/capibaraent-cli)\n",
"bugtrack_url": null,
"license": null,
"summary": "A flexible multimodal AI library for advanced contextual understanding and deployment.",
"version": "1.2.1",
"project_urls": {
"Homepage": "https://github.com/anacronic-io/capibaraGPT-v2"
},
"split_keywords": [
"ai",
"nlp",
"multimodal",
"machine-learning",
"deep-learning",
"language-models",
"ethics",
"tpu",
"training",
"deployment"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "e764b6aa8d839b5c1614dfd9cee1cb0daaea7a6a04da25c22fcb0fe6597fe848",
"md5": "3d126efa8d8f68f16d8c7e8180301bd6",
"sha256": "0a42255fa8935fe042b314a81a751d4406d3666e1781f5e6a621690b4dc004a1"
},
"downloads": -1,
"filename": "capibara_ent-1.2.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "3d126efa8d8f68f16d8c7e8180301bd6",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 38424,
"upload_time": "2024-11-05T21:20:48",
"upload_time_iso_8601": "2024-11-05T21:20:48.093978Z",
"url": "https://files.pythonhosted.org/packages/e7/64/b6aa8d839b5c1614dfd9cee1cb0daaea7a6a04da25c22fcb0fe6597fe848/capibara_ent-1.2.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a558ebad67149362da809553c0d7beb8e31fa750b991f6a200d1f56d13629a74",
"md5": "8e56d742c19aafa1f4795b3e9250d729",
"sha256": "251242483c1586ef2013d647bf48d29df17271a4c2d9e176daecee1527a0a380"
},
"downloads": -1,
"filename": "capibara_ent-1.2.1.tar.gz",
"has_sig": false,
"md5_digest": "8e56d742c19aafa1f4795b3e9250d729",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 28349,
"upload_time": "2024-11-05T21:20:49",
"upload_time_iso_8601": "2024-11-05T21:20:49.703428Z",
"url": "https://files.pythonhosted.org/packages/a5/58/ebad67149362da809553c0d7beb8e31fa750b991f6a200d1f56d13629a74/capibara_ent-1.2.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-11-05 21:20:49",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "anacronic-io",
"github_project": "capibaraGPT-v2",
"github_not_found": true,
"lcname": "capibara-ent"
}
JSON
