| Name | mlstac JSON |
| Version |
0.3.2
JSON |
| download |
| home_page | https://github.com/csaybar/isp-models |
| Summary | A machine learning model-sharing specification based on STAC MLM and Safetensors. |
| upload_time | 2025-02-26 14:19:12 |
| maintainer | None |
| docs_url | None |
| author | Cesar Aybar |
| requires_python | <4.0,>=3.10 |
| license | None |
| keywords |
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| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# MLSTAC: Machine Learning with STAC
[](https://pypi.org/project/mlstac/)
[](https://pypi.org/project/mlstac/)
[](https://github.com/csaybar/isp-models/blob/main/LICENSE)
[](https://csaybar.github.io/isp-models/)
MLSTAC is a Python library that simplifies working with machine learning models using STAC (SpatioTemporal Asset Catalog) metadata standards. Load models with just a few lines of code regardless of where they're stored or how they're formatted.
## Features
- **Unified API**: Work with ML models using a consistent interface
- **Multiple Storage Backends**: Support for local, HTTP(S), FTP, S3, and Google Cloud Storage
- **Smart Visualization**: Automatic detection of Jupyter/Colab notebooks for enhanced display
- **Efficient Downloads**: Stream large model files for optimal performance
- **STAC Metadata**: Rich model information using standard metadata format
## Installation
```bash
pip install mlstac
```
## Quick Start
```python
from mlstac import ModelLoader
# Load a model by ID (will fetch metadata from the registry)
model = ModelLoader("resnet50")
# Download the model to a local directory
model.download("./models")
# Load the model for inference
inference_model = model.load_compiled_model()
# Get example data for testing
example_data = model.load_example_data()
# Run inference
result = inference_model(example_data)
```
## Working with Models
MLSTAC provides a simple, consistent interface for working with ML models:
```python
# View model information
model = ModelLoader("efficientnet")
# Prints rich information about the model in a notebook or terminal
# Get model summary as a dictionary
summary = model.get_model_summary()
# Download only specific components
model.download(
"./models",
trainable_model=True,
compiled_model=True,
example_data=False
)
# Load for training
trainable = model.load_trainable_model()
```
## Model Storage Support
MLSTAC supports multiple ways to access models:
- **Snippet IDs**: Short IDs that resolve to models in the registry
- **URLs**: HTTP(S) or FTP URLs to model files
- **Cloud Storage**: AWS S3 and Google Cloud Storage
- **Local Paths**: File paths on your local system
## Documentation
For complete documentation, visit our [documentation site](https://csaybar.github.io/isp-models/).
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the terms of the MIT license.
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"description": "# MLSTAC: Machine Learning with STAC\n\n[](https://pypi.org/project/mlstac/)\n[](https://pypi.org/project/mlstac/)\n[](https://github.com/csaybar/isp-models/blob/main/LICENSE)\n[](https://csaybar.github.io/isp-models/)\n\nMLSTAC is a Python library that simplifies working with machine learning models using STAC (SpatioTemporal Asset Catalog) metadata standards. Load models with just a few lines of code regardless of where they're stored or how they're formatted.\n\n## Features\n\n- **Unified API**: Work with ML models using a consistent interface\n- **Multiple Storage Backends**: Support for local, HTTP(S), FTP, S3, and Google Cloud Storage\n- **Smart Visualization**: Automatic detection of Jupyter/Colab notebooks for enhanced display\n- **Efficient Downloads**: Stream large model files for optimal performance\n- **STAC Metadata**: Rich model information using standard metadata format\n\n## Installation\n\n```bash\npip install mlstac\n```\n\n## Quick Start\n\n```python\nfrom mlstac import ModelLoader\n\n# Load a model by ID (will fetch metadata from the registry)\nmodel = ModelLoader(\"resnet50\")\n\n# Download the model to a local directory\nmodel.download(\"./models\")\n\n# Load the model for inference\ninference_model = model.load_compiled_model()\n\n# Get example data for testing\nexample_data = model.load_example_data()\n\n# Run inference\nresult = inference_model(example_data)\n```\n\n## Working with Models\n\nMLSTAC provides a simple, consistent interface for working with ML models:\n\n```python\n# View model information\nmodel = ModelLoader(\"efficientnet\")\n# Prints rich information about the model in a notebook or terminal\n\n# Get model summary as a dictionary\nsummary = model.get_model_summary()\n\n# Download only specific components\nmodel.download(\n \"./models\", \n trainable_model=True, \n compiled_model=True,\n example_data=False\n)\n\n# Load for training\ntrainable = model.load_trainable_model()\n```\n\n## Model Storage Support\n\nMLSTAC supports multiple ways to access models:\n\n- **Snippet IDs**: Short IDs that resolve to models in the registry\n- **URLs**: HTTP(S) or FTP URLs to model files\n- **Cloud Storage**: AWS S3 and Google Cloud Storage\n- **Local Paths**: File paths on your local system\n\n## Documentation\n\nFor complete documentation, visit our [documentation site](https://csaybar.github.io/isp-models/).\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## License\n\nThis project is licensed under the terms of the MIT license.\n",
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