Metadata-Version: 2.4
Name: onex-sdk
Version: 0.1.2
Summary: Framework-agnostic neural signal monitoring
Home-page: https://github.com/onex-ai/onex-sdk
Author: OneX AI Team
Author-email: OneX AI Team <support@onex.io>
License: MIT
Project-URL: Homepage, https://getonex.ai/
Project-URL: Documentation, https://getonex.ai/
Project-URL: Repository, https://getonex.ai/
Project-URL: Issues, https://getonex.ai/
Keywords: machine-learning,observability,monitoring,mlops
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: requests>=2.28.0
Requires-Dist: numpy>=1.21.0
Provides-Extra: pytorch
Requires-Dist: torch>=1.12.0; extra == "pytorch"
Provides-Extra: tensorflow
Requires-Dist: tensorflow>=2.8.0; extra == "tensorflow"
Provides-Extra: jax
Requires-Dist: jax>=0.3.0; extra == "jax"
Provides-Extra: all
Requires-Dist: torch>=1.12.0; extra == "all"
Requires-Dist: tensorflow>=2.8.0; extra == "all"
Requires-Dist: jax>=0.3.0; extra == "all"
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: black>=22.0.0; extra == "dev"
Requires-Dist: twine>=4.0.0; extra == "dev"
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

# OneX Observability SDK

The OneX SDK provides framework-agnostic utilities to monitor neural
signals and export them to the OneX observability platform. It detects
popular machine learning frameworks (PyTorch, TensorFlow, JAX) and
attaches lightweight instrumentation to running models.

## Installation

```bash
pip install onex-sdk
```

Optional extras are available for framework-specific monitoring:

```bash
# PyTorch support
pip install onex-sdk[pytorch]

# TensorFlow support
pip install onex-sdk[tensorflow]

# JAX support
pip install onex-sdk[jax]
```

## Quick Start

```python
from onex import OneXMonitor

monitor = OneXMonitor(api_key="your-api-key")
model = monitor.watch(model)
```

## Development

Create a virtual environment and install the development requirements:

```bash
pip install -r requirements-dev.txt
```

Then run the tests with `pytest`.

