Framework-agnostic neural signal monitoring
Project description
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
pip install onex-sdk
Optional extras are available for framework-specific monitoring:
# PyTorch support
pip install onex-sdk[pytorch]
# TensorFlow support
pip install onex-sdk[tensorflow]
# JAX support
pip install onex-sdk[jax]
Quick Start
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:
pip install -r requirements-dev.txt
Then run the tests with pytest.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file onex_sdk-0.1.23.tar.gz.
File metadata
- Download URL: onex_sdk-0.1.23.tar.gz
- Upload date:
- Size: 23.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
72974181d4745e78cc3f8f8c0685634f9efeaa9ed467a2d641cdf26076b1ac77
|
|
| MD5 |
67fe1893e41e4f94fcd2510239ea2799
|
|
| BLAKE2b-256 |
1544307be63a0d085d3bef78e1bda1652315c3aba83792680afb924886277641
|
File details
Details for the file onex_sdk-0.1.23-py3-none-any.whl.
File metadata
- Download URL: onex_sdk-0.1.23-py3-none-any.whl
- Upload date:
- Size: 24.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
9d383654c344bc07fe987420c07bacfc04f689ab704ffe4d910a73bc97498aa2
|
|
| MD5 |
2b7ec2f85b3e3842730ea301fd772b61
|
|
| BLAKE2b-256 |
3c37a35356b22d09e4f736f79c23fb3767b24ab94e78c99863100793328556b3
|
