<img src="./zeno.png" width="250px"/>
[](https://badge.fury.io/py/zenoml)
[](https://lbesson.mit-license.org/) [](https://gitter.im/zeno-ml-eval/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
Zeno is a general-purpose framework for evaluating machine learning models.
It combines a **Python API** with an **interactive UI** to allow users to discover, explore, and analyze the performance of their models across diverse use cases.
Zeno can be used for any data type or task with [modular views](https://zenoml.com/docs/views/) for everything from object detection to audio transcription.
## Quickstart
Install the Zeno Python package from PyPI:
```bash
pip install zenoml
```
### Command Line
To get started, run the following command to initialize a Zeno project. It will walk you through creating the `zeno.toml` configuration file:
```bash
zeno init
```
Then run `zeno zeno.toml`.
### Jupyter Notebook
You can also run Zeno directly from Jupyter notebooks or lab. The `zeno` command takes a dictionary of configuration options as input. See [the docs](https://zenoml.com/docs/configuration) for a full list of options. In this example we pass the minimum options for exploring a non-tabular dataset:
```python
import pandas as pd
from zeno import zeno
df = pd.read_csv("/path/to/metadata/file.csv")
zeno({
"metadata": df, # Pandas DataFrame with a row for each instance
"view": "audio-transcription", # The type of view for this data/task
"data_path": "/path/to/raw/data/", # The folder with raw data (images, audio, etc.)
"data_column": "id" # The column in the metadata file that contains the relative paths of files in data_path
})
```
## Learn More
Check out examples and additional documentation:
- [Introduction](https://zenoml.com/docs/intro) - Learn more about Zeno.
- [Quickstart](http://zenoml.com/docs/quickstart) - Setup Zeno with your own data and models.
- [Documentation & API](http://zenoml.com/docs/intro) - Full documentation and API reference.
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