<img src="https://zenoml.com/img/zeno.png" width="250px"/>
[](https://badge.fury.io/py/zenoml)
[](https://lbesson.mit-license.org/)
[](https://cabreraalex.com/paper/zeno)
[](https://discord.gg/km62pDKAkE)
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.
### Demos
| **Image Classification** | **Audio Transcription** | **Image Generation** | **Dataset Chatbot** | **Sensor Classification** |
| :---------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
| Imagenette | Speech Accent Archive | DiffusionDB | LangChain + Notion | MotionSense |
| [](https://zeno-ml-imagenette.hf.space/) | [](https://zeno-ml-audio-transcription.hf.space/) | [](https://zeno-ml-diffusiondb.hf.space/) | [](https://zeno-ml-langchain-qa.hf.space/) | [](https://zeno-ml-imu-classification.hf.space) |
| [code](https://huggingface.co/spaces/zeno-ml/imagenette/tree/main) | [code](https://huggingface.co/spaces/zeno-ml/audio-transcription/tree/main) | [code](https://huggingface.co/spaces/zeno-ml/diffusiondb/tree/main) | [code](https://huggingface.co/spaces/zeno-ml/audio-transcription/tree/main) | [code](https://huggingface.co/spaces/zeno-ml/imu-classification/tree/main) |
<br />
https://user-images.githubusercontent.com/4563691/220689691-1ad7c184-02db-4615-b5ac-f52b8d5b8ea3.mp4
## 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
```
Take a look at the [configuration documentation](https://zenoml.com/docs/configuration) for additional `toml` file options like adding model functions.
Start Zeno with `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
})
```
You can pass a list of decorated function references directly Zeno as you add models and metrics.
## Citation
Please reference our [CHI'23 paper](https://arxiv.org/pdf/2302.04732.pdf)
```bibtex
@inproceedings{cabrera23zeno,
author = {Cabrera, Ángel Alexander and Fu, Erica and Bertucci, Donald and Holstein, Kenneth and Talwalkar, Ameet and Hong, Jason I. and Perer, Adam},
title = {Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning},
year = {2023},
isbn = {978-1-4503-9421-5/23/04},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3544548.3581268},
doi = {10.1145/3544548.3581268},
booktitle = {CHI Conference on Human Factors in Computing Systems},
location = {Hamburg, Germany},
series = {CHI '23}
}
```
## Community
Chat with us on our [Discord channel](https://discord.gg/km62pDKAkE) or leave an issue on this repository if you run into any issues or have a request!
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"description": "<img src=\"https://zenoml.com/img/zeno.png\" width=\"250px\"/>\n\n[](https://badge.fury.io/py/zenoml)\n\n[](https://lbesson.mit-license.org/)\n[](https://cabreraalex.com/paper/zeno)\n[](https://discord.gg/km62pDKAkE)\n\nZeno is a general-purpose framework for evaluating machine learning models.\nIt 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.\nZeno 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.\n\n### Demos\n\n| **Image Classification** | **Audio Transcription** | **Image Generation** | **Dataset Chatbot** | **Sensor Classification** |\n| :---------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |\n| Imagenette | Speech Accent Archive | DiffusionDB | LangChain + Notion | MotionSense |\n| [](https://zeno-ml-imagenette.hf.space/) | [](https://zeno-ml-audio-transcription.hf.space/) | [](https://zeno-ml-diffusiondb.hf.space/) | [](https://zeno-ml-langchain-qa.hf.space/) | [](https://zeno-ml-imu-classification.hf.space) |\n| [code](https://huggingface.co/spaces/zeno-ml/imagenette/tree/main) | [code](https://huggingface.co/spaces/zeno-ml/audio-transcription/tree/main) | [code](https://huggingface.co/spaces/zeno-ml/diffusiondb/tree/main) | [code](https://huggingface.co/spaces/zeno-ml/audio-transcription/tree/main) | [code](https://huggingface.co/spaces/zeno-ml/imu-classification/tree/main) |\n\n<br />\n\nhttps://user-images.githubusercontent.com/4563691/220689691-1ad7c184-02db-4615-b5ac-f52b8d5b8ea3.mp4\n\n## Quickstart\n\nInstall the Zeno Python package from PyPI:\n\n```bash\npip install zenoml\n```\n\n### Command Line\n\nTo get started, run the following command to initialize a Zeno project. It will walk you through creating the `zeno.toml` configuration file:\n\n```bash\nzeno init\n```\n\nTake a look at the [configuration documentation](https://zenoml.com/docs/configuration) for additional `toml` file options like adding model functions.\n\nStart Zeno with `zeno zeno.toml`.\n\n### Jupyter Notebook\n\nYou 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:\n\n```python\nimport pandas as pd\nfrom zeno import zeno\n\ndf = pd.read_csv(\"/path/to/metadata/file.csv\")\n\nzeno({\n \"metadata\": df, # Pandas DataFrame with a row for each instance\n \"view\": \"audio-transcription\", # The type of view for this data/task\n \"data_path\": \"/path/to/raw/data/\", # The folder with raw data (images, audio, etc.)\n \"data_column\": \"id\" # The column in the metadata file that contains the relative paths of files in data_path\n})\n\n```\n\nYou can pass a list of decorated function references directly Zeno as you add models and metrics.\n\n## Citation\n\nPlease reference our [CHI'23 paper](https://arxiv.org/pdf/2302.04732.pdf)\n\n```bibtex\n@inproceedings{cabrera23zeno,\n author = {Cabrera, \u00c1ngel Alexander and Fu, Erica and Bertucci, Donald and Holstein, Kenneth and Talwalkar, Ameet and Hong, Jason I. and Perer, Adam},\n title = {Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning},\n year = {2023},\n isbn = {978-1-4503-9421-5/23/04},\n publisher = {Association for Computing Machinery},\n address = {New York, NY, USA},\n url = {https://doi.org/10.1145/3544548.3581268},\n doi = {10.1145/3544548.3581268},\n booktitle = {CHI Conference on Human Factors in Computing Systems},\n location = {Hamburg, Germany},\n series = {CHI '23}\n}\n```\n\n## Community\n\nChat with us on our [Discord channel](https://discord.gg/km62pDKAkE) or leave an issue on this repository if you run into any issues or have a request!\n",
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