This repository has been deprecated in favor of ZenoHub and is no longer actively maintained.

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 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
code	code	code	code	code

https://user-images.githubusercontent.com/4563691/220689691-1ad7c184-02db-4615-b5ac-f52b8d5b8ea3.mp4

Quickstart

Install the Zeno Python package from PyPI:

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:

zeno init

Take a look at the configuration documentation 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 for a full list of options. In this example we pass the minimum options for exploring a non-tabular dataset:

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 fil