# AnswerDotAI/fastprogress

Simple and flexible progress bar for Jupyter Notebook and console

Repository: https://github.com/AnswerDotAI/fastprogress
Canonical: https://ross.abutalabs.com/products/fastprogress
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: python, developer-tools, plots, jupyter-notebook
Last push: 2026-08-06T06:46:58+00:00

## Health v2 (maintenance only)
Score: 92/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 83, longevity 100
- inputs: {"age_days": 2938, "days_push": 27, "days_rel": 116, "gap_med": 0, "n_releases_24m": 8}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1098, forks 105 (observed 2026-08-28T04:03:34.800762+00:00)

## What it is
fastprogress is a fast and simple Python progress bar library that works in both Jupyter Notebooks and the console. It supports nested progress bars, dynamic comments, and live graph plotting during loops, making it well suited for machine learning training loops.

## Use cases
- show a progress bar for a long-running loop in a notebook
- display nested progress bars for inner and outer loops
- plot training and validation loss live during model training
- track progress of batch processing in the console
- write status messages alongside a progress bar

## When to choose
- you need lightweight progress bars in Jupyter or terminal
- you want live metric plots inside a training loop
- you need nested loop progress with comments

## When to avoid
- you need rich dashboards or web-based monitoring like TensorBoard
- you are not working in Python or notebooks/consoles

## Facets
- artifact type: library
- maturity: stable
- function: developer-tools, data-visualization
- domain: developer-tools, machine-learning, data-visualization
- platform: python, cross-platform
- tags: progress-bar, jupyter-notebook, nested-progress, training-loop

## Member repositories
- AnswerDotAI/fastprogress (main) score 92

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:34.800762+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:46:12.131794+00:00, confidence not recorded.
  - readme: https://github.com/AnswerDotAI/fastprogress (fetched 2026-08-28T04:03:34.800762+00:00, sha 929862a63523)
  - registry_pypi: https://pypi.org/pypi/fastprogress/json (fetched 2026-08-29T12:49:34.839209+00:00, sha cf741548b048)
- Data as of 2026-08-30T08:39:29.467469+00:00.