---
id: fastprogress
version: "1.1.6"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# fastprogress — A nested progress with plotting options for fastai

License: permissive · Maintenance: active · Downloads: 1.6M/mo

## What it is and what it does

fastprogress wraps iterators to display real-time progress bars in Jupyter notebooks and console environments. It supports nested progress tracking (parent and child bars), live comments on each bar, and optional matplotlib-based graph plotting that updates as your loop runs. The package is commonly used in machine learning training loops to visualize epoch progress, loss curves, and validation metrics simultaneously.

The library depends on fastcore and python-fasthtml for its core functionality. It targets Python 3.10+ and is designed to be lightweight and easy to integrate into existing loops with minimal code changes. Output can be written to files when the script is redirected, preserving only the `.write()` method output.

Use it for:

- Track multi-level training loops with separate progress bars for epochs and batches while plotting loss curves live.
- Monitor long-running data processing pipelines in Jupyter notebooks with nested progress and status comments.
- Display console progress for batch jobs with optional graph visualization of metrics over time.
- Provide real-time feedback during hyperparameter sweeps or grid searches with nested iteration tracking.
- Log training progress to files while showing interactive progress in Jupyter during development.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Displays nested progress bars with optional live plotting for Jupyter notebooks and console output, useful for tracking multi-level loops and training workflows.

Yes. Active maintenance, no known vulnerabilities, permissive license, and low install friction make it a safe choice. Install if you need nested progress tracking with optional live plotting in Jupyter or console environments, especially for training loops or multi-level iterations.

## Install

pip install fastprogress
uv add fastprogress
poetry add fastprogress

## Installing fastprogress

Before you install: Low friction: pure Python wheel with only two runtime dependencies (fastcore and python-fasthtml). Active maintenance with recent releases; last commit 2026-08-06.

License in practice: Apache-2.0 permissive license allows use in commercial and private projects with minimal restrictions.

Quickstart:

pip install fastprogress

from fastprogress.fastprogress import master_bar
from time import sleep

for i in (mb := master_bar(range(10))):
    for j in mb.progress(range(100)):
        sleep(0.01)
    mb.write(f'Finished loop {i}.')

Requires Python 3.10 or later.

Verify before relying:

- Whether plotting features work outside Jupyter (e.g., in headless environments or pure console).
- Performance characteristics with very large iteration counts or frequent graph updates.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 1.6M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

progress bar jupyter notebook, nested progress tracking, training loop visualization, console progress display, live plot during iteration, jupyter-integration, training-visualization

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