# ogrisel/parallel_ml_tutorial

Tutorial on scikit-learn and IPython for parallel machine learning

Repository: https://github.com/ogrisel/parallel_ml_tutorial
Canonical: https://ross.abutalabs.com/products/parallel_ml_tutorial
Language: Jupyter Notebook
License Family: other
Last push: 2016-10-04T04:50:13+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4983, "days_push": 3620, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1589, forks 584 (observed 2026-08-28T04:05:08.071496+00:00)

## What it is
A Jupyter notebook tutorial on parallel machine learning with scikit-learn and IPython, covering scalable feature extraction, parallel cross-validation, and grid search on an IPython cluster. It includes a PyCon 2013 video recording and materials for running clusters on Amazon EC2 spot instances with StarCluster.

## Use cases
- learn parallel machine learning with scikit-learn
- run cross validation and grid search in parallel with IPython
- scale text classification feature extraction
- set up an IPython cluster on Amazon EC2
- optimize memory usage with numpy memory mapping
- understand common predictive modeling errors

## When to choose
- learning scikit-learn parallelism concepts from a guided tutorial
- following along with the PyCon 2013 video course
- studying historical IPython cluster and StarCluster workflows

## When to avoid
- you need up-to-date tooling (uses IPython 2.x and scikit-learn 0.15)
- you want modern distributed ML (use dask, ray, or joblib instead)
- you need maintained or licensed material (no license, last release 2016)

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, data-science, benchmarking
- domain: machine-learning, tutorials, data-science
- platform: python, cross-platform
- tags: scikit-learn, ipython, jupyter-notebook, parallel-computing, distributed-computing, text-classification, hyperparameter-tuning, ec2, starcluster

## Member repositories
- ogrisel/parallel_ml_tutorial (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:08.071496+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 2026-08-30T03:54:58.150117+00:00, confidence not recorded.
  - readme: https://github.com/ogrisel/parallel_ml_tutorial (fetched 2026-08-28T04:05:08.071496+00:00, sha 521458c1f76c)
- Data as of 2026-08-30T08:39:29.467469+00:00.