SHshcheklein·Nov 11, 2024Submission
CML: Continuous Machine Learning CI/CD for ML
2 points · Open discussioniterative
cml
JavaScript2020–2024
iterative / cml
cml
♾️ CML - Continuous Machine Learning | CI/CD for ML
AI & Machine LearningDeveloper ToolsCommand LineScience & EngineeringLearning & ReferenceDevOps & Cloud
3Distinct people
9Mentions
0Explicit recommenders
4.2KGitHub stars
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What it is
♾️ CML - Continuous Machine Learning | CI/CD for ML
GitHub
Where it is used
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 PM UTC.
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Mentions over time
HN mentions by year9 dated mentions
Observed mentions: 2020-07-07 13:59:41 UTC to 2024-11-11 18:58:25 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 3 | 1 | 2 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 1 | 1 | 0 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 5 | 5 | 0 |
What Hacker News says
9 sourcesContinuous Machine Learning – CI/CD for AI
1 points · Open discussionCML – Continuous Machine Learning CI/CD for ML
1 points · Open discussionCML: CI/CD for Machine Learning Projects
1 points · Open discussionCML – Continuous Machine Learning, CI/CD for ML
1 points · Open discussionCML: CI/CD for Machine Learning Projects
4 points · Open discussionShow HN: CML – CI/CD for Machine Learning with GitHub Actions and Gitlab CI
1 points · Open discussionThanks, and good questions!
1. Yes! Let me link some reports and example repos:
- A basic classification problem with scikit learn: https://github.com/iterative/cml_base_case/pull/2
- CML with DVC & Vega-Lite graphs:
https://github.com/iterative/cml_dvc_case/pull/4
- Neural style transfer with EC2 GPU:
https://github.com/iterative/cml_cloud_case/pull/2
2. If training fails, you'll be notified that your run failed in the GitHub Action dashboard (or GitLab CI/CD dashboard). See here for some real life examples of failure ;) : https://github.com/iterative/cml_cloud_case/actions
3. CML reports are markdown documents, so you can write any kind of text to them. If your metrics are output in a file `metrics.txt`, you can have your runner execute `cat metrics.txt >> report.md` and then have CML pass on the report to GitHub/GitLab. Likewise, any
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graphing library is supported because you can add standard image files (.png, .jpg) to the report. So custom metrics and custom graphs. We like DVC for managing and plotting metrics, but we're biased because we also maintain it.
4. Yep, GitHub Actions is pretty powerful and flexible. Works with whatever external services you can connect to your Action!
5. It's not strictly a Docker technology. We use Docker images preinstalled with the CML library in our examples, but you can just install the library with npm in your own image. https://github.com/iterative/cml#using-your-own-docker-image
Let me know if there's anything else I can tell you about
Hi, I'm one of the project creators. Continuous Machine Learning (CML) is an open source project to help ML projects use CI/CD with Github Actions and Gitlab CI (https://github.com/iterative/cml).
CML automatically generates human-readable reports with metrics and data viz in every pull/merge request, and helps you use storage and GPU/CPU resources from cloud services. CML addresses three hurdles for making ML compatible with CI:
1. In ML, pass/fail tests aren’t enough. Understanding model performance might require data visualizations and detailed metric reports. CML automatically generates custom reports after every CI run with visual elements like tables and graphs. You can even get a Tensorboard.dev link as part of your report.
2. Dataset changes need to trigger feedback just like source code. CML works with DVC so dataset changes tri
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gger automatic training and testing.
3.Hardware for ML is an ecosystem in itself. We’ve developed use cases with CML and Docker Machine to automatically provision and deploy cloud compute instances (CPU & GPU) for model training.
Our philosophy is that ML projects- and MLOps practices- should be built on top of traditional software tools and CI systems, and not as a separate platform. Our goal is to extend DevOps’ wins from software development to ML. Check out our project site (https://cml.dev) and repo, and please let us know what you think!