Examples for using ONNX Runtime for model training.
active 2023-10-10 → 2026-05-20 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 954 days from 2023-10-10 to 2026-05-20. Pushes: 14 total, peak 3 in a day. Pull requests: 22 total, peak 3 in a day. Issues: 17 total, peak 3 in a day. Comments: 44 total, peak 6 in a day. Stars: 77 total, peak 2 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| carzh | 17 | 1 | 4 | 8 |
| galran | 15 | 2 | 1 | 6 |
| baijumeswani | 11 | 2 | 0 | 4 |
| ajindal1 | 10 | 6 | 4 | 0 |
| edgchen1 | 9 | 2 | 3 | 2 |
| GeorgeS2019 | 9 | 0 | 0 | 8 |
| dependabot[bot] | 9 | 0 | 9 | 0 |
| hanbitmyths | 7 | 0 | 0 | 4 |
| wschin | 6 | 0 | 0 | 4 |
| prathikr | 4 | 0 | 0 | 2 |
| kshama-msft | 4 | 0 | 0 | 2 |
| sunejas | 3 | 0 | 0 | 1 |
| jingyanwangms | 2 | 1 | 1 | 0 |
| Kevinpsk | 2 | 0 | 0 | 1 |
| microsoft-github-policy-service[bot] | 1 | 0 | 0 | 1 |
| atom2-source | 1 | 0 | 0 | 1 |
| Leo5050xvjf | 1 | 0 | 0 | 0 |
| bil-ash | 1 | 0 | 0 | 0 |
| Nittur | 1 | 0 | 0 | 0 |
| hunt-47 | 1 | 0 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
- Pull request#209dependabot[bot]2026-05-20 17:19
- Issue comment#206microsoft-github-policy-service[bot]2025-10-31 06:04Checkpoint from VS Code for coding agent session
- Issue comment#203atom2-source2025-04-19 07:56onnxruntime-training web llama example
- Issue#205Nittur2024-12-30 13:43Faced some issue while calling performInference for mobileBert on Android
- Issue#203bil-ash2024-12-10 02:29onnxruntime-training web llama example
- Issue#201Leo5050xvjf2024-10-25 07:09Can ONNX Runtime Training Handle Models with Different Outputs for Training and Inference?
- Pull request#198dependabot[bot]2024-10-17 22:10
- Pull request#196dependabot[bot]2024-08-09 18:26
- Pull request#194dependabot[bot]2024-07-05 23:25
- Issue comment#192carzh2024-07-02 22:31Updated installation instructions for on-device training package
- Issue comment#192GeorgeS20192024-07-02 21:14Updated installation instructions for on-device training package
- Issue comment#192GeorgeS20192024-07-02 21:07Updated installation instructions for on-device training package
- Issue comment#192GeorgeS20192024-07-02 21:03Updated installation instructions for on-device training package
- Issue comment#192carzh2024-07-02 21:01Updated installation instructions for on-device training package
- Issue comment#192GeorgeS20192024-07-02 20:48Updated installation instructions for on-device training package
- Issue comment#192GeorgeS20192024-06-29 05:10Updated installation instructions for on-device training package
- Issue comment#192GeorgeS20192024-06-29 05:09Updated installation instructions for on-device training package
- Issue comment#192carzh2024-06-29 05:03Updated installation instructions for on-device training package
- Issue#193GeorgeS20192024-06-29 04:52More c# examples
- Issue comment#190GeorgeS20192024-06-29 04:50Phi-3 and Llama-3 tutorial
- Issue comment#192GeorgeS20192024-06-29 04:45Updated installation instructions for on-device training package
- Pull request#192carzh2024-06-25 21:36
- Issue comment#189prathikr2024-06-20 23:10train models with huggingface dataset ??
- Issue#189prathikr2024-06-20 23:10train models with huggingface dataset ??
- Issue comment#175prathikr2024-06-20 23:10Training a BERT model is failing on android mobile device
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 77 stars here means stars gained during the window, not the repo's star count.