Higgsfield: multi node training without crying (6.2k stars)
What it does
All text below is quoted directly from the project README.
>Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters
How it's all done?
- We install all the required tools in your server (Docker, your project's deploy keys, higgsfield binary).
- Then we generate deploy & run workflows for your experiments.
- As soon as it gets into Github, it will automatically deploy your code on your nodes.
- Then you access your experiments' run UI through Github, which will launch experiments and save the checkpoints.
>pip install higgsfield==0.0.3
Repository
higgsfield-ai/higgsfield
Repository stats
|Metric|Value|
|:-|:-|
|Stars|6,223|
|Forks|1.1k|
|Last release|v0.0.4-rc (2024-03-23)|
|Languages|Jupyter Notebook 82.3%, Python 16.2%, Jinja 1.5%|
|Contributors|4|
|AI contributor accounts (Claude / Codex / Cursor / Copilot)|None|
|AI co-authored commits|None|
Main contributor
|Field|Value|
|:-|:-|
|Name|Omar Änwar|
|GitHub|@arpanetus|
|Commits to this repo|25 (top contributor)|
|Bio|Applied FOMO-Ops Engineer|
|Followers|154|
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14 points
Mentions
Higgsfield: multi node training without crying (6.2k stars) What it does All text below is quoted directly from the project README. >Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters How it's all done? - We install all the required tools in your server (Docker, your project's deploy keys, higgsfield binary). - Then we generate deploy & run workflows for your experiments. - As soon as it gets into Github, it will automatically deploy your code on your nodes. - Then you access your experiments' run UI through Github, which will launch experiments and save the checkpoints. >pip install higgsfield==0.0.3 Repository higgsfield-ai/higgsfield Repository stats |Metric|Value| |:-|:-| |Stars|6,223| |Forks|1.1k| |Last release|v0.0.4-rc (2024-03-23)| |Languages|Jupyter Notebook 82.3%, Python 16.2%, Jinja 1.5%| |Contributors|4| |AI contributor accounts (Claude / Codex / Cursor / Copilot)|None| |AI co-authored commits|None| Main contributor |Field|Value| |:-|:-| |Name|Omar Änwar| |GitHub|@arpanetus| |Commits to this repo|25 (top contributor)| |Bio|Applied FOMO-Ops Engineer| |Followers|154| ────────────────────────────── 📌 How many great GitHub repos have you discovered and completely forgotten about? Don't let another useful tool disappear into your Reddit feed. Get the community's best repos, organized by category, plus key takeaways from real developer discussions, delivered to your inbox twice a week. 📬 Never lose track of great repos again. Subscribe free.