Flower Labs and Starcloud are sharing a major milestone: the successful execution of a decentralized AI workload using Flower on an operational Starcloud satellite.
Given the code quality and rigid testing, SQLite is probably the last project that should be rewritten. It'd be great to see all other C code rewritten first!
We use Pyenv successfully for developing the Flower open-source project. We use a few simple Bash scripts to manage virtual environments with different Python versions via pyenv and the pyenv-virtualenv plugin.
The main scripts are `venv-create.sh`, `venv-delete.sh` and `bootstrap.sh`. `venv-reset.sh` pulls these three scripts together to make reinstalling your venv a single command.
The big opportunity on the edge is access to more data. Especially with the rise of end-to-end encryption, applications will be able to use more (and more diverse) data on the edge to get better model performance. It's generally true that training on beefier infrastructure is easier, but in the long run, nothing can beat access to better data. And edge hardware has gotten a lot faster over the last few years.
One of the Flower maintainers here. The code example is primarily meant as a demonstrator to show that it's possible to fine-tune these models in a federated way on devices as small as a Raspberry Pi 5.
The bigger takeaway is that we're close to being able to train/fine-tune models with much better performance by accessing vastly more data on the edge, in a federated way.
Markdown is only going to get more popular as AI agents usage grows.