shardingsphere VS pytorch-lightning

Opposite to what the documentation tells, the full prefix is jdbc:shardingsphere:absolutepath. I've opened a PR to fix the documentation.

🔗 Release Notes

ElasticJob, one of the sub-projects of the Apache ShardingSphere community, is a distributed scheduling solution oriented towards Internet applications and massive tasks.

Following 1.5 months in development, Apache ShardingSphere 5.2.1 is released. Our community merged 614 PRs from teams and individuals around the world. The resulting 5.2.1 release has been optimized in terms of features, performance, testing, documentation, examples, etc.

🔗 Update Logs

GitHub Issues

[8] https://github.com/apache/shardingsphere/pull/17914 [9] https://github.com/apache/shardingsphere/blob/2c9936497214b8a654cb56d43583f62cd7a6b76b/shardingsphere-proxy/shardingsphere-proxy-frontend/shardingsphere-proxy-frontend-core/src/main/java/org/apache/shardingsphere/proxy/frontend/netty/ServerHandlerInitializer.java [10] https://shardingsphere.apache.org/document/current/cn/downloads/

Relevant Links: GitHub issue

Release Notes

It's very easy to get started, right in your Terminal, no fees! No credit card at all.

And there are cloud providers like https://replicate.com/ and https://lightning.ai/ that will let you use your LLM via an API key just like you did with OpenAI if you need that.

You don't need OpenAI - nobody does.

https://see.stanford.edu/Course/CS229 https://lightning.ai/ https://www.youtube.com/watch?v=00s9ireCnCw&t=57s https://towardsdatascience.com/

I've been wondering on what is the recommended method of saving logits/activations using PyTorch Lightning. I've looked at Callbacks, Loggers and ModelHooks but none of the use-cases seem to be for this kind of activity (even if I were to create my own custom variants of each utility). The ModelCheckpoint Callback in its utility makes me feel like custom Callbacks would be the way to go but I'm not quite sure. This closed GitHub issue does address my issue to some extent.

Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase , lucidrains, Stable Diffusion, Lightning and Hugging Face. Thanks for open-sourcing!

For lightweight use, pytorch-lightning is too heavy, and its source code will be very difficult for beginners to read, at least for me.

For the A100 GPU, theoretical performance is the same for FP16/BF16 and both rely on the same number of bits, meaning memory should be the same. However since it's quite newly added to PyTorch, performance seems to still be dependent on underlying operators used (pytorch lightning debugging in progress here).