what can I really do with mojo at the moment?

[strange coyote]

Hi, I have been following Mojo since release but still haven’t seen any project i could use.

1. We always see mojo is meant to be used for ai but as far as i know there is no pytorch/flux or similar. So how would i be able to train my goof vector to sequence model? What would be the real diff vs using pytorch with cuda?
2. I have another usecase where i build high speed libraries with rust that scan over hashmaps in memory and provide ms response. Would mojo allow me to do something similar?

[hoary blade]

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[keen heart]

You might want to look at https://github.com/mzaks/mojo-hash for hashmap related implementations in Mojo. In case you haven't seen, there is Basalt https://github.com/basalt-org/basalt which is among the most mature, higher level, high quality, Mojo projects. Not a pytorch replacement for now, but showcasing what is and will be possible with Mojo.

For me, getting involved in Mojo now is a bit like speculating on its promising future. It is still young, breaking changes with every new version and quite some rough edges to deal with. But no doubt for me, it is fast already, low level language features for vectorization and parallelization are already available for example, and there is a team of very skilled people behind this ambitious project.

[strange coyote]

I definitely want to to give it a try. I have a local search solver written in julia which has to run ml inferance of a model trained in pytorch. Right now i load the model as onnx but my idea would be to build a runnable in mojo and check how fast is inference time. I read this is possible to be done with python and mojo so i guess will be the sane with julia. Similarly i will try to compare performance of small rust libraries with mojo. Idea is to load some data in memory and aggregate as per user calls in ms