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View Code? Open in Web Editor NEWA Jax-based library for designing and training transformer models from scratch.
License: MIT License
A Jax-based library for designing and training transformer models from scratch.
License: MIT License
Creating a custom random module that abstracts Jax's verbose process can be a useful way to simplify random number generation and make your code more readable.
Creating a custom dropout layer that abstracts the complicated dropout implementation in Flax/Jax can make it easier to use dropout in various Jax setups.
With the latest jax=='0.4.25'
and jaxlib=='0.4.25'
I get:
import nanodl
# AttributeError: module 'jax.random' has no attribute 'KeyArray'
Hi @HMUNACHI ,
Your dataloader code is heavily referred to the jax-dataloader repository, which is totally okay. But could you kindly cite the repository for reference?
Thank you!
Norms across most Attention based implementations have epsilon value set to dropout probability
Implementation of IJEPA
ReadTheDocs is failing.
I'm a Cyber Security researcher and developer of PackjGuard [1] to address open-source software supply chain attacks.
During my research, I detected a deleted package in this repository.
Specifically, the package nanodl
mentioned in file README
at line 43 does not exist on the public PyPI registry. A bad actor can hijack this package to propagate malicious code.
Not only your apps/services using https://github.com/HMUNACHI/nanodl
repo code are vulnerable to this attack, but the users of your open-source Github repo could also fall victim.
You could read more about such attacks here: https://medium.com/@alex.birsan/dependency-confusion-4a5d60fec610
Please highlight this in file README and register a placeholder package for nanodl
on public PyPI soon to remediate.
To automatically fix such issues in future, please install PackjGuard Github app [1].
Thanks!
Hey, great job with nanodl!
I was just looking through the code and noticed that when in Lambda's Trainer the gradients are not being averaged across devices here:
nanodl/nanodl/__src/models/lamda.py
Lines 564 to 565 in 18c7f8e
Not sure if this is happening elsewhere but usually to keep the weights in sync you apply a jax.lax.pmean
over the gradients before passing them to apply_gradients
, e.g.
grads = jax.lax.pmean(grads, axis_name='devices')
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