cgarciae

4 ms·

1.
NNX – Neural Networks for JAX(github.com)
2 points by cgarciae 3y ago|1 comments
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by cgarciae 3y ago
NNX is a Neural Networks library for JAX that provides a simple yet powerful module system that adheres to standard Python semantics. Its aim is to combine the robustness of Flax with a simplified, Pythonic API akin to that of PyTorch.
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by cgarciae 6y ago
I think we have to make a distiction here: - On one hand, having access to these large scale language models that can do few-shot learning is incredibly useful for the industry as in can be easily deployed to solve thosands of simple tasks.
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by cgarciae 6y ago
Side note: I like Pytorch but eager pytorch is not faster the jax.jit or tf.function code
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by cgarciae 6y ago
One thing not mentioned in the original "Why Swift for Tensorflow" document and was a mayor source of conflict when the differentiable programming feature was formally proposed by the S4TF as a standard Swift feature: Swift has no
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by cgarciae 6y ago
I think the ML community really needs a better language than Python but not because of the ML part, that works really good, its because of the Data Engineering part (which is 80-90% of most projects) where python really struggles for being
7.
by cgarciae 8y ago
No repo like LIME? Would love to test this.
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by cgarciae 8y ago
Hey, thanks for all the feedback. I will change the naming since its something most of you have agreed is a good change. The goal I have for Pypeline is much simpler: let you easily setup data pipelines where you leverage processes, threads
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by cgarciae 8y ago
Sounds like a group_by and then do a function per group? Pypeline doesn't have grouping but sounds like Spark or Dask should the the job.
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by cgarciae 8y ago
Dask might be lightweight internally but resorting to it just to solve a simple task that requires concurrency is not "simple". Streamz looks nice! However: "Streamz relies on the Tornado framework for concurrency. This allow
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by cgarciae 8y ago
jajajajaja
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by cgarciae 8y ago
Nice to hear that. When I wrote this: "Pypeline was designed to solve simple medium data tasks that require concurrency and parallelism but where using frameworks like Spark or Dask feel exaggerated or unnatural." it was actually
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by cgarciae 8y ago
3. If you first have to put in all the data first you cant handle streams. Pypeline accepts non-terminating iterables and also gives you back possibly non-terminating iterables.
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by cgarciae 8y ago
Thanks! Did take a look at mpipe (its actually referenced in the readme). But mpipe has its flaws: 1. It uses None as the stage terminator, this is VERY error prone, what if you actually want to send None? Pypeline uses a special private te
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by cgarciae 8y ago
I use BEAM for my Dataflow jobs. But their local "DirectRunner" is just for testing purposes. As with Spark, BEAM is a huge beast, Pypeline was created with simplicity in mind, its a pure python library, no dependencies.
16.
by cgarciae 8y ago
Point taken! Thanks a lot for your feedback. Just a few points: * pypeline is already taken :( * My main reason for this was because initially I was thinking that you did an `import pypeln as pl` and then called things like e.g. `pl.pr.map`
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Pypeline: A Python library for creating concurrent data pipelines(github.com)
121 points by cgarciae 8y ago|46 comments
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Making an Unlimited Number of Requests with Python Aiohttp and Pypeln(medium.com)
1 points by cgarciae 8y ago|0 comments