Mehdi2277

7 ms·

1.
by Mehdi2277 3mo ago
That aligns pretty well with a past job. Those two areas were very popular user interests. Third one was cosmetics like skincare routine.
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by Mehdi2277 2y ago
I'm very happy with pyright. Most bug reports are fixed within a week and new peps/features are added very rapidly usually before pep is accepted (under experimental flag). Enough that I ended up dropping pylint and consider pyrig
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by Mehdi2277 2y ago
Uv is installer not a build backend. It’s similar to pip. If you install library with uv it will call backend like setuptools as needed. It is not a replacement for setuptools.
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by Mehdi2277 2y ago
If I assume you mean LLM like models similar to chatgpt that is pretty debated in the community. Several years ago many people in ML community believed we were at plateau and that throwing more compute/money would not give significant
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by Mehdi2277 2y ago
My experience working on ml at couple faang like companies is gpus actually tend to be too fast compute wise and often models are unable to come close to theoretical nvidia flops numbers. In that very frequently bottlenecks from profiling a
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by Mehdi2277 2y ago
I’ve worked at companies with async training. Async training does help on fault tolerance and also can assist with training thoroughput by being less reliant on slowest machine. It does add meaningful training noise and when we did experime
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by Mehdi2277 2y ago
This is fair guess on intuition but working in recommender space on both content/ad recommendation, content understanding signals have pretty consistently across two companies and many projects tended to underwhelm and key signals are
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by Mehdi2277 2y ago
I’ve occasionally worked with more dynamic models (tree structured decoding). They are generally not a good fit for trying to max gpu thoroughput. A lot of magic of transformers and large language models is about pushing gpu as much we can
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by Mehdi2277 3y ago
Having used it heavily it is nowhere near painless. Where can you get a TPU? To train models you basically need to use GCP services. There are multiple services that offer TPU support, Cloud AI Platform, GKE, and Vertex AI. For GPU you can
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by Mehdi2277 3y ago
23 comes from 2023. Pip uses calendar based versioning. The 23.3.1 means 2023, 3rd quarter, second release. The last number is for bug fix release. The first two numbers are purely date based and do not follow semvar.
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by Mehdi2277 3y ago
PEP 646 Variadic generics, https://peps.python.org/pep-0646/ , was made for this specific use case but mypy is still working on implementing it. And even with it, it's expected several more peps are needed to make
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by Mehdi2277 3y ago
Having worked at similar companies on similar systems usually A/B experiments and smaller probability of an action bigger weight it must have to matter much overall. The constants are generally done through some ab tests to get them in
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by Mehdi2277 4y ago
Python documentation defines standard library and includes venv, https://docs.python.org/3/library/venv.html , as part of it. There is also python steering council/core developer group that has had discussions
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by Mehdi2277 4y ago
It’s opposite. Venv is built into python and part of standard library. It is part of cpython repo and developed by python maintainers. Some Linux distributions remove it from standard library but windows/Mac always has it. The Linux di
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by Mehdi2277 4y ago
The issue is most of my commits have little meaning and often include in progress commits where code is not even functional/tests may crash. Especially as I have tooling that builds artifacts for me based on commits/runs ci based
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by Mehdi2277 4y ago
Pyright's vendoring of stubs or pylance's? Pylance vendors extra stubs (microsoft type stubs), but pyright itself does not. Pyright does vendor typeshed, but all type checkers including mypy vendor typeshed. Narrowing on object tr
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by Mehdi2277 4y ago
Pyright is pretty recent and started development years after mypy. But some of pyright's design goals (laziness for LSP) would have made it very difficult to do in mypy without a very large refactor/rewrite. Pyright is implemented
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by Mehdi2277 4y ago
Typescript similarity is expected as maintainer of pyright does talk with typescript maintainers and sometimes pyright adds features inspired by typescript's inference. I think the major reasons for mypy is if you are working on a libr
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by Mehdi2277 4y ago
Django is one library that has some apis that are beyond type system and likely difficult to ever fully describe in type system. Some of this it handles with a custom mypy plugin to overrule/extend some type rules. No other python type
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by Mehdi2277 4y ago
I’ve heavily used both mypy/pyright. The semantic differences are mainly in areas where type system is not specific enough on exact behavior expected. As an example if you have a function that has overloads how do you decide which over
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by Mehdi2277 4y ago
That trivial way only lazily shallow imports. I don’t see a good way to do a lazy deep import. A lot of libraries I import, then transitively import hundreds or more of other files. The file I import I may only need a small subset of those
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by Mehdi2277 4y ago
Numpy has a large amount of python code. C is definitely used in places, but majority of numpy is python. Not all operations need C implementations especially as many can be built on top of other python functions which do wrap C. Tensorflow
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by Mehdi2277 4y ago
For a while I used both mypy and pyright for my team’s codebase. After about half a year I eventually dropped mypy . I think type checking is valuable just that most of errors mypy detected pyright also caught and using newer type features
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by Mehdi2277 4y ago
That's not been my experience much at all work or research wise. Tensorflow, pytorch, jax are still very dominant. I've worked at several companies and interviewed at several dozen for ML roles. They have 100% been python/c++
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by Mehdi2277 4y ago
Python is also the dominant language for machine learning which does care for performance. The person who made recent nogil work is one of the core maintainers of key ML library. The standard workaround is ML libraries, the performance sens
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by Mehdi2277 4y ago
It's implemented in 3.11. Documentation still hasn't been written for it yet though and that's why the issue is still open, https://github.com/python/cpython/issues/90908 .
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by Mehdi2277 4y ago
Partly motivated? The primary focus was ML space. The creators of PEP 646 work at Facebook with goal of supporting pytorch tensor dimension tracking. That was main motivation and many of the features/design of that pep were based on wh
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by Mehdi2277 4y ago
I quite like monitoring dashboards and use them frequently. Mainly for examining system health and debugging operational issues. My previous team had some business metric charts/dashboards related to system we worked on that we reviewe
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by Mehdi2277 4y ago
Arm CPUs including M1 macs do not support avx. AVX is an x86 specific thing.
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by Mehdi2277 4y ago
I don’t understand this as distinction with file systems. Networked file systems exist and I have a lot of production code that shares data as files in either some networked file/blob system. Often databases even just store a reference

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