DataProfiler
jax
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DataProfiler | jax | |
---|---|---|
61 | 82 | |
1,349 | 27,509 | |
2.6% | 3.8% | |
7.0 | 10.0 | |
1 day ago | 2 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
DataProfiler
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Data Profiler 0.9.0 -- offering a massive improvement to memory usage during profiling of large datasets
Great call out -- would you be willing to write up an issue for that on the repo? Thank you! https://github.com/capitalone/DataProfiler/issues/new/choose
- FLiPN-FLaNK Stack Weekly for 20 March 2023
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Miller – tool for querying, shaping, reformatting data in CSV, TSV, and JSON
My team built a similar tool in Python to load any delimited file, json, parquet and Avro with one command:
https://github.com/capitalone/DataProfiler
Effectively loads anything into a dataframe
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PyTorch vs. TensorFlow in 2022
The thing is, tensorflow has more ability to run cross platform.
I help maintain https://github.com/capitalone/DataProfiler
Our sensitive data detection library is exported to iOS, android, and Java; in addition to Python. We also run distributed and federated use cases with custom layers. All of which are improved in tensorflow.
That said, I’d use pytorch if I could. Simply put, it has a better user experience.
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Fast CSV Processing with SIMD
I really should write up how we did delimiter and quote detection in this library:
https://github.com/capitalone/DataProfiler
It turns out delimited files IMO are much harder to parse than say, JSON. Largely because they have so many different permutations. The article covers CSVs, but many files are tab or null separated. We’ve even seen @ separated with ‘ for quotes.
Given the above, it should still be possible to use the method described. I’m guessing you’d have to detect the separators and quote chars first, however. You’d have to also handle empty rows and corrupted rows (which happen often enough).
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Dask – a flexible library for parallel computing in Python
Having used both ray, dask, and writing custom threads, my personal view is that while there are advantages I wouldn’t want to use any of these unless absolutely necessary.
My personal approach for most of these tasks are to try to break down the problem to be as asynchronous as possible. Then you can create threads.
The nice thing about dask is really the way you can effectively use it as a pandas dataframe.
Having said that, we opted to write our own parallelization for this library:
https://github.com/capitalone/DataProfiler
As opposed to using the dask frame. Effectively, it’s a high overhead and easier to maintain the threading ourselves given the particular approaches taken.
That said, if I was working with large pandas dataframes, id likely use dask. For large datasets which couldn’t be stored in memory of use ray.io
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Launch HN: Metaplane (YC W20) – Datadog for Data
My team has worked on a library for a similar purpose:
https://github.com/capitalone/DataProfiler
Load any document, profile and monitor the profiles for changes that would impact downstream applications.
Very common problem, you all are in a great space! Very interested and will check out!
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Show HN: Graphsignal – Production Model Monitoring
We built a very similar application internally with our open source library: https://github.com/capitalone/dataprofiler
Effectively, you can monitor changes between profiles:
# Load a CSV file
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Miller CLI – Like Awk, sed, cut, join, and sort for CSV, TSV and JSON
Not exactly the same, but we wrote a library to easily load any delimited type of file and finds header (even if not first row). It also works to load JSON, Parquet, AVRO and loads it into a dataframe. Not CLI exactly, but pretty easy:
https://github.com/capitalone/dataprofiler
Anyway, pretty interesting Miller CLI
jax
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The Elements of Differentiable Programming
The dual numbers exist just as surely as the real numbers and have been used well over 100 years
https://en.m.wikipedia.org/wiki/Dual_number
Pytorch has had them for many years.
https://pytorch.org/docs/stable/generated/torch.autograd.for...
JAX implements them and uses them exactly as stated in this thread.
https://github.com/google/jax/discussions/10157#discussionco...
As you so eloquently stated, "you shouldn't be proclaiming things you don't actually know on a public forum," and doubly so when your claimed "corrections" are so demonstrably and totally incorrect.
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Julia GPU-based ODE solver 20x-100x faster than those in Jax and PyTorch
On your last point, as long as you jit the topmost level, it doesn't matter whether or not you have inner jitted functions. The end result should be the same.
Source: https://github.com/google/jax/discussions/5199#discussioncom...
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Apple releases MLX for Apple Silicon
The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.
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MatX: Efficient C++17 GPU numerical computing library with Python-like syntax
>
Are they even comparing apples to apples to claim that they see these improvements over NumPy?
> While the code complexity and length are roughly the same, the MatX version shows a 2100x over the Numpy version, and over 4x faster than the CuPy version on the same GPU.
NumPy doesn't use GPU by default unless you use something like Jax [1] to compile NumPy code to run on GPUs. I think more honest comparison will mainly compare MatX running on same CPU like NumPy as focus the GPU comparison against CuPy.
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
Actually that never changed. The README has always had an example of differentiating through native Python control flow:
https://github.com/google/jax/commit/948a8db0adf233f333f3e5f...
The constraints on control flow expressions come from jax.jit (because Python control flow can't be staged out) and jax.vmap (because we can't take multiple branches of Python control flow, which we might need to do for different batch elements). But autodiff of Python-native control flow works fine!
Development seems not to have dropped at all from the contributions page: https://github.com/google/jax/graphs/contributors
Don’t know about usage and uptake though.
You're right! Maybe we should revise that... I made https://github.com/google/jax/pull/17851, comments welcome!
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Julia and Mojo (Modular) Mandelbrot Benchmark
For a similar "benchmark" (also Mandelbrot) but took place in Jax repo discussion: https://github.com/google/jax/discussions/11078#discussionco...
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Functional Programming 1
2. https://github.com/fantasyland/fantasy-land (A bit heavy on jargon)
Note there is a python version of Ramda available on pypi and there’s a lot of FP tidbits inside JAX:
3. https://pypi.org/project/ramda/ (Worth making your own version if you want to learn, though)
4. For nested data, JAX tree_util is epic: https://jax.readthedocs.io/en/latest/jax.tree_util.html and also their curry implementation is funny: https://github.com/google/jax/blob/4ac2bdc2b1d71ec0010412a32...
Anyway don’t put FP on a pedestal, main thing is to focus on the core principles of avoiding external mutation and making helper functions. Doesn’t always work because some languages like Rust don’t have legit support for currying (afaik in 2023 August), but in those cases you can hack it with builder methods to an extent.
Finally, if you want to understand the middle of the midwit meme, check out this wiki article and connect the free monoid to the Kleene star (0 or more copies of your pattern) and Kleene plus (1 or more copies of your pattern). Those are also in regex so it can help you remember the regex symbols. https://en.wikipedia.org/wiki/Free_monoid?wprov=sfti1
The simplest example might be {0}^* in which case
0: “” // because we use *
- Codon: Python Compiler
What are some alternatives?
Numba - NumPy aware dynamic Python compiler using LLVM
functorch - functorch is JAX-like composable function transforms for PyTorch.
julia - The Julia Programming Language
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Cython - The most widely used Python to C compiler
jax-windows-builder - A community supported Windows build for jax.
mesh-transformer-jax - Model parallel transformers in JAX and Haiku
dex-lang - Research language for array processing in the Haskell/ML family
tensorflow - An Open Source Machine Learning Framework for Everyone
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️
brax - Massively parallel rigidbody physics simulation on accelerator hardware.