cunumeric
DataProfiler
cunumeric | DataProfiler | |
---|---|---|
9 | 61 | |
595 | 1,363 | |
0.0% | 1.0% | |
8.5 | 6.3 | |
1 day ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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cunumeric
- Announcing Chapel 1.32
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Is Parallel Programming Hard, and, If So, What Can You Do About It? [pdf]
I am biased because this is my research area, but I have to respectfully disagree. Actor models are awful, and the only reason it's not obvious is because everything else is even more awful.
But if you look at e.g., the recent work on task-based models, you'll see that you can have literally sequential programs that parallelize automatically. No message passing, no synchronization, no data races, no deadlocks. Read your programs as if they're sequential, and you immediately understand their semantics. Some of these systems are able to scale to thousands of nodes.
An interesting example of this is cuNumeric, which allows you to take sequential Python programs that use NumPy, and by changing one line (the import statement), run automatically on clusters of GPUs. It is 100% pure awesomeness.
https://github.com/nv-legate/cunumeric
(I don't work on cuNumeric, but I do work on the runtime framework that cuNumeric uses.)
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GPT in 60 Lines of NumPy
I know this probably isn't intended for performance, but it would be fun to run this in cuNumeric [1] and see how it scales.
[1]: https://github.com/nv-legate/cunumeric
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Dask โ a flexible library for parallel computing in Python
If you want built-in GPU support (and distributed), you should check out cuNumeric (released by NVIDIA in the last week or so). Also avoids needing to manually specify chunk sizes, like it says in a sibling comment.
https://github.com/nv-legate/cunumeric
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Julia is the better language for extending Python
Try dask
Distribute your data and run everything as dask.delayed and then compute only at the end.
Also check out legate.numpy from Nvidia which promises to be a drop in numpy replacement that will use all your CPU cores without any tweaks on your part.
https://github.com/nv-legate/legate.numpy
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Learning more about HPC as a python guy
Something for the HPC tools category: https://github.com/nv-legate/legate.numpy
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Unifying the CUDA Python Ecosystem
You might be interested in Legate [1]. It supports the NumPy interface as a drop-in replacement, supports GPUs and also distributed machines. And you can see for yourself their performance results; they're not far off from hand-tuned MPI.
[1]: https://github.com/nv-legate/legate.numpy
Disclaimer: I work on the library Legate uses for distributed computing, but otherwise have no connection.
- Legate NumPy: An Aspiring Drop-In Replacement for NumPy at Scale
DataProfiler
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LongRoPE: Extending LLM Context Window Beyond 2M Tokens
It's been possible to skip tokenization for a long time, my team and I did it here - https://github.com/capitalone/DataProfiler
For what it's worth, we actually were working with LSTMs with nearly a billion params back in 2016-2017 area. Transformers made it far more effective to train and execute, but ultimately LSTMs are able to achieve similar results, though slow & require more training data.
- Data Profiler โ What's in your data?
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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
- Release 0.8.3 ยท capitalone/DataProfiler
What are some alternatives?
cupy - NumPy & SciPy for GPU
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
CudaPy - CudaPy is a runtime library that lets Python programmers access NVIDIA's CUDA parallel computation API.
pyWhat - ๐ธ Identify anything. pyWhat easily lets you identify emails, IP addresses, and more. Feed it a .pcap file or some text and it'll tell you what it is! ๐งโโ๏ธ
CUDA.jl - CUDA programming in Julia.
usaddress - :us: a python library for parsing unstructured United States address strings into address components
numba - NumPy aware dynamic Python compiler using LLVM
XlsxWriter - A Python module for creating Excel XLSX files.
legate.pandas - An Aspiring Drop-In Replacement for Pandas at Scale
superset - Apache Superset is a Data Visualization and Data Exploration Platform
grcuda - Polyglot CUDA integration for the GraalVM
vtuber-livechat-dataset - ๐ VTuber 1B: Billion-scale Live Chat and Moderation Event Dataset