cupy
papermill
cupy | papermill | |
---|---|---|
21 | 26 | |
7,787 | 5,636 | |
1.2% | 0.7% | |
9.9 | 8.0 | |
5 days ago | 10 days ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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.
cupy
- CuPy: NumPy and SciPy for GPU
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Keras 3.0
I did not expect anything interesting, but this is actually cool.
> A full implementation of the NumPy API. Not something "NumPy-like" — just literally the NumPy API, with the same functions and the same arguments.
I suppose it's like https://cupy.dev/
- Progress on No-GIL CPython
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Fedora 40 Eyes Dropping Gnome X11 Session Support
What was the difference in runtime performance, and did you try CuPy?
https://github.com/cupy/cupy :
> CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. CuPy acts as a drop-in replacement to run existing NumPy/SciPy code on NVIDIA CUDA or AMD ROCm platforms.
Projects using CuPy:
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How does one optimize their functions?
It's more effort though. You will likely have to format your data in specific ways for the GPU to efficiently process it. I've done this kind of thing with PyTorch tensors, but there are also math-specific libraries like CuPy. If you only have millions, Numpy should be fine.
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Speed Up Your Physics Simulations (250x Faster Than NumPy) Using PyTorch. Episode 1: The Boltzmann Distribution
I'd also recommend checking out CuPy which aims to fully re-implement the Numpy api for CUDA GPUs, while taking advantage of Nvidia's specialized libraries like cuBLAS, cuRAND, cuSOLVER etc. The tradeoff being that it only works with Nvidia GPUs.
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ELI5: Why doesn't numpy work on GPUs?
u/Spataner's answer is great. If you WANT GPU-enabled numpy functions, I would check out CuPy: https://cupy.dev/
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Help!!! Training neural net in vs code
Not sure how VS Code is relevant here as it's just you IDE, shouldn't have any influence on this. Now, seeing as you're using numpy (which has no gpu support), you could try and use something like CuPy in place of numpy. I'm not sure about the interoperability because I've never used this myself, but if you're lucky it could be as simple as just replacing all numpy calls with the same CuPy calls (or replacing all import numpy as np with import cupy as np ).
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What's the best thing/library you learned this year ?
Cupy replicates the numpy and scipy APIs but runs on the GPU.
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Making Python fast for free – adventures with mypyc
For that, you can use cupy[0], PyTorch[1] or Tensorflow[2]. They all mimic the numpy's API with the possibility to use your GPU.
[0] https://cupy.dev/
papermill
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Spreadsheet errors can have disastrous consequences – yet we keep making them
Pandas docs > Comparison with spreadsheets: https://pandas.pydata.org/docs/getting_started/comparison/co...
Pandas docs > I/O > Excel files: https://pandas.pydata.org/docs/user_guide/io.html#excel-file...
nteract/papermill: https://github.com/nteract/papermill :
> papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. [...]
> This opens up new opportunities for how notebooks can be used. For example:
> - Perhaps you have a financial report that you wish to run with different values on the first or last day of a month or at the beginning or end of the year, using parameters makes this task easier.
"The World Excel Championship is being broadcast on ESPN" (2022) https://news.ycombinator.com/item?id=32420925 :
> Computational notebook speedrun ideas:
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Jupyter Kernel Architecture
There is Papermill ... https://github.com/nteract/papermill
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Git and Jupyter Notebooks Guide
https://github.com/jupyter/enhancement-proposals/pull/103#is...
Papermill is one tool for running Jupyter notebooks as reports; with the date in the filename. https://papermill.readthedocs.io/en/latest/
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JupyterLab 4.0
You may be interested in papermill to address the parametrized analysis problem [1]. I think (but I'm not positive) this is what the data team at a previous job used to automate running notebooks for all sorts nightly reports.
[1] https://papermill.readthedocs.io/en/latest/#
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Show HN: Mercury – convert Jupyter Notebooks to Web Apps without code rewriting
I'm using Papermill to operationalize Notebooks (https://github.com/nteract/papermill), it e.g. also has airflow support. I'm really happy with papermill for automatic notebook execution, in my field it's nice that we can go very quickly from analysis to operations -- while having super transparent "logging" in the executed notebooks.
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What's the best thing/library you learned this year ?
papermill bcpandas fastapi
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Does the Jupyter API allow using Jupyter from the CL?
But you can execute your notebook using Jupyter-run or papermill.
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Running Jupyter notebooks in parallel
As a first option, we will use Papermill, which has a Python API that allows us to run different notebooks using some functions:
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Tips for using Jupyter Notebooks with GitHub
Papermill can also target cloud storage outputs for hosting rendered notebooks, execute notebooks from custom Python code, and even be used within distributed data pipelines like Dagster (see Dagstermill). For more information, see the papermill documentation.
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Three Tools for Executing Jupyter Notebooks
Papermill Source Code
What are some alternatives?
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
nbconvert - Jupyter Notebook Conversion
Numba - NumPy aware dynamic Python compiler using LLVM
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
scikit-cuda - Python interface to GPU-powered libraries
airflow-notebook - This repository is no longer maintained.
TensorFlow-object-detection-tutorial - The purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch
nbdev - Create delightful software with Jupyter Notebooks
bottleneck - Fast NumPy array functions written in C
voila - Voilà turns Jupyter notebooks into standalone web applications
dpnp - Data Parallel Extension for NumPy
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts