box
tensorflow
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box | tensorflow | |
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31 | 221 | |
809 | 182,323 | |
- | 0.7% | |
7.8 | 10.0 | |
12 days ago | 5 days ago | |
R | C++ | |
MIT License | 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.
box
- Trying to Replicate Excel financial Functions
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Can someone explain how R project are organized and deployed?
As for organising code within a project, as mentioned packages really don’t allow this beyond collation order. The best solution in this space is the ‘box’ package which implements a fully-featured module system for R. ‘box’ notably gets used by some folks to implement large-scale Shiny applications; if this is what you’re after, I would recommend the ‘rhino’ framework, which builds upon Siny and ‘box’.
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Good practice with long R scripts - any examples?
You can write amazing, clean, modular code with the box package.
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Does anyone feel like R is actually vastly worse for dependency/environment management than Python?
I would look into box https://github.com/klmr/box if you haven’t heard of it already
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"Managing large codebases in R" webinar (Oct. 6, 2022)
Shapeless plug: check out the already mentioned ‘box’, I think it’s strictly superior to ‘import’ (but I’m biased).
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Get tsarted wiht R using this Cheat Sheet - DataCamp
By contrast, R code doesn’t need to change the working directory at all! Having to do so hides other flaws in the code. For instance, when trying to load code or data, use the tools provided by R. That is, write packages and use system.file or, when not writing packages, use ‘box’.
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Which R function do you find somewhat tricky?
‘box’ fixes that.
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Restructuring a large R project. Need advice on how to wire up file paths and associated objects.
I think your use-case is best addressed by the ‘targets’ package. But I would also recommend checking out the ‘box’ package for a more general way of structuring R projects in modules which isn’t supported well natively by R (disclaimer: I wrote that package). Writing R code as modules fundamentally side-steps the issue of having to deal with absolute paths. Instead, all code and data are either contained in the module or can be accessed relative to the working directory.
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[Q] Loading `dplyr` packages within a function but not outside of it
However, using ‘box’, as recommended in another comment, allows you to achieve the same effect with less (and cleaner) code, by declaring your imports locally with the box::use function.
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Is it possible to see what functions are used from what library?
If you're writing you're own code you can use packageName::functionName(), or the box package. Which is definitely useful on larger codebases.
tensorflow
- TensorFlow-metal on Apple Mac is junk for training
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐
- One .gitignore to rule them all
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10 Github repositories to achieve Python mastery
Explore here.
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GitHub and Developer Ecosystem Control
Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.
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Non-determinism in GPT-4 is caused by Sparse MoE
Right but that's not an inherent GPU determinism issue. It's a software issue.
https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.
Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.
Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.
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Can someone explain how keras code gets into the Tensorflow package?
and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:
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Is it even possible to design a ML model without using Python or MATLAB? Like using C++, C or Java?
Exactly what language do you think TensorFlow is written in? :)
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How to do deep learning with Caffe?
You can use Tensorflow's deep learning API for this.
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When the documentation has TODOs
Since you've specifically mentioned ML, here's Tenserflow's GitHub. I'm sure a quick glance through that will change your mind.
What are some alternatives?
renv - renv: Project environments for R.
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
ggplot2 - An implementation of the Grammar of Graphics in R
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
rnim - A bridge between R and Nim
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
tidytable - Tidy interface to 'data.table'
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
workflowr - Organize your project into a research website
scikit-learn - scikit-learn: machine learning in Python
lintr - Static Code Analysis for R
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.