D How To Organize Deep Learning Projects On Github R Reddit

While this post is mainly for my beginner friends, I’m sure there are some tips that anyone can use! So you’ve been learning some coding and maybe even machine learning and now you are ready to put your knowledge into action! Projects are excellent ways to develop and solidify new skills, as well as show potential employers your capabilities! With that said, how you structure and showcase your work is important. If a recruiter or interviewer finds your github projects to be sloppy or not user-friendly, it wont matter how awesome your new anime recommender system with an integrated web app is (and yes, it’s... Data projects are not only ways to develop skills, but also ways to show off how well you can organize and present your work.

It pays off to take the extra time and make your repos look professional. Δdocument.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); A simple and well designed structure is essential for any Deep Learning project, so after a lot practice and contributing in pytorch projects here's a pytorch project template that combines simplicity, best practice for... The main idea is that there's much same stuff you do every time when you start your pytorch project, so wrapping all this shared stuff will help you to change just the core idea... So, here’s a simple pytorch template that help you get into your main project faster and just focus on your core (Model Architecture, Training Flow, etc) In order to decrease repeated stuff, we recommend to use a high-level library.

You can write your own high-level library or you can just use some third-part libraries such as ignite, fastai, mmcv … etc. This can help you write compact but full-featured training loops in a few lines of code. Here we use ignite to train mnist as an example. In a nutshell here's how to use this template, so for example assume you want to implement ResNet-18 to train mnist, so you should do the following: You will find a template file and a simple example in the model and trainer folder that shows you how to try your first model simply. This post describes best practices for organizing machine learning projects that I have found to be highly effective during my PhD in machine learning.

Python is a great language for machine learning. Python includes a bunch of libraries that are super useful for ML: Git version control is extremely useful for keeping machine learning projects organized. Git is a tool that can be used to keep track of all changes that you make to your code. A Git “repository” is a directory containing your code files. Git uses space-efficient techniques so that it does not store multiple copies of your code but instead stores relative changes between old files and new files.

Git helps keep your directory of code files clean and organized because only the most recent versions are “obviously” present (although you can easily access any version of your code at any time). You choose when to mark that changes have occurred, using a “commit” which bundles together specific changes to your code along with a written description that you provide. Git repositories also make it easy to share code and collaborate. On the whole, Git is a way better solution to preserving old code functionality than saving a million different versions of your code under “myscript_v1.py”, “dataprocessing_v56.py”, “utils_73.py” and so on. Git version control is available through GitHub, GitLab, and Bitbucket to name a few. I use GitHub most frequently.

Once you have GitHub set up and are used to using it, it takes about 2 minutes to set up a new repository. Toolbox of models, callbacks, and datasets for AI/ML researchers. Also PyTorch Lighting gives example in this repo https://github.com/PyTorchLightning/pytorch-lightning-bolts InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database.

Automate critical tasks and eliminate the need to move data externally. Download now. Detectron2 is a platform for object detection, segmentation and other visual recognition tasks. You can check these repos: https://github.com/open-mmlab/mmdetection https://github.com/facebookresearch/detectron2 A pragmatic guide to organizing your machine learning projects I just want to start with a brief disclaimer.

This is how I personally organize my projects and it’s what works for me, that doesn’t necessarily mean it will work for you. However, there is definitely something to be said about how good organization streamlines your workflow. Building my ML framework the way I do allows me to work in a very plug n’ play way: I can train, change, adjust my models without making too many changes to my code. The first thing I do whenever I start a new project is to make a folder and name it something appropriate, for example, "MNIST" or "digit_recognition". Inside the main project folder, I always create the same subfolders: notes, input, src, models, notebooks. Don’t forget to add a README.md file as well!

As intuitive as this breakdown seems, it makes one hell of a difference in productivity. When I build my projects I like to automate as much as possible. That is, I try to repeat myself as little as possible and I like to change things like models and hyperparameters with as little code as I can. Let’s look to build a very simple model to classify the MNIST dataset. For those of you that have been living under a rock, this dataset is the de facto "hello world" of computer vision. The data files train.csv and test.csv contain gray-scale images of hand-drawn digits, from zero through nine.

Given the pixel intensity values (each column represents a pixel) of an image, we aim to identify which digit the image is. This is a supervised problem. Please note that the models I am creating are by no means the best for classifying this dataset, that isn’t the point of this blog post. So, how do we start? Well, as with most things data science, we first need to decide on a metric. By looking at a count plot (sns.countplot() (this was done in a Jupyter notebook in the notebooks folder!)) we can see that the distribution of labels is fairly uniform, so plain and simple accuracy...

Learn how to structure your GitHub repositories, manage issues, and implement workflows that boost productivity. This comprehensive guide provides actionable strategies for developers to organize GitHub projects for maximum efficiency. GitHub has become the backbone of modern software development, but many teams struggle with organizational chaos as projects grow. Whether you're a solo developer managing personal projects or part of a large team coordinating complex codebases, how you organize your GitHub presence directly impacts productivity and collaboration. According to a 2023 Stack Overflow Developer Survey, developers spend up to 30% of their time managing code organization and project structure rather than writing new code. This guide will help you reclaim that lost time through strategic GitHub organization.

One of the first major decisions any team faces is whether to use a monorepo (single repository containing multiple projects) or multi-repo (separate repositories for each project) approach. Companies like Google and Facebook have famously adopted monorepo approaches for their massive codebases, while others like Amazon prefer more distributed repository structures.

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