How to Use Git Tools: From Beginner to Expert

How to Use Git Tools: From Beginner to Expert

Learn Everything you need to know about Git in Just 2 hours

In this this course, we first explain the basic working principle of the distributed version control software Git. Then, we explain the star features of Git: branching and merging. We will see how branching allows you to make parallel histories inside one project. This is a key element to easy collaboration, and a good understanding of branching and merging is a prerequisite for effectively using remote services such as Gitlab and Github.

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Now that you are an expert on git, in this somewhat longer and last video in the series we finally explain how we can use Gitlab and other remote git services to collaborate on git projects. More specifically, you will get an Introduction to different hosting platforms, then get the following

• Basic explanation of how remotes work, then
• Demonstration of creating a new remote repository on Gitlab + a short tour of the site
• Demonstration of clone, pull and push with a simulated collaboration
• How I propose we work on our gitlab projects
• Why do we use the -u modifyer with push?

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You can get Git for your system from git-scm dot com. The lectures in this course are only meant as an introductory presentation, not as an exhaustive course. You will find all relevant details in the Pro-Git book by Chanon and Straub. For specific questions you will 99% of times find answers on stackexchange.

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In the bonus section, which about using the GPU to substantially speed up all kinds of numerical problems. You will Conventional wisdom dictates that for fast numeric you need to be a C/C++ wizz. It turns out that you can get quite far with only python. In this video, I explain how you can use Cupy together with Numba to perform calculations on NVIDIA GPU’s. Production quality is not the best, but I hope you may find it useful.

Introduction: GPU programming in python, why?

Cupy intro

Cupy demonstration in Google colab

Cupy summary

Numba.cuda and kernels intro

Grids, blocks and threads

Matrix multiplication kernel

Tiled matrix multiplication kernel and shared memory

Numba.cuda demonstration in Google colab

Final remarks

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