5 Very Cool Open Source Machine Learning Projects Tools

There is a huge demand for open-source machine learning projects tools in 2022

Machine learning is one of the leading tech trends that is popularly accepted and leveraged across the globe. And open source is something that can be modified as they are accessible to everyone. It promotes the free exchange of ideas within a community to build creative and technological innovations or ideas. Because of the advantages and demand, open-source machine learning projects are being encouraged. Here are a few cool open-source machine learning tools.

Apache Cassandra

Apache Cassandra is a distributed and decentralized database designed to manage massive amounts of structured and unstructured data across the world. It was developed at Facebook for inbox search and open sourced in July 2008. One of Cassandra’s most essential features is its elastic and linear scalability, which enables a consistently fast response time. Data is automatically replicated to multiple nodes for fault tolerance and easy distribution.

Ansible

Ansible is a radically simple IT automation system. It handles configuration management, application deployment, cloud provisioning, ad-hoc task execution, network automation, and multi-node orchestration. Ansible makes complex changes like zero-downtime rolling updates with load balancers easy.

Eclipse

Eclipse is an open-source IDE (Integrated Development Environment) and has been around since 2001. The Eclipse IDE is part of the efforts of the Eclipse Foundation, a non-profit corporation steering the development of numerous open-source projects.

TensorFlow

TensorFlow is a popular open-source machine learning framework for artificial intelligence and computer vision applications that was created by the Google Brain Team in 2015 under an Apache 2.0 open-source license. TensorFlow python library is used for fast numerical computing with data flow graphs. It allows developers to focus on training and inference of deep neural networks.

Kubernetes

Kubernetes is an open-source container orchestration platform and also known as k8s or kube. Kubernetes an OG in the container space is open-source system for automating containerized computer applications building, deployment, testing, scaling resources and applications in real-time. K9s Kubernetes, the Kubernetes CLI that makes it easier to navigate, observe, and manage your Kubernetes clusters.

Contributing to open source comes with too many pros. So, these are some good open-source projects to contribute.

stumble across a variable you don’t know, you just check the declaration. If you gave it a meaningful name, that already gives you a big clue what it is, what it’s doing, and where it’s needed.

Compare that to Python.

There you pretty much invent variables as you go. If you didn’t give it a meaningful name or at least left a comment about it, your future self will be messed up.

If you’re stringing together a big project, you might be integrating C packages, Fortran packages, and more. There are many advantages to this: C packages might not exist in Python, and are usually faster. Scientific packages often exist only in Fortran for legacy reasons.

In effect, you’re going to have to use compilers like ‘gcc’, ‘gfortran’, and perhaps others more.

And that’s a hassle! The documentation for integrating C modules in your Python code is more than 4,500 words long — twice as long as this article! And the documentation for Fortran isn’t that much shorter either.

Building your whole project in C might be slower to code at first. But you’ll prevent situations where you have to mess around with multiple compilers and interfaces.

C is so old that there’s packages for almost anything. Even user-friendly machine learning packages.

The global interpreter lock, or GIL, has been around since day zero of Python. It made memory management incredibly easy for the end user.

In smaller projects at least, developers don’t have to think about computer memory at all when they use Python. Compare that to C where you literally reserve bits of memory for every single variable!

Basically, the GIL counts how many times a variable has been referenced in every section of the code. If the variable is no longer needed, then it frees the memory space it occupies.

In small projects, the GIL helps with performance-boosting because unnecessary memory space is wiped out.

But in bigger projects there’s a problem: the GIL doesn’t like multithreading.

This is a very performance-boosting way of executing programs where several instruction threads run independently on the same process resources. Machine learning models are great to train this way.

There’s just one little problem: the GIL only works on one thread at a time.

So if variable A is getting executed on thread 1, while thread 2 is already finished with A, then its memory might end up getting deleted. It just depends where the GIL happens to be at the time.