Colaboratory, also called Google Colab is a product of Google Research and is used to write and run Python and other languages from your browser.
What it is
Here is a beginner’s guide that perfectly complements this article.
Colab is a hosted Jupyter, installed and configured, so that we do not have to do anything on our computer but simply work from the browser, on cloud resources.
It works exactly like Jupyter. They are Notebooks or notebooks based on cells that can be texts, images or code, in this Python step, because unlike Jupyter Colab at the moment you can only use the Python kernel, they talk about implementing later others like R, Scala, etc, but no date is said.
It is a very fast way to test code without having to configure our computer and to get into the world of Machine Learning, Deep Learning, artificial intelligence and data science. Ideal also for teachers because being based on Jupyter we can share projects with other people just as if we were using Jupyter Hub.
We can use any python functionality, we can use TensorFlow, Keras, Numpy, come on all your libraries.
It offers us a free GPU and TPU service,
They are part of the group of developers of https://colaboratory.jupyter.org/welcome/
The service is free but we need a Gmail account. Notebooks data is stored in our Google Drive. And we can save and load notebooks from Github as well. Besides importing projects coming from Jupyter or exporting them as well. It works with .ipynb
It is clear that hardware resources are limited. You will not be able to create projects that need a large amount of computation. If you like this system and want to use it for advanced projects you can always pay for the Pro or Pro + version. I am going to focus on the free one.
In its day I already talked about that a way to use Jupyter in a free version is to use the free version.
Google’s Machine Learning Crash Course is built on Colab and I’m finishing it. Soon I will tell you how it goes
If you are interested in Machine Learning, see what courses are available.
Why use Colab? Advantages
Because it’s a very quick and easy way to be able to set up courses and information about Python programming and share it with other people or with students in case you are a teacher.
In my case I have a compatibility problem between TensorFlow and my CPU, so at the moment I will use it to make different examples and tests with TensorFlow and Keras.
Disadvantages
Well, we can only use Pyhton
And we use yet another Google product and continue to feed and depend more and more on the technological giant “Don’t be Evil”.
Differences between Colab and Jupyter
- Colab es un servicio alojado, un Jupyter alojado, mietnras que Jupyter lo usas en tu pc
- Colab, aunque es gratuito si quieres potencia de cómputo tiense que irte a la versión de pago
- Al ser alojado, puedes compartir los notebook con la gente
- En Colab solo puedes usar Python, en cambio en Jupyter puedes instalar todo tipo de Kernels, R, Bash, javascript, etc