Welcome back.
In this video we're going to talk about the key features and why Google Colab is so popular.
Also we're going to talk about some limitations to be aware of free access to GPUs and TPUs.
This is the killer feature.
Training complex AI models requires immense computational power, which is expensive.
Colab provides access to those resources for free.
In order to upload the files or run some files, we can go to File Locate.
In drive, you can upload upload the files to your drive and located in drive Google drive open in playground
mode.
Create a new notebook in drive.
Open notebook upload notebook.
So in this course, I prepared those notebooks and other notebooks in the later stages, but I want
from you to follow me step by step.
All those files and notebooks are available in the resources folder, so please upload them step by
step as I am doing here.
Okay.
File upload notebook.
Select the notebook.
Select Basics Variables operators and click open.
Zero configuration required.
You don't need to be a system administrator to set up a Python environment with complex dependencies.
Dependencies.
It's very simple.
So you can see the notebook opened.
And in the next videos we're gonna learn about those step by step.
Okay.
And we're going to run the codes and learn about the basic variables and operators in Python.
You can share your notebooks via a link just like a Google document.
So here from file you can save save a copy GitHub copy in drive, move to the bin, move rename and
others.
Okay.
Also you can go to the edit select all cells.
This is by the way a cell.
So here if I need to create a code this is a cell okay.
You can run them.
Run all the cells for here I'm running 22 cells okay.
So here you can notice that we are executing most common data science and ML libraries like TensorFlow,
PyTorch, OpenCV are pre-installed.
You can install any other library with a simple pip install command.
Don't worry, we're gonna see everything later.
In the next video we're gonna talk about the view tab, table of contents, notebook info, execute
code history, start slash slideshow, and others.
Collapse sections.
Expand sections so you can expand sections by simple click.
And you can notice that we have The.
When we run our application, we run our application.
Here we are executing all the cells.
So here you can stop, you can run again.
And also you can click here and clear output.
Okay.
Let me scroll up.
The free version doesn't provide unlimited compute.
The your virtual machine will disconnect after a period of inactivity typically 90 minutes.
Like for example, if you if you have one hour and a half, you don't make anything.
It will disconnect and has a maximum continuous runtime, typically 12 hours.
Your work is saved, but the computational environment is reset.
Also in the free version, the availability of the best GPUs like a ten and V ten, is not guaranteed
in the free tier and is subject to your usage pattern and overall availability.
Why this is important.
If you go to the runtime, we can run all.
Run before run selection, run cell and below.
Those are not important.
Interrupt execution, restart session, restart session and runtime disconnect and delete runtime.
The most important thing is change runtime.
Here we have runtime type are Julia and Python three.
We are interested in in Python three.
Here we have the accelerators, the hardware accelerators.
You can learn about them here.
The type the types of GPUs and TPUs that are available in Colab vary over time.
This is necessary for Colab to be able to provide access to those resources free of charge.
You can access premium GPUs subject to availability by purchasing one of our paid plans.
If you are planning to execute and make very complex executions later when later when you create a very
big project, if you have a lot of data set, you need to process many things.
You, uh, you should, uh, purchase a GPU.
Okay.
But for now, we are using our free version.
Also, if you have a computer with, uh, with a good GPU, like, uh, 30, 70, 40, 60, 50, 70,
50, 80, 50, 90, those are flagships.
So there is no need to buy any of those plans.
If you would like to access to a specific dedicated hardware, explore using GCP market space.
Colab.
But we don't need all of those things.
I'm just showing you that you can use CPU, T4, GPU, um Click okay.
Uh, you can use this TPU, but A10s and L4, l4 GPU and those are not, uh, accessible only if you
want to access premium GPUs.
Okay.
You can use CPU or GPU runtime version.
We use the latest recommended click save.
Then we need to restart the runtime.
We are restarting the runtime automatically and restart session okay.
Here we have the indication when you are connected we get this green tick and you can see the Ram and
the disk.
We are using one over 12 gigabyte and disk 39 over 112 gigabyte.
Okay, this is the Colab.
Also, you can access tools that command palette settings.
Um, you can set the the editor font size for example 15 monospace font and others.
Okay the help.
Report a bug and others.
Okay, so this is a very simple thing.
We have commands.
We have code, text, runtime and the restart sessions interrupt execution from this bar and the slide
bar to the left we have this table of content.
We got the table of content like a summary for the file we have here the find and replace.
We have this code snippet the secrets and the folder.
Here we're going to upload some of our files.
So please pay attention to this icon okay.
Thank you guys.
Let's learn about the basics and the variables and operators in Python.