Google Colab Tutorial for Beginners | Get Started with Google Colab
Summary
TLDRGoogle Colab, auch bekannt als Caller, ist eine hervorragende Plattform für Einsteiger in künstliche Intelligenz und Data Science. Es bietet vorinstallierte Bibliotheken und Werkzeuge, ermöglicht das Schreiben und Teilen von Python-Code im Browser und ist überall verfügbar. Colab organisiert Projekte in Notebooks, die direkt in Google Drive gespeichert werden. Es unterstützt auch Hardware-Beschleuniger wie GPU und TPU für komplexere AI-Projekte und ermöglicht die Installation zusätzlicher Bibliotheken. Die einfache Freigabe von Code und die Möglichkeit, Notebooks herunterzuladen, machen Colab zu einer beliebten Wahl in der Data-Science-Community.
Takeaways
- 🌐 Google Colab ist eine großartige Plattform zum Einstieg in künstliche Intelligenz und Data Science.
- 💻 Es bietet alles, was du brauchst, mit vorinstallierten Bibliotheken und Werkzeugen, die für dich eingerichtet sind.
- 📱 Du kannst Python-Code direkt in deinem Browser schreiben, ausführen und teilen, ganz ohne Konfiguration.
- 🔗 Mit einem einfachen Link kannst du deinen Code mit anderen teilen.
- 📁 Alles, was du in Colab machst, wird in deinem Google-Konto gespeichert und ist über Google Drive erreichbar.
- 📓 Colab ist wie ein Jupyter-Notebook, das auf Google-Servern gehostet wird, mit zusätzlichen Funktionen.
- 🛠️ Du kannst Code-Zellen erstellen und Python-Code schreiben, der auf Google-Servern und nicht auf deinem Laptop ausgeführt wird.
- 🔀 Du kannst Hardware-Beschleuniger wie GPU oder TPU auswählen, um deine KI- oder Data-Science-Projekte zu beschleunigen.
- 🆓 Google Colab bietet sogar kostenlose Zugriffsmöglichkeiten auf GPUs und TPUs.
- 📚 Die wichtigsten Data-Science- und KI-Bibliotheken sind bereits in Colab vorinstalliert und können mit Import-Anweisungen verwendet werden.
- 🔄 Wenn du eine neue Notebook-Datei erstellen möchtest, kannst du dies über 'Datei' > 'Neues Notebook' tun.
Q & A
Was ist Google Colaboratory und wofür wird es verwendet?
-Google Colaboratory, oder kurz 'Colab', ist eine browserbasierte Plattform, die es ermöglicht, Python-Code auszuführen. Sie wird hauptsächlich für Datenwissenschaft und künstliche Intelligenz (KI) verwendet, da alle wichtigen Bibliotheken und Tools vorinstalliert sind.
Welche Vorteile bietet Google Colab im Vergleich zur lokalen Entwicklung?
-Ein Vorteil von Google Colab ist, dass keine Software installiert oder eingerichtet werden muss. Alle wichtigen Bibliotheken sind vorinstalliert, und der Code wird auf Google-Servern ausgeführt, sodass der Nutzer sofort mit dem Programmieren beginnen kann.
Wie können Nutzer ihre Arbeit in Google Colab speichern und teilen?
-Alle Notebooks, die in Google Colab erstellt werden, werden automatisch im Google Drive des Nutzers gespeichert. Die Notebooks können einfach über einen Link geteilt werden, sodass jeder mit einem Internetzugang und einem Google-Konto darauf zugreifen kann.
Was ist eine Zelle in Google Colab und welche Arten von Zellen gibt es?
-In Google Colab gibt es zwei Arten von Zellen: Codezellen, in denen Python-Code geschrieben und ausgeführt wird, und Textzellen, in denen Text und Formatierungen wie Bilder, Videos oder Listen verwendet werden können. Letztere nutzen die Markup-Sprache Markdown.
Wie können Hardwarebeschleuniger in Google Colab verwendet werden?
-Google Colab bietet die Möglichkeit, Hardwarebeschleuniger wie GPUs und TPUs zu verwenden, um rechenintensive Aufgaben zu beschleunigen. Diese Beschleuniger können über die Option 'Runtime' und 'Change runtime type' aktiviert werden.
Wann sollte ein Hardwarebeschleuniger verwendet werden?
-Für einfache Python- oder maschinelle Lernmodelle reicht oft ein CPU aus. Für größere und komplexere Modelle ist der Einsatz einer GPU oder TPU ratsam, da diese die Berechnungen erheblich beschleunigen.
Was passiert, wenn ein Notebook in Google Colab längere Zeit inaktiv ist?
-Wenn ein Notebook längere Zeit nicht verwendet wird, wird es von den Google-Servern getrennt, um Ressourcen zu sparen. Der Code bleibt jedoch gespeichert und das Notebook kann jederzeit erneut verbunden und der Code wieder ausgeführt werden.
Wie können zusätzliche Bibliotheken in Google Colab installiert werden?
-Um eine Bibliothek zu installieren, die nicht standardmäßig in Google Colab enthalten ist, kann der Code mit einem Ausrufezeichen (!) vor dem Befehl 'pip install' ausgeführt werden. Zum Beispiel: '!pip install libraryname'.
Welche Dateiformate unterstützt Google Colab zum Speichern von Notebooks?
-Google Colab speichert Notebooks standardmäßig im IPYNB-Format, das auch von Jupyter Notebooks verwendet wird. Es gibt jedoch auch die Möglichkeit, Notebooks als Python-Dateien (.py) herunterzuladen.
Warum ist Google Colab besonders bei der KI- und Datenwissenschaftsgemeinschaft beliebt?
-Google Colab ist aufgrund seiner Einfachheit, der vorinstallierten Bibliotheken und der Möglichkeit, leistungsstarke Hardware wie GPUs und TPUs kostenlos zu nutzen, besonders bei KI- und Datenwissenschaftlern beliebt. Zudem lassen sich Notebooks einfach teilen und kollaborativ bearbeiten.
Outlines
💻 Einführung in Google Colab
Google Colab, auch bekannt als Colab, ist eine hervorragende Plattform für Einsteiger in die Welt der künstlichen Intelligenz und Data Science. Es bietet alles, was man für den Einstieg benötigt, einschließlich vorinstallierter und vorkonfigurierter Hauptbibliotheken und Tools. Colab ermöglicht es, Python-Code im Browser zu schreiben, auszuführen und zu teilen, ohne jegliche Konfiguration. Der Code wird auf Google-Servern und nicht lokal auf dem eigenen Laptop ausgeführt. Colab ist auch mit Jupyter Notebooks vergleichbar, bietet aber zusätzliche Funktionen, die es in der Data Science- und KI-Community beliebt machen. Die Notebooks sind in Google Drive gespeichert und können problemlos geteilt werden.
🔄 Hardware-Akzelerierer in Google Colab
In Google Colab können Benutzer zwischen verschiedenen Servertypen wählen, einschließlich CPU, GPU und TPU. Während CPU standardmäßig zur Verfügung steht, können GPU und TPU angefordert werden, um die Berechnungsleistung für komplexere KI- oder Data Science-Projekte zu erhöhen. TPU (Tensor Processing Unit) ist speziell für die Beschleunigung von KI-Code konzipiert. Colab ermöglicht es, alle wichtigen Data Science- und KI-Bibliotheken und -Tools direkt über Import-Anweisungen zu verwenden, da sie vorinstalliert sind. Zusätzlich können mit einem Ausrufezeichen neue Bibliotheken installiert werden.
📚 Nützliche Funktionen von Google Colab
Google Colab ermöglicht es, Code in Notebooks zu schreiben, die in Google Drive gespeichert und geteilt werden können. Die Plattform unterstützt die Verwendung von Code- und Textzellen, wobei后者 zur Erklärung von Code oder zum Einbinden von Bildern und Videos verwendet werden kann. Die Verwendung von Hardware-Akzelerierern wie GPU und TPU ist für die Verarbeitung großer KI- oder Data Science-Modelle von Vorteil. Colab ist eine ideale Umgebung zum Erlernen und Anwenden von Data Science- und KI-Bibliotheken wie TensorFlow, NumPy, pandas, Matplotlib und PyTorch. Die Plattform ist auch dafür bekannt, dass sie es ermöglicht, Tutorials und Projekte problemlos zu teilen und zu speichern.
Mindmap
Keywords
💡Google Colab
💡Data Science
💡Künstliche Intelligenz
💡Jupyter Notebook
💡Hardware-Beschleuniger
💡Code-Zelle
💡Text-Zelle
💡Markdown
💡Runtime
💡Libraries
Highlights
Google Colaboratory, or Colab, is an excellent starting point for artificial intelligence and data science.
All major libraries and tools are pre-installed and pre-set up in Colab.
You can start coding data science and AI without installing any software.
Colab allows you to write, run, and share Python code in your browser.
You can access Colab from any device without configuration.
Your work in Colab is saved under your Google account and can be accessed like Google Drive files.
Colab is essentially a Jupyter notebook hosted on Google servers with additional features.
Everything in Colab is organized under notebooks.
You can create a new notebook to start working on your projects.
It's helpful to rename your notebooks for better organization.
Colab executes code on Google servers, not on your local machine.
You can run code cells using the play button or keyboard shortcuts.
There are two types of cells in Colab: code cells and text cells.
Text cells allow you to write in Markdown, which supports rich text formatting.
Colab offers the option to use hardware accelerators like GPUs and TPUs for free.
Notebooks will disconnect from Google servers after being idle for a while to save resources.
TPUs are designed for accelerating AI code and are accessible in Colab.
Colab comes with pre-installed major data science and AI libraries.
You can install additional libraries in Colab using an exclamation mark prefix.
Colab notebooks can be saved to your Google Drive and shared with others.
Colab is popular for sharing tutorials due to its ease of accessibility and shareability.
Notebooks are in .ipnb format, which is ideal for data science and machine learning.
This tutorial covers everything you need to get started with Google Colab.
Transcripts
google collaboratory or caller for short
is the best way to get started with
artificial intelligence and data science
everything you need all the major
libraries and tools are already
pre-installed for you and pre-set up for
you
so by the time it takes you to search
how to install another software you can
already be
get up and running coding your data
science and artificial intelligence code
with google call up you can
write run and share python code in your
browser and this allows you to get
started from your phone tablet or laptop
without any configuration and you can
immediately share your code with anyone
with a simple link
and everything you do in call app will
be saved under your google account so
you can
access them share them just like the
rest of your files under your google
drive if you are familiar with the
jupyter notebooks google collab is
essentially
a jupiter notebook hosted on google
servers with additional functionalities
that makes it especially popular among
data science artificial intelligence
communities
so without further ado let's go ahead
and get started with call up so first
thing you want to do
is to search for google call up and it
should be the one that says
callup.research.google.com let's go into
that
so the very first thing you should know
about google call up is that everything
is organized under notebooks so first
thing you see when you come to the
website
are basically your recent notebooks and
the first one you will see will probably
be the welcome to collaborator notebook
so to get started first thing you want
to do is to go ahead and create a new
notebook
so the first time you open a collab
notebook it will name it for you
with untitled 0 in this case it is 3 for
me
and one thing you want to do is to
rename this notebook so that it's more
meaningful to us it can be something
like
collab intro tutorial since many times
you'll be working
with multiple notebooks at a time it can
be really helpful to appropriately name
your notebooks so if you come back to it
months after writing it it will be
descriptive
and much easier for you to know what
what you were doing in that notebook and
everything i do in this notebook will be
saved to my google drive under my google
account so when first we take a look
at google call up the first thing we
will see and the major thing we will see
is
something called a code cell and a play
button we can go ahead and write some
python code here and it will be executed
not on our laptop
but it will be run on google servers and
currently we are not connected at
servers
but we don't have to click here to be
connected but instead
usually what you want to do is to write
some code here and it will be
automatically connect to google servers
so let's write a simple python code
now there are a couple ways i can run
this code first one and the default one
is to click on display button
and we just ran our first python code
usually you'll want to use
keyboard shortcuts and the shortcut to
run a single cell
is command enter which does the same
thing as clicking on that play button
but most of the time chances are you'll
want to run a call cell
and create a new call cell under it so
you can keep coding to do that
we will use shift enter
which i just did notice that there are
two type of cells
i can create one of them is a code cell
and this is where we write our code
another one is a text cell so although
it says text cell
text is not the only thing we can write
here once you write some text
using the icons above we can turn it
into bold
italic title as well as other features
such as
images or videos lists and bullet points
and there's actually even a language for
that called
markdown language and as you're getting
started with collab it's not necessary
to learn
markdown language but if you are
interested you can do that
especially later on in your artificial
intelligence or data science journey
for now what you should know is that we
can create code cells that are modular
and we can also
use text cells to make more explanations
or display images that will be helpful
and maybe explain our code better with
some rich text formats so when we write
code inside
code cells we said that it's not running
in our own laptop but
it's running at google servers and there
are actually three types of servers that
google can allocate to us
and we can change it from here if you go
to runtime and
change runtime type you will see that
you have the option to have a hardware
accelerator right now by default you are
not given a hardware accelerator but if
you need one
it's possible to have either a gpu or a
tpu
so why would you need them if you're
just getting started and writing python
code or
simple machine learning code that runs
fast enough most likely you'll be fine
with the default version
which has no hardware accelerator
meaning that it will run
on cpus but as your artificial
intelligence code gets larger and more
complex as you do more operations that
operations you make will take more time
and you'll want to use some kind of
hardware accelerator
and the most common hardware accelerator
is a gpu
and we have access to free gpus within
collapse
one thing you will notice is that when
you change the hardware accelerator type
from num to gpu
or a tpu it will basically tell you that
if you don't need a hardware accelerator
do not use a hardware accelerator
and this way google can allocate its
gpus or tpus
to people who need more computational
power for now i'm just writing simple
python code so i can change it to none
one thing you should know is that when
you leave your notebook unused for hours
your notebook will be disconnected from
google servers to more efficiently
allocate
its resources but do not worry when that
happens all of your code will be
still safe and nothing changes on them
but you will need to rerun your code and
to do that you can also do it from
runtime
and run all so we talked about cpus and
gpus that you can connect to
but there's also another one which we
can access from
change runtime type and hardware
accelerator and that was
tpu and tpus are tensor processing units
and do not be scared by the name these
are basically processing
units or processing chips that are
specifically designed
for significantly accelerating
artificial intelligence code
and we also have free access to them
from cola
and this can be especially useful if you
have larger machine learning models
or artificial intelligence models but if
you are using
a smaller model you will be most likely
fine with a gpu
or none hardware accelerator
so until now we just seen python code
but we didn't see anything about data
science machine learning or
artificial intelligence code but one of
the major advantages of using google
colab is that you can't only code python
code in the browser
but all of the major data science and
artificial intelligence libraries and
tools come already
pre-installed in collab and we can start
using them
with import statements such as
so in order to use tensorflow numpy
pandas
matbaltip and pytorch and many more of
them since they're already pre-installed
for us we can
start using them with these import
statements and if you want we can also
check their versions and as you can see
we're using the latest versions
of the libraries such as using
tensorflow 2.4
which is the latest one as of right now
and if you're not familiar with
any of these do not worry as you get
started with colab it will be much
easier to
learn them as you're coding them so as
you are getting started you are not
required to know what this code does or
whether these libraries are about and
you can learn them one video at a time
and every now and then you will want to
use libraries
that are not inside collab and when you
want to do that you will want to
prefix your code with an exclamation
mark
and with that you can install other
libraries to call up as well
and when you want to use them you can
start using them with
import statements as well so as you are
working within a notebook
if you want to create a new notebook you
can do that from file
and new notebook and similar notebook
actions such as opening the existing
ones
you can also do them from here as well a
lot of the times
many of the machine learning data
science or
artificial intelligence codes are
written in collab and
when you're learning from them one thing
you want to do is to save a copy
in drive and when you save a copy and
drive you can have a copy of the
tutorial
and save it under your google drive as
well
and if i do that i will have all the
code from that previous notebook
except in this one i'm not connected
which i can connect in a second as well
and i will have this title change
to a copy of follow-up intro tutorial
from collab info tutorial and if i wanna
run this code you can either run that
code
cell by cell or you can
run that from runtime and run all
which will run all the cells
sequentially
and that's one of the reason why collab
is so popular among
data science and machine learning
communities and you can find ldap
tutorials
are written in collab for ease of
shareability because you can literally
share them with a single
link and everyone that has an internet
connection and the google account
can have access to that code and run
them themselves
as well as modifying it
and if you want to download the notebook
you can also do that from here
under file you can go download this
notebook as a ipnb file
which is what we are using here and you
can also download it as a python file
one thing you should know is that
currently we're working with the ipad b
file and this is the same file format
that you use in jupyter notebooks
and ipamb files are more popular with
data science and machine learning
because they have additional
capabilities on top of python files some
of those features
are called cells which you can save your
outputs of the code cells
you can also change the order of the
code cells more easily
from these up or down arrows you can
delete them
comment them and they're overall in a
more modular structure
that are a better fit for data science
and machine learning code so these were
everything you needed to get started
with google collab
as you just have seen you can just
search for google call up
and get coding within seconds without
any setup
and everything you do will be saved
under your google account and you can
reach them from your google drive
and share them with a single link so
this was it for this tutorial
if you learned better by reading by the
way i also have this tutorial in article
format as well
and i will make sure to put a link to it
in the description box with that i hope
you got some value out of this video if
you want to see more machine learning
and artificial intelligence tutorials
make sure to like and subscribe and i
will see you next time
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