Table of contents

Section
Code Text Copy to Drive
Notebook

Gemini

Welcome to Colab!

Explore the Gemini API

The Gemini API gives you access to Gemini models created by Google DeepMind. Gemini models are built from the ground up to be multimodal, so you can reason seamlessly across text, images, code, and audio.

How to get started
  1. Go to Google AI Studio and log in with your Google account.
  2. Create an API key.
  3. Use a quickstart for Python, or call the REST API using curl.
Explore use cases

To learn more, check out the Gemini cookbook or visit the Gemini API documentation.


Gemini

Colab now has AI features powered by Gemini. The video below provides information on how to use these features, whether you're new to Python, or a seasoned veteran.

Thumbnail for a video showing 3 AI-powered Google Colab features

Gemini


Gemini

What is Colab?

Colab, or "Colaboratory", allows you to write and execute Python in your browser, with

  • Zero configuration required
  • Access to GPUs free of charge
  • Easy sharing

Whether you're a student, a data scientist or an AI researcher, Colab can make your work easier. Watch Introduction to Colab or Colab Features You May Have Missed to learn more, or just get started below!


Gemini

Getting started

The document you are reading is not a static web page, but an interactive environment called a Colab notebook that lets you write and execute code.

For example, here is a code cell with a short Python script that computes a value, stores it in a variable, and prints the result:


Gemini
seconds_in_a_day = 24 * 60 * 60
seconds_in_a_day
86400

Gemini

To execute the code in the above cell, select it with a click and then either press the play button to the left of the code, or use the keyboard shortcut "Command/Ctrl+Enter". To edit the code, just click the cell and start editing.

Variables that you define in one cell can later be used in other cells:


Gemini
seconds_in_a_week = 7 * seconds_in_a_day
seconds_in_a_week
604800

Gemini

Colab notebooks allow you to combine executable code and rich text in a single document, along with images, HTML, LaTeX and more. When you create your own Colab notebooks, they are stored in your Google Drive account. You can easily share your Colab notebooks with co-workers or friends, allowing them to comment on your notebooks or even edit them. To learn more, see Overview of Colab. To create a new Colab notebook you can use the File menu above, or use the following link: create a new Colab notebook.

Colab notebooks are Jupyter notebooks that are hosted by Colab. To learn more about the Jupyter project, see jupyter.org.


Gemini

Data science

With Colab you can harness the full power of popular Python libraries to analyze and visualize data. The code cell below uses numpy to generate some random data, and uses matplotlib to visualize it. To edit the code, just click the cell and start editing.


Gemini

You can import your own data into Colab notebooks from your Google Drive account, including from spreadsheets, as well as from Github and many other sources. To learn more about importing data, and how Colab can be used for data science, see the links below under Working with Data.


Gemini
import numpy as np
import IPython.display as display
from matplotlib import pyplot as plt
import io
import base64

ys = 200 + np.random.randn(100)
x = [x for x in range(len(ys))]

fig = plt.figure(figsize=(43), facecolor='w')
plt.plot(x, ys, '-')
plt.fill_between(x, ys, 195, where=(ys > 195), facecolor='g', alpha=0.6)
plt.title("Sample Visualization", fontsize=10)

data = io.BytesIO()
plt.savefig(data)
image = F"data:image/png;base64,{base64.b64encode(data.getvalue()).decode()}"
alt = "Sample Visualization"
display.display(display.Markdown(F"""![{alt}]({image})"""))
plt.close(fig)

Gemini

Colab notebooks execute code on Google's cloud servers, meaning you can leverage the power of Google hardware, including GPUs and TPUs, regardless of the power of your machine. All you need is a browser.

For example, if you find yourself waiting for pandas code to finish running and want to go faster, you can switch to a GPU Runtime and use libraries like RAPIDS cuDF that provide zero-code-change acceleration.


Gemini

To learn more about accelerating pandas on Colab, see the 10 minute guide or US stock market data analysis demo.


Gemini

Machine learning

With Colab you can import an image dataset, train an image classifier on it, and evaluate the model, all in just a few lines of code.


Gemini

Colab is used extensively in the machine learning community with applications including:

  • Getting started with TensorFlow
  • Developing and training neural networks
  • Experimenting with TPUs
  • Disseminating AI research
  • Creating tutorials

To see sample Colab notebooks that demonstrate machine learning applications, see the machine learning examples below.


Gemini

Gemini

Featured examples