This document provides an overview and tutorial of using Google Colab. It discusses: - The speaker's background and experience in big data, AI, and machine learning - An introduction to Google Colab and its key features like GPU/TPU acceleration and hardware limitations - A tutorial on connecting to Colab, accessing files from Google Drive, and comparing CPU and GPU performance - Examples of using Colab for flower classification with Keras and TPU as well as homework on iris classification

1. 國立臺北護理健康大學 NTUNHS
Google CoLab
Orozco Hsu
2022-03-07
1

2. About me
• Education
• NCU (MIS)、NCCU (CS)
• Work Experience
• Telecom big data Innovation
• AI projects
• Retail marketing technology
• User Group
• TW Spark User Group
• TW Hadoop User Group
• Taiwan Data Engineer Association Director
• Research
• Big Data/ ML/ AIOT/ AI Columnist
2

3. Tutorial
Content
3
Compare different runtime
Flower classification in TPU accelerator
Homework
Register Google CoLab and let’s started

4. Code
• Download code
• https://github.com/orozcohsu/ntunhs_2022_01.git
• Folder/file
• 20220307_inter_master/run.ipynb
4

5. Code
5
Click button
Open it with Colab
Copy it to your
google drive
Check your google
drive

6. Google CoLab
• A jupyter notebook based coding environment for python developer
and most popular python packages such like numpy, scikit-learn,
tensorflow, keras, pandas and matplotlib…
• It also supports hardware accelerate runtime for model building.
• GPU (Graphics Processing Unit): Nvidia K80, T4, P4 or P100
• TPU (Tensor Processing Unit): Cloud TPU | Google Cloud
• The notebook instance lasts 12 hours running and kernel session for 1
hour.
• Pay 9.99 USD/month choosing better plans (CoLab Pro or more)
• Plan price: Google Colab
6

7. Google CoLab
• Which One Do You Choose For Training Your Deep Neural Net?
7
Ref: https://www.predictiveanalyticsworld.com/machinelearningtimes/should-you-choose-a-gpu-or-a-tpu-to-train-your-machine-learning-models/10460/
check_colab_gpu_tpu.ipynb

8. Google CoLab
• Get a google account and use google drive service
• Launch a CoLab service
• Jupyter files repository
• Training, test dataset repository
• Model files repository
• Fast launcher with github repository ipynb file
8

9. Google CoLab
• CoLab limitation
• It can give you instances with 12GB of RAM and GPU for 12 hours (Max.) for
free use
• Save checkpoints during training to avoid time limitation
• Not for cryptocurrency mining
• Check CoLab FAQ
• More details: https://research.google.com/colaboratory/faq.html
• TPU price: https://cloud.google.com/tpu/pricing
9

10. Let’s started
10

11. Let’s started
11

12. Let’s started
12

13. Let’s started
13

14. Let’s started
14

15. Let’s started
15

16. Let’s started
• A new web jupyter notebook (has connected) called the runtime.
• Using web browser interacting with CoLab (jupyter notebook) called a
web session.
• Python running through python-kernel (runtime)
16

17. Let’s started
• Set auto-refresh CoLab webpage, avoid to have an idle timeout
• Press F12, run above code in console
• Don’t close the webpage until you won’t use it anymore
17
function ConnectButton(){
console.log("Connect pushed");
document.querySelector("#top-toolbar > colab-connect-button").shadowRoot.querySelector("#connect").click()
}
setInterval(ConnectButton,60000);
After executed, your CoLab webpage will sometime switch back working area
automatically in order to avoid the idle timeout
Ref: https://research.google.com/colaboratory/faq.html#idle-timeouts

18. Let’s started
18
File name and
configuration
Cell (coding area)
Session connection
Cell control
panel
Command palette (type logs or scratch or editor)
kernel running?

19. Let’s started
• Make a connection
19
Local runtime: https://research.google.com/colaboratory/local-runtimes.html

20. Let’s started
20
After connecting to a hosted runtime

21. Let’s started
21
Return runtime resource to google

22. Let’s started
• Runtime (virtual machine) description
22

23. Let’s started
• Using cgroup to control
linux resources
• Check:
https://shekhargulati.co
m/2019/01/03/how-
docker-uses-cgroups-to-
set-resource-limits/
23

24. Let’s started
• Check runtime disk space and directories
24
Working folder

25. Let’s started
25

26. Let’s started
26
Google provides poor TPU to free usage, and more advanced TPU is chargeable.

27. Let’s started
• Mount a resource from google drive and access it (Using pydrive)
27
colab_google_drive.ipynb
get folder id

28. Compare different runtime
• GPU vs CPU performance comparison
28
tf2_cpu_gpu_colab.ipynb

29. Other Topics
• Google Cloud Platform (GCP)
• https://cloud.google.com/
• CoLab integrates with BigQuery
• CoLab integrates with GCS (TPU only)
• To more fully use the parallelism, and to avoid bottlenecking on data transfer
29

30. Other Topics
• New features of CoLab
• Charts visualization
• Downloading Datasets into Google Drive via Google Colab
• https://towardsdatascience.com/downloading-datasets-into-google-drive-via-
google-colab-bcb1b30b0166
30
interacting_table.ipynb
colab_charts.ipynb

31. Flower classification
• The model will take as input a photo of a flower and return whether it
is a daisy, dandelion, rose, sunflower, or tulip.
• Using keras framework on TPU tensorflow 2.x.
31
The code is from: https://colab.research.google.com/notebooks/tpu.ipynb
tpu_colab.ipynb

32. Homework
• Try to create a iris classification project on colab follow the link
below.
• https://medium.com/@yosik81/machine-learning-in-30-minutes-
with-python-and-google-colab-6e6dfb77f5e1
32