Part 1: Create a Virtual Machine (VM)

• Follow the instructions to create a VM.

• Add a name.

• Set your boot disk.

• Complete your VM details.

In this example we will use an Ubuntu 20.04 LTS server.

• Allow HTTP and HTTPS traffic.

Install Jupyter Notebook on GCP.

Ensure you have Python installed on your machine. Jupyter Notebook supports Python 3.3 or later. You can install Python 3 by executing:

sudo apt-get update -y && sudo apt-get upgrade -y

Step 1: Install pip

pip is a package manager for Python. It's used to install and manage Python libraries. Execute the following command to install pip for Python 3:

sudo apt install python3 python3-pip -y

Step 2: Upgrade pip (Optional)

It’s a good practice to have the latest version of pip. Upgrade pip using the following command:

pip install --upgrade pip

Step 3: Install Jupyter Notebook

Now, install Jupyter Notebook using pip:

pip3 install jupyter

Close your SSH windows, and start again.

Step 4: Start and Access Jupyter Notebook

Launch Jupyter Notebook by executing the following command:

jupyter notebook --ip='*' --NotebookApp.token='' --NotebookApp.password=''

Or

~/.local/bin/jupyter-notebook --ip='*' --NotebookApp.token='' --NotebookApp.password=''

If you like you can setup a token or password:

~/.local/bin/jupyter-notebook --ip='*' --NotebookApp.token='supersecret1234' --NotebookApp.password=''

On GCP, open port 8888 or allow access to all.

• Click on default

• Acces the firewall

• Add a new rule.

• Create the rule and save it.

• Return to your VM instances.

• Open your web browser and type the following URL:

http://{your-ip}:8888/

Part 2: Create a bucket

• Follow the instructions to create a Bucket.
• First, navigate to the Bucket space.

• Then create a new Bucket.

• Then, add a new Bucket name.

• Click the confirm button.

• Load a dataset from Kaggle (IMDB Movies Dataset).

https://www.kaggle.com/datasets/harshitshankhdhar/imdb-dataset-of-top-1000-movies-and-tv-shows?resource=download

• Upload the dataset.

Part 3: Create a new dataset on BigQuery

• Navigate to the BigQuery interface.

• Create a new dataset

• Let's call it kaggle_data.

• Create a new table.

• Set the sourceto the Google Cloud Storage.

• Then, select your dataset from your Bucket.

• Finally, set your table name to movies and the schema to auto-detect.

• Let's run some simple queries - Click on your dataset and QUERY option in a new tab.

• Run a simple query.

  Do not forget to add a star(*) after the SELECT statement.

  SELECT * FROM `class-380310.kaggle_data.movies` LIMIT 10;
  

• Explore queries using SQL-like statements.

SELECT * FROM `class-380310.kaggle_data.movies` 
WHERE Series_Title Like "%Lord%";

Part 4: Access the data from Python

• Create a new IAM policy for BigQuery

• Access the service accounts.

• Create a new service account.

• Complete the service account details.

  Complete a name and a description.

• Navigate back to IAM and create the new principals.

• Add the new role.

Type BigQuery and select Admin.

• The new role should be dispalyed in your list.

• Let's create a new key to access the BigQuery dataset from outside. Go to Service accounts and select Manage keys.


• Add a new key.

• Download the JSON file into your local folder.

• Create a new Python script (I am using colab).

https://colab.research.google.com/drive/1br-6wQoanZGz_Zg0jayCOZI9_zthP3S9?usp=sharing

!pip install google-cloud-bigquery

from google.cloud import bigquery
from google.oauth2 import service_account

credentialsPath = r'class-380310-6fb7133281e5.json'

credentials = service_account.Credentials.from_service_account_file(credentialsPath)

client = bigquery.Client(credentials=credentials)

query = 'CALL class-380310.kaggle_data.movies'

results = client.query(query)

print(results)

query = client.query("""
  SELECT * FROM `class-380310.kaggle_data.movies` LIMIT 10
 """)

results = query.result()
for row in results:
	print(row)

Make sure you upload your key - or have your key in the same directory with your scirpts (if you run it locally)!

• FInally run your Python scripts!

Now delete everything you done!

• Navigate to Buckets and delete the Bucket.
• Navigate to BigQuery and delete your dataset.
• Navigate to the IAM and delete your principal (use the pen to edit the principal and delete the role).
• Navigate to the IAM → Service accounts and delete your BigQuery service account.

Part 5: Access the data from Python

• Create a new VM with more resources.

• Change the boot disk to Ubuntu 24.04 LTS *

Connect to the VM using SSH

1. Update your VM.

sudo apt-get update

2. Install python.

sudo apt-get install python3-pip -y

3. Download Ollama.

curl -fsSL https://ollama.com/install.sh | sh

4. Download and run the model.

ollama run llama2

• Ask a question!

How many Oscars has Sir Ian McKellen won?

You can exit using:

/bye

5. Create a Modelfile, for example using pico.

Run the following command.

pico Modelfile

6. Then enter the following text.

FROM llama2

# set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1

# set the system message
SYSTEM """
You are Mario from Super Mario Bros. Answer as Mario, the assistant, only.
"""

• Press together control and S to save.

• Then, control and X to exit.

7. Next, create and run the model:

ollama create mario -f ./Modelfile

• Then run the next command.

ollama run mario

• Now discuss with Mario!

Hi there!

You should reveive a response from Mario.

Press Control and C to break the chat at any time.

8. Install Ollama library

pip install ollama

9. Create a new Python script using pico.

pico test.py

10. Paste the following code, and then run your code!

import ollama

stream = ollama.chat(
    model='llama2',
    messages=[{'role': 'user', 'content': 'Who is Bowser? Show me only 2-3 lines'}],
    stream=True,
)

for chunk in stream:
  print(chunk['message']['content'], end='', flush=True)

• Or you can get a response from Mario, adjust your code to the mario model!

import ollama

stream = ollama.chat(
    model='mario',
    messages=[{'role': 'user', 'content': 'Who is Bowser? Show me only 2-3 lines'}],
    stream=True,
)

for chunk in stream:
  print(chunk['message']['content'], end='', flush=True)

11. Run your script!

python3 test.py

Optional material to setup a UI:

Install Docker

apt install docker.io

Install the web-ui docker container.

docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main

Go to VM at port 3000

import ollama

stream = ollama.chat(
    model='llama2',
    messages=[{'role': 'user', 'content': 'Why is the sky blue?'}],
    stream=True,
)

for chunk in stream:
  print(chunk['message']['content'], end='', flush=True)