Setting up Ollama on Python workbench and UU VRE workspaces

Dependencies

This tutorial assumes you have an installation of uv (Python package manager) on your workspace. Most SURF Research Cloud workspaces now come with uv preinstalled. If (in the rare case) you are using a workspace without any components, then you can install uv yourself with:

curl -LsSf https://astral.sh/uv/install.sh | sh

This tutorial also assumes you have a storage volume attached to your workspace.

Create a virtual environment

Open a terminal

• In the UU VRE workspace To open a new terminal, click the + button in the file browser and select the terminal in the new Launcher tab
  
• Python Workbench, click applications in the topright, and click terminal

Create a project folder

cd data/<the name of your storage volume>
mkdir project
cd project

Install Ollama

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

Create a virtual environment

uv venv --python 3.12
source .venv/bin/activate
uv pip install ipykernel
uv pip install ollama

Pull a model

Find models here: https://ollama.com/search

e.g.:

ollama pull gpt-oss

Create a jupyter kernel

python -m ipykernel install --user --name ollama --display-name "Ollama"

Create a new notebook and use

• In UU VRE: Click the + button and select the notebook with “Ollama” kernel in the new Launcher tab.

• In Python Workbench Desktop:

  uv pip install jupyter
  jupyter lab

Copy this example code, if needed change the model name to the model that you have downloaded and run the cell:

from ollama import chat
from ollama import ChatResponse

response: ChatResponse = chat(model='gpt-oss', messages=[
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
])
print(response['message']['content'])
# or access fields directly from the response object
print(response.message.content)