Cog plays nicely with Jupyter notebooks.
First, add jupyterlab
to the python_packages
array in your cog.yaml
file:
build:
python_packages:
- "jupyterlab==3.3.4"
Cog can run notebooks in the environment you've defined in cog.yaml
with the following command:
cog run -p 8888 jupyter notebook --allow-root --ip=0.0.0.0
You can also import a notebook into your Cog Predictor file.
First, export your notebook to a Python file:
jupyter nbconvert --to script my_notebook.ipynb # creates my_notebook.py
Then import the exported Python script into your predict.py
file. Any functions or variables defined in your notebook will be available to your predictor:
from cog import BasePredictor, Input
import my_notebook
class Predictor(BasePredictor):
def predict(self, prompt: str = Input(description="string prompt")) -> str:
output = my_notebook.do_stuff(prompt)
return output