Quick Start - Jupyter Notebooks (MNIST)¶
In this tutorial, we will use the Valohai Jupyter Notebook Extension to build a machine learning model using the MNIST dataset.
To use jupyter notebook extension, you will need:
- Docker (https://docs.docker.com/install)
valohai/mnist_notebook docker image from the DockerHub.
docker pull valohai/mnist_notebook
If you want to use an image without examples in it, use
3. Start the notebook server¶
Run the notebook server on local port 8888.
docker run -p 8888:8888 valohai/mnist_notebook
Open in browser: http://127.0.0.1:8888
If you want to run your own notebooks, use:
docker run -p 8888:8888 -v "$PWD":/home/jovyan/work valohai/jupyhai
This mounts your current working directory into the container.
- All the files in your current working directory will be available within the container
- All changes within the mounted folder will persist after shutting down
4. Open a notebook¶
Create new notebook in the /work folder or choose tf-mnist-valohai.ipynb.
5. Sign in¶
Press the Valohai button in the toolbar and login using your Valohai credentials.
6. Configure settings¶
Press the Valohai button in the toolbar and go to settings window.
Select the following:
- Project: Valohai project where the executions will be version controlled
- Environment: Environment type for the cloud executions (E.g. AWS p2.xlarge)
- Docker Image: Docker image that provides the required libraries (E.g. TensorFlow)
These are the same settings you would choose when using Valohai website, CLI or valohai.yaml.
Once you are happy with your selections. Press save.
7. Create an execution¶
Press the Valohai button in the toolbar and select Create execution.
The gizmo for the new execution will appear to the right.
8. Watch the results¶
Each of the colored gizmos on the right side of the page signify a single Valohai execution. You can click the #1 > Notebook button to download the finished notebook back to your local machine.
9. Parameterize the notebook¶
Parameterizing a notebook happens through cell tags. Tags are a standard Jupyter feature.
Here we mark the first cell with a
parameters tag, which means all variables are considered to be
Valohai parameters, just like you would define in the valohai.yaml.
10. Download the inputs¶
Here we marked the first cell with
inputs tag and ran it in Valohai.
All the variables in this cell will be considered as Valohai input URIs for the execution, just like in the valohai.yaml.
11. Reusing the parameterized notebook¶
Now you can run notebook based experiments without a notebook!
learning_rate is parameterized, you can change it via Valohai web interface and run
additional experiments without even opening the notebook.
When I try to download my outputs back from a finished execution, I get
404: Not Found
Always use the notebook server through
http://127.0.0.1:8888 instead of