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Add notebook packaging guide to docs (#690)
add notebook packaging guide Co-authored-by: Jun Sheng <jscheng@google.com> Co-authored-by: Ishmeet Mehta <ishmeetm@google.com>
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# Packaging Jupyter notebook as deployable code | ||
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Jupyter notebook is widely used by data scientists and machine learning experts in their day to day work to interactively and iteratively develop. However, the `ipynb` format is typically not used as a deployable or packagable artifact. There are two scenarios that notebooks are converted to deployable/package artifacts: | ||
1. Model training tasks needed to convert to batch jobs to scale up with more computational resources | ||
1. Model inference tasks needed to convert to an API server to serve the end-user requests | ||
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In this guide we will showcase two different tools which may help faciliate converting your notebook to a deployable/packageable raw python library. | ||
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This process can also be automated utilizing Continuous Integration (CI) tools such as [Cloud Build](https://cloud.google.com/build/). | ||
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## Use jupytext to convert notebook to raw python and containerize | ||
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1. Update the notebook to `Pair Notebook with Percent Format` | ||
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Jupytext comes with recent jupyter notebook or jupyter-lab. In addition to just converting from `ipynb` to python, it can pair between the formats. This allows for updates made in `ipynb` to be propagated to python and vice versa. | ||
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To pair the notebook, simply use the pair function in the File menu: | ||
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![jupyter-pairing](../images/notebook/jupyter-pairing.png) | ||
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In this example we use the file [gpt-j-online.ipynb](https://github.com/GoogleCloudPlatform/ai-on-gke/blob/main/ray-on-gke/examples/notebooks/gpt-j-online.ipynb):![jupyter-gpt-j-online-ipynb](../images/notebook/jupyter-gpt-j-online-ipynb.png) | ||
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1. After pairing, we get the generated raw python: | ||
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![jupyter-gpt-j-online-py](../images/notebook/jupyter-gpt-j-online-py.png) | ||
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**NOTE**: This conversion can also be performed via the `jupytext` cli with the following command: | ||
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```sh | ||
jupytext --set-formats ipynb,py:percent \ | ||
--to py gpt-j-online.ipynb | ||
``` | ||
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1. Extract the module dependencies | ||
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In the notebook environment, users typically install required python modules using `pip install` commands, but in the container environment, these dependencies need to be installed into the container prior to executing the python library. | ||
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We can use the `pipreqs` tool to generate the dependencies. Add the following snippet in a new cell of your notebook and run it: | ||
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```sh | ||
!pip install pipreqs | ||
!pipreqs --scan-notebooks | ||
``` | ||
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The following is an example output: | ||
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![jupyter-generate-requirements](../images/notebook/jupyter-generate-requirements.png) | ||
**NOTE**: (the `!cat requirements.txt` line is an example of the generated `requirements.txt`) | ||
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1. Create the Dockerfile | ||
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To create the docker image of your generated raw python, we need to create a `Dockerfile`, below is an example. Replace `_THE_GENERATED_PYTHON_FILE_` with your generated python file: | ||
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```Dockerfile | ||
FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04 | ||
RUN apt-get update && \ | ||
apt-get -y --no-install-recommends install python3-dev gcc python3-pip git && \ | ||
rm -rf /var/lib/apt/lists/* | ||
RUN pip3 install --no-cache-dir -r requirements.txt | ||
COPY _THE_GENERATED_PYTHON_FILE_ /_THE_GENERATED_PYTHON_FILE_ | ||
ENV PYTHONUNBUFFERED 1 | ||
CMD python3 /_THE_GENERATED_PYTHON_FILE_ | ||
``` | ||
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1. [Optional] Lint and remove unused code | ||
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Using `pylint` to validate the generated code is a good practice. Pylint can detect unordered `import` statements, unused code and provide code readability suggestions. | ||
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To use `pylint`, create a new cell in your notebook, run the code below and replace `_THE_GENERATED_PYTHON_FILE_` to your filename: | ||
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```sh | ||
!pip install pylint | ||
!pylint _THE_GENERATED_PYTHON_FILE_ | ||
``` | ||
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## Use nbconvert to convert notebook to raw python | ||
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We can convert a Jupyter notebook to python script using nbconvert tool. | ||
The nbconvert tool is available inside your Jupyter notebook environment in Google Colab Enterprise. If you are in another environment and it is not available, it can be found [here](https://pypi.org/project/nbconvert/) | ||
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1. Run the nbconvert command in your notebook. In this example, we are using `gsutil` to copy the notebook to the Colab Enterprise notebook. | ||
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```sh | ||
!jupyter nbconvert --to python Fine-tune-Llama-Google-Colab.ipynb | ||
``` | ||
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Below is an example of the commands | ||
![jupyter-nbconvert](../images/notebook/jupyter-nbconvert.png) |