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This section describes how you can use the
python-pachyderm client from within the Pachyderm IDE.
You need to have Pachyderm IDE installed as described in Deploy the Pachyderm IDE.
When you deploy the Pachyderm IDE, you get JupyterHub and a customized JupyterLab UI running next to your Pachyderm cluster.
Before we proceed, let's clarify the following terms as they might be confusing for first-time users:
JupyterHub is a popular data science platform that enables users to quickly spin out multiple single-tenant Jupyter Notebook server instances.
Jupyter Notebooks provide a means for conducting experiments with data and code written in Python which is familiar to many data scientists. Because of the built-in rich-text support, visualizations, the easy-to-use web interface, many enterprise users prefer Jupyter Notebooks to the classic Terminal prompt. JupyterHub brings all the benefits of Jupyter Notebooks without the need to install or configure anything on user machines except for a web browser.
JupyterLab is an alternative UI for the classic Jupyter Notebook IDE that enables you to author and test notebooks and code.
python-pachyderm is an official Python client for Pachyderm. For Python developers who prefer to communicate with Pachyderm directly through the API, rather than by using the
python-pachydermis the right choice.
python-pachyderm is preinstalled in your Pachyderm IDE.
Difference in Pipeline Creation Methods¶
python-pachyderm supports the standard create_pipeline method that is also available through the Pachyderm CLI and UI. When you use
create_pipeline, you need to build a new Docker image and push it to an image registry every time you update the code in your pipeline. Users that are less familiar with Docker might find this process a bit cumbersome. However, you must use this method for all non-Python code.
When you use
python-pachyderm, in addition to the
create_pipeline method, you can use the create_python_pipeline function that does not require you to include your code in a Docker image and rebuild it each time you make a change. Instead, this function creates a PFS repository called
<pipeline_name>_source and puts the source code into it. Also, it creates a
<pipeline_name>_build repository to build Python dependencies. Therefore, when you use
create_python_pipeline, your DAG includes two additional repositories for each pipeline. Because of that, you do not need to build a new Docker image every time you change something in your pipeline code. You can run your code instantly. This method is ideal for users who want to avoid building Docker images.
While you can mix and match pipeline creation methods in Pachyderm IDE, you might eventually want to pick one method that works for your use case. It is a matter of personal preference which method to use. While some users, those who write code in Python, in particular, might find it convenient to avoid the Docker build workflow, others might want to enable Docker in JupyterHub or build Docker images from their local machines.