Setup for both methods

Create a Conda Environment

We will again be utilizing YML files to define our conda environments. In addition to the packages we need for our analysis, we will also need to install the ipykernel package to enable our jupyter notebook to access the conda environment.

An example YML file may look something like:

name: notebook_env
channels:
  - conda-forge
  - bioconda
dependencies:
  - ipykernel
  - numpy
  - pandas
  - matplotlib
  - seaborn
  - scanpy

You will then need to manually create the conda environment. Assuming you had saved the file as notebook_env.yml, you can create the environment by running:

conda env create -f notebook_env.yml

Method 1 Overview - Jupyter & VSCode & Conda

It can be helpful to work directly in VSCode and Jupyter Notebooks when developing your pipelines. This will enable you to take advantage of both the features of VSCode and Jupyter Notebooks to make your development process more efficient.

Just like for our nextflow processes, we will want to carefully define a conda environment for the analysis we will be performing in our jupyter notebooks. These analyses being done in your notebook can often be some of the most biologically relevant and interesting results. It is critically important that these analyses are reproducible and transparent.

VSCode Requirements

You will need to install the following extensions for VSCode:

  • Python
  • Jupyter Notebook

You can install these extensions by opening the extensions view in VSCode (View > Extensions) and searching for the extensions listed above.

VSCode

If you open up VSCode, you can make a jupyter notebook. All this entails is making a new file and saving it with a .ipynb extension. The jupyter extension will automatically recognize this as a jupyter notebook and enable all of the standard jupyter notebook features.

VS Code Kernel

If you open this notebook and look in the top right corner, you should see the Select Kernel option. Please select this and then select Python Environments....

Select Kernel

From the dropdown menu, find the name of the conda environment you created, in our example it would be notebook_env, and select it. This will enable you to run your jupyter notebook using the conda environment you created.

Method 2 Overview - JupyterLab & Conda

BU SCC OnDemand also supports the use of JupyterLab, which is a feature-rich interface that provides a more modern and user-friendly experience for working with Jupyter Notebooks.

Under Interactive Apps, please select Jupyter Notebook to launch a jupyter notebook server on a compute node.

Please ensure that you do the following (you may refer to the image for reference as well):

  1. Load the miniconda module under List of modules to load

  2. Use the command listed below for your Pre-Launch Command

conda activate notebook_env
  1. Select the lab interface under Interface

OnDemand Jupyter

This will create a JupyterLab session with your conda environment activated. You can then use the built-in jupyter notebook interface to create and run your notebooks and access and use the packages installed in your conda environment.

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