Install kernels and libraries in an EMR Studio Workspace - Amazon EMR
Amazon EMR on EC2Amazon EMR on EKSAmazon EMR Serverless

Install kernels and libraries in an EMR Studio Workspace

Each Amazon EMR Studio Workspace comes with a set of pre-installed libraries and kernels.

Kernels and libraries on clusters that run on Amazon EC2

You can also customize the environment for EMR Studio in the following ways when you use EMR clusters running on Amazon EC2:

  • Install Jupyter Notebook kernels and Python libraries on a cluster primary node – When you install libraries using this option, all Workspaces attached to the same cluster share those libraries. You can install kernels or libraries from within a notebook cell or while connected using SSH to the primary node of a cluster.

  • Use notebook-scoped libraries – When Workspace users install and use libraries from within a notebook cell, those libraries only available to that notebook alone. This option lets different notebooks using the same cluster work without worrying about conflicting library versions.

EMR Studio Workspaces have the same underlying architecture as EMR Notebooks. You can install and use Jupyter Notebook kernels and Python libraries with EMR Studio in the same way you would with EMR Notebooks. For instructions, see Installing and using kernels and libraries.

Kernels and libraries on Amazon EMR on EKS clusters

Amazon EMR on EKS clusters include the PySpark and Python 3.7 kernels with a set of pre-installed libraries. Amazon EMR on EKS does not support installing additional libraries or clusters.

Each Amazon EMR on EKS cluster comes with the following Python and PySpark libraries installed:

  • Python – boto3, cffi, future, ggplot, jupyter, kubernetes, matplotlib, numpy, pandas, plotly, pycryptodomex, py4j, requests, scikit-learn, scipy, seaborn

  • PySpark – ggplot, jupyter, matplotlib, numpy, pandas, plotly, pycryptodomex, py4j, requests, scikit-learn, scipy, seaborn

Kernels and libraries on EMR Serverless applications

Each EMR Serverless application comes with the following Python and PySpark libraries installed:

  • Python – ggplot, matplotlib, numpy, pandas, plotly, bokeh, scikit-learn, scipy, seaborn

  • PySpark – ggplot, matplotlib,numpy, pandas, plotly, bokeh, scikit-learn, scipy, seaborn