Running Parallel Computing on Jupyter Notebook: A tutorial on how to utilize Jupyter Notebook for parallel computing, including how to use tools like IPython parallel and Dask.

3 min readJul 19, 2023

Introduction

Jupyter Notebook is a great tool for data analysis and machine learning. However, when dealing with large datasets and computationally intensive tasks, performance can become an issue.

Luckily, there are several ways to achieve parallel computing and distribute tasks across multiple CPU cores from within Jupyter Notebooks. This can significantly speed up your analysis and model training.

This blog post will cover two popular options for parallel computing on Jupyter Notebook:

Note

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In-browser editing of code
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Understanding Parallel Computing:

What is Parallel Computing?

Gain a foundational understanding of parallel computing and its benefits, including increased computational speed, improved scalability, and efficient utilization of resources.

Types of Parallel Computing

Explore different approaches to parallel computing, such as task parallelism and data parallelism, and understand when each approach is applicable.

1) IPython parallel

IPython parallel (ipyparallel) allows you to parallelize Python code across multiple engines.

Install ipyparallel:

! sudo pip install ipyparallel

Start the IPython controller:

ipcontroller --port=8888

Start as many engines as CPU cores you want to use:

ipengine --ip=127.0.0.1 --port=8888

From your Jupyter Notebook, connect to the controller:

from ipyparallel import Client

c = Client(profile='default')

Distribute tasks to engines using:

c[:].apply_async(function, args)

Results are collected and returned as a list.

2) Dask

Dask provides advanced parallelism and out-of-core computing. It has a lot of useful tools:

dask.array for large arrays
dask.dataframe for parallel dataframes
dask.distributed for cluster computing

You can install and use dask from within Jupyter.

For example, to parallelize a map operation:

  import dask.array as da

  x = da.from_array(array, chunks=(1000,))

  result = x.map_blocks(func, dtype='f8').compute()

Best Practices for Parallel Computing

Load Balancing: Distribute the workload evenly across parallel workers to achieve optimal performance and minimize idle time.
Minimize Communication Overhead: Reduce unnecessary data transfers and communication between workers to enhance computational efficiency.
Scalability Considerations: Keep scalability in mind when designing parallel computations, ensuring that the system can handle increasing workloads as the data size grows.
Monitoring and Debugging: Utilize monitoring tools and techniques to identify bottlenecks, diagnose issues, and optimize the performance of your parallel code.

Conclusion

Jupyter Notebook is not just a tool for sequential data analysis; it also provides powerful capabilities for parallel computing. By incorporating tools like IPython parallel and Dask into your Jupyter Notebook workflows, you can harness the power of parallelism, enabling faster and more scalable computations. Whether you choose IPython parallel for task distribution or Dask for distributed computing, parallel computing in Jupyter Notebook opens up a world of possibilities for data scientists and researchers, allowing them to tackle complex problems and process large-scale data with ease. Embrace the parallel computing paradigm in Jupyter Notebook and unlock a new level of productivity and performance in your data analysis endeavors.