# Using IPython for parallel computing
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## Installing IPython Parallel
As of 4.0, IPython parallel is now a standalone package called {mod}`ipyparallel`. You can install it with:
`
pip install ipyparallel
`
or:
`
conda install ipyparallel
`
As of IPython Parallel 7, this will include installing/enabling an extension for both the classic Jupyter Notebook and JupyterLab ≥ 3.0.
## Quickstart
IPython Parallel
A quick example to:
allocate a cluster (collection of IPython engines for use in parallel)
run a collection of tasks on the cluster
wait interactively for results
cleanup resources after the task is done
```python import time import ipyparallel as ipp
task_durations = [1] * 25 # request a cluster with ipp.Cluster() as rc:
# get a view on the cluster view = rc.load_balanced_view() # submit the tasks asyncresult = view.map_async(time.sleep, task_durations) # wait interactively for results asyncresult.wait_interactive() # retrieve actual results result = asyncresult.get()
# at this point, the cluster processes have been shutdown ```
<video autoplay loop width=”100%”> <source src=”_static/basic.mp4”/> </video>
Follow the [tutorial][] to learn more.
[tutorial]: ./tutorial/index
## Contents
tutorial/index reference/index ```
# IPython Parallel API
# Indices and tables
{ref}`genindex`
{ref}`modindex`
{ref}`search`