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Diagnostics (distributed)
=========================
The :doc:`Dask distributed scheduler <scheduling>` provides feedback in two
forms:
1. A progress bar suitable for interactive use in consoles or notebooks
2. An interactive dashboard, containing several plots and tables with live
information
Progress bar
------------
.. currentmodule:: dask.distributed
.. autosummary::
progress
The dask.distributed progress bar differs from the ``ProgressBar`` used for
:doc:`local diagnostics <diagnostics-local>`.
The ``progress`` function takes a Dask object that is executing in the background.
.. code-block:: python
# Single machine progress bar
from dask.diagnostics import ProgressBar
with ProgressBar():
x.compute()
# Distributed scheduler ProgressBar
from dask.distributed import Client, progress
client = Client() # use dask.distributed by default
x = x.persist() # start computation in the background
progress(x) # watch progress
x.compute() # convert to final result when done if desired
Dashboard
---------
.. currentmodule:: dask.distributed
.. autosummary::
Client
If `Bokeh <https://bokeh.pydata.org/en/latest/>`_ is installed
then the dashboard will start up automatically whenever the scheduler is created.
For local use this happens automatically when you create a client with no arguments
.. code-block:: python
from dask.distributed import Client
client = Client() # start distributed scheduler locally. Launch dashboard
It is typically served at http://localhost:8787/status ,
but may be served elsewhere if this port is taken.
The address of the dashboard will be displayed if you are in a Jupyter Notebook.
There are numerous pages with information about task runtimes, communication,
statistical profiling, load balancing, memory use, and much more.
For more information we recommend the following video guide:
.. raw:: html
<iframe width="560"
height="315"
src="https://www.youtube.com/embed/N_GqzcuGLCY"
frameborder="0"
allow="autoplay; encrypted-media"
allowfullscreen>
</iframe>
External Documentation
----------------------
More in-depth technical documentation about Dask's distributed scheduler is
available at https://distributed.dask.org/en/latest
API
---
.. autofunction:: progress
|
