docs/source/diagnostics-distributed.rst
:orphan:
The :doc:Dask distributed scheduler <scheduling> provides live feedback in two
forms:
For information on the Dask dashboard see :doc:dashboard.
.. currentmodule:: dask.distributed
.. autosummary:: get_task_stream Client.profile performance_report
You can capture some of the same information that the dashboard presents for
offline processing using the get_task_stream and Client.profile
functions. These capture the start and stop time of every task and transfer,
as well as the results of a statistical profiler.
.. code-block:: python
with get_task_stream(plot='save', filename="task-stream.html") as ts: x.compute()
client.profile(filename="dask-profile.html")
history = ts.data
Additionally, Dask can save many diagnostics dashboards at once including the
task stream, worker profiles, bandwidths, etc. with the performance_report
context manager:
.. code-block:: python
from dask.distributed import performance_report
with performance_report(filename="dask-report.html"):
## some dask computation
The following video demonstrates the performance_report context manager in greater
detail:
.. raw:: html
<iframe width="560"
height="315"
src="https://www.youtube.com/embed/nTMGbkS761Q"
frameborder="0"
allow="autoplay; encrypted-media"
allowfullscreen>
</iframe>
.. 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
from dask.diagnostics import ProgressBar
with ProgressBar(): x.compute()
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
Some computer networks may restrict access to certain ports or only allow access from certain machines. If you are unable to access the dashboard then you may want to contact your IT administrator.
Some common problems and solutions follow:
Specify an accessible port
Some clusters restrict the ports that are visible to the outside world. These
ports may include the default port for the web interface, ``8787``. There are
a few ways to handle this:
1. Open port ``8787`` to the outside world. Often this involves asking your
cluster administrator.
2. Use a different port that is publicly accessible using the
``--dashboard-address :8787`` option on the ``dask-scheduler`` command.
3. Use fancier techniques, like `Port Forwarding`_
Port Forwarding
~~~~~~~~~~~~~~~
If you have SSH access then one way to gain access to a blocked port is through
SSH port forwarding. A typical use case looks like the following:
.. code:: bash
local$ ssh -L 8000:localhost:8787 user@remote
remote$ dask-scheduler # now, the web UI is visible at localhost:8000
remote$ # continue to set up dask if needed -- add workers, etc
It is then possible to go to ``localhost:8000`` and see Dask Web UI. This same approach is
not specific to dask.distributed, but can be used by any service that operates over a
network, such as Jupyter notebooks. For example, if we chose to do this we could
forward port 8888 (the default Jupyter port) to port 8001 with
``ssh -L 8001:localhost:8888 user@remote``.
Required Packages
~~~~~~~~~~~~~~~~~
Bokeh must be installed in your scheduler's environment to run the dashboard. If it's not the dashboard page will instruct you to install it.
Depending on your configuration, you might also need to install ``jupyter-server-proxy`` to access the dashboard.
API
---
.. autofunction:: progress
.. autofunction:: get_task_stream