Diagnostics (distributed)¶
The Dask distributed scheduler provides live feedback in two forms:
An interactive dashboard containing many plots and tables with live information
A progress bar suitable for interactive use in consoles or notebooks
Dashboard¶
For information on the Dask dashboard see Dashboard Diagnostics.
Capture diagnostics¶
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.
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:
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:
Progress bar¶
The dask.distributed
progress bar differs from the ProgressBar
used for
local diagnostics.
The progress
function takes a Dask object that is executing in the background:
# Progress bar on a single-machine scheduler
from dask.diagnostics import ProgressBar
with ProgressBar():
x.compute()
# Progress bar with the distributed scheduler
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
Connecting to the Dashboard¶
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:
Open port
8787
to the outside world. Often this involves asking your cluster administrator.Use a different port that is publicly accessible using the
--dashboard-address :8787
option on thedask-scheduler
command.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:
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.