Debugging parallel programs is hard. Normal debugging tools like logging and using pdb to interact with tracebacks stop working normally when exceptions occur in far-away machines, different processes, or threads.

Dask has a variety of mechanisms to make this process easier. Depending on your situation, some of these approaches may be more appropriate than others.

These approaches are ordered from lightweight or easy solutions to more involved solutions.


One of the most basic methods of debugging is to simply print values and inspect them. However, when using Python’s built-in print() function with Dask, those prints often happen on remote machines instead of in the user’s Python session, which typically isn’t the experience developers want when debugging.

Because of this, Dask offers a dask.distributed.print function which acts just like Python’s built-in print() but also forwards the printed output to the client side Python session. This makes distributed debugging feel more like debugging locally.


When a task in your computation fails, the standard way of understanding what went wrong is to look at the exception and traceback. Often people do this with the pdb module, IPython %debug or %pdb magics, or by just looking at the traceback and investigating where in their code the exception occurred.

Normally when a computation executes in a separate thread or a different machine, these approaches break down. To address this, Dask provides a few mechanisms to recreate the normal Python debugging experience.

Inspect Exceptions and Tracebacks

By default, Dask already copies the exception and traceback wherever they occur and reraises that exception locally. If your task failed with a ZeroDivisionError remotely, then you’ll get a ZeroDivisionError in your interactive session. Similarly you’ll see a full traceback of where this error occurred, which, just like in normal Python, can help you to identify the troublesome spot in your code.

However, you cannot use the pdb module or %debug IPython magics with these tracebacks to look at the value of variables during failure. You can only inspect things visually. Additionally, the top of the traceback may be filled with functions that are Dask-specific and not relevant to your problem, so you can safely ignore these.

Both the single-machine and distributed schedulers do this.

Use the Single-Threaded Scheduler

Dask ships with a simple single-threaded scheduler. This doesn’t offer any parallel performance improvements but does run your Dask computation faithfully in your local thread, allowing you to use normal tools like pdb, %debug IPython magics, the profiling tools like the cProfile module, and snakeviz. This allows you to use all of your normal Python debugging tricks in Dask computations, as long as you don’t need parallelism.

The single-threaded scheduler can be used, for example, by setting scheduler='single-threaded' in a compute call:

>>> x.compute(scheduler='single-threaded')

For more ways to configure schedulers, see the scheduler configuration documentation.

This only works for single-machine schedulers. It does not work with dask.distributed unless you are comfortable using the Tornado API (look at the testing infrastructure docs, which accomplish this). Also, because this operates on a single machine, it assumes that your computation can run on a single machine without exceeding memory limits. It may be wise to use this approach on smaller versions of your problem if possible.

Rerun Failed Task Locally

If a remote task fails, we can collect the function and all inputs, bring them to the local thread, and then rerun the function in hopes of triggering the same exception locally where normal debugging tools can be used.

With the single-machine schedulers, use the rerun_exceptions_locally=True keyword:

>>> x.compute(rerun_exceptions_locally=True)

On the distributed scheduler use the recreate_error_locally method on anything that contains Futures:

>>> x.compute()

>>> %pdb
>>> future = client.compute(x)
>>> client.recreate_error_locally(future)

Remove Failed Futures Manually

Sometimes only parts of your computations fail, for example, if some rows of a CSV dataset are faulty in some way. When running with the distributed scheduler, you can remove chunks of your data that have produced bad results if you switch to dealing with Futures:

>>> import dask.dataframe as dd
>>> df = ...           # create dataframe
>>> df = df.persist()  # start computing on the cluster

>>> from distributed.client import futures_of
>>> futures = futures_of(df)  # get futures behind dataframe
>>> futures
[<Future: status: finished, type: pd.DataFrame, key: load-1>
 <Future: status: finished, type: pd.DataFrame, key: load-2>
 <Future: status: error, key: load-3>
 <Future: status: pending, key: load-4>
 <Future: status: error, key: load-5>]

>>> # wait until computation is done
>>> while any(f.status == 'pending' for f in futures):
...     sleep(0.1)

>>> # pick out only the successful futures and reconstruct the dataframe
>>> good_futures = [f for f in futures if f.status == 'finished']
>>> df = dd.from_delayed(good_futures, meta=df._meta)

This is a bit of a hack, but often practical when first exploring messy data. If you are using the concurrent.futures API (map, submit, gather), then this approach is more natural.

Inspect Scheduling State

Not all errors present themselves as exceptions. For example, in a distributed system workers may die unexpectedly, your computation may be unreasonably slow due to inter-worker communication or scheduler overhead, or one of several other issues. Getting feedback about what’s going on can help to identify both failures and general performance bottlenecks.

For the single-machine scheduler, see local diagnostic documentation. The rest of the section will assume that you are using the distributed scheduler where these issues arise more commonly.

Web Diagnostics

First, the distributed scheduler has a number of diagnostic tools showing dozens of recorded metrics like CPU, memory, network, and disk use, a history of previous tasks, allocation of tasks to workers, worker memory pressure, work stealing, open file handle limits, etc. Many problems can be correctly diagnosed by inspecting these pages. By default, these are available at http://scheduler:8787/ where scheduler should be replaced by the address of the scheduler. See diagnosing performance docs for more information.


The scheduler, workers, and client all emits logs using Python’s standard logging module. By default, these emit to standard error. When Dask is launched by a cluster job scheduler (SGE/SLURM/YARN/Mesos/Marathon/Kubernetes/whatever), that system will track these logs and will have an interface to help you access them. If you are launching Dask on your own, they will probably dump to the screen unless you redirect stderr to a file .

You can control the logging verbosity in the Configuration, for example, the ~/.config/dask/*.yaml files. Defaults currently look like the following:

  distributed: info
  distributed.client: warning
  bokeh: error

Logging for specific components like distributed.client, distributed.scheduler, distributed.nanny, distributed.worker, etc. can each be independently configured. So, for example, you could add a line like distributed.worker: debug to get very verbose output from the workers.

Furthermore, you can explicitly assign handlers to loggers. The following example assigns both file (“output.log”) and console output to the scheduler and workers. See the python logging documentation for information on the meaning of specific terms here.

  version: 1
      class: logging.handlers.RotatingFileHandler
      filename: output.log
      level: INFO
      class: logging.StreamHandler
      level: INFO
      level: INFO
        - file
        - console
      level: INFO
        - file
        - console


If you are using the distributed scheduler from a single machine, you may be setting up workers manually using the command line interface or you may be using LocalCluster which is what runs when you just call Client():

>>> from dask.distributed import Client, LocalCluster
>>> client = Client()  # This is actually the following two commands

>>> cluster = LocalCluster()
>>> client = Client(cluster.scheduler.address)

LocalCluster is useful because the scheduler and workers are in the same process with you, so you can easily inspect their state while they run (they are running in a separate thread):

>>> cluster.scheduler.processing
{'worker-one:59858': {'inc-123', 'add-443'},
 'worker-two:48248': {'inc-456'}}

You can also do this for the workers if you run them without nanny processes:

>>> cluster = LocalCluster(nanny=False)
>>> client = Client(cluster)

This can be very helpful if you want to use the Dask distributed API and still want to investigate what is going on directly within the workers. Information is not distilled for you like it is in the web diagnostics, but you have full low-level access.

Inspect state with IPython

Sometimes you want to inspect the state of your cluster but you don’t have the luxury of operating on a single machine. In these cases you can launch an IPython kernel on the scheduler and on every worker, which lets you inspect state on the scheduler and workers as computations are completing.

This does not give you the ability to run %pdb or %debug on remote machines. The tasks are still running in separate threads, and so are not easily accessible from an interactive IPython session.

For more details, see the Dask distributed IPython docs.