Anaconda Enterprise: Documentation

# Frequently Asked Questions

More questions can be found on StackOverflow at http://stackoverflow.com/search?tab=votes&q=dask%20distributed

## How do I use external modules?

Use `client.upload_file`. For more detail, see the API docs and a StackOverflow question “Can I use functions imported from .py files in Dask/Distributed?” This function supports both standalone file and setuptools’s `.egg` files for larger modules.

## Too many open file descriptors?

Your operating system imposes a limit to how many open files or open network connections any user can have at once. Depending on the scale of your cluster the `dask-scheduler` may run into this limit.

By default most Linux distributions set this limit at 1024 open files/connections and OS-X at 128 or 256. Each worker adds a few open connections to a running scheduler (somewhere between one and ten, depending on how contentious things get.)

If you are on a managed cluster you can usually ask whoever manages your cluster to increase this limit. If you have root access and know what you are doing you can change the limits on Linux by editing `/etc/security/limits.conf`. Instructions are here under the heading “User Level FD Limits”: http://www.cyberciti.biz/faq/linux-increase-the-maximum-number-of-open-files/

## Error when running dask-worker about `OMP_NUM_THREADS`

For more problems with OMP_NUM_THREADS, see http://stackoverflow.com/questions/39422092/error-with-omp-num-threads-when-using-dask-distributed

## Does Dask handle Data Locality?

Yes, both data locality in memory and data locality on disk.

Often it’s much cheaper to move computations to where data lives. If one of your tasks creates a large array and a future task computes the sum of that array, you want to be sure that the sum runs on the same worker that has the array in the first place, otherwise you’ll wait for a long while as the data moves between workers. Needless communication can easily dominate costs if we’re sloppy.

The Dask Scheduler tracks the location and size of every intermediate value produced by every worker and uses this information when assigning future tasks to workers. Dask tries to make computations more efficient by minimizing data movement.

Sometimes your data is on a hard drive or other remote storage that isn’t controlled by Dask. In this case the scheduler is unaware of exactly where your data lives, so you have to do a bit more work. You can tell Dask to preferentially run a task on a particular worker or set of workers.

For example Dask developers use this ability to build in data locality when we communicate to data-local storage systems like the Hadoop File System. When users use high-level functions like `dask.dataframe.read_csv('hdfs:///path/to/files.*.csv'` Dask talks to the HDFS name node, finds the locations of all of the blocks of data, and sends that information to the scheduler so that it can make smarter decisions and improve load times for users.

## PermissionError [Errno 13] Permission Denied: `/root/.dask`

This error can be seen when starting distributed through the standard process control tool `supervisor` and running as a non-root user. This is caused by `supervisor` not passing the shell environment variables through to the subprocess, head to this section of the supervisor documentation to see how to pass the `$HOME` and `$USER` variables through.