# Worker Resources

Access to scarce resources like memory, GPUs, or special hardware may constrain how many of certain tasks can run on particular machines.

For example, we may have a cluster with ten computers, four of which have two GPUs each. We may have a thousand tasks, a hundred of which require a GPU and ten of which require two GPUs at once. In this case we want to balance tasks across the cluster with these resource constraints in mind, allocating GPU-constrained tasks to GPU-enabled workers. Additionally we need to be sure to constrain the number of GPU tasks that run concurrently on any given worker to ensure that we respect the provided limits.

This situation arises not only for GPUs but for many resources like tasks that require a large amount of memory at runtime, special disk access, or access to special hardware. Dask allows you to specify abstract arbitrary resources to constrain how your tasks run on your workers. Dask does not model these resources in any particular way (Dask does not know what a GPU is) and it is up to the user to specify resource availability on workers and resource demands on tasks.

## Example

We consider a computation where we load data from many files, process each one with a function that requires a GPU, and then aggregate all of the intermediate results with a task that takes up 70GB of memory.

We operate on a three-node cluster that has two machines with two GPUs each and one machine with 100GB of RAM.

When we set up our cluster we define resources per worker:

    dask worker scheduler:8786 --resources "GPU=2"
    dask worker scheduler:8786 --resources "GPU=2"
    dask worker scheduler:8786 --resources "MEMORY=100e9"

When we submit tasks to the cluster we specify constraints per task

    from distributed import Client
    client = Client('scheduler:8786')

    data = [client.submit(load, fn) for fn in filenames]
    processed = [client.submit(process, d, resources={'GPU': 1}) for d in data]
    final = client.submit(aggregate, processed, resources={'MEMORY': 70e9})

Equivalently, we can specify resource constraints using the dask annotations machinery:

    with dask.annotate(resources={'GPU': 1}):
        processed = [client.submit(process, d) for d in data]
    with dask.annotate(resources={'MEMORY': 70e9}):
        final = client.submit(aggregate, processed)

## Specifying Resources

Resources can be specified in several ways. The easiest option will depend on exactly how your cluster is being created.

From the command line

Resources can be provided when starting the worker process, as shown above:

    dask worker scheduler:8786 --resources "GPU=2"

The keys are used as the resource name and the values are parsed into a numeric value.

From Dask’s configuration system

Alternatively, resources can be specified using Dask’s configuration system.

    from distributed import LocalCluster

    with dask.config.set({"distributed.worker.resources.GPU": 2}):
        cluster = LocalCluster()

The configuration will need to be set in the process that’s spawning the actual worker. This might be easiest to achieve by specifying resources as an environment variable (shown in the next section).

From environment variables

Like any other Dask config value, resources can be specified as environment variables before starting the process. Using Bash syntax

    $ DASK_DISTRIBUTED__WORKER__RESOURCES__GPU=2 dask worker
    ...

This might be the easiest solution if you aren’t able to pass options to the `distributed.Worker` class.

## Resources are applied separately to each worker process

If you are using `dask worker --nworkers <nworkers>` the resource will be applied separately to each of the `nworkers` worker processes. Suppose you have 2 GPUs on your machine, if you want to use two worker processes, you have 1 GPU per worker process so you need to do something like this:

    dask worker scheduler:8786 --nworkers 2 --resources "GPU=1"

Here is an example that illustrates how to use resources to ensure each task is run inside a separate process, which is useful to execute non thread-safe tasks or tasks that uses multithreading internally:

    dask worker scheduler:8786 --nworkers 3 --nthreads 2 --resources "process=1"

With the code below, there will be at most 3 tasks running concurrently and each task will run in a separate process:

    from distributed import Client
    client = Client('scheduler:8786')

    futures = [client.submit(non_thread_safe_function, arg,
                             resources={'process': 1}) for arg in args]

## Resources are Abstract

Resources listed in this way are just abstract quantities. We could equally well have used terms “mem”, “memory”, “bytes” etc. above because, from Dask’s perspective, this is just an abstract term. You can choose any term as long as you are consistent across workers and clients.

It’s worth noting that Dask separately track number of cores and available memory as actual resources and uses these in normal scheduling operation.

## Resources with collections

You can also use resources with Dask collections, like arrays and delayed objects. You can annotate operations on collections with specific resources that should be required to perform the computation using the dask annotations machinery.

    # Read note below!
    dask.config.set({"optimization.fuse.active": False})
    x = da.read_zarr(...)
    with dask.annotate(resources={'GPU': 1}):
        y = x.map_blocks(func1)
    z = y.map_blocks(func2)
    z.compute()

Note

This feature is currently supported for dataframes only when `with dask.annotate(...):` wraps the compute() or persist() call; in that case, the annotation applies to the whole graph, starting from and excluding any previously persisted collections.

For other collections, like arrays and delayed objects, annotations can get lost during the optimization phase. To prevent this issue, you must set:

    >>> dask.config.set({"optimization.fuse.active": False})

Or in dask.yaml:

    optimization:
      fuse:
        active: false

A possible workaround, that also works for dataframes, can be to perform intermediate calls to persist(). Note however that this can significantly impact optimizations and reduce overall performance.

    x = dd.read_parquet(...)
    with dask.annotate(resources={'GPU': 1}):
        y = x.map_partitions(func1).persist()
    z = y.map_partitions(func2)
    del y  # Release distributed memory for y as soon as possible
    z.compute()