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})

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, dataframes, and delayed objects. You can pass a dictionary mapping keys of the collection to resource requirements during compute or persist calls.

x = dd.read_csv(...)
y = x.map_partitions(func1)
z = y.map_parititons(func2)

z.compute(resources={tuple(y._keys()): {'GPU': 1})

In some cases (such as the case above) the keys for y may be optimized away before execution. You can avoid that either by requiring them as an explicit output, or by passing the optimize_graph=False keyword.

z.compute(resources={tuple(y._keys()): {'GPU': 1}, optimize_graph=False)