It is easy to get started with Dask delayed, but using it well does require some experience. This page contains suggestions for best practices, and includes solutions to common problems.
Call delayed on the function, not the result¶
Dask delayed operates on functions like
dask.delayed(f)(x, y), not on their results like
dask.delayed(f(x, y)). When you do the latter, Python first calculates
f(x, y) before Dask has a chance to step in.
Compute on lots of computation at once¶
To improve parallelism, you want to include lots of computation in each compute call.
Ideally, you want to make many
dask.delayed calls to define your computation and
dask.compute only at the end. It is ok to call
in the middle of your computation as well, but everything will stop there as
Dask computes those results before moving forward with your code.
for x in L: y = dask.delayed(f)(x) y.compute() # calling compute after every delayed call stops parallelism
results =  for x in L: y = dask.delayed(f)(x) results.append(y) results = dask.compute(*results) # call compute after you have collected many delayed calls
Don’t mutate inputs¶
Your functions should not change the inputs directly.
@dask.delayed def f(x): x += 1 return x
@dask.delayed def f(x): return x + 1
If you need to use a mutable operation, then make a copy within your function first:
@dask.delayed def f(x): x = copy(x) x += 1 return x
Avoid global state¶
Ideally, your operations shouldn’t rely on global state. Using global state might work if you only use threads, but when you move to multiprocessing or distributed computing then you will likely encounter confusing errors.
L =  @dask.delayed def f(x): L.append(x)
Don’t rely on side effects¶
Delayed functions only do something if they are computed. You will always need to pass the output to something that eventually calls compute.
dask.delayed(f)(1, 2, 3) # this has no effect
x = dask.delayed(f)(1, 2, 3) ... dask.compute(x, ...) # need to call compute for something to happen
Break up computations into many pieces¶
dask.delayed function call is a single operation from Dask’s perspective.
You achieve parallelism by having many delayed calls, not by using only a
single one: Dask will not look inside a function decorated with
and parallelize that code internally. To accomplish that, it needs your help to
find good places to break up a computation.
def load(filename): ... def process(data): ... def save(data): ... @dask.delayed def f(filenames): results =  for filename in filenames: data = load(filename) data = process(data) results.append(save(data)) return results dask.compute(f(filenames)) # this is only a single task
@dask.delayed def load(filename): ... @dask.delayed def process(data): ... @dask.delayed def save(data): ... def f(filenames): results =  for filename in filenames: data = load(filename) data = process(data) results.append(save(data)) return results dask.compute(f(filenames)) # this has many tasks and so will parallelize
Avoid too many tasks¶
Every delayed task has an overhead of a few hundred microseconds. Usually this
is ok, but it can become a problem if you apply
dask.delayed too finely. In
this case, it’s often best to break up your many tasks into batches or use one
of the Dask collections to help you.
results =  for x in range(1000000000): # Too many dask.delayed calls y = dask.delayed(f)(x) results.append(y)
# Use collections import dask.bag as db b = db.from_sequence(1000000000, npartitions=1000) b = b.map(f)
# Or batch manually def batch(seq): sub_results =  for x in seq: sub_results.append(f(x)) return sub_results batches =  for i in range(0, 1000000000, 1000000): # in steps of 1000000 result_batch = dask.delayed(batch, range(i, i + 1000000)) batches.append(result_batch)
Avoid calling delayed within delayed functions¶
Often, if you are new to using Dask delayed, you place
everywhere and hope for the best. While this may actually work, it’s usually
slow and results in hard-to-understand solutions.
Usually you never call
@dask.delayed def process_all(L): result =  for x in L: y = dask.delayed(f)(x) result.append(y) return result
Instead, because this function only does delayed work, it is very fast and so there is no reason to delay it.
def process_all(L): result =  for x in L: y = dask.delayed(f)(x) result.append(y) return result
Don’t call dask.delayed on other Dask collections¶
When you place a Dask array or Dask DataFrame into a delayed call, that function will receive the NumPy or Pandas equivalent. Beware that if your array is large, then this might crash your workers.
Instead, it’s more common to use methods like
df.map_partitions, or to turn your arrays or DataFrames into many delayed
import dask.dataframe as dd df = dd.read_csv('/path/to/*.csv') dask.delayed(train)(df) # might as well have used Pandas instead
import dask.dataframe as dd df = dd.read_csv('/path/to/*.csv') df.map_partitions(train) # or partitions = df.to_delayed() delayed_values = [dask.delayed(train)(part) for part in partitions]
However, if you don’t mind turning your Dask array/DataFrame into a single chunk, then this is ok.
Avoid repeatedly putting large inputs into delayed calls¶
Every time you pass a concrete result (anything that isn’t delayed) Dask will hash it by default to give it a name. This is fairly fast (around 500 MB/s) but can be slow if you do it over and over again. Instead, it is better to delay your data as well.
This is especially important when using a distributed cluster to avoid sending your data separately for each function call.
x = np.array(...) # some large array results = [dask.delayed(train)(x, i) for i in range(1000)]
Every call to
dask.delayed(train)(x, ...) has to hash the NumPy array
x, which slows things down.
x = np.array(...) # some large array x = dask.delayed(x) # delay the data, hashing once results = [dask.delayed(train)(x, i) for i in range(1000)]