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Using Multiple CPU Cores

When running locally, tasks are executed as separate processes. The operating system takes care of allocating them to separate CPU cores, so they will actually execute in parallel assuming that enough CPU cores are available. Hence, your flow can utilize multiple cores without you having to do anything special besides defining branches in the flow.

When running remotely on @batch or @kubernetes, branches are mapped to separate jobs that are executed in parallel, allowing you to scale horizontally to any number of parallel tasks. In addition, you may take advantage of multiple CPU cores inside a task. This may happen automatically if you use a modern ML library like PyTorch or Scikit Learn, or you may parallelize functions explicitly, as explained below.

Mapping items in parallel‚Äč

Metaflow provides a utility function called parallel_map that helps take advantage of multiple CPU cores. This function is almost equivalent to Pool().map in the Python's built-in multiprocessing library. The main differences are the following:

  • parallel_map supports lambdas and any other callables of Python.
  • parallel_map does not suffer from bugs present in multiprocessing.
  • parallel_map can handle larger amounts of data.

You may also use parallel_map to parallelize simple operations that might be too cumbersome to implement as separate steps.

Here is an extension of our previous example that implements a multicore sum() by partitioning the matrix by row:

from metaflow import FlowSpec, step, batch, parallel_map

class BigSum(FlowSpec):

@resources(memory=60000, cpu=8)
def start(self):
import numpy
import time
big_matrix = numpy.random.ranf((80000, 80000))
t = time.time()
parts = parallel_map(lambda i: big_matrix[i:i+10000].sum(),
range(0, 80000, 10000))
self.sum = sum(parts)
self.took = time.time() - t

def end(self):
print("The sum is %f." % self.sum)
print("Computing it took %dms." % (self.took * 1000))

if __name__ == '__main__':

Note that we use cpu=8 to request enough CPU cores, so our parallel_map can benefit from optimal parallelism. Disappointingly, in this case the parallel sum is not faster than the original simple implementation due to the overhead of launching separate processes in parallel_map. A less trivial operation might see a much larger performance boost.