CODEWITHSUNDEEP
pythondask
JRR
JRR

Reputation: 6152

dask distributed memory error

I got the following error on the scheduler while running Dask on a distributed job:

distributed.core - ERROR -
Traceback (most recent call last):
  File "/usr/local/lib/python3.4/dist-packages/distributed/core.py", line 269, in write
    frames = protocol.dumps(msg)
  File "/usr/local/lib/python3.4/dist-packages/distributed/protocol.py", line 81, in dumps
    frames = dumps_msgpack(small)
  File "/usr/local/lib/python3.4/dist-packages/distributed/protocol.py", line 153, in dumps_msgpack
    payload = msgpack.dumps(msg, use_bin_type=True)
  File "/usr/local/lib/python3.4/dist-packages/msgpack/__init__.py", line 47, in packb
    return Packer(**kwargs).pack(o)
  File "msgpack/_packer.pyx", line 231, in msgpack._packer.Packer.pack (msgpack/_packer.cpp:231)
  File "msgpack/_packer.pyx", line 239, in msgpack._packer.Packer.pack (msgpack/_packer.cpp:239)
MemoryError

Is this running out of memory on the scheduler or on one of the workers? Or both??

Upvotes: 2

Views: 2706

Answers (1)

MRocklin
MRocklin

Reputation: 57319

The most common cause of this error is trying to collect too much data, such as occurs in the following example using dask.dataframe:

df = dd.read_csv('s3://bucket/lots-of-data-*.csv')
df.compute()

This loads all of the data into RAM across the cluster (which is fine), and then tries to bring the entire result back to the local machine by way of the scheduler (which probably can't handle your 100's of GB of data all in one place.) Worker-to-client communications pass through the Scheduler, so it is the first single machine to receive all of the data and the first machine likely to fail.

If this is the case then you instead probably want to use the Executor.persist method, to trigger computation but leave it on the cluster.

df = dd.read_csv('s3://bucket/lots-of-data-*.csv')
df = e.persist(df)

Generally we only use df.compute() for small results that we want to view in our local session.

Upvotes: 2

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