text: Hello everyone. Just wanted to know whether I can perform Dask array computations inside a delayed function, something like this.
  def dask_delayed_example(X): mean = X.mean(axis=0) std = X.std(axis=0) return X - mean X = df.to_dask_array(lengths=True) res = dask_delayed_example(X)
  Is Dask delayed only meant for parallelizing computation or can it also be used to perform computation on larger-than-memory datasets in a distributed manner?
  Hi there ! I'm trying to use
  dask-ssh as follows:
  % dask-ssh --scheduler master1 node1 --------------------------------------------------------------- Dask.distributed v1.27.0 Worker nodes: 0: node1 scheduler node: master1:8786 --------------------------------------------------------------- [ scheduler master1:8786 ] : /home/applis/anaconda/envs/py3v19.04/bin/python -m distributed.cli.dask_scheduler --port 8786 [ worker node1 ] : /home/applis/anaconda/envs/py3v19.04/bin/python -m None master1:8786 --nthreads 0 --host node1 --memory-limit auto
  but I cannot connect a client to the scheduler and logging does not provide more information.
  Client('master1:8786') fails with
  OSError: Timed out trying to connect... and I can not access the web ui at master1:8787. However, running
  dask-scheduler and
  dask-workerwork fine. Any suggestion ?
  da.from_array(large_numpy_array)was blowing up workers, as (counterintuitively) the
  large_numpy_arrayis not actually partitioned along the expected chunks. Each worker appears to get a full copy of
  large_numpy_arrayregardless of the chunking.
  from_array(large_array, chunks=chunks)[0].compute(), for example, does not allocate data to workers the way one would expect from the chunking
  scatter, is that right?