What is the most efficient way to create a `dask.array` from a dask.Series of list?
The series consists of 5 million lists 300 of elements.
It is currently divide into 500 partitions.
Currently I am trying:

```
pt = [delayed(np.array)(y)
      for y in
      [delayed(list)(x)
       for x in series.to_delayed()]]
da = delayed(dask.array.concatenate)(pt, axis=1)
da = dask.array.from_delayed(da, (vec.size.compute(), 300), dtype=float)

```

The idea is to convert each partition into a numpy array and stitch 
those together into a `dask.array`.
This code is taking forever to run though.
A numpy array can be built from this data quite quickly from this data sequentially as long as there is enough RAM.