ParallelPostFit excessive scheduler memory use and CancelledError

Notebook with MCVE and all notes: https://nbviewer.jupyter.org/gist/rikturr/43336377678018d01d4f21f19dd7ef11

When using `ParallelPostFit` to train with pandas/numpy objects then predict on dask objects, I noticed that the scheduler memory use runs extremely high. Many times I would get a `CancelledError` and scheduler dying when calling `.predict()` with pretty small data sizes (refer to notebook for full code with outputs):

```python
X_train, X_test, y_train, y_test = ...
rf = ParallelPostFit(
RandomForestClassifier(n_estimators=100, random_state=seed, n_jobs=-1)
)
_ = rf.fit(X_train, y_train)

preds = rf.predict(X_test)
_ = preds.compute() # failure happens on this line after ~40 minutes
```

The scheduler memory balloons to npartitions of `X_test` * size of `rf`, which can get into the multiple GBs very fast. I noticed that each time an operation would get called on `preds`, this memory exchange would happen again. I realize that this is because `ParallePostFit` uses `map_partitions` behind the scenes, but does not broadcast the model objects. This causes Dask to send the object through the scheduler to each worker _each time you do something with `preds`_ (unless of course you persisted it).

Workaround is to broadcast the model object then use `map_partitions` yourself instead of the `ParallelPostFit` wrapper:

```python
rf = RandomForestClassifier(n_estimators=100, random_state=seed, n_jobs=-1)
_ = rf.fit(X_train, y_train)

rf_fut = client.scatter(rf, broadcast=True)

def dask_predict(df, model):
return model.predict(df)

preds = X_test.map_partitions(
dask_predict,
model=rf_fut,
meta=np.array([1])
)
```

I plan to follow up with a PR to fix this in the `ParallelPostFit` class