# Dill

For some functions or tasks it can be useful to not rely on pickle, but on some more powerful serialization backends like dill. `dill` isn’t installed by default. See Dill for more information on installing the dependencies.

One specific example where `dill` shines is when using start method `spawn` (the default on Windows) in combination with iPython or Jupyter notebooks. `dill` enables parallelizing more exotic objects like lambdas and functions defined in iPython and Jupyter notebooks. For all benefits of `dill`, please refer to the dill documentation.

Once the dependencies have been installed, you can enable it using the `use_dill` flag:

    with WorkerPool(n_jobs=4, use_dill=True) as pool:
        ...

Note

When using `dill` it can potentially slow down processing. This is the cost of having a more reliable and powerful serialization backend.