# Launch Tasks from Tasks

Sometimes it is convenient to launch tasks from other tasks. For example you may not know what computations to run until you have the results of some initial computations.

## Motivating example

We want to download one piece of data and turn it into a list. Then we want to submit one task for every element of that list. We don’t know how long the list will be until we have the data.

So we send off our original `download_and_convert_to_list` function, which downloads the data and converts it to a list on one of our worker machines:

    future = client.submit(download_and_convert_to_list, uri)

But now we need to submit new tasks for individual parts of this data. We have three options.

1. Gather the data back to the local process and then submit new jobs from the local process

2. Gather only enough information about the data back to the local process and submit jobs from the local process

3. Submit a task to the cluster that will submit other tasks directly from that worker

## Gather the data locally

If the data is not large then we can bring it back to the client to perform the necessary logic on our local machine:

    >>> data = future.result()                  # gather data to local process
    >>> data                                    # data is a list
    [...]

    >>> futures = e.map(process_element, data)  # submit new tasks on data
    >>> analysis = e.submit(aggregate, futures) # submit final aggregation task

This is straightforward and, if `data` is small then it is probably the simplest, and therefore correct choice. However, if `data` is large then we have to choose another option.

## Submit tasks from client

We can run small functions on our remote data to determine enough to submit the right kinds of tasks. In the following example we compute the `len` function on `data` remotely and then break up data into its various elements.

    >>> n = client.submit(len, data)            # compute number of elements
    >>> n = n.result()                          # gather n (small) locally

    >>> from operator import getitem
    >>> elements = [client.submit(getitem, data, i) for i in range(n)]  # split data

    >>> futures = client.map(process_element, elements)
    >>> analysis = client.submit(aggregate, futures)

We compute the length remotely, gather back this very small result, and then use it to submit more tasks to break up the data and process on the cluster. This is more complex because we had to go back and forth a couple of times between the cluster and the local process, but the data moved was very small, and so this only added a few milliseconds to our total processing time.

### Extended Example

Computing the Fibonacci numbers involves a recursive function. When the function is run, it calls itself using values it computed. We will use this as an example throughout this documentation to illustrate different techniques of submitting tasks from tasks.

    def fib(n):
        if n < 2:
            return n
        a = fib(n - 1)
        b = fib(n - 2)
        return a + b

    print(fib(10))  # prints "55"

We will use this example to show the different interfaces.

## Submit tasks from worker

Note: this interface is new and experimental. It may be changed without warning in future versions.

We can submit tasks from other tasks. This allows us to make decisions while on worker nodes.

To submit new tasks from a worker that worker must first create a new client object that connects to the scheduler. There are three options for this:

1. `dask.delayed` and `dask.compute`

2. `get_client` with `secede` and `rejoin`

3. `worker_client`

### dask.delayed

The Dask delayed behaves as normal: it submits the functions to the graph, optimizes for less bandwidth/computation and gathers the results. For more detail, see dask.delayed.

    from distributed import Client
    from dask import delayed, compute

    @delayed
    def fib(n):
        if n < 2:
            return n
        # We can use dask.delayed and dask.compute to launch
        # computation from within tasks
        a = fib(n - 1)  # these calls are delayed
        b = fib(n - 2)
        a, b = compute(a, b)  # execute both in parallel
        return a + b

    if __name__ == "__main__":
        # these features require the dask.distributed scheduler
        client = Client()

        result = fib(10).compute()
        print(result)  # prints "55"

### Getting the client on a worker

The `get_client` function provides a normal Client object that gives full access to the dask cluster, including the ability to submit, scatter, and gather results.

    from distributed import Client, get_client, secede, rejoin

    def fib(n):
        if n < 2:
            return n
        client = get_client()
        a_future = client.submit(fib, n - 1)
        b_future = client.submit(fib, n - 2)
        a, b = client.gather([a_future, b_future])
        return a + b

    if __name__ == "__main__":
        client = Client()
        future = client.submit(fib, 10)
        result = future.result()
        print(result)  # prints "55"

However, this can deadlock the scheduler if too many tasks request jobs at once. Each task does not communicate to the scheduler that they are waiting on results and are free to compute other tasks. This can deadlock the cluster if every scheduling slot is running a task and they all request more tasks.

To avoid this deadlocking issue we can use `secede` and `rejoin`. These functions will remove and rejoin the current task from the cluster respectively.

    def fib(n):
        if n < 2:
            return n
        client = get_client()
        a_future = client.submit(fib, n - 1)
        b_future = client.submit(fib, n - 2)
        secede()
        a, b = client.gather([a_future, b_future])
        rejoin()
        return a + b

### Connection with context manager

The `worker_client` function performs the same task as `get_client`, but is implemented as a context manager. Using `worker_client` as a context manager ensures proper cleanup on the worker.

    from dask.distributed import Client, worker_client

    def fib(n):
        if n < 2:
            return n
        with worker_client() as client:
            a_future = client.submit(fib, n - 1)
            b_future = client.submit(fib, n - 2)
            a, b = client.gather([a_future, b_future])
        return a + b

    if __name__ == "__main__":
        client = Client()
        future = client.submit(fib, 10)
        result = future.result()
        print(result)  # prints "55"

Tasks that invoke `worker_client` are conservatively assumed to be long running. They can take a long time, waiting for other tasks to finish, gathering results, etc. In order to avoid having them take up processing slots the following actions occur whenever a task invokes `worker_client`.

1. The thread on the worker running this function secedes from the thread pool and goes off on its own. This allows the thread pool to populate that slot with a new thread and continue processing additional tasks without counting this long running task against its normal quota.

2. The Worker sends a message back to the scheduler temporarily increasing its allowed number of tasks by one. This likewise lets the scheduler allocate more tasks to this worker, not counting this long running task against it.

Establishing a connection to the scheduler takes a few milliseconds and so it is wise for computations that use this feature to be at least a few times longer in duration than this.