Overview — Anaconda Enterprise 4.4 documentation

Anaconda Enterprise: Documentation

# Overview

An explanation of dask task graphs.

## Motivation

Normally, humans write programs and then compilers/interpreters interpret them (for example `python`, `javac`, `clang`). Sometimes humans disagree with how these compilers/interpreters choose to interpret and execute their programs. In these cases humans often bring the analysis, optimization, and execution of code into the code itself.

Commonly a desire for parallel execution causes this shift of responsibility from compiler to human developer. In these cases, we often represent the structure of our program explicitly as data within the program itself.

A common approach to parallel execution in user-space is task scheduling. In task scheduling we break our program into many medium-sized tasks or units of computation, often a function call on a non-trivial amount of data. We represent these tasks as nodes in a graph with edges between nodes if one task depends on data produced by another. We call upon a task scheduler to execute this graph in a way that respects these data dependencies and leverages parallelism where possible, multiple independent tasks can be run simultaneously.

Many solutions exist. This is a common approach in parallel execution frameworks. Often task scheduling logic hides within other larger frameworks (Luigi, Storm, Spark, IPython Parallel, and so on) and so is often reinvented.

Dask is a specification that encodes task schedules with minimal incidental complexity using terms common to all Python projects, namely dicts, tuples, and callables. Ideally this minimum solution is easy to adopt and understand by a broad community.

## Example

Consider the following simple program:

    def inc(i):
        return i + 1

    def add(a, b):
        return a + b

    x = 1
    y = inc(x)
    z = add(y, 10)

We encode this as a dictionary in the following way:

    d = {'x': 1,
         'y': (inc, 'x'),
         'z': (add, 'y', 10)}

While less pleasant than our original code, this representation can be analyzed and executed by other Python code, not just the CPython interpreter. We don’t recommend that users write code in this way, but rather that it is an appropriate target for automated systems. Also, in non-toy examples, the execution times are likely much larger than for `inc` and `add`, warranting the extra complexity.

## Schedulers

The `dask` library currently contains a few schedulers to execute these graphs. Each scheduler works differently, providing different performance guarantees and operating in different contexts. These implementations are not special and others can write different schedulers better suited to other applications or architectures easily. Systems that emit dask graphs (like `dask.array`, `dask.bag`, and so on) may leverage the appropriate scheduler for the application and hardware.