.. _ray-dag-guide:

Lazy Computation Graphs with the Ray DAG API
============================================

With ``ray.remote`` you have the flexibility of running an application where
computation is executed remotely at runtime. For a ``ray.remote`` decorated
class or function, you can also use ``.bind`` on the body to build a static
computation graph.

.. note::

     Ray DAG is designed to be a developer facing API where recommended use cases
     are

     1) Locally iterate and test your application authored by higher level libraries.

     2) Build libraries on top of the Ray DAG APIs.


.. note::
    
    Ray has introduced an experimental API for high-performance workloads that is
    especially well suited for applications using multiple GPUs. This API is built on top of
    the Ray DAG API.

    See :ref:`Ray Compiled Graph <ray-compiled-graph>` for more details.


When ``.bind()`` is called on a ``ray.remote`` decorated class or function, it will
generate an intermediate representation (IR) node that act as backbone and
building blocks of the DAG that is statically holding the computation graph
together, where each IR node is resolved to value at execution time with
respect to their topological order.

The IR node can also be assigned to a variable and passed into other nodes as
arguments.

Ray DAG with functions
----------------------

The IR node generated by ``.bind()`` on a ``ray.remote`` decorated function is
executed as a Ray Task upon execution which will be solved to the task output.

This example shows how to build a chain of functions where each node can be
executed as root node while iterating, or used as input args or kwargs of other
functions to form more complex DAGs.

Any IR node can be executed directly ``dag_node.execute()`` that acts as root
of the DAG, where all other non-reachable nodes from the root will be ignored.

.. tab-set::

    .. tab-item:: Python

        .. literalinclude:: ./doc_code/ray-dag.py
          :language: python
          :start-after: __dag_tasks_begin__
          :end-before: __dag_tasks_end__


Ray DAG with classes and class methods
--------------------------------------

The IR node generated by ``.bind()`` on a ``ray.remote`` decorated class is
executed as a Ray Actor upon execution. The Actor will be instantiated every
time the node is executed, and the classmethod calls can form a chain of
function calls specific to the parent actor instance.

DAG IR nodes generated from a function, class or classmethod can be combined
together to form a DAG.

.. tab-set::

    .. tab-item:: Python

        .. literalinclude:: ./doc_code/ray-dag.py
          :language: python
          :start-after: __dag_actors_begin__
          :end-before: __dag_actors_end__



Ray DAG with custom InputNode
-----------------------------

``InputNode`` is the singleton node of a DAG that represents user input value at
runtime. It should be used within a context manager with no args, and called
as args of ``dag_node.execute()``

.. tab-set::

    .. tab-item:: Python

        .. literalinclude:: ./doc_code/ray-dag.py
          :language: python
          :start-after: __dag_input_node_begin__
          :end-before: __dag_input_node_end__

Ray DAG with multiple MultiOutputNode
-------------------------------------

``MultiOutputNode`` is useful when you have more than 1 output from a DAG. ``dag_node.execute()``
returns a list of Ray object references passed to ``MultiOutputNode``. The below example
shows the multi output node of 2 outputs.

.. tab-set::

    .. tab-item:: Python

        .. literalinclude:: ./doc_code/ray-dag.py
          :language: python
          :start-after: __dag_multi_output_node_begin__
          :end-before: __dag_multi_output_node_end__

Reuse Ray Actors in DAGs
------------------------
Actors can be a part of the DAG definition with the ``Actor.bind()`` API.
However, when a DAG finishes execution, Ray kills Actors created with ``bind``.

You can avoid killing your Actors whenever DAG finishes by creating Actors with ``Actor.remote()``.

.. tab-set::

    .. tab-item:: Python

        .. literalinclude:: ./doc_code/ray-dag.py
          :language: python
          :start-after: __dag_actor_reuse_begin__
          :end-before: __dag_actor_reuse_end__


More resources
--------------

You can find more application patterns and examples in the following resources
from other Ray libraries built on top of Ray DAG API with the same mechanism.

|  `Ray Serve Compositions of Models <https://docs.ray.io/en/master/serve/model_composition.html>`_
|  `Visualization of Ray Compiled Graph <https://docs.ray.io/en/latest/ray-core/compiled-graph/profiling.html#visualization>`_