Graph concepts and state machine#

A graph is composed of the following:

  • Step: A Step runs a function or class handler or a REST API call. MLRun comes with a list of pre-built steps that include data manipulation, readers, writers and model serving. You can also write your own steps using standard Python functions or custom functions/classes, or can be a external REST API (the special $remote class).

  • Router: A special type of step is a router with routing logic and multiple child routes/models. The basic routing logic is to route to the child routes based on the event.path. More advanced or custom routing can be used, for example, the ensemble router sends the event to all child routes in parallel, aggregates the result and responds.

  • Queue: A queue or stream that accepts data from one or more source steps and publishes to one or more output steps. Queues are best used to connect independent functions/containers. Queues can run in-memory or be implemented using a stream, which allows it to span processes/containers.

The Graph server has two modes of operation (topologies):

  • Router topology (default): A minimal configuration with a single router and child tasks/routes. This can be used for simple model serving or single hop configurations.

  • Flow topology: A full graph/DAG. The flow topology is implemented using two engines: async (the default) is based on Storey and asynchronous event loop; and sync, which supports a simple sequence of steps.

This section presents:

The Event object#

The Graph state machine accepts an Event object (similar to a Nuclio Event) and passes it along the pipeline. An Event object hosts the event body along with other attributes such as path (http request path), method (GET, POST, …), andid (unique event ID).

In some cases the events represent a record with a unique key, which can be read/set through the event.key. Records have associated event.time that, by default, is the arrival time, but can also be set by a step.

The Task steps are called with the event.body by default. If a task step needs to read or set other event elements (key, path, time, …) you should set the task full_event argument to True.

Task steps support optional input_path and result_path attributes that allow controlling which portion of the event is sent as input to the step, and where to update the returned result.

For example, for an event body {"req": {"body": "x"}}, input_path="req.body" and result_path="resp" the step gets "x" as the input. The output after the step is {"req": {"body": "x"}: "resp": <step output>}. Note that input_path and result_path do not work together with full_event=True.

The Context object#

The step classes are initialized with a context object (when they have context in their __init__ args). The context is used to pass data and for interfacing with system services. The context object has the following attributes and methods.

Attributes:

  • logger: Central logger (Nuclio logger when running in Nuclio).

  • verbose: True if in verbose/debug mode.

  • root: The graph object.

  • current_function: When running in a distributed graph, the current child function name.

Methods:

  • get_param(key, default=None): Get the graph parameter by key. Parameters are set at the serving function (e.g. function.spec.parameters = {"param1": "x"}).

  • get_secret(key): Get the value of a project/user secret.

  • get_store_resource(uri, use_cache=True): Get the mlrun store object (data item, artifact, model, feature set, feature vector).

  • get_remote_endpoint(name, external=False): Return the remote nuclio/serving function http(s) endpoint given its [project/]function-name[:tag].

  • Response(headers=None, body=None, content_type=None, status_code=200): Create a nuclio response object, for returning detailed http responses.

Example, using the context:

 if self.context.verbose:
        self.context.logger.info('my message', some_arg='text')
    x = self.context.get_param('x', 0)

Topology#

Router#

Once you have a serving function, you need to choose the graph topology. The default is router topology. With the router topology you can specify different machine learning models. Each model has a logical name. This name is used to route to the correct model when calling the serving function.

from sklearn.datasets import load_iris

# set the topology/router
graph = fn.set_topology("router")

# Add the model
fn.add_model("model1", class_name="ClassifierModel", model_path="https://s3.wasabisys.com/iguazio/models/iris/model.pkl")

# Add additional models
#fn.add_model("model2", class_name="ClassifierModel", model_path="<path2>")

# create and use the graph simulator
server = fn.to_mock_server()
x = load_iris()['data'].tolist()
result = server.test("/v2/models/model1/infer", {"inputs": x})

print(result)
> 2021-11-02 04:18:36,925 [info] model model1 was loaded
> 2021-11-02 04:18:36,926 [info] Initializing endpoint records
> 2021-11-02 04:18:36,965 [info] Loaded ['model1']
{'id': '6bd11e864805484ea888f58e478d1f91', 'model_name': 'model1', 'outputs': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]}

Flow#

Using the flow topology, you can specify tasks, which typically manipulate the data. The most common scenario is pre-processing of data prior to the model execution.

Note

Once the topology is set, you cannot change an existing function topology.

In this topology, you build and connect the graph (DAG) by adding steps using the step.to() method, or by using the graph.add_step() method.

The step.to() is typically used to chain steps together. graph.add_step can add steps anywhere on the graph and has before and after parameters to specify the location of the step.

fn2 = mlrun.code_to_function("serving_example_flow",
                             kind="serving", 
                             image="mlrun/mlrun")

graph2 = fn2.set_topology("flow")     

graph2_enrich = graph2.to("storey.Extend", name="enrich", _fn='({"tag": "something"})')

# add an Ensemble router with two child models (routes)
router = graph2.add_step(mlrun.serving.ModelRouter(), name="router", after="enrich")
router.add_route("m1", class_name="ClassifierModel", model_path='https://s3.wasabisys.com/iguazio/models/iris/model.pkl')
router.respond()

# Add additional models
#router.add_route("m2", class_name="ClassifierModel", model_path=path2)

# plot the graph (using Graphviz)
graph2.plot(rankdir='LR')
../_images/f707b7a3e0019266c2b046f092c113e7adabe36fb14dd1e09c3e4969237b8bb6.svg
fn2_server = fn2.to_mock_server()

result = fn2_server.test("/v2/models/m1/infer", {"inputs": x})

print(result)
> 2021-11-02 04:18:42,142 [info] model m1 was loaded
> 2021-11-02 04:18:42,142 [info] Initializing endpoint records
> 2021-11-02 04:18:42,183 [info] Loaded ['m1']
{'id': 'f713fd7eedeb431eba101b13c53a15b5'}

Building distributed graphs#

Graphs can be hosted by a single function (using zero to n containers), or span multiple functions where each function can have its own container image and resources (replicas, GPUs/CPUs, volumes, etc.). It has a root function, which is where you configure triggers (http, incoming stream, cron, …), and optional downstream child functions.

You can specify the function attribute in Task or Router steps. This indicates where this step should run. When the function attribute is not specified it runs on the root function. function="*" means the step can run in any of the child functions.

Steps on different functions should be connected using a Queue step (a stream).

Adding a child function:

```python
fn.add_child_function('enrich', 
                      './entity_extraction.ipynb', 
                      image='mlrun/mlrun',
                      requirements=["storey", "sklearn"])
```

See a full example with child functions.

A distributed graph looks like this:

distributed graph

Error handling#

Graph steps might raise an exception. If you want to have an error handling flow, you can specify an exception handling step/branch that is triggered on error. The error handler step receives the event that entered the failed step, with two extra attributes: event.origin_state indicates the name of the failed step; and event.error holds the error string.

Use the graph.error_handler() (apply to all steps) or step.error_handler() (apply to a specific step) if you want the error from the graph or the step to be fed into a specific step (catcher).

Example of setting an error catcher per step:

graph.add_step("MyClass", name="my-class", after="pre-process").error_handler("catcher")
graph.add_step("ErrHandler", name="catcher", full_event=True, after="")

Note

Additional steps can follow the catcher step.

Using the example in Getting started with model serving, you can add an error handler as follows:

graph2_enrich.error_handler("catcher")
graph2.add_step("ErrHandler", name="catcher", full_event=True, after="")
<mlrun.serving.states.TaskStep at 0x7fd46e557750>

Now, display the graph again:

graph2.plot(rankdir='LR')
<mlrun.serving.states.TaskStep at 0x7fd46e557750>

Exception stream#

The graph errors/exceptions can be pushed into a special error stream. This is very convenient in the case of distributed and production graphs.

To set the exception stream address (using v3io streams uri):

fn_preprocess2.spec.error_stream = err_stream