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cloudmesh / cloudmesh-flow

Workflow for cloudmesh
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Workflow Management

Cloudmesh includes a simple tool for managing workflows. A cloudmesh workflow is a set of Python functions (that we call tasks or nodes) that form the workflow. The tasks can have dependencies. Thus, some may be run in sequence and some in parallel. You can visualize your workflows, including the monitoring the current status of the tasks, and the dependency network between them.

Prerequisit

To use this tool, you will need to already have

installed. Flow uses the same MongoDB that cloudmesh-cloud uses. You can set it up by follwowing our instructions in the manual.

Instalation

Please download cloudmesh-flow from github

$ git clone https://github.com/cloudmesh/cloudmesh-flow.git

To complet the install do 

$ cd cloudmesh-flow
$ pip install -e .

The commands are available by calling the flow commands via cms.

The first thing you have to do is to start the cloudmesh database in which we store information about the tasks as well as their status throughout the execution. If you have not yet started it please execute:

$ cms init

This command has to be called only once. Now you can test that the installation went well by calling

$ cms flow list

to list your current tasks. The command should complete correctly but output nothing if you have now prior workflows integrated.

Adding Tasks

There are several ways to add tasks to your workflow. One way is to use the commandline

$ cms flow node add TASKNAME

For example, to add a task named a call

cms flow node add a

This creates a reference to the task in the database, and adds the task to your default workflow, which is named workflow

By default, all tasks are run in parallel. Typically, you willl have multiple tasks that you need to run, some in sequence some in parallel. To add tasks a, b, and c, where a and b can be run in parallel but c depends on a, execute the following sequence:

$ cms flow node add a
$ cms flow node add b
$ cms flow node add c
$ cms flow edge add c a

This creates the 3 above nodes, and then adds the dependency. You can check that the nodes were added successfully by running

$ cms flow list

Your output should look similar to this:

Node "a" dependecies []
Node "b" dependencies []
Node "c" dependencies ["a"]

Multiple Workflows

Many times a user wants to have several different workflows defined. You can manage several workflows by passing the --flowname parameter to the cms flow command. For example, to add a new node in the workflow workflow2, run

$ cms flow node add d --flowname=workflow2

Now you will have 2 workflows, the default one with the nodes we added earlier, and the flow workflow2. You can see the different results by running the list command on each

$ cms flow list
$ cms flow list --flowname=workflow2

Adding A Complete Flow

While this process works for smaller workflows, it can be tedious to add 10 or 15 tasks via the command line. You can add a flow by defining a flowstring, which specifies many tasks and their dependencies all at once.

A flowstring is a single string with a list of tasks, separated by either a semicolon (;) or a double pipe (||). The semicolon indicates sequential dependency and the pipe indicates parallel. A sample flowstring for the flow we defined above is

"a;c||b"

Node a is joined sequentially with node c and in parallel with node b.

Adding a Flow String

Currently only supported in a YAML file.

Adding a Flow YAML Definition

You can also specify a flow in a YAML file. The file must have two properties, flowstring and flowname. This allows you to add an entire flow at once unders a specfic name. See the examples directory for details.

An example workflow specified as YAML is available at

https://github.com/cloudmesh/cloudmesh-flow/blob/main/example/sampleflow.yaml

Running Flows

Workflows nodes correspond to Python functions that are attached to specific workflow classes. To run a workflow, you need to specify the task definitions. The easiest way to see this in action is in the examples directory.

Defining a Workflow is simple: declare a class with one method for each node, and have the class inherit from the BaseWorkFlow base class. The method names should correspond to your node names, for example if you node is named "a" you must define a method named a() At the bottom of your file, copy the entry point code that is listed on each of the example files:

if __name__ == "__main__":
    Flow = MyFlow(sys.argv[0])
    Flow.runCommand(sys.argv[1])

This this allows our workflow engine to call your methods. Anything you return at the end of your function will be inserted into the database as the result of running that node. You should return a dict, which will be accessible via the result field in the db. Note that your filename should be of the form $flowname-flow.py. This makes it so the result parser can grab the result of your workflow methods and insert them into the database corresponding to the current run of $flowname.

Visualizing

The package cloudmesh.flow.visualize visualizes the workflows that have been created by the cloudmesh.flow package. This package consists of a rest service which essentially provides the following two methods:

Start the visualization service

To start the visualization service run the following command:

$ cms flow visualize start

The visualization rest service will then be available at the address

http://127.0.0.1:8080/flow/

To stop the rest service run the follwoing command:

$ cms flow visualize stop

At the page http://127.0.0.1:8080/flow/ you can find the list of all the submitted workflows.

http://127.0.0.1:8080/flow/{#fig:flow-visualize-home}

By selecting a workflow you can view a visualization of the workflow. The visualization of the workflow is getting updated every three seconds.

http://127.0.0.1:8080/flow/{workflow}{#fig:flow-visualize-workflow}

A node in a visualization has a color. The color represents the status of the task associated with the node. If the task is pending for execution, its color will be blue, if it is running then the color is orange and if it is finished then its color is green and if it is in error state, then its color is red. Otherwise the color is violet. On the right you can view the graph information including the name of the nodes and the dependencies between nodes. The features progress, modified time and done are added to the visualization package but currently these data are not present in the flow database or if present the data-access class does not retrieve them.

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