Developer Workflow tutorial

Creating new workflows

In this section we are going to write a very simple AiiDA workflow. Before starting this tutorial, we assume that you have successfully completed the Developer calculation plugin tutorial and have your input and output plugins ready to use with this tutorial.

This tutorial creates a workflow for the addition of three numbers. Number could be an integer or a float value. All three numbers will be passed as parameters to the workflow in dictionary format (e.g. {"a": 1, "b": 2.2, "c":3}).

To demonstrate how a workflow works, we will perform the sum of three numbers in two steps:

  1. Step 1: temp_value = a + b
  2. Step 2: sum = temp_value + c

A workflow in AiiDA is a python script with several user defined functions called steps. All AiiDA functions are available inside “steps” and calculations or sub-workflows can be launched and retrieved. The AiiDA daemon executes a workflow and handles all the operations starting from script loading, error handling and reporting, state monitoring and user interaction with the execution queue. The daemon works essentially as an infinite loop, iterating several simple operations:

  1. It checks the running step in all the active workflows, if there are new calculations attached to a step it submits them.
  2. It retrieves all the finished calculations. If one step of one workflow exists where all the calculations are correctly finished it reloads the workflow and executes the next step as indicated in the script.
  3. If a workflow’s next step is the exit one, the workflow is terminated and the report is closed.


Since the daemon is aware only of the classes present at the time of its launch, make sure you restart the daemon every time you add a new workflow, or modify an existing one. To restart a daemon, use following command:

verdi daemon restart

Let’s start to write a workflow step by step. First we have to import some packages:

from aiida.common import aiidalogger
from aiida.orm.workflow import Workflow
from aiida.orm import Code, Computer
from import ParameterData
from aiida.common.exceptions import InputValidationError

In order to write a workflow, we must create a class by extending the Workflow class from aiida.orm.workflow. This is a fundamental requirement, since the subclassing is the way AiiDA understand if a class inside the file is an AiiDA workflow or a simple utility class. In the class, you need to re-define an __init__ method as shown below (in the current code version, this is a requirement). Create a new file, which has the same name as the class you are creating (in this way, it will be possible to load it with WorkflowFactory), in this case, with the following content:

class AddnumbersWorkflow(Workflow):
    This workflow takes 3 numbers as an input and gives
    its addition as an output.
    Workflow steps:
    passed parameters: a,b,c
    1st step: a + b = step1_result
    2nd step: step1_result + c = final_result

    def __init__(self, **kwargs):
        super(AddnumbersWorkflow, self).__init__(**kwargs)

Once the class is defined a user can add methods to generate calculations, download structures or compute new structures starting form a query in previous AiiDA calculations present in the DB. Here we will add simple helper function to validate the input parameters which will be the dictionary with keys a, b and c. All dictionary values should be of type integer or float.

def validate_input(self):
    Check if the passed parameters are of type int or float
    else raise exception
    # get parameters passed to workflow when it was
    # initialised. These parameters can not be modified
    # during an execution
    params = self.get_parameters()

    for k in ['a','b','c']:
            # check if value is int or float
            if not (isinstance(params[k], int) or isinstance(params[k], float)):
                raise InputValidationError("Value of {} is not of type int or float".format(k))
        except KeyError:
            raise InputValidationError("Missing input key {}".format(k))

    # add in report
    self.append_to_report("Starting workflow with params: {0}".format(params))

In the above method we have used append_to_report workflow method. Once the workflow is launched, the user interactions are limited to some events (stop, relaunch, list of the calculations). So most of the times it is very useful to have custom messages during the execution. Hence, workflow is equipped with a reporting facility self.append_to_report(string), where the user can fill with any text and can retrieve both live and at the end of the execution.

Now we will add the method to launch the actual calculations. We have already done this as part of plugin exercise and hence we do not discuss it in detail here.

def get_calculation_sum(self, a, b):
        launch new calculation
        :param a: number
        :param b: number
        :return: calculation object, already stored
        # get code/executable file
        codename = 'sum'
        code = Code.get_from_string(codename)

        computer_name = 'localhost'
        computer = Computer.get(computer_name)

        # create new calculation
        calc = code.new_calc()
        calc.label = "Add two numbers"
        calc.description = "Calculation step in a workflow to add more than two numbers"
        calc.set_max_wallclock_seconds(30*60) # 30 min
        calc.set_resources({"num_machines": 1})

        # pass input to the calculation
        parameters = ParameterData(dict={'x1': a,'x2':b,})

        # store calculation in database
        return calc

Now we will write the first step which is one of the main components in the workflow. In the example below, the start method is decorated with Workflow.step making it a very unique kind of method, automatically stored in the database as a container of calculations and sub-workflows.

    def start(self):
        Addition for first two parameters passed to workflow
        when it was initialised

        except InputValidationError:

        # get first parameter passed to workflow when it was initialised.
        a = self.get_parameter("a")
        # get second parameter passed to workflow when it was initialised.
        b = self.get_parameter("b")

        # start first calculation
        calc = self.get_calculation_sum(a, b)

        # add in report
        self.append_to_report("First step calculation is running...")

        # attach calculation in workflow to access in next steps

        # go to next step

Several functions are available to the user when coding a workflow step, and in the above method we have used basic ones discussed below:

  • self.get_parameters(): with this method we can retrieve the parameters passed to the workflow when it was initialized. Parameters cannot be modified during an execution, while attributes can be added and removed.
  • self.attach_calculation(calc): this is a key point in the workflow, and something possible only inside a step method. Every JobCalculation, generated in the method itself or retrieved from other utility methods, is attached to the workflow’s step. They are then launched and executed completely by the daemon, without the need of user interaction. Any number of calculations can be attached. The daemon will poll the servers until all the step calculations will be finished, and only after that it will call the next step.
  • this is the final part of a step, where the user points the engine about what to do after all the calculations in the steps (on possible sub-workflows, as we will see later) are terminated. The argument of this function has to be a Workflow.step decorated method of the same workflow class, or in case this is the last step to be executed, you can use the common method self.exit which is always present in each Workflow subclass. Note that while this call typically occurs at the end of the function, this is not required and you can call the next() method as soon as you can decide which method should follow the current one. As it can be seen above, we can use some python logic (if, ...) to decide what the next method is going to be (above, we directly point to self.exit if the input is invalid).


remember to call and NOT!! In the first case, we are correctly passing the method stage2 to next. In the second case we are instead immediately running the stage2 method, something we do not want to do (we need to wait for the current step to finish), and passing its return value to (which is wrong).

The above start step calls method validate_input() to validate the input parameters. When the workflow will be launched through the start method, the AiiDA daemon will load the workflow, execute the step, launch all the calculations and monitor their state.

Now we will create a second step to retrieve the addition of first two numbers from the first step and then we will add the third input number. Once all the calculations in the start step will be finished, the daemon will load and execute the next step i.e. stage2, shown below:

    def stage2(self):
        Get result from first calculation and add third value passed
        to workflow when it was initialised
        # get third parameter passed to workflow when it was initialised.
        c = self.get_parameter("c")
        # get result from first calculation
        start_calc = self.get_step_calculations(self.start)[0]

        # add in report
        self.append_to_report("Result of first step calculation is {}".format(

        # start second calculation
        result_calc = self.get_calculation_sum(start_calc.res.sum, c)

        # add in report
        self.append_to_report("Second step calculation is done..")

        # attach calculation in workflow to access in next steps

        # go to next step

The new feature used in the above step is:

  • self.get_step_calculations(Workflow.step): anywhere after the first step we may need to retrieve and analyze calculations executed in a previous steps. With this method we can have access to the list of calculations of a specific workflows step, passed as an argument.

Now in the last step of the workflow we will retrieve the results from stage2 and exit the workflow by calling method:

    def stage3(self):
        Get the result from second calculation and add it as final
        result of this workflow
        # get result from second calculation
        second_calc = self.get_step_calculations(self.stage2)[0]

        # add in report
        self.append_to_report("Result of second step calculation is {}".format(

        # add workflow result

        # add in report
        self.append_to_report("Added value to workflow results")

        # Exit workflow

The new features used in the above step are:

  • self.add_result(): When all calculations are done it is useful to tag some of them as results, using custom string to be later searched and retrieved. Similarly to the get_step_calculations, this method works on the entire workflow and not on a single step.
  • This is the final part of each workflow. Every workflow inheritate a fictitious step called exit that can be set as a next to any step. As the names suggest, this implies the workflow execution finished correctly.

Running a workflow

After saving the workflow inside a python file (i.e.`) located in the ``aiida/workflows directory, we can launch the workflow simply invoking the specific workflow class and executing the start() method inside the verdi shell or in a python script (with the AiiDA framework loaded).


Don’t forget to (re)start your daemon at this point!

In this case, let’s use the verdi shell. In the shell we execute:

AddnumbersWorkflow = WorkflowFactory("addnumbers")
params = {"a":2, "b": 1.4, "c": 1}
wobject = AddnumbersWorkflow(params=params)

In the above example we initialized the workflow with input parameters as a dictionary. The WorkflowFactory will work only if you gave the correct name both the python file and to the class. Otherwise, you can just substitute that line with a suitable import like:

from aiida.orm.workflows.addnumbers import AddnumbersWorkflow

We launched the workflow using start() method after storing it. Since start is a decorated workflow step, the workflow is added to the workflow to the execution queue monitored by the AiiDA daemon.

We now need to know what is going on. There are basically two main ways to see the workflows that are running: by printing the workflow list or a single workflow report.
  • Workflow list

    From the command line we run:

    >> verdi workflow list

    This will list all the running workflows, showing the state of each step and each calculation (and, when present, each sub-workflow). It is the fastest way to have a snapshot of what your AiiDA workflow daemon is working on. An example output right after the AddnumbersWorkflow submission should be:

    + Workflow AddnumbersWorkflow (pk: 76) is RUNNING [0h:00m:14s ago]
    |-* Step: start [->stage2] is RUNNING
    | | Calculation ('Number sum', pk: 739) is TOSUBMIT

    The pk number of each workflow is reported, a unique ID identifying that specific execution of the workflow, something necessary to retrieve it at any other time in the future (as explained in the next point).

  • Workflow report

    As explained, each workflow is equipped with a reporting facility the user can use to log any intermediate information, useful to debug the state or show some details. Moreover the report is also used by AiiDA as an error reporting tool: in case of errors encountered during the execution, the AiiDA daemon will copy the entire stack trace in the workflow report before halting its execution. To access the report we need the specific pk of the workflow. From the command line you would run:

    verdi workflow report PK_NUMBER

    while from the verdi shell the same operation requires to use the get_report() method:

    >> load_workflow(PK_NUMBER).get_report()

    In both variants, PK_NUMBER is the pk number of the workflow we want the report of. The load_workflow function loads a Workflow instance from its pk number, or from its uuid (given as a string).

    Once launched, the workflows will be handled by the daemon until the final step or until some error occurs. In the last case, the workflow gets halted and the report can be checked to understand what happened.

  • Workflow result

    As explained, when all the calculations are done it is useful to tag some nodes or quantities as results, using a custom string to be later searched and retrieved. This method works on the entire workflow and not on a single step.

    To access the results we need the specific pk of the workflow. From the verdi shell, you can use the get_report() method:

    >> load_workflow(PK_NUMBER).get_results()


  • Killing a workflow

    A user can also kill a workflow while it is running. This can be done with the following verdi command:

    >> verdi workflow kill PK_NUMBER_1 PK_NUMBER_2 PK_NUMBER_N

    where several pk numbers can be given. A prompt will ask for a confirmation; this can be avoided by using the -f option.

    An alternative way to kill an individual workflow is to use the kill method. In the verdi shell type:

    >> load_workflow(PK_NUMBER).kill()


In the exercise you have to write a workflow for the addition of six numbers, using the workflow we just wrote as subworkflows.

For this workflow use:

  • Input parameters:
    params = {“w1”: {“a”: 2, “b”: 2.1, “c”: 1}, “w2”: {“a”: 2, “b”: 2.1, “c”: 4}}
  • start step:
    Use two sub workflows (the ones developed above) for the addition of three numbers:
    • Sub workflow with input w1 and calculate its sum (temp_result1)
    • Sub workflow with input w2 and calculate its sum (temp_result2)
  • stage2 step:
    final_result = temp_result1 + temp_result2 Add final_result to the workflow results and exit the workflow.

Some notes and tips:

  • You can attach a subworkflow similarly to how you attach a calculation: in the step, create the new subworkflow, set its parameters using set_parameters, store it, call the start() method, and then call self.attach_workflow(wobject) to attach it to the current step.
  • If you want to pass intermediate data from one step to another, you can set the data as a workflow attibute: in a step, call self.add_attribute(attr_name, attr_value), and retrieve it in another step using attr_value = self.get_attribute(attr_name). Values can be any JSON-serializable value, or an AiiDA node.