Defining Jobs using FireTasks

In the installation tutorial, we ran a simple script that performed echo "howdy, your job launched successfully!" >> howdy.txt". Looking inside fw_test.yaml, that command was defined within a task labeled Script Task:

spec:
  _tasks:
  - _fw_name: Script Task
    parameters:
      script: echo "howdy, your job launched successfully!" >> howdy.txt
      use_shell: true

The Script Task is one type of FireTask, which is a predefined job template written in Python. The Script Task in particular refers Python code inside FireWorks that runs an arbitrary shell script that you give it. You can use the Script Task to run almost any job (without worrying that it’s all done within a Python layer). However, you might want to set up custom job templates that are more explicit and reusable. In this section, we’ll demonstrate how to accomplish this with FireTasks, as well as provide more details on the Script Task.

This tutorial can be completed from the command line. Some knowledge of Python is helpful, but not required. In this tutorial, we will run examples on the central server for simplicity. One could just as easily run them on a FireWorker if you’ve set one up.

Running multiple FireTasks

You can run multiple tasks within the same FireWork. For example, the first step of your FireWork might write an input file that the second step processes. Let’s create a FireWork where the first step prints howdy.txt, and the second step counts the number of words in that file.

  1. Navigate to the tasks tutorial directory on your FireServer:

    cd <INSTALL_DIR>/fw_tutorials/firetask
  2. Look inside the file fw_multi.yaml. You should see two instances of Script Task inside our spec. Remember, our spec contains all the information needed to run our job. The second Script Task runs the wc -w command to count the number of characters in howdy.txt and exports the result to words.txt:

    spec:
      _tasks:
      - _fw_name: Script Task
        parameters:
          script: echo "howdy, your job launched successfully!" > howdy.txt
          use_shell: true
      - _fw_name: Script Task
        parameters:
          script: wc -w < howdy.txt > words.txt
          use_shell: true
  3. Run this multi-step FireWork on your FireServer:

    lp_run.py reset <TODAY'S DATE>
    lp_run.py add fw_multi.yaml
    rlauncher_run.py singleshot

Tip

You can run all three of these commands on a single line by separating them with a semicolon. This will reset the database, insert a FW, and run it within a single command.

You should see two files written out to the system, howdy.txt and words.txt, confirming that you successfully ran a two-step job!

Note

The only way to communicate information between FireTasks within the same FireWork is by writing and reading files, such as in our example. If you want to perform more complicated information transfer, you might consider Creating a custom FireTask or defining a workflow that connects FireWorks instead.

Using ScriptTask

While running arbitrary shell scripts is flexible, it’s not particularly well-organized. The command (echo), its arguments ("howdy, your job launched successfully!"), and its output (howdy.txt) are all intermingled within the same line. If we separated these components, it would be easier to do a data-parallel task where the same commands are run for multiple arguments. Let’s explore a better way to define our multi-step job:

  1. Staying in the firetasks tutorial directory, remove any output from the previous step:

    rm howdy.txt fw.json words.txt
  2. Look inside the file fw_better_multi.yaml:

    spec:
      _tasks:
      - _fw_name: Script Task
        parameters:
          script: cat -t
          stdin_key: echo_text
          stdout_file: howdy.txt
      - _fw_name: Script Task
        parameters:
          script: wc -w
          stdin_file: howdy.txt
          stdout_file: words.txt
      echo_text: howdy, your job launched successfully!

    You should see two FireTasks as before. However, this time, the spec contains more than just _tasks - it also contains an echo_text parameter that’s separated from the _tasks. We can replace the echo_text parameter with arbitrary data, and the same _tasks will process that data. Thus, performing the same tasks on multiple data is just a matter of changing a single parameter.

    Under the hood, the first Script Task is getting its input from the echo_text parameter (we defined its stdin_key to be echo_text). It is then writing its output to howdy.txt. The second Script Task is reading in howdy.txt, performing the wc -w command, and writing its output words.txt.

    Note

    We have changed the command from echo (in earlier examples) to cat -t - this is because cat -t can easily take in input from a standard input stream, which is how the echo_text parameter is being fed in.

  3. Run the FireWork on the central server to confirm that this new formulation also works as intended:

    lp_run.py reset <TODAY'S DATE>
    lp_run.py add fw_better_multi.yaml
    rlauncher_run.py singleshot

At this point, you might want to change the echo_text parameter to something other than howdy, your job launched successfully!, reinsert the FireWork, and re-run the Rocket. Your custom text should get printed to howdy.txt and the number of words should change appropriately.

Creating a custom FireTask

Because the Script Task can run arbitrary shell scripts, it can in theory run any type of job and is an ‘all-encompassing’ FireTask. However, if you are comfortable with some basic Python, it is better to define your own custom FireTasks (job templates) for the codes you run. A custom FireTask can clarify the usage of your code and guard against unintended behavior by restricting the commands that can be executed.

Even if you plan to only use Script Task, we suggest that you still read through the next portion before continuing with the tutorial. We’ll be creating a custom FireTask that adds one or more numbers using Python’s sum() function, and later building workflows using this (and similar) FireTasks:

Note

You can place code for custom FireTasks in the user_packages directory of FireWorks; it will be discovered there. If you want to place your FireTasks in a package outside of FireWorks, please read the FireWorks configuration tutorial.

  1. Staying in the firetasks tutorial directory, remove any output from the previous step:

    rm howdy.txt fw.json words.txt
  2. Let’s first look at what a custom FireTask looks like in Python. Look inside the file addition_task.py which defines the Addition Task:

    class AdditionTask(FireTaskBase, FWSerializable):
    
        _fw_name = "Addition Task"
    
        def run_task(self, fw_spec):
            input_array = fw_spec['input_array']
            m_sum = sum(input_array)
    
            print "The sum of {} is: {}".format(input_array, m_sum)
    
            return FWAction('CONTINUE', {'sum': m_sum})
    
  3. A few notes about what’s going on:

  • In the class definition, we are extending FireTaskBase to tell FireWorks that this is a FireTask.
  • A special parameter named _fw_name is set to Addition Task. This parameter sets what this FireTask will be called by the outside world.
  • The run_task() method is a special method name that gets called when the task is run. It can take in a FireWork object’s specification (fw_spec).
  • This FireTask first reads the input_array parameter of the FireWork’s spec.
  • It then sums all the values it finds in the input_array parameter of the FireWork’s spec using Python’s sum() function.
  • The FireTask then prints both the inputs and the sum to the standard out.
  • Finally, the task returns a FWAction object. We’ll discuss this object in greater detail in future tutorials. For now, it is sufficient to know that this is an instruction that says we should CONTINUE with the workflow, and store the sum we computed in the database (inside the FireWork’s stored_data section).
  1. Now let’s define a FireWork that runs this FireTask to add the numbers 1 and 2. Look inside the file fw_adder.yaml for this new FireWork definition:

    spec:
      _tasks:
      - _fw_name: Addition Task
        parameters: {}
      input_array:
      - 1
      - 2
  2. Let’s match up this FireWork’s spec with our code for our custom FireWork:

  • The _fw_name parameter is set to the same value as in our code for the FireTask (Addition Task). This is how FireWorks knows to run your custom FireTask rather than Script Task or some other FireTask.
  • This spec has an input_array field defined to 1 and 2. Remember that our Python code was grabbing the values in the input_array, summing them, and printing them to standard out.
  1. When you are comfortable that you roughly understand how a custom FireTask is set up, try running the FireWork on the central server to confirm that the Addition Task works:

    lp_run.py reset <TODAY'S DATE>
    lp_run.py add fw_adder.yaml
    rlauncher_run.py --silencer singleshot

Note

The --silencer option suppresses log messages.

# Confirm that the sum is not only printed to the screen, but also stored in our FireWork in the stored_data section:

lp_run.py get_fw 1

Next up: Workflows!

With custom FireTasks, you can go beyond the limitations of running shell commands and execute arbitrary Python code templates. Furthermore, these templates can operate on data from the spec of the FireWork. For example, the Addition Task used the input_array from the spec to decide what numbers to add. By using the same FireWork with different values in the spec, one could execute a data-parallel application.

While one could construct an entire workflow by chaining together multiple FireTasks within a single FireWork, this is often not ideal. For example, we might want to switch between different FireWorkers for different parts of the workflow depending on the computing requirements for each step. Or, we might have a restriction on walltime that necessitates breaking up the workflow into more atomic steps. Finally, we might want to employ complex branching logic or error-correction that would be cumbersome to employ within a single FireWork. The next step in the tutorial is to explore connecting together FireWorks into a workflow.

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