Asynchronous Job Executions

This section describes how to execute a job asynchronously, but on the same machine. Consequently this does not count as a remote or distributed execution, and hence does not depend on Turi Distributed. For the sake of completeness it is still included in this chapter.

Let's start with the "Hello World" of deployment examples: adding two numbers. In the following code, we will do the following:

  • Write a simple python function to add two numbers.
  • Execute the function asynchronously on your local machine.

First, let's create the Python function. Then pass the name and the function keyword arguments that you want to run with into job.create().

import graphlab as gl

def add(x, y):
    return x + y

# Execute the job.
job = gl.deploy.job.create(add, x=1, y=1)

Note that the parameter names in the kwargs of the job.create call need to match the parameter names in the definition of your method (x and y in this example).

To get the results of this execution, simply call job.get_results().

print job.get_results()

To get the status of this execution, simply call job.get_status()

print job.get_status()

If the execution of this function throws an exception, we can get the exception type, message, and traceback from the job metrics. See job.get_metrics().

# Will fail since y is None
job = gl.deploy.job.create(add, x=1, y=None)
metrics = job.get_metrics()

print metrics
| task_name | status |      start_time     | run_time | exception |
|    add    | Failed | 2015-05-07 11:13:40 |   None   | TypeError |
|       exception_message       |      exception_traceback      |
| unsupported operand type(s... | Traceback (most recent cal... |
[1 rows x 7 columns]
# get exception type and exception message
print metrics[0]['exception'] + ": " + metrics[0]['exception_message']
TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'

To visualize this job execution, use

That should give you a sense of the types of tasks that can be accomplished with this API. In the following more practical example, we build a recommender and then execute it remotely.