The Job class is a generic representation of jobs running through a project’s queue. Many tasks involved in modeling, such as creating a new model or computing feature impact for a model, will use a job to track the worker usage and progress of the associated task.

Checking the Contents of the Queue

To see what jobs running or waiting in the queue for a project, use the Project.get_all_jobs method.

from datarobot.enums import QUEUE_STATUS

jobs_list = project.get_all_jobs()  # gives all jobs queued or inprogress
jobs_by_type = {}
for job in jobs_list:
    if job.job_type not in jobs_by_type:
        jobs_by_type[job.job_type] = [0, 0]
    if job.status == QUEUE_STATUS.QUEUE:
        jobs_by_type[job.job_type][0] += 1
        jobs_by_type[job.job_type][1] += 1
for type in jobs_by_type:
    (num_queued, num_inprogress) = jobs_by_type[type]
    print('{} jobs: {} queued, {} inprogress'.format(type, num_queued, num_inprogress))

Cancelling a Job

If a job is taking too long to run or no longer necessary, it can be cancelled easily from the Job object.

from datarobot.enums import QUEUE_STATUS

bad_jobs = project.get_all_jobs(status=QUEUE_STATUS.QUEUE)
for job in bad_jobs:

Retrieving Results From a Job

Once you’ve found a particular job of interest, you can retrieve the results once it is complete. Note that the type of the returned object will vary depending on the job_type. All return types are documented in Job.get_result.

from datarobot.enums import JOB_TYPE

time_to_wait = 60 * 60  # how long to wait for the job to finish (in seconds) - i.e. an hour
assert my_job.job_type == JOB_TYPE.MODEL
my_model = my_job.get_result_when_complete(max_wait=time_to_wait)

Model Jobs

Model creation is an asynchronous process. This means that when explicitly invoking new model creation (with project.train or model.train for example) all you get is the id of the process, responsible for model creation. With this id you can get info about the model that is being created or the model itself, when the creation process is finished. For this you should use the ModelJob class.

Get an existing ModelJob

To retrieve existing ModelJob use ModelJob.get method. For this you need the id of Project that is used for model creation and the id of ModelJob. Having ModelJob might be useful if you want to know parameters of model creation, automatically chosen by the API backend, before actual model was created.

If model is already created, ModelJob.get will raise PendingJobFinished exception

import time

import datarobot as dr

blueprint_id = '5506fcd38bd88f5953219da0'
model_job_id = project.train(blueprint_id)
model_job = dr.ModelJob.get(,
>>> 64.0

# wait for model to be created (in a very inefficient way)
time.sleep(10 * 60)
model_job = dr.ModelJob.get(,
>>> datarobot.errors.PendingJobFinished

# get the job attached to the model
>>> Model('5d518cd3962d741512605e2b')

Get a created model

After model is created, you can use ModelJob.get_model to get newly created model.

import datarobot as dr

model = dr.ModelJob.get_model(,

wait_for_async_model_creation function

If you just want to get the created model after getting the ModelJob id, you can use the wait_for_async_model_creation function. It will poll for the status of the model creation process until it’s finished, and then will return the newly created model. Note the differences below between datetime partitioned projects and non-datetime-partitioned projects.

from datarobot.models.modeljob import wait_for_async_model_creation

# used during training based on blueprint
model_job_id = project.train(blueprint, sample_pct=33)
new_model = wait_for_async_model_creation(,

# used during training based on existing model
model_job_id = existing_model.train(sample_pct=33)
new_model = wait_for_async_model_creation(

# For datetime-partitioned projects, use project.train_datetime. Note that train_datetime returns a ModelJob instead
# of just an id.
model_job = project.train_datetime(blueprint)
new_model = wait_for_async_model_creation(,