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

Get an existing ModelJob

To retrieve existing ModelJob use ModelJob.get method. For this you need id of Project that is used for model creation and id of ModelJob. Having ModelJob might be useful if you want to know parameters of model creation, automatically chosen by 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 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 created model after getting ModelJob id, you can use wait_for_async_model_creation function. It will poll for status of model creation process until it’s finished, and then will return newly created model.

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(