Model creation is asynchronous process. This means than when explicitly invoking
new model creation (with
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
Get an existing ModelJob¶
To retrieve existing ModelJob use
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
import time import datarobot as dr blueprint_id = '5506fcd38bd88f5953219da0' model_job_id = project.train(blueprint_id) model_job = dr.ModelJob.get(project=project.id, model_job_id=model_job_id) model_job.sample_pct >>> 64.0 # wait for model to be created (in a very inefficient way) time.sleep(10 * 60) model_job = dr.ModelJob.get(project=project.id, model_job_id=model_job_id) >>> 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(project=project.id, model_job_id=model_job_id)
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( project_id=project.id, model_job_id=model_job_id, ) # used during training based on existing model model_job_id = existing_model.train(sample_pct=33) new_model = wait_for_async_model_creation( project_id=existing_model.project_id, model_job_id=model_job_id, )