kumoai.pquery.TrainingTableJob

kumoai.pquery.TrainingTableJob#

class kumoai.pquery.TrainingTableJob[source]#

Bases: JobInterface[str, GenerateTrainTableRequest, GenerateTrainTableJobResource], KumoProgressFuture[TrainingTable]

A representation of an ongoing training table generation job in the Kumo platform.

import kumoai

# See `PredictiveQuery` documentation:
pquery = kumoai.PredictiveQuery(...)

# If a training table is generated in nonblocking mode, the response
# will be of type `TrainingTableJob`:
training_table_job = pquery.generate_training_table(non_blocking=True)

# You can also construct a `TrainingTableJob` from a job ID, e.g.
# one that is present in the Kumo Jobs page:
training_table_job = kumoai.TrainingTableJob("trainingjob-...")

# Get the status of the job:
print(training_table_job.status())

# Attach to the job, and poll progress updates:
training_table_job.attach()

# Cancel the job:
training_table_job.cancel()

# Wait for the job to complete, and return a `TrainingTable`:
training_table_job.result()
Parameters:

job_id (str) – ID of the training table generation job.

__init__(job_id)[source]#
property id: str#

The unique ID of this training table generation process.

result()[source]#

Returns the resolved state of the future.

Raises:

Exception – If the future is complete but in a failed state due to an exception being raised, this method will raise the same exception.

Return type:

TrainingTable

future()[source]#

Returns the concurrent.futures.Future object wrapped by this future. It is not recommended to access this object directly.

Return type:

Future[TrainingTable]

status()[source]#

Returns the status of a running training table generation job.

Return type:

JobStatusReport

cancel()[source]#

Cancels a running training table generation job, and raises an error if cancellation failed.

Return type:

None

load_config()[source]#

Load the full configuration for this training table generation job.

Returns:

Complete configuration including plan, pquery_id, graph_snapshot_id, etc.

Return type:

GenerateTrainTableRequest

attach(interval_s=4.0)#

Allows a user to attach to a running job and view its progress.

Parameters:

interval_s (float) – Time interval (seconds) between polls, minimum value allowed is 4 seconds.

Return type:

TypeVar(T)

delete_tags(tags)#

Removes the tags from the job.

Parameters:

tags (list[str]) – The tags to remove.

Return type:

bool

done()#

Returns True if this future has been resolved with result(), or False if this future is still in-progress. Note that this method will return True if the future is complete, but in a failed state, and that this method will return False if the job is complete, but the future has not been awaited.

Return type:

bool

get_tags()#

Returns the tags of the job.

Return type:

dict[str, str]

classmethod search_by_tags(tags, limit=10)#

Returns a list of job instances from a set of job tags.

Parameters:

  • tags (Mapping[str, str]) – Tags by which to search.

  • limit (int) – Max number of jobs to list, default 10.

Return type:

list[Self]

Example

>>> tags = {'pquery_name': 'my_pquery_name'}
>>> jobs = BatchPredictionJob.search_by_tags(tags)
Search limited to 10 results based on the `limit` parameter.
Found 2 jobs.
update_tags(tags)#

Updates the tags of the job.

Parameters:

tags (Mapping[str, Optional[str]]) – The tags to update. Note that the value ‘none’ will remove the tag. If the tag is not present, it will be added.

Return type:

bool

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