JobMonitor.get_status()

Retrieves the current status of a job, along with the associated model identifier, job metadata, and results.

The job status can be one of the following:

Status	Description
`QUEUED`	A job is queued and it is waiting for execution.
`COMPLETED`	A job has been completed (e.g., the model has been trained).
`RUNNING`	A job is currently beeing executed.
`CANCELED`	The job has been canceled by the user.
`FAILED`	The job has failed (an error occured).

Returns

The method returns a JSON object containing the following fields:

Field	Description
`job_id`	A unique identifier for the job. This ID can be used to retrieve the job via the `JobManager`’s `fetch_job` method.
`status`	The current state of the job (e.g., queued, completed, running, canceled, failed)
`started_at`	The timestamp indicating when the job was created.
`finished_at`	The timestamp indicating when the job finished execution.
`updated_at`	The timestamp of the most recent status update for the job.
`experiment_name`	The experiment name that uniquely identifies the dataset used in the job, following the format `dataset_name_task_type_task_name`.
`job_type`	The type of job being executed (`train`, `inference` or `train_inference`). Jobs initiated by the `fit()` method are of type `train`, jobs initiated by the `predict()` method are of type `inference`, while jobs initiated by the `fit_predict()` method are of type `train_inference`.
`model_run_id`	A unique identifier assigned to the trained model associated with the job. For `train` or `train_inference` jobs, this is the ID of the model that was trained in the job; for `inference` job, it is the ID of the model used for inference.
`result`	A dictionary containing details such as per-epoch validation metrics, training metrics, best epoch, and related information.
`export_paths`	A dictionary containing file paths to the exported `predictions` and `embeddings` generated by the job. This is only relevant for `inference` and `train_inference` jobs.
`source_views`	A dictionary containing the secure data views of the `predictions` and `embeddings` generated by the job. This is only relevant for `inference` and `train_inference` jobs.

Example

# Create a job and send it to the queue
train_job = trainer.fit(dataset=dataset)

train_job.get_status()