MachineLearningMixin#
- class MachineLearningMixin#
Bases:
object
Methods Summary
clone_machinelearning_workflow
(project_id, ...)Clone the Machine Learning Workflow.
delete_machinelearning_job
(project_id, ...)Delete a Machine Learning job.
delete_machinelearning_workflow
(project_id, ...)Delete a Machine Learning workflow.
get_machinelearning_job
(project_id, ...[, ...])Return a particular Machine Learning job.
get_machinelearning_jobs
(project_id, ...[, ...])Return all the Machine Learning jobs for a particular Machine Learning workflow.
get_machinelearning_workflow
(project_id, ...)Return a specific Machine Learning Workflow in a project.
get_machinelearning_workflow_assets
(...[, ...])Return all the binary assets like trained models associated with a Machine Learning Workflow.
get_machinelearning_workflows
(project_id)Return all Machine Learning workflows for a project.
kill_machinelearning_job
(project_id, ...)Kills a Machine Learning job if it is running.
modify_machinelearning_workflow
(project_id, ...)Modify an existing Machine Learning workflow.
new_machinelearning_job
(project_id, ...[, ...])Create a new Machine Learning job.
new_machinelearning_workflow
(project_id, ...)Create a new Machine Learning Workflow.
run_machinelearning_job
(project_id, ...)Schedules a Machine Learning job to run now.
run_machinelearning_workflow
(project_id, ...)Run a Machine Learning workflow directly on Squirro items.
wait_for_machinelearning_job
(project_id, ...)Wait for the first run of the Machine Learning job to complete.
Methods Documentation
- clone_machinelearning_workflow(project_id, ml_workflow_id, name=None, type=None)#
Clone the Machine Learning Workflow.
- Parameters
project_id (
str
) – Id of the Squirro project.ml_workflow_id (
str
) – Id of the Machine Learning workflow.name (
Optional
[str
]) – Optional name of Machine learning workflow. If not specified, the name of the workflow will be the same as the original one.type (
Optional
[str
]) – Optional parameter to define type of the Machine learning workflow. Possible values are other, query, query_default, ais, published, document_embedder_queries, document_embedder_queries_default. If not specified, the type of the workflow will be the same as the original one.
- delete_machinelearning_job(project_id, ml_workflow_id, ml_job_id)#
Delete a Machine Learning job.
- Parameters
project_id – Id of the Squirro project.
ml_workflow_id – Id of the Machine Learning workflow.
ml_job_id – Id of the Machine Learning job.
- delete_machinelearning_workflow(project_id, ml_workflow_id)#
Delete a Machine Learning workflow.
- Parameters
project_id – Id of the Squirro project.
ml_workflow_id – Id of the Machine Learning workflow.
- get_machinelearning_job(project_id, ml_workflow_id, ml_job_id, include_run_log=None, last_n_log_lines=None, include_results=None)#
Return a particular Machine Learning job.
- Parameters
project_id – Id of the Squirro project.
ml_workflow_id – Id of the Machine Learning workflow.
ml_job_id – Id of the Machine Learning job.
include_run_log – Boolean flag to optionally fetch the last run log of the job.
last_n_log_lines – Integer to fetch only the last n lines of the last run log.
include_run_log – Boolean flag to optionally fetch the last run results.
- get_machinelearning_jobs(project_id, ml_workflow_id, include_internal_jobs=None)#
Return all the Machine Learning jobs for a particular Machine Learning workflow.
- Parameters
project_id – Id of the Squirro project.
ml_workflow_id – Id of the Machine Learning workflow.
include_internal_jobs – Bool, whether or not to include the internal jobs. Generally, you should not need to get these jobs as these jobs are used to optimize the inference runs.
- get_machinelearning_workflow(project_id, ml_workflow_id)#
Return a specific Machine Learning Workflow in a project.
- Parameters
project_id – Id of the project.
ml_workflow_id – Id of the Machine Learning workflow.
- get_machinelearning_workflow_assets(project_id, ml_workflow_id, write_to_disk=None)#
Return all the binary assets like trained models associated with a Machine Learning Workflow.
- Parameters
project_id – Id of the project.
ml_workflow_id – Id of the Machine Learning workflow.
write_to_disk – Boolean. Wheather or not to write the exported ML workflow assets to the disk.
- get_machinelearning_workflows(project_id)#
Return all Machine Learning workflows for a project.
- Parameters
project_id – Id of the Squirro project.
- kill_machinelearning_job(project_id, ml_workflow_id, ml_job_id)#
Kills a Machine Learning job if it is running.
- Parameters
project_id – Id of the Squirro project.
ml_workflow_id – Id of the Machine Learning workflow.
ml_job_id – Id of the Machine Learning job.
- modify_machinelearning_workflow(project_id, ml_workflow_id, name=None, config=None, ml_models=None, type=None)#
Modify an existing Machine Learning workflow.
- Parameters
project_id – Id of the Squirro project.
ml_workflow_id – Id of the Machine Learning workflow.
name – Name of Machine learning workflow.
config – Dictionary of Machine Learning workflow config. Detailed documentation here: https://squirro.atlassian.net/wiki/spaces/DOC/pages/337215576/Squirro+Machine+Learning+Documentation # noqa
ml_models – Directory with ml_models to be uploaded into the workflow path.
type – Optional parameter to define type of the Machine learning workflow. Possible values are other, query, query_default, ais, published, document_embedder_queries, document_embedder_queries_default. If not specified, the default type is other.
- new_machinelearning_job(project_id, ml_workflow_id, type, scheduling_options=None)#
Create a new Machine Learning job.
- Parameters
project_id – Id of the Squirro project.
ml_workflow_id – Id of the Machine learning workflow.
type – Type of the Machine Learning job. Possible values are training and inference.
scheduling_options – Scheduling options for the job
Example:
>>> client.new_machinelearning_job( project_id='2aEVClLRRA-vCCIvnuEAvQ', ml_workflow_id='129aVASaFNPN3NG10-ASDF', type='training', scheduling_options={"time_based":{"repeat_every":"1d"}}) '13nv0va0svSDv3333v'
- new_machinelearning_workflow(project_id, name, config, ml_models=None, type=None)#
Create a new Machine Learning Workflow.
- Parameters
project_id – Id of the Squirro project.
name – Name of Machine learning workflow.
config – Dictionary of Machine learning workflow config. Detailed documentation here: https://squirro.atlassian.net/wiki/spaces/DOC/pages/337215576/Squirro+Machine+Learning+Documentation # noqa
ml_models – Directory with ml_models to be uploaded into the workflow path
type – Optional parameter to define type of the Machine learning workflow. Possible values are other, query, query_default, ais, published, document_embedder_queries, document_embedder_queries_default. If not specified, the default type is other.
- run_machinelearning_job(project_id, ml_workflow_id, ml_job_id)#
Schedules a Machine Learning job to run now.
- Parameters
project_id – Id of the Squirro project.
ml_workflow_id – Id of the Machine Learning workflow.
ml_job_id – Id of the Machine Learning job.
- run_machinelearning_workflow(project_id, ml_workflow_id, data, asynchronous=False)#
Run a Machine Learning workflow directly on Squirro items.
- Parameters
project_id – Id of the Squirro project.
ml_workflow_id – Id of the Machine Learning workflow.
data –
Data to run through Machine Learning workflow. -response_format: Format of the response. Valid options are:
standard: The default format of the response. Fields returned
by the Machine Learning workflow are wrapped into a list. - plain: Fields returned from the Machine Learning workflow are returned as they are, without wrapping.
asynchronous – Whether or not to run Machine Learning workflow asynchronously (recommended for large data batches).
- wait_for_machinelearning_job(project_id, ml_workflow_id, ml_job_id, max_wait_time=600)#
Wait for the first run of the Machine Learning job to complete. Often useful in automated scripts where you would want to wait for a job to finish after setting it up to inspect logs or results.
- Parameters
project_id – Id of the Squirro project.
ml_workflow_id – Id of the Machine Learning workflow.
ml_job_id – Id of the Machine Learning job.
max_wait_time – Maximum time to wait for Machine Learning job (default: 600s).