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jobs

jobs

Module containing all dagster jobs

Functions

job_clearlocks 

job_clearlocks()

Clear locks from given lock entries

Source code in niceml/dagster/jobs/jobs.py 
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@job(
    config=hydra_conf_mapping_factory(),
)
def job_clearlocks():
    """Clear locks from given lock entries"""
    clear_locks()  # pylint: disable=no-value-for-parameter

job_copy_exp 

job_copy_exp()

Copy an experiment from one location to another

Source code in niceml/dagster/jobs/jobs.py 
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@job(
    config=hydra_conf_mapping_factory(),
    resource_defs={
        "locations": locations_resource,
    },
)
def job_copy_exp():
    """Copy an experiment from one location to another"""
    copy_exp()  # pylint: disable=no-value-for-parameter

job_data_generation 

job_data_generation()

Job for data generation

Source code in niceml/dagster/jobs/jobs.py 
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@job(config=hydra_conf_mapping_factory())
def job_data_generation():
    """Job for data generation"""

    current_data_location = data_generation()  # pylint: disable=no-value-for-parameter
    current_data_location = split_data(
        current_data_location
    )  # pylint: disable=no-value-for-parameter
    current_data_location = crop_numbers(
        current_data_location
    )  # pylint: disable=no-value-for-parameter
    current_data_location = image_to_tabular_data(current_data_location)
    df_normalization(current_data_location)

job_eval 

job_eval()

Job for evaluating experiment

Source code in niceml/dagster/jobs/jobs.py 
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@job(config=hydra_conf_mapping_factory(), resource_defs={"mlflow": mlflow_tracking})
def job_eval():
    """Job for evaluating experiment"""
    filelock_dict = acquire_locks()  # pylint: disable=no-value-for-parameter
    exp_context = localize_experiment()  # pylint: disable=no-value-for-parameter
    exp_context = eval_copy_exp(exp_context)  # pylint: disable=no-value-for-parameter
    exp_context, datasets, filelock_dict = prediction(
        exp_context, filelock_dict
    )  # pylint: disable=no-value-for-parameter
    exp_context, filelock_dict = analysis(
        exp_context, datasets, filelock_dict
    )  # pylint: disable=no-value-for-parameter
    release_locks(filelock_dict)  # pylint: disable=no-value-for-parameter
    exptests(exp_context)  # pylint: disable=no-value-for-parameter

job_train 

job_train()

Job for training an experiment

Source code in niceml/dagster/jobs/jobs.py 
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@job(config=hydra_conf_mapping_factory(), resource_defs={"mlflow": mlflow_tracking})
def job_train():
    """Job for training an experiment"""
    filelock_dict = acquire_locks()  # pylint: disable=no-value-for-parameter
    exp_context = experiment()  # pylint: disable=no-value-for-parameter
    exp_context, filelock_dict = train(
        exp_context, filelock_dict
    )  # pylint: disable=no-value-for-parameter
    exp_context, datasets, filelock_dict = prediction(
        exp_context, filelock_dict
    )  # pylint: disable=no-value-for-parameter
    exp_context, filelock_dict = analysis(
        exp_context, datasets, filelock_dict
    )  # pylint: disable=no-value-for-parameter
    release_locks(filelock_dict)  # pylint: disable=no-value-for-parameter
    exptests(exp_context)  # pylint: disable=no-value-for-parameter