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Add TaskTypes to main namespace (#453)

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* add tasktypes to main namespace

* add tasktypes to pipe decorator example

* minor linting

Co-authored-by: Johnathan Alexander <jalexander86@gatech.edu>
This commit is contained in:
J Alexander 2021-09-22 02:34:45 -05:00 committed by GitHub
parent fd83f8c2cb
commit dd3d4cec94
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2 changed files with 8 additions and 3 deletions
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4
clearml/__init__.py
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@ -9,12 +9,15 @@ from .storage import StorageManager
from .errors import UsageError
from .datasets import Dataset
TaskTypes = Task.TaskTypes
if not PY2:
from .automation.controller import PipelineController
__all__ = [
"__version__",
"Task",
"TaskTypes",
"InputModel",
"OutputModel",
"Model",
@ -28,6 +31,7 @@ else:
__all__ = [
"__version__",
"Task",
"TaskTypes",
"InputModel",
"OutputModel",
"Model",

7
examples/pipeline/pipeline_from_decorator.py
View File

@ -1,10 +1,11 @@
from clearml.automation.controller import PipelineDecorator
from clearml import TaskTypes
# Make the following function an independent pipeline component step
# notice all package imports inside the function will be automatically logged as
# required packages for the pipeline execution step
@PipelineDecorator.component(return_values=['data_frame'], cache=True)
@PipelineDecorator.component(return_values=['data_frame'], cache=True, task_type=TaskTypes.data_processing)
def step_one(pickle_data_url: str, extra: int = 43):
print('step_one')
# make sure we have scikit-learn for this step, we need it to use to unpickle the object
@ -26,7 +27,7 @@ def step_one(pickle_data_url: str, extra: int = 43):
# required packages for the pipeline execution step.
# Specifying `return_values` makes sure the function step can return an object to the pipeline logic
# In this case, the returned tuple will be stored as an artifact named "processed_data"
@PipelineDecorator.component(return_values=['processed_data'], cache=True,)
@PipelineDecorator.component(return_values=['processed_data'], cache=True, task_type=TaskTypes.data_processing)
def step_two(data_frame, test_size=0.2, random_state=42):
print('step_two')
# make sure we have pandas for this step, we need it to use the data_frame
@ -45,7 +46,7 @@ def step_two(data_frame, test_size=0.2, random_state=42):
# required packages for the pipeline execution step
# Specifying `return_values` makes sure the function step can return an object to the pipeline logic
# In this case, the returned object will be stored as an artifact named "model"
@PipelineDecorator.component(return_values=['model'], cache=True,)
@PipelineDecorator.component(return_values=['model'], cache=True, task_type=TaskTypes.training)
def step_three(data):
print('step_three')
# make sure we have pandas for this step, we need it to use the data_frame