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https://github.com/clearml/clearml
synced 2025-04-30 11:04:23 +00:00
Fix Multi-pipeline support
This commit is contained in:
allegroai
parent
2c214e9848
commit
51c8c84bc4
@ -20,9 +20,11 @@ from .. import Logger
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from ..automation import ClearmlJob
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from ..backend_interface.task.populate import CreateFromFunction
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from ..backend_interface.util import get_or_create_project, exact_match_regex
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from ..config import get_remote_task_id
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from ..debugging.log import LoggerRoot
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from ..model import BaseModel, OutputModel
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from ..task import Task
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from ..utilities.process.mp import leave_process
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from ..utilities.proxy_object import LazyEvalWrapper, flatten_dictionary
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@ -131,6 +133,7 @@ class PipelineController(object):
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self._thread = None
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self._pipeline_args = dict()
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self._pipeline_args_desc = dict()
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self._pipeline_args_type = dict()
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self._stop_event = None
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self._experiment_created_cb = None
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self._experiment_completed_cb = None
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@ -980,8 +983,8 @@ class PipelineController(object):
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# also trigger node monitor scanning
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self._scan_monitored_nodes()
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def add_parameter(self, name, default=None, description=None):
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# type: (str, Optional[Any], Optional[str]) -> None
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def add_parameter(self, name, default=None, description=None, param_type=None):
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# type: (str, Optional[Any], Optional[str], Optional[str]) -> None
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"""
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Add a parameter to the pipeline Task.
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The parameter can be used as input parameter for any step in the pipeline.
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@ -992,10 +995,13 @@ class PipelineController(object):
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:param name: String name of the parameter.
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:param default: Default value to be put as the default value (can be later changed in the UI)
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:param description: String description of the parameter and its usage in the pipeline
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:param param_type: Optional, parameter type information (to used as hint for casting and description)
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"""
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self._pipeline_args[str(name)] = str(default or '')
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self._pipeline_args[str(name)] = default
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if description:
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self._pipeline_args_desc[str(name)] = str(description)
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if param_type:
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self._pipeline_args_type[str(name)] = param_type
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def get_parameters(self):
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# type: () -> dict
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@ -1148,6 +1154,7 @@ class PipelineController(object):
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self._task._set_parameters(
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{'{}/{}'.format(self._args_section, k): v for k, v in params.items()},
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__parameters_descriptions=self._pipeline_args_desc,
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__parameters_types=self._pipeline_args_type,
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__update=True,
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)
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params['_continue_pipeline_'] = False
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@ -2197,6 +2204,7 @@ class PipelineDecorator(PipelineController):
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_debug_execute_step_function = False
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_default_execution_queue = None
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_multi_pipeline_instances = []
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_multi_pipeline_call_counter = -1
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_atexit_registered = False
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def __init__(
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@ -2877,6 +2885,7 @@ class PipelineDecorator(PipelineController):
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def internal_decorator(*args, **kwargs):
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pipeline_kwargs = dict(**(kwargs or {}))
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pipeline_kwargs_types = dict()
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inspect_func = inspect.getfullargspec(func)
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if args:
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if not inspect_func.args:
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@ -2892,12 +2901,39 @@ class PipelineDecorator(PipelineController):
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default_kwargs.update(pipeline_kwargs)
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pipeline_kwargs = default_kwargs
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if inspect_func.annotations:
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pipeline_kwargs_types = {
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str(k): inspect_func.annotations[k] for k in inspect_func.annotations}
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# run the entire pipeline locally, as python functions
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if cls._debug_execute_step_function:
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ret_val = func(**pipeline_kwargs)
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LazyEvalWrapper.trigger_all_remote_references()
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return ret_val
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# check if we are in a multi pipeline
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force_single_multi_pipeline_call = False
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if multi_instance_support and cls._multi_pipeline_call_counter >= 0:
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# check if we are running remotely
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if not Task.running_locally():
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# get the main Task property
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t = Task.get_task(task_id=get_remote_task_id())
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if str(t.task_type) == str(Task.TaskTypes.controller):
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# noinspection PyBroadException
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try:
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# noinspection PyProtectedMember
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multi_pipeline_call_counter = int(
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t._get_runtime_properties().get('multi_pipeline_counter', None))
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# NOTICE! if this is not our call we LEAVE immediately
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# check if this is our call to start, if not we will wait for the next one
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if multi_pipeline_call_counter != cls._multi_pipeline_call_counter:
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return
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except Exception:
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# this is not the one, so we should just run the first
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# instance and leave immediately
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force_single_multi_pipeline_call = True
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if default_queue:
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cls.set_default_execution_queue(default_queue)
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@ -2918,13 +2954,23 @@ class PipelineDecorator(PipelineController):
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a_pipeline._clearml_job_class.register_hashing_callback(a_pipeline._adjust_task_hashing)
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# add pipeline arguments
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if pipeline_kwargs:
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a_pipeline.get_parameters().update(pipeline_kwargs)
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for k in pipeline_kwargs:
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a_pipeline.add_parameter(
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name=k,
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default=pipeline_kwargs.get(k),
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param_type=pipeline_kwargs_types.get(k)
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)
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# sync multi-pipeline call counter (so we know which one to skip)
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if Task.running_locally() and multi_instance_support and cls._multi_pipeline_call_counter >= 0:
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# noinspection PyProtectedMember
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a_pipeline._task._set_runtime_properties(
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dict(multi_pipeline_counter=str(cls._multi_pipeline_call_counter)))
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# serialize / deserialize state only if we are running locally
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a_pipeline._start(wait=False)
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# sync arguments back
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# sync arguments back (post deserialization and casting back)
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for k in pipeline_kwargs.keys():
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if k in a_pipeline.get_parameters():
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pipeline_kwargs[k] = a_pipeline.get_parameters()[k]
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@ -2968,6 +3014,12 @@ class PipelineDecorator(PipelineController):
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# now we can raise the exception
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if triggered_exception:
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raise triggered_exception
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# Make sure that if we do not need to run all pipelines we forcefully leave the process
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if force_single_multi_pipeline_call:
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leave_process()
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# we will never get here
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return pipeline_result
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if multi_instance_support:
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@ -3045,6 +3097,8 @@ class PipelineDecorator(PipelineController):
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"""
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def internal_decorator(*args, **kwargs):
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cls._multi_pipeline_call_counter += 1
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# if this is a debug run just call the function (no parallelization).
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if cls._debug_execute_step_function:
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return func(*args, **kwargs)
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@ -3069,7 +3123,7 @@ class PipelineDecorator(PipelineController):
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# make sure we wait for the subprocess.
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p.daemon = False
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p.start()
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if parallel:
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if parallel and Task.running_locally():
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cls._multi_pipeline_instances.append((p, queue))
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return
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else:
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