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73 lines
3.8 KiB
ReStructuredText
73 lines
3.8 KiB
ReStructuredText
.. _task_managment:
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=================================
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Task Management
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=================================
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.. currentmodule:: qlib
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Introduction
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=============
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The `Workflow <../component/introduction.html>`_ part introduces how to run research workflow in a loosely-coupled way. But it can only execute one ``task`` when you use ``qrun``.
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To automatically generate and execute different tasks, ``Task Management`` provides a whole process including `Task Generating`_, `Task Storing`_, `Task Running`_ and `Task Collecting`_.
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With this module, users can run their ``task`` automatically at different periods, in different losses, or even by different models.
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An example of the entire process is shown `here <https://github.com/microsoft/qlib/tree/main/examples/taskmanager/task_manager_rolling.py>`_.
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Task Generating
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===============
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A ``task`` consists of `Model`, `Dataset`, `Record` or anything added by users.
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The specific task template(/definition/config) can be viewed in
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`Task Section <../component/workflow.html#task-section>`_.
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Even though the task template is fixed, users can customize their ``TaskGen`` to generate different ``task`` by task template.
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Here is the base class of ``TaskGen``:
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.. autoclass:: qlib.workflow.task.gen.TaskGen
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:members:
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``Qlib`` provider a class `RollingGen <https://github.com/microsoft/qlib/tree/main/qlib/workflow/task/gen.py>`_ to generate a list of ``task`` of the dataset in different date segments.
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This class allows users to verify the effect of data from different periods on the model in one experiment.
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Task Storing
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===============
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To achieve higher efficiency and the possibility of cluster operation, ``Task Manager`` will store all tasks in `MongoDB <https://www.mongodb.com/>`_.
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Users **MUST** finished the configuration of `MongoDB <https://www.mongodb.com/>`_ when using this module.
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Users need to provide the URL and database name of ``task`` storing like this.
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.. code-block:: python
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from qlib.config import C
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C["mongo"] = {
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"task_url" : "mongodb://localhost:27017/", # your MongoDB url
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"task_db_name" : "rolling_db" # database name
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}
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The CRUD methods of ``task`` can be found in TaskManager.
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More methods can be seen in the `Github <https://github.com/microsoft/qlib/tree/main/qlib/workflow/task/manage.py>`_.
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.. autoclass:: qlib.workflow.task.manage.TaskManager
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:members:
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Task Running
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===============
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After generating and storing those ``task``, it's time to run the ``task`` which are in the *WAITING* status.
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``Qlib`` provides a method called ``run_task`` to run those ``task`` in task pool, however, users can also customize how tasks are executed.
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An easy way to get the ``task_func`` is using ``qlib.model.trainer.task_train`` directly.
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It will run the whole workflow defined by ``task``, which includes *Model*, *Dataset*, *Record*.
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.. autofunction:: qlib.workflow.task.manage.run_task
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Task Collecting
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===============
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To see the results of ``task`` after running or to update something, ``Qlib`` provides a ``TaskCollector`` to collect the tasks by filter condition (optional).
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Here are some methods in this class.
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.. autoclass:: qlib.workflow.task.collect.TaskCollector
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:members:
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``Qlib`` provides a concrete `example <https://github.com/microsoft/qlib/tree/main/examples/taskmanager/task_manager_rolling_with_updating.py>`_, including a whole process of `Task Generating`_ (using `RollingGen <https://github.com/microsoft/qlib/tree/main/qlib/workflow/task/gen.py>`_), `Task Storing`_, `Task Running`_ and `Task Collecting`_.
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Besides, the `example <https://github.com/microsoft/qlib/tree/main/examples/taskmanager/task_manager_rolling_with_updating.py>`_ uses a ``ModelUpdater`` inherited from ``TaskCollector``, which can update the inferences and retrain the model if it is out of date.
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Actually, the model updating can be viewed as a subset of ``Online Serving``. |