Metadata-Version: 1.1
Name: hdeeprm
Version: 0.1.0b1
Summary: Evaluate Deep Reinforcement Learning policies for heterogeneous Workload Management
Home-page: https://github.com/RaMdsC/hdeeprm
Author: Adrián Herrera
Author-email: adr.her.arc.95@gmail.com
License: MIT
Description: HDeepRM
        =======
        
        Framework for evaluating Workload Management policies based on
        Deep Reinforcement Learning for Heterogeneous Clusters.
        
        .. include-overview-start
        
        Overview
        --------
        
        HDeepRM is a Python framework for evaluating Workload Management policies
        based on Deep Reinforcement Learning for Heterogeneous Clusters. It
        leverages the `Batsim ecosystem <https://gitlab.inria.fr/batsim>`_
        for simulating a heterogeneous Workload Management context. This is composed
        of the Simulator, `Batsim <https://gitlab.inria.fr/batsim/batsim>`_ and the
        Decision System, `PyBatsim <https://gitlab.inria.fr/batsim/pybatsim>`_.
        
        HDeepRM provides a heterogeneity layer on top of PyBatsim, which adds support
        for user-defined resource hierarchies. Memory and bandwidth conflicts are added
        along with interdependence when consolidating or scattering jobs across the
        data centre.
        
        It offers a flexible API for developing Deep Reinforcement Learning agents.
        These may be trained by providing real workload traces in
        `SWF format <http://www.cs.huji.ac.il/labs/parallel/workload/swf.html>`_ along
        with platforms defined in the format specified in `Platforms <TODO>`_. They can
        be further evaluated and tested against traditional policies.
        
        Installation Prerequisites
        ~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        HDeepRM is distributed as a Python package on
        `PyPi <https://pypi.org/project/hdeeprm/>`_.
        In order to download and install it, the following software is needed:
        
        - Python3.6+, find your OS in this
          `installation guide <https://realpython.com/installing-python/>`_.
        - Pip, the Python package manager. If not already available with the Python
          installation, follow the
          `official guide <https://pip.pypa.io/en/stable/installing/>`_.
        
        Installation
        ~~~~~~~~~~~~
        
        In order to install HDeepRM, just download the package from PyPi:
        
        .. code-block:: bash
        
          pip install --user hdeeprm
        
        If ``pip`` is mapped to Python 2.x, try:
        
        .. code-block:: bash
        
          pip3 install --user hdeeprm
        
        When working with multiple Python versions, use:
        
        .. code-block:: bash
        
          python3.6 -m pip install --user hdeeprm
        
        This should download the ``hdeeprm`` package with all its dependencies,
        which are:
        
        - ``defusedxml`` >= 0.5.0: secure XML generation and parsing.
        - ``gym`` >= 0.11.0: environment, actions and observations definitions.
        - ``lxml`` >= 4.3.1: generation of the XML tree. Backend for ``defusedxml``.
        - ``numpy`` >= 1.16.1: efficient data structure operations.
        - ``procset`` >= 1.0: closed-interval sets for resource selection.
        - ``pybatsim`` >= 3.1.0: decision system and main interface to interact
          with Batsim.
        - ``torch`` >= 1.0.1: deep learning library for agent definition.
        
        Usage Prerequisites
        ~~~~~~~~~~~~~~~~~~~
        
        The simulation side is done by Batsim, which is needed in order to run
        HDeepRM experiments. Follow the `official installation docs
        <https://batsim.readthedocs.io/en/latest/installation.html>`_ for instructions.
        
        Usage
        ~~~~~
        
        In order to experiment with HDeepRM, an integrated launcher is provided:
        
        .. code-block:: bash
        
          hdeeprm-launch <agent.py> <options.json> --inmodel <saved_model.pt> --outmodel <to_save_model.pt>
        
        The ``agent.py`` file contains your developed agent for evaluation.
        See `agent examples <TODO>`_ for reference.
        
        The ``options.json`` specifies the experiment parameters. The JSON structure
        is as follows:
        
        .. code-block:: json
        
          {
            "seed",
            "nb_resources",
            "nb_jobs",
            "workload_file_path",
            "platform_file_path,
            "pybatsim": {
              "log_level",
              "env": {
                "objective",
                "queue_sensitivity"
              },
              "agent": {
                "policy_pair",
                "run",
                "hidden",
                "lr",
                "gamma"
              }
            }
          }
        
        Global options:
        
        * ``seed`` - The random seed for evaluation reproducibility.
        * ``nb_resources`` - Total number of cores in the simulated platform.
        * ``nb_jobs`` - Total number of jobs to generate in the workload.
        * ``workload_file_path`` - Location of the original SWF formatted workload.
        * ``platform_file_path`` - Location of the original
          HDeepRM JSON formatted platform.
        
        PyBatsim options:
        
        * ``log_level`` - Logging level for showing insights from the simulation. See `Logging <https://docs.python.org/3.6/howto/logging.html>`_ for reference on possible values.
        
        PyBatsim - Environment options:
        
        * ``objective`` - Metric to be optimised by the agent. See `Objectives <TODO>`_ for an explanation and recognised values.
        * ``queue_sensitivity`` - Sensitivity of the observation to variations in job queue size. See `Hyperparameters - Queue Sensitivity <TODO>`_.
        
        PyBatsim - Agent options:
        
        * ``policy_pair`` - For :class:`~hdeeprm.agent.ClassicAgent` and derived only. The job and resource selection policies. Policy pairs are further described in `Environment - Action Space <TODO>`_.
        * ``run`` - For :class:`~hdeeprm.agent.LearningAgent` and derived only.
          Type of run for the learning agent, can be *train* or *test*.
          When training, the agent's inner model is updated,
          whereas testing is meant for evaluation purposes.
        * ``hidden`` - For :class:`~hdeeprm.agent.LearningAgent` and derived only. Number of units in each hidden layer from the agent's inner model. See `Hyperparameters - Hidden units <TODO>`_.
        * ``lr`` - For :class:`~hdeeprm.agent.LearningAgent` and derived only. Learning rate for updating the agent's inner model. See `Hyperparameters - Learning rate <TODO>`_.
        * ``gamma`` - For :class:`~hdeeprm.agent.LearningAgent` and derived only. Discount factor for rewards. See `Hyperparameters - Reward Discount Factor <TODO>`_.
        
        This is an example of an ``options.json`` file
        for a :class:`~hdeeprm.agent.ClassicAgent`:
        
        .. code-block:: json
        
          {
            "seed": 2009,
            "nb_resources": 2280,
            "nb_jobs": 10000,
            "workload_file_path": "/workspace/workloads/my_workload.swf",
            "platform_file_path: "/workspace/platforms/my_platform.json",
            "pybatsim": {
              "log_level": "DEBUG",
              "env": {
                "objective": "avg_utilization",
                "queue_sensitivity": 0.05
              },
              "agent": {
                "policy_pair": "shortest-high_flops"
              }
            }
          }
        
        
        This is another example of an ``options.json`` file,
        in this case for a :class:`~hdeeprm.agent.LearningAgent`:
        
        .. code-block:: json
        
          {
            "seed": 1995,
            "nb_resources": 2280,
            "nb_jobs": 10000,
            "workload_file_path": "/workspace/workloads/my_workload.swf",
            "platform_file_path: "/workspace/platforms/my_platform.json",
            "pybatsim": {
              "log_level": "WARNING",
              "env": {
                "objective": "makespan",
                "queue_sensitivity": 0.01
              },
              "agent": {
                "run": "train",
                "hidden": 128,
                "lr": 0.001,
                "gamma": 0.99
              }
            }
          }
        
        The ``inmodel`` optional argument may be used for providing a path
        to a previously trained and saved model. HDeepRM will load this model
        before starting the run.
        
        The ``outmodel`` optional argument may be specified as a path for
        saving the model after the run is finished. If not provide, the model
        won't be saved. This is usually combined with *train* runs.
        
        .. include-overview-end
        
Keywords: deep reinforcement learning workload management job scheduling resource simulator framework heterogeneous cluster batsim
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
