update github woorkflow config
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@ -32,12 +32,12 @@ Steps to reproduce the behavior:
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## Environment
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- MARO version (e.g., v0.1.1a1):
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- MARO scenario (`CIM`, `Citi Bike`):
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- MARO scenario (`CIM`, `Citi Bike`, `VM Scheduling`):
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- MARO component (`Simulation`, `RL`, `Distributed Training`):
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- Orchestration platform (`GraSS on Azure`, `AKS on Azure`):
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- How you installed MARO (`pip`, `source`):
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- OS (`Linux`, `Windows`, `macOS`):
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- Python version (`3.6`, `3.7`):
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- Python version (`3.7`, `3.8`, `3.9`):
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- Docker image (e.g., maro2020/maro:latest):
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- CPU/GPU:
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- Any other relevant information:
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@ -31,8 +31,9 @@ Please also add other related information/contexts/dependencies here.
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- [ ] Mac OS
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- [ ] Linux
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- Python version:
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- [ ] 3.6
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- [ ] 3.7
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- [ ] 3.8
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- [ ] 3.9
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- Key information snapshot(s):
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## Needs Follow Up Actions
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@ -13,7 +13,7 @@ jobs:
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strategy:
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matrix:
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os: [ubuntu-latest, windows-latest, macos-latest]
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python-version: [3.6, 3.7]
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python-version: [3.7, 3.8, 3.9]
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steps:
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- uses: actions/checkout@v2
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@ -21,10 +21,10 @@ jobs:
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steps:
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# Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
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- uses: actions/checkout@v2
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- name: Set up Python 3.6
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- name: Set up Python 3.7
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uses: actions/setup-python@v2
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with:
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python-version: 3.6
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python-version: 3.7
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- name: Build image
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run: |
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@ -1,4 +1,3 @@
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name: test
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on:
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@ -14,7 +13,7 @@ jobs:
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strategy:
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matrix:
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os: [ubuntu-18.04, windows-latest, macos-latest]
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python-version: [3.6, 3.7, 3.8, 3.9]
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python-version: [3.7, 3.8, 3.9]
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steps:
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- uses: actions/checkout@v2
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@ -2,6 +2,7 @@ name: test_with_cli
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on:
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pull_request_review:
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branches: [ master, v0.1, v0.2, v0.3 ]
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types: [ submitted ]
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workflow_dispatch:
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@ -10,7 +10,7 @@ in local private network and run your training job in On-Premises distributed en
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Prerequisites
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-------------
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* Linux with Python 3.6+
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* Linux with Python 3.7+
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* `Install Powershell <https://docs.microsoft.com/en-us/powershell/scripting/install/installing-powershell?view=powershell-7.1>`_ if you are using Windows Server
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* For master node vm, need install flask, gunicorn, and redis.
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@ -96,4 +96,4 @@ grass-on-premises-join-cluster
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config:
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install_node_runtime: true
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install_node_gpu_support: false
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install_node_gpu_support: false
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@ -5,7 +5,7 @@ the multi-processes in the localhost environment.
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Prerequisites
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-------------
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* Linux with Python 3.6+
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* Linux with Python 3.7+
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* Redis
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Cluster Management
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@ -25,7 +25,7 @@ Install MARO from Source (\ `Editable Mode <https://pip.pypa.io/en/stable/refere
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* Prerequisites
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* `Python >= 3.6, < 3.8 <https://www.python.org/downloads/>`_
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* `Python >= 3.7 <https://www.python.org/downloads/>`_
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* C++ Compiler
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* Linux or Mac OS X: ``gcc``
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@ -12,7 +12,7 @@ local mode for the finished epoch under real-time mode.
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Dependency
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----------
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Env-geographic's startup depends on **docker** and **docker-compose**.
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Env-geographic's startup depends on **docker** and **docker-compose**.
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Therefore, users need to install docker on the machine and ensure that it can run normally.
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User could get docker through `Docker installation <https://docs.docker.com/get-docker/>`_.
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@ -44,7 +44,7 @@ Firstly, user need to start the local database with command:
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----
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After the command is executed successfully, user
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could view the local data with localhost:9000 by default.
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could view the local data with localhost:9000 by default.
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If the default port is occupied, user could obtain the access port of each container
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through the following command:
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@ -91,7 +91,7 @@ To send data to database, there are 2 compulsory steps:
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User needs to set the value of the environment variable
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"MARO_STREAMIT_ENABLED" to "true". If user wants to specify the experiment name,
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set the environment variable "MARO_STREAMIT_EXPERIMENT_NAME". If user does not
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set the environment variable "MARO_STREAMIT_EXPERIMENT_NAME". If user does not
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set this value, a unique experiment name would be processed automatically. User
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could check the experiment name through database. It should be noted that when
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selecting a topology, user must select a topology with specific geographic
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@ -118,7 +118,7 @@ package **streamit** with following code before environment initialization:
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# Initialize environment and send basic information of experiment to database.
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env = Env(scenario="cim", topology="global_trade.22p_l0.1",
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start_tick=0, durations=100)
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for ep in range(EPISODE_NUMBER):
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# Send experimental data to database by episode.
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streamit.episode(ep)
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@ -144,8 +144,8 @@ command:
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----
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Generally, the backend service runs on a local machine, occupying port 5000.
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If port 5000 is already occupied, the program would find another free port starting from 5000.
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Generally, the backend service runs on a local machine, occupying port 5000.
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If port 5000 is already occupied, the program would find another free port starting from 5000.
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To be specific, if user would like to run the backend service in docker rather on a local machine, please
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run the shell script run_docker.sh under the folder maro\maro\cli\maro_real_time_vis. It should be noted that
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@ -1,6 +1,6 @@
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#!/bin/bash
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# this script used build maro packages for linux
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# this script used build maro packages for linux
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cd "$(dirname $(readlink -f $0))/.."
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@ -8,4 +8,4 @@ bash ./scripts/compile_cython.sh
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# NOTE: currently we only support python3.6 and 3.7, need to be clearfy the python and packages version
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# about manylinux: https://github.com/pypa/manylinux
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docker run --rm -v "$PWD":/maro quay.io/pypa/manylinux2010_x86_64 bash /maro/scripts/build_wheel.sh
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docker run --rm -v "$PWD":/maro quay.io/pypa/manylinux2010_x86_64 bash /maro/scripts/build_wheel.sh
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3
setup.py
3
setup.py
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'Operating System :: Unix',
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"Programming Language :: Python",
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.6",
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"Programming Language :: Python :: 3.7",
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"Programming Language :: Python :: 3.8",
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"Programming Language :: Python :: 3.9",
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"Topic :: Scientific/Engineering :: Artificial Intelligence"],
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python_requires=">=3.6",
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python_requires=">=3.7",
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setup_requires=[
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"numpy<1.20.0",
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],
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