doc refined

docs/source/conf.py

@@ -39,7 +39,7 @@
               "sphinx.ext.coverage",
               "sphinx.ext.napoleon",
               "sphinx.ext.viewcode",
-              "sphinx_markdown_tables"
+              "sphinx_markdown_tables",
               ]
 
 napoleon_google_docstring = True

docs/source/index.rst

@@ -10,7 +10,7 @@ Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcemen
 learning as a Service (RaaS) for real-world resource optimization. It can be
 applied to many important industrial domains, such as container inventory
 management in logistics, bike repositioning in transportation, virtual machine
-provisioning in data centers, and asset management in finance. Besides 
+provisioning in data centers, and asset management in finance. Besides
 `Reinforcement Learning <https://www.andrew.cmu.edu/course/10-703/textbook/BartoSutton.pdf>`_ (RL), it
 also supports other planning/decision mechanisms, such as
 `Operations Research <https://en.wikipedia.org/wiki/Operations_research>`_.

docs/source/installation/grass_cluster_provisioning_on_azure.rst

@@ -9,111 +9,99 @@ on Azure and run your training job in a distributed environment.
 Prerequisites
 -------------
 
-
 * `Install the Azure CLI and login <https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest>`_
 * `Install docker <https://docs.docker.com/engine/install/>`_ and
   `Configure docker to make sure it can be managed as a non-root user <https://docs.docker.com/engine/install/linux-postinstall/#manage-docker-as-a-non-root-user>`_
 
 Cluster Management
 ------------------
 
-
 * Create a cluster with a `deployment <#grass-azure-create>`_
 
-.. code-block:: sh
-
-   # Create a grass cluster with a grass-create deployment
-   maro grass create ./grass-azure-create.yml
+  .. code-block:: sh
 
+    # Create a grass cluster with a grass-create deployment
+    maro grass create ./grass-azure-create.yml
 
 * Scale the cluster
 
-.. code-block:: sh
+  .. code-block:: sh
 
-   # Scale nodes with 'Standard_D4s_v3' specification to 2
-   maro grass node scale my_grass_cluster Standard_D4s_v3 2
-
-Check `VM Size <https://docs.microsoft.com/en-us/azure/virtual-machines/sizes>`_
-to see more node specifications.
+    # Scale nodes with 'Standard_D4s_v3' specification to 2
+    maro grass node scale my_grass_cluster Standard_D4s_v3 2
 
+  Check `VM Size <https://docs.microsoft.com/en-us/azure/virtual-machines/sizes>`_
+  to see more node specifications.
 
 * Delete the cluster
 
-.. code-block:: sh
-
-   # Delete a grass cluster
-   maro grass delete my_grass_cluster
+  .. code-block:: sh
 
+    # Delete a grass cluster
+    maro grass delete my_grass_cluster
 
 * Start/stop nodes to save costs
 
-.. code-block:: sh
+  .. code-block:: sh
 
-   # Start 2 nodes with 'Standard_D4s_v3' specification
-   maro grass node start my_grass_cluster Standard_D4s_v3 2
+    # Start 2 nodes with 'Standard_D4s_v3' specification
+    maro grass node start my_grass_cluster Standard_D4s_v3 2
 
-   # Stop 2 nodes with 'Standard_D4s_v3' specification
-   maro grass node stop my_grass_cluster Standard_D4s_v3 2
+    # Stop 2 nodes with 'Standard_D4s_v3' specification
+    maro grass node stop my_grass_cluster Standard_D4s_v3 2
 
 Run Job
 -------
 
-
 * Push your training image
 
-.. code-block:: sh
-
-   # Push image 'my_image' to the cluster
-   maro grass image push my_grass_cluster --image-name my_image
+  .. code-block:: sh
 
+    # Push image 'my_image' to the cluster
+    maro grass image push my_grass_cluster --image-name my_image
 
 * Push your training data
 
-.. code-block:: sh
-
-   # Push data under './my_training_data' to a relative path '/my_training_data' in the cluster
-   # You can then assign your mapping location in the start-job deployment
-   maro grass data push my_grass_cluster ./my_training_data/* /my_training_data
+  .. code-block:: sh
 
+    # Push data under './my_training_data' to a relative path '/my_training_data' in the cluster
+    # You can then assign your mapping location in the start-job deployment
+    maro grass data push my_grass_cluster ./my_training_data/* /my_training_data
 
 * Start a training job with a `deployment <#grass-start-job>`_
 
-.. code-block:: sh
-
-   # Start a training job with a start-job deployment
-   maro grass job start my_grass_cluster ./grass-start-job.yml
+  .. code-block:: sh
 
+    # Start a training job with a start-job deployment
+    maro grass job start my_grass_cluster ./grass-start-job.yml
 
 * Or, schedule batch jobs with a `deployment <#grass-start-schedule>`_
 
-.. code-block:: sh
-
-   # Start a training schedule with a start-schedule deployment
-   maro grass schedule start my_grass_cluster ./grass-start-schedule.yml
+  .. code-block:: sh
 
+    # Start a training schedule with a start-schedule deployment
+    maro grass schedule start my_grass_cluster ./grass-start-schedule.yml
 
 * Get the logs of the job
 
-.. code-block:: sh
-
-   # Get the logs of the job
-   maro grass job logs my_grass_cluster my_job_1
+  .. code-block:: sh
 
+    # Get the logs of the job
+    maro grass job logs my_grass_cluster my_job_1
 
 * List the current status of the job
 
-.. code-block:: sh
-
-   # List the current status of the job
-   maro grass job list my_grass_cluster
+  .. code-block:: sh
 
+    # List the current status of the job
+    maro grass job list my_grass_cluster
 
 * Stop a training job
 
-.. code-block:: sh
+  .. code-block:: sh
 
-   # Stop a training job
-   maro grass job stop my_job_1
+    # Stop a training job
+    maro grass job stop my_job_1
 
 Sample Deployments
 ------------------

docs/source/installation/k8s_cluster_provisioning_on_azure.rst

@@ -9,7 +9,6 @@ on Azure and run your training job in a distributed environment.
 Prerequisites
 -------------
 
-
 * `Install the Azure CLI and login <https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest>`_
 * `Install and set up kubectl <https://kubernetes.io/docs/tasks/tools/install-kubectl/>`_
 * `Install docker <https://docs.docker.com/engine/install/>`_ and
@@ -18,92 +17,82 @@ Prerequisites
 Cluster Management
 ------------------
 
-
 * Create a cluster with a `deployment <#k8s-azure-create>`_
 
-.. code-block:: sh
-
-   # Create a k8s cluster
-   maro k8s create ./k8s-azure-create.yml
+  .. code-block:: sh
 
+    # Create a k8s cluster
+    maro k8s create ./k8s-azure-create.yml
 
 * Scale the cluster
 
-.. code-block:: sh
+  .. code-block:: sh
 
-   # Scale nodes with 'Standard_D4s_v3' specification to 2
-   maro k8s node scale my_k8s_cluster Standard_D4s_v3 2
-
-Check `VM Size <https://docs.microsoft.com/en-us/azure/virtual-machines/sizes>`_
-to see more node specifications.
+    # Scale nodes with 'Standard_D4s_v3' specification to 2
+    maro k8s node scale my_k8s_cluster Standard_D4s_v3 2
 
+  Check `VM Size <https://docs.microsoft.com/en-us/azure/virtual-machines/sizes>`_
+  to see more node specifications.
 
 * Delete the cluster
 
-.. code-block:: sh
+  .. code-block:: sh
 
-   # Delete a k8s cluster
-   maro k8s delete my_k8s_cluster
+    # Delete a k8s cluster
+    maro k8s delete my_k8s_cluster
 
 Run Job
 -------
 
-
 * Push your training image
 
-.. code-block:: sh
-
-   # Push image 'my_image' to the cluster
-   maro k8s image push my_k8s_cluster --image-name my_image
+  .. code-block:: sh
 
+    # Push image 'my_image' to the cluster
+    maro k8s image push my_k8s_cluster --image-name my_image
 
 * Push your training data
 
-.. code-block:: sh
-
-   # Push data under './my_training_data' to a relative path '/my_training_data' in the cluster
-   # You can then assign your mapping location in the start-job deployment
-   maro k8s data push my_k8s_cluster ./my_training_data/* /my_training_data
+  .. code-block:: sh
 
+    # Push data under './my_training_data' to a relative path '/my_training_data' in the cluster
+    # You can then assign your mapping location in the start-job deployment
+    maro k8s data push my_k8s_cluster ./my_training_data/* /my_training_data
 
 * Start a training job with a `deployment <#k8s-start-job>`_
 
-.. code-block:: sh
-
-   # Start a training job with a start-job deployment
-   maro k8s job start my_k8s_cluster ./k8s-start-job.yml
+  .. code-block:: sh
 
+    # Start a training job with a start-job deployment
+    maro k8s job start my_k8s_cluster ./k8s-start-job.yml
 
 * Or, schedule batch jobs with a `deployment <#k8s-start-schedule>`_
 
-.. code-block:: sh
-
-   # Start a training schedule with a start-schedule deployment
-   maro k8s schedule start my_k8s123_cluster ./k8s-start-schedule.yml
+  .. code-block:: sh
 
+    # Start a training schedule with a start-schedule deployment
+    maro k8s schedule start my_k8s123_cluster ./k8s-start-schedule.yml
 
 * Get the logs of the job
 
-.. code-block:: sh
-
-   # Logs will be exported to current directory
-   maro k8s job logs my_k8s_cluster my_job_1
+  .. code-block:: sh
 
+    # Logs will be exported to current directory
+    maro k8s job logs my_k8s_cluster my_job_1
 
 * List the current status of the job
 
-.. code-block:: sh
-
-   # List current status of jobs
-   maro k8s job list my_k8s_cluster my_job_1
+  .. code-block:: sh
 
+    # List current status of jobs
+    maro k8s job list my_k8s_cluster my_job_1
 
 * Stop a training job
 
-.. code-block:: sh
+  .. code-block:: sh
 
-   # Stop a training job
-   maro k8s job stop my_k8s_cluster my_job_1
+    # Stop a training job
+    maro k8s job stop my_k8s_cluster my_job_1
 
 Sample Deployments
 ------------------

docs/source/installation/pip_install.rst

@@ -5,76 +5,62 @@ Package
 Install MARO from `PyPI <https://pypi.org/project/pymaro/#files>`_
 ----------------------------------------------------------------------
 
-
-* 
-  Max OS / Linux
+* Max OS / Linux
 
   .. code-block:: sh
 
-     pip install pymaro
+    pip install pymaro
 
-* 
-  Windows
+* Windows
 
-  .. code-block:: powershell
+  .. code-block::
 
-     # Install torch first, if you don't have one.
-     pip install torch===1.6.0 torchvision===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
+    # Install torch first, if you don't have one.
+    pip install torch===1.6.0 torchvision===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
 
-     pip install pymaro
+    pip install pymaro
 
 Install MARO from Source (\ `Editable Mode <https://pip.pypa.io/en/stable/reference/pip_install/#editable-installs>`_\ )
 ------------------------------------------------------------------------------------------------------------------------
 
-
-* 
-  Prerequisites
-
+* Prerequisites
 
   * `Python >= 3.6, < 3.8 <https://www.python.org/downloads/>`_
   * C++ Compiler
 
     * Linux or Mac OS X: ``gcc``
     * Windows: `Build Tools for Visual Studio 2017 <https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools&rel=15>`_
 
-* 
-  Enable Virtual Environment
-
+* Enable Virtual Environment
 
-  * 
-    Mac OS / Linux
+  * Mac OS / Linux
 
     .. code-block:: sh
 
        # If your environment is not clean, create a virtual environment firstly.
        python -m venv maro_venv
        source ./maro_venv/bin/activate
 
-  * 
-    Windows
+  * Windows
 
     .. code-block:: powershell
 
-       # If your environment is not clean, create a virtual environment firstly.
-       python -m venv maro_venv
-       .\maro_venv\Scripts\activate
-
-* 
-  Install MARO
+      # If your environment is not clean, create a virtual environment firstly.
+      python -m venv maro_venv
+      .\maro_venv\Scripts\activate
 
+* Install MARO
 
-  * 
-    Mac OS / Linux
+  * Mac OS / Linux
 
     .. code-block:: sh
 
-       # Install MARO from source.
-       bash scripts/install_maro.sh
+      # Install MARO from source.
+      bash scripts/install_maro.sh
 
-  * 
-    Windows
+  * Windows
 
     .. code-block:: powershell
 
-       # Install MARO from source.
-       .\scripts\install_maro.bat
+      # Install MARO from source.
+      .\scripts\install_maro.bat