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docs: headings update (#186)
* docs: headings update * no bold entries on home page
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docs/how-to/dss.rst

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@@ -7,8 +7,8 @@ This guide describes how to manage Data Science Stack (DSS).
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DSS is a Command Line Interface (CLI)-based environment and distributed as a `snap`_.
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Install DSS
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-----------
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Install
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--------
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.. note::
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To install DSS, ensure you have previously installed `Snap`_ and `MicroK8s`_.
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dss
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Start DSS
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Initialise
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You can initialise DSS through ``dss initialize``.
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This command:
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dss create my-notebook --image=pytorch
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dss create my-notebook --image=kubeflownotebookswg/jupyter-scipy:v1.8.0
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Remove DSS
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----------
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Remove
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-------
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You can remove DSS from your MicroK8s cluster through ``dss purge``.
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This command purges all the DSS components, including:
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Waiting for namespace dss to be deleted...
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Success: All DSS components and notebooks purged successfully from the Kubernetes cluster.
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Get DSS status
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--------------
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Get status
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-----------
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You can check the DSS status through ``dss status``.
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This command provides a quick way to check the status of your DSS environment, including the MLflow status and whether a GPU is detected in your environment.
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MLflow URL: http://10.152.183.68:5000
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GPU acceleration: Disabled
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List DSS commands
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-----------------
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List commands
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--------------
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You can get the list of available commands for DSS through the ``dss`` command with the ``--help`` option:
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docs/how-to/mlflow.rst

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Access MLflow
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-------------
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You can access the MLflow User Interface (UI) within your Data Science Stack (DSS) environment through a web browser,
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You can access the MLflow User Interface (UI) within your DSS environment through a web browser,
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by navigating to the URL associated with MLflow.
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This UI allows you to interact directly with your MLflow experiments and models.
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Once you know the URL, open a web browser and enter the URL into the address bar.
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This will direct you to the MLflow interface.
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Get MLflow logs
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---------------
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Get logs
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You can retrieve logs for MLflow within your Data Science Stack (DSS) environment.
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You can retrieve MLflow logs within your DSS environment.
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Retrieving logs is a critical task for maintaining and troubleshooting MLflow.
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To get MLflow logs, use the ``dss logs`` command with the ``--mlflow`` option:
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[2024-04-30 07:57:54 +0000] [26] [INFO] Booting worker with pid: 26
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Get MLflow artefacts
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--------------------
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Get artefacts
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--------------
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MLflow artefacts, including `models <https://mlflow.org/docs/latest/models.html>`_, `experiments <https://mlflow.org/docs/latest/tracking.html#experiments>`_ and `runs <https://mlflow.org/docs/latest/tracking.html#runs>`_ are stored within your DSS environment.
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They can be accessed and downloaded from the :ref:`MLflow UI <access_mlflow>`.
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You can access and download them from the :ref:`MLflow UI <access_mlflow>`.
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See also
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--------

docs/index.rst

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.. grid-item:: :doc:`Tutorial <tutorial/index>`
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**Get started** - a hands-on introduction to DSS for newcomers
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Get started - a hands-on introduction to DSS for newcomers
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.. grid-item:: :doc:`How-to guides <how-to/index>`
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**Step-by-step guides** covering key operations and common tasks with DSS
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Step-by-step guides covering key operations and common tasks with DSS
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.. grid:: 1 1 2 2
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.. grid-item:: :doc:`Explanation <explanation/index>`
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**Discussion and clarification** of key topics
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Discussion and clarification of key topics
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docs/tutorial/get-started.rst

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.. _set_microk8s:
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Setting up MicroK8s
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-------------------
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Set up MicroK8s
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---------------
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DSS relies on a container orchestration system, capable of exposing the host GPUs to the workloads.
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`MicroK8s`_ is used as the orchestration system.

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