From 6d08290ddee47a606eb227c6ee1088a4de8ed05c Mon Sep 17 00:00:00 2001 From: nhennigan Date: Wed, 23 Sep 2026 14:21:22 -0700 Subject: [PATCH 1/2] Update references from 'K8s' to 'Kubernetes' for Canonical Kubernetes We are trying to become more consistent with how we refer to Canonical Kubernetes. Canonical K8s can sometimes be confused for Charmed Kubernetes so I have expanded K8s across this docset to be more explicit. Signed-off-by: nhennigan --- docs/explanation/dss-arch.rst | 14 ++++++-------- docs/how-to/dss.rst | 10 +++++----- docs/how-to/enable-gpus/enable-intel-gpu.rst | 10 +++++----- docs/how-to/enable-gpus/enable-nvidia-gpu.rst | 2 +- docs/index.rst | 4 ++-- docs/reuse/links.txt | 2 +- docs/tutorial/get-started.rst | 12 ++++++------ 7 files changed, 26 insertions(+), 28 deletions(-) diff --git a/docs/explanation/dss-arch.rst b/docs/explanation/dss-arch.rst index 0375481..2489387 100644 --- a/docs/explanation/dss-arch.rst +++ b/docs/explanation/dss-arch.rst @@ -4,7 +4,7 @@ DSS architecture This guide provides an overview of the Data Science Stack (DSS) architecture, its main components, and their interactions. DSS is a ready-to-run environment for Machine Learning (ML) and Data Science (DS). -It's built on open-source tooling, including `Canonical K8s`_, JupyterLab, and `MLflow `_. +It's built on open-source tooling, including `Canonical Kubernetes`_, JupyterLab, and `MLflow `_. DSS is distributed as a `snap`_ and usable on any Ubuntu workstation. This provides robust security management and user-friendly version control, enabling seamless updates and auto-rollback in case of failure. @@ -79,9 +79,9 @@ Orchestration ~~~~~~~~~~~~~ DSS requires a container orchestration solution. -DSS relies on `Canonical K8s`_, a lightweight Kubernetes distribution. +DSS relies on `Canonical Kubernetes`_, a lightweight Kubernetes distribution. -Therefore, Canonical K8s needs to be deployed before installing DSS on the host machine. +Therefore, Canonical Kubernetes needs to be deployed before installing DSS on the host machine. It must be configured with local storage support to handle persistent volumes used by DSS. .. _gpu_support: @@ -93,7 +93,7 @@ DSS can run with or without the use of GPUs. If needed, follow `NVIDIA GPU Operator `_ for deployment details. DSS does not automatically install the tools and libraries required for running GPU workloads. -It relies on Canonical K8s for the required operating-system drivers. +It relies on Canonical Kubernetes for the required operating-system drivers. It also depends on the chosen image, for example, CUDA when working with NVIDIA GPUs. .. caution:: @@ -103,7 +103,7 @@ Storage ^^^^^^^ DSS expects a default `storage class `_ in the Kubernetes deployment, which is used to persist Jupyter Notebooks and MLflow artefacts. -In Canonical K8s, a local storage class should be configured to provision Kubernetes' *PersistentVolumeClaims* (`PVCs `_). +In Canonical Kubernetes, a local storage class should be configured to provision Kubernetes' *PersistentVolumeClaims* (`PVCs `_). A shared PVC is used across all Jupyter Notebooks to share and persist data. MLflow also uses its dedicated PVC to store the logged artefacts. @@ -142,7 +142,5 @@ This includes the GPU Operator for managing access and usage. Accessibility ------------- -Jupyter Notebooks and MLflow can be accessed from a web browser through the Pod IP that is given access through Canonical K8s. +Jupyter Notebooks and MLflow can be accessed from a web browser through the Pod IP that is given access through Canonical Kubernetes. See :ref:`access_notebook` and :ref:`access_mlflow` for more details. - -.. _Canonical K8s: https://snapcraft.io/k8s diff --git a/docs/how-to/dss.rst b/docs/how-to/dss.rst index 8f145b5..e359cf1 100644 --- a/docs/how-to/dss.rst +++ b/docs/how-to/dss.rst @@ -11,7 +11,7 @@ Install ------- .. note:: - To install DSS, ensure you have previously installed `Snap`_ and `Canonical K8s`_. + To install DSS, ensure you have previously installed `Snap`_ and `Canonical Kubernetes`_. You can install DSS using ``snap`` as follows: @@ -31,7 +31,7 @@ Initialise You can initialise DSS through ``dss initialize``. This command: -* Stores credentials for the Canonical K8s cluster. +* Stores credentials for the Canonical Kubernetes cluster. * Allocates storage for your DSS Jupyter Notebooks. * Deploys an `MLflow `_ model registry. @@ -39,7 +39,7 @@ This command: dss initialize --kubeconfig "$(sudo k8s config)" -The ``--kubeconfig`` option is used to provide your Canonical K8s cluster's kubeconfig. +The ``--kubeconfig`` option is used to provide your Canonical Kubernetes cluster's kubeconfig. .. note:: Note the use of quotes for the ``--kubeconfig`` option. Without them, the content may be interpreted by your shell. @@ -63,7 +63,7 @@ You should expect an output like this: Remove ------ -You can remove DSS from your Canonical K8s cluster through ``dss purge``. +You can remove DSS from your Canonical Kubernetes cluster through ``dss purge``. This command purges all the DSS components, including: * All Jupyter Notebooks. @@ -72,7 +72,7 @@ This command purges all the DSS components, including: .. note:: - This action removes the components of the DSS environment, but it does not remove the DSS CLI or your Canonical K8s cluster. + This action removes the components of the DSS environment, but it does not remove the DSS CLI or your Canonical Kubernetes cluster. To remove those, `delete their snaps `_. .. code-block:: bash diff --git a/docs/how-to/enable-gpus/enable-intel-gpu.rst b/docs/how-to/enable-gpus/enable-intel-gpu.rst index 5f3fb05..4f40e91 100644 --- a/docs/how-to/enable-gpus/enable-intel-gpu.rst +++ b/docs/how-to/enable-gpus/enable-intel-gpu.rst @@ -5,7 +5,7 @@ Enable Intel GPUs This guide describes how to configure Data Science Stack (DSS) to utilise the Intel GPUs on your machine. -You can do so by enabling the Intel device plugin on your `Canonical K8s`_ cluster. +You can do so by enabling the Intel device plugin on your `Canonical Kubernetes`_ cluster. Prerequisites ------------- @@ -44,7 +44,7 @@ If the drivers are correctly installed, you should see information about your GP Enable the Intel GPU plugin --------------------------- -To ensure DSS can utilise Intel GPUs, you have to enable the Intel GPU plugin in your Canonical K8s cluster. +To ensure DSS can utilise Intel GPUs, you have to enable the Intel GPU plugin in your Canonical Kubernetes cluster. 1. Use `kubectl kustomize` to build the plugin YAML configuration files: @@ -61,7 +61,7 @@ To allow multiple containers to utilise the same GPU, run: sed -i 's/enable-monitoring/enable-monitoring\n - -shared-dev-num=10/' gpu_plugin.yaml -2. Apply the built YAML files to your Canonical K8s cluster: +2. Apply the built YAML files to your Canonical Kubernetes cluster: .. code-block:: bash @@ -69,7 +69,7 @@ To allow multiple containers to utilise the same GPU, run: kubectl apply -f node_feature_rules.yaml kubectl apply -f gpu_plugin.yaml -The Canonical K8s cluster is now configured to recognise and utilise your Intel GPU. +The Canonical Kubernetes cluster is now configured to recognise and utilise your Intel GPU. .. note:: After the YAML configuration files have been applied, they can be safely deleted. @@ -77,7 +77,7 @@ The Canonical K8s cluster is now configured to recognise and utilise your Intel Verify the Intel GPU plugin --------------------------- -To verify the Intel GPU plugin is installed and the Canonical K8s cluster recognises your GPU, run: +To verify the Intel GPU plugin is installed and the Canonical Kubernetes cluster recognises your GPU, run: .. code-block:: bash diff --git a/docs/how-to/enable-gpus/enable-nvidia-gpu.rst b/docs/how-to/enable-gpus/enable-nvidia-gpu.rst index ea2903f..32412eb 100644 --- a/docs/how-to/enable-gpus/enable-nvidia-gpu.rst +++ b/docs/how-to/enable-gpus/enable-nvidia-gpu.rst @@ -3,7 +3,7 @@ Enable NVIDIA GPUs ================== -This guide describes how to configure Data Science Stack (DSS) to utilise your NVIDIA GPUs within a Canonical K8s environment. +This guide describes how to configure Data Science Stack (DSS) to utilise your NVIDIA GPUs within a Canonical Kubernetes environment. DSS supports GPU acceleration by leveraging the `NVIDIA GPU Operator`_. The operator ensures that the necessary components, including drivers and runtime, are set up correctly to enable GPU workloads. diff --git a/docs/index.rst b/docs/index.rst index 5ee02f4..63609a6 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -5,9 +5,9 @@ Data Science Stack documentation ================================ Data Science Stack (DSS) is a ready-to-run environment for Machine Learning (ML) and data science. -It's built on open-source tooling, including Canonical K8s, JupyterLab, and MLflow, and is usable on any Ubuntu/Snap-enabled workstation. +It's built on open-source tooling, including Canonical Kubernetes, JupyterLab, and MLflow, and is usable on any Ubuntu/Snap-enabled workstation. -DSS provides a Command Line Interface (CLI) for managing containerised ML environment images such as PyTorch or TensorFlow, on top of Canonical K8s. +DSS provides a Command Line Interface (CLI) for managing containerised ML environment images such as PyTorch or TensorFlow, on top of Canonical Kubernetes. Typically, creating ML environments on a workstation involves complex and hard-to-reverse configurations. DSS solves this problem by providing accessible, production-ready, isolated, and reproducible ML environments that fully utilise a workstation's GPUs. diff --git a/docs/reuse/links.txt b/docs/reuse/links.txt index e88ca9e..05e8712 100644 --- a/docs/reuse/links.txt +++ b/docs/reuse/links.txt @@ -3,7 +3,7 @@ .. _How to publish documentation on Read the Docs: https://library.canonical.com/documentation/publish-on-read-the-docs .. _Example product documentation: https://canonical-example-product-documentation.readthedocs-hosted.com/ -.. _Canonical K8s: https://snapcraft.io/k8s +.. _Canonical Kubernetes: https://snapcraft.io/k8s .. _Charmed MLflow: https://documentation.ubuntu.com/charmed-mlflow/en/latest/ .. _Code of conduct: https://ubuntu.com/community/ethos/code-of-conduct .. _Contribute: https://github.com/canonical/data-science-stack/blob/main/CONTRIBUTING.md diff --git a/docs/tutorial/get-started.rst b/docs/tutorial/get-started.rst index 986f5c8..2a625c0 100644 --- a/docs/tutorial/get-started.rst +++ b/docs/tutorial/get-started.rst @@ -4,7 +4,7 @@ Get started with DSS ==================== This guide describes how you can get started with Data Science Stack (DSS). -From setting up Canonical K8s in your host environment, all the way to running your first notebook. +From setting up Canonical Kubernetes in your host environment, all the way to running your first notebook. Data Science Stack is a ready-made environment that makes it seamless to run GPU-enabled containerised Machine Learning (ML) environments. It provides easy access to a solution for developing and optimising ML models, utilising your machine's GPUs and allowing users to utilise different ML environment images based on their needs. @@ -18,14 +18,14 @@ Requirements .. _set_canonical_k8s: -Set up Canonical K8s +Set up Canonical Kubernetes -------------------- DSS relies on a container orchestration system, capable of exposing the host GPUs to the workloads. -`Canonical K8s`_ is used as the orchestration system. -All the workloads and state managed by DSS are running on top of Canonical K8s. +`Canonical Kubernetes`_ is used as the orchestration system. +All the workloads and state managed by DSS are running on top of Canonical Kubernetes. -You can install Canonical K8s using ``snap`` as follows: +You can install Canonical Kubernetes ``k8s snap`` as follows: .. code-block:: bash @@ -50,7 +50,7 @@ Now, install the DSS CLI using the following command: Initialise DSS -------------- -Next, you need to initialise DSS on top of Canonical K8s and prepare MLflow: +Next, you need to initialise DSS on top of Canonical Kubernetes and prepare MLflow: .. code-block:: bash From d4ae0ec6379753af71923a75804271605e7e0449 Mon Sep 17 00:00:00 2001 From: nhennigan Date: Wed, 23 Sep 2026 14:35:17 -0700 Subject: [PATCH 2/2] Fix build error --- docs/tutorial/get-started.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorial/get-started.rst b/docs/tutorial/get-started.rst index 2a625c0..1fbfbf1 100644 --- a/docs/tutorial/get-started.rst +++ b/docs/tutorial/get-started.rst @@ -19,7 +19,7 @@ Requirements .. _set_canonical_k8s: Set up Canonical Kubernetes --------------------- +---------------------------- DSS relies on a container orchestration system, capable of exposing the host GPUs to the workloads. `Canonical Kubernetes`_ is used as the orchestration system.