diff --git a/content/en/_index.html b/content/en/_index.html index 128c6f56cf..3cdc49c3b1 100644 --- a/content/en/_index.html +++ b/content/en/_index.html @@ -50,10 +50,10 @@

What is Kubeflow?

alt="Jupyter Logo" />
-
Notebooks
+
Workbenches

- Kubeflow includes services to create and manage interactive Jupyter - notebooks. You can customize your notebook deployment and your compute resources to + Kubeflow includes services to create and manage interactive Workbenches. + You can customize your workbench deployment and your compute resources to suit your data science needs. Experiment with your workflows locally, then deploy them to a cloud when you're ready.

diff --git a/content/en/docs/about/community.md b/content/en/docs/about/community.md index daa3b813e9..aec574ec7e 100644 --- a/content/en/docs/about/community.md +++ b/content/en/docs/about/community.md @@ -161,7 +161,7 @@ The following table outlines which components are maintained by each Working Gro - Notebook Controller + Workbench Controller diff --git a/content/en/docs/components/central-dash/customizing-menu.md b/content/en/docs/components/central-dash/customizing-menu.md index 2078972784..48d4439b2c 100644 --- a/content/en/docs/components/central-dash/customizing-menu.md +++ b/content/en/docs/components/central-dash/customizing-menu.md @@ -40,7 +40,7 @@ data: { "type": "item", "link": "/jupyter/", - "text": "Notebooks", + "text": "Workbenches", "icon": "book" }, . @@ -98,7 +98,7 @@ data: { "type": "item", "link": "/jupyter/", - "text": "Notebooks", + "text": "Workbenches", "icon": "book" }, . diff --git a/content/en/docs/components/central-dash/overview.md b/content/en/docs/components/central-dash/overview.md index 71e96f12eb..c6e65980b5 100644 --- a/content/en/docs/components/central-dash/overview.md +++ b/content/en/docs/components/central-dash/overview.md @@ -11,10 +11,10 @@ Your Kubeflow deployment includes a central dashboard that provides quick access to the Kubeflow components deployed in your cluster. The dashboard includes the following features: -- Shortcuts to specific actions, a list of recent pipelines and notebooks, and +- Shortcuts to specific actions, a list of recent pipelines and workbenches, and metrics, giving you an overview of your jobs and cluster in one view. - A housing for the UIs of the components running in the cluster, including - **Pipelines**, **Katib**, **Notebooks**, and more. + **Pipelines**, **Katib**, **Workbenches**, and more. - A [registration flow](/docs/components/central-dash/registration-flow/) that prompts new users to set up their namespace if necessary. @@ -24,7 +24,7 @@ The Kubeflow UIs include the following: * **Home**: Home, the central hub to access recent resources, active experiments, and useful documentation. -* **Notebook Servers**: To manage [Notebook servers](/docs/components/notebooks/). +* **Workbench Servers**: To manage [Workbench servers](/docs/components/notebooks/). * **TensorBoards**: To manage TensorBoard servers. * **Models**: To manage deployed [KFServing models](/docs/components/kfserving/kfserving/). * **Volumes**: To manage the cluster's Volumes. @@ -125,4 +125,4 @@ You can access Kubeflow via `kubectl` and port-forwarding as follows: option](/docs/components/multi-tenancy/) where you can set up a single namespace for a shared deployment or configure multi-tenancy for your Kubeflow deployment. -* [Set up your Jupyter notebooks](/docs/components/notebooks/setup/) in Kubeflow. +* [Set up your Workbenches](/docs/components/notebooks/setup/) in Kubeflow. diff --git a/content/en/docs/components/central-dash/registration-flow.md b/content/en/docs/components/central-dash/registration-flow.md index 814623a6fd..7ba3dd5f32 100644 --- a/content/en/docs/components/central-dash/registration-flow.md +++ b/content/en/docs/components/central-dash/registration-flow.md @@ -61,5 +61,5 @@ with your namespace available in the dropdown list at the top of the screen: ## Next steps -* [Set up a Jupyter notebook](/docs/components/notebooks/setup/) in Kubeflow. +* [Set up a Workbench](/docs/components/notebooks/setup/) in Kubeflow. * Read more about [multi-tenancy in Kubeflow](/docs/components/multi-tenancy/). diff --git a/content/en/docs/components/multi-tenancy/getting-started.md b/content/en/docs/components/multi-tenancy/getting-started.md index 10003c267b..c7064358b9 100644 --- a/content/en/docs/components/multi-tenancy/getting-started.md +++ b/content/en/docs/components/multi-tenancy/getting-started.md @@ -29,36 +29,36 @@ to which you have view or modify access. alt="Select active profile " class="mt-3 mb-3 border border-info rounded"> -This guide illustrates the user isolation functionality using the Jupyter -notebooks service which is the first service in the system to have full +This guide illustrates the user isolation functionality using the Kubeflow +workbenches service which is the first service in the system to have full integration with the multi-user isolation functionality. -After you select an active profile, the Notebooks Servers UI -displays only the active notebook servers in the currently selected -profile. All other notebook servers remain hidden from you. If you switch -the active profile, the view switches the list of active notebooks -appropriately. You can connect to any of the listed notebook servers and -view and modify the existing Jupyter notebooks available in the server. +After you select an active profile, the Workbenches Servers UI +displays only the active workbench servers in the currently selected +profile. All other workbench servers remain hidden from you. If you switch +the active profile, the view switches the list of active workbenches +appropriately. You can connect to any of the listed workbench servers and +view and modify the existing Kubeflow workbenches available in the server. -For example, the following image shows the list of notebook servers available +For example, the following image shows the list of workbench servers available in a user's primary profile: List of notebooks in active profile -When an unauthorized user accesses the notebooks in this profile, they see an +When an unauthorized user accesses the workbenches in this profile, they see an error: Error listing notebooks in inacessible profile -When you create Jupyter notebook servers from the Notebooks Servers UI, -the notebook pods are created in your active profile. If you don't have +When you create workbench servers from the Workbenches Servers UI, +the workbench pods are created in your active profile. If you don't have modify access to the active profile, you can only browse currently active -notebook servers and access the existing notebooks but cannot create -new notebook servers in that profile. You can create notebook +workbench servers and access the existing workbenches but cannot create +new workbench servers in that profile. You can create workbench servers in your primary profile which you have view and modify access to. ## Onboarding a new user @@ -285,8 +285,8 @@ profiles in the system along with their contributors. The contributors have access to all the Kubernetes resources in the -namespace and can create notebook servers as well as access -existing notebooks. +namespace and can create workbench servers as well as access +existing workbenches. ## Managing contributors manually diff --git a/content/en/docs/components/multi-tenancy/overview.md b/content/en/docs/components/multi-tenancy/overview.md index 973891ae05..e73bbecd53 100644 --- a/content/en/docs/components/multi-tenancy/overview.md +++ b/content/en/docs/components/multi-tenancy/overview.md @@ -30,7 +30,7 @@ With multi-user isolation, Users are authenticated and authorized, and then prov ## Current integration -These Kubeflow Components can support multi-user isolation: Central Dashboard, Notebooks, Pipelines, AutoML (Katib), KFServing. Furthermore, resources created by the notebooks (for example, training jobs and deployments) also inherit the same access. +These Kubeflow Components can support multi-user isolation: Central Dashboard, Workbenches, Pipelines, AutoML (Katib), KFServing. Furthermore, resources created by the workbenches (for example, training jobs and deployments) also inherit the same access. Important notes: Multi-user isolation has several configurable dependencies, especially those related to how Kubeflow is configured with the underlying Kubernetes cluster’s identity management system. Additionally, Kubeflow multi-user isolation doesn’t provide hard security guarantees against malicious attempts to infiltrate another user’s profile. diff --git a/content/en/docs/components/notebooks/_index.md b/content/en/docs/components/notebooks/_index.md index e854977805..288a92003f 100644 --- a/content/en/docs/components/notebooks/_index.md +++ b/content/en/docs/components/notebooks/_index.md @@ -1,5 +1,5 @@ +++ -title = "Kubeflow Notebooks" -description = "Documentation for Kubeflow Notebooks" +title = "Kubeflow Workbenches" +description = "Documentation for Kubeflow Workbenches" weight = 10 +++ diff --git a/content/en/docs/components/notebooks/api-reference/_index.md b/content/en/docs/components/notebooks/api-reference/_index.md index 301bba1ddb..1bd9c6fdf0 100644 --- a/content/en/docs/components/notebooks/api-reference/_index.md +++ b/content/en/docs/components/notebooks/api-reference/_index.md @@ -1,5 +1,5 @@ +++ title = "API Reference" -description = "Reference documentation for Kubeflow Notebooks" +description = "Reference documentation for Kubeflow Workbenches" weight = 900 +++ \ No newline at end of file diff --git a/content/en/docs/components/notebooks/container-images.md b/content/en/docs/components/notebooks/container-images.md index 4a83e4b818..b205e62cdb 100644 --- a/content/en/docs/components/notebooks/container-images.md +++ b/content/en/docs/components/notebooks/container-images.md @@ -1,11 +1,11 @@ +++ title = "Container Images" -description = "About Container Images for Kubeflow Notebooks" +description = "About Container Images for Kubeflow Workbenches" weight = 30 +++ -Kubeflow Notebooks natively supports three types of notebooks, [JupyterLab](https://github.com/jupyterlab/jupyterlab), [RStudio](https://github.com/rstudio/rstudio), and [Visual Studio Code (code-server)](https://github.com/cdr/code-server), but any web-based IDE should work. -Notebook servers run as containers inside a Kubernetes Pod, which means the type of IDE (and which packages are installed) is determined by the Docker image you pick for your server. +Kubeflow Workbenches natively supports three types of workbenches, [JupyterLab](https://github.com/jupyterlab/jupyterlab), [RStudio](https://github.com/rstudio/rstudio), and [Visual Studio Code (code-server)](https://github.com/cdr/code-server), but any web-based IDE should work. +Workbench servers run as containers inside a Kubernetes Pod, which means the type of IDE (and which packages are installed) is determined by the Docker image you pick for your server. ## Images @@ -13,7 +13,7 @@ We provide a number of [example container images](https://github.com/kubeflow/ku ### Base Images -These images provide a common starting point for Kubeflow Notebook containers. +These images provide a common starting point for Kubeflow Workbench containers. See [custom images](#custom-images) to learn how to extend them with your own packages. Dockerfile | Registry | Notes @@ -43,15 +43,15 @@ Dockerfile | Registry | Notes ### Image Dependency Chart -This flow-chart shows how our notebook container images depend on each other. +This flow-chart shows how our workbench container images depend on each other. A flow-chart showing how notebook container images depend on each other ## Custom Images -Packages installed by users __after spawning__ a Kubeflow Notebook will only last the lifetime of the pod (unless installed into a PVC-backed directory). +Packages installed by users __after spawning__ a Kubeflow Workbench will only last the lifetime of the pod (unless installed into a PVC-backed directory). To ensure packages are preserved throughout Pod restarts users will need to either: 1. [Build custom images that include them](https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers#custom-images), or @@ -59,7 +59,7 @@ To ensure packages are preserved throughout Pod restarts users will need to eith ### Image Requirements -For Kubeflow Notebooks to work with a container image, the image must: +For Kubeflow Workbenches to work with a container image, the image must: - expose an HTTP interface on port `8888`: - kubeflow sets an environment variable `NB_PREFIX` at runtime with the URL path we expect the container be listening under - kubeflow uses IFrames, so ensure your application sets `Access-Control-Allow-Origin: *` in HTTP response headers @@ -71,5 +71,5 @@ For Kubeflow Notebooks to work with a container image, the image must: ## Next steps -- Use your container image by specifying it when spawning your notebook server. +- Use your container image by specifying it when spawning your workbench server. (See the [quickstart guide](/docs/components/notebooks/quickstart-guide/).) \ No newline at end of file diff --git a/content/en/docs/components/notebooks/jupyter-tensorflow-examples.md b/content/en/docs/components/notebooks/jupyter-tensorflow-examples.md index 16e8ae3828..e6e825b1e4 100644 --- a/content/en/docs/components/notebooks/jupyter-tensorflow-examples.md +++ b/content/en/docs/components/notebooks/jupyter-tensorflow-examples.md @@ -1,6 +1,6 @@ +++ title = "Jupyter TensorFlow Examples" -description = "Examples using Jupyter and TensorFlow in Kubeflow Notebooks" +description = "Examples using Jupyter and TensorFlow in Kubeflow Workbenches" weight = 40 +++ @@ -9,7 +9,7 @@ weight = 40 (adapted from [tensorflow/tensorflow - mnist_softmax.py](https://github.com/tensorflow/tensorflow/blob/r1.4/tensorflow/examples/tutorials/mnist/mnist_softmax.py)) -1. When creating your notebook server choose a [container image](/docs/components/notebooks/container-images/) which has Jupyter and TensorFlow installed. +1. When creating your workbench server choose a [container image](/docs/components/notebooks/container-images/) which has Jupyter and TensorFlow installed. 2. Use Jupyter's interface to create a new **Python 3** notebook. @@ -55,4 +55,4 @@ weight = 40 - See a [simple example](https://github.com/kubeflow/examples/tree/master/pipelines/simple-notebook-pipeline) of creating Kubeflow pipelines in a Jupyter notebook. - Build machine-learning pipelines with the [Kubeflow Pipelines SDK](/docs/components/pipelines/sdk/sdk-overview/). -- Learn the advanced features available from a Kubeflow notebook, such as [submitting Kubernetes resources](/docs/components/notebooks/submit-kubernetes/) or [building Docker images](/docs/components/notebooks/custom-notebook/). +- Learn the advanced features available from a Kubeflow workbench, such as [submitting Kubernetes resources](/docs/components/notebooks/submit-kubernetes/) or [building Docker images](/docs/components/notebooks/custom-notebook/). diff --git a/content/en/docs/components/notebooks/overview.md b/content/en/docs/components/notebooks/overview.md index ba765824fb..044d9eb870 100644 --- a/content/en/docs/components/notebooks/overview.md +++ b/content/en/docs/components/notebooks/overview.md @@ -1,22 +1,22 @@ +++ title = "Overview" -description = "An overview of Kubeflow Notebooks" +description = "An overview of Kubeflow Workbenches" weight = 5 +++ {{% stable-status %}} -## What is Kubeflow Notebooks? +## What is Kubeflow Workbenches? -Kubeflow Notebooks provides a way to run web-based development environments inside your Kubernetes cluster by running them inside Pods. +Kubeflow Workbenches provides a way to run web-based development environments inside your Kubernetes cluster by running them inside Pods. Some key features include: - Native support for [JupyterLab](https://github.com/jupyterlab/jupyterlab), [RStudio](https://github.com/jupyterlab/jupyterlab), and [Visual Studio Code (code-server)](https://github.com/cdr/code-server). -- Users can create notebook containers directly in the cluster, rather than locally on their workstations. -- Admins can provide standard notebook images for their organization with required packages pre-installed. -- Access control is managed by Kubeflow's RBAC, enabling easier notebook sharing across the organization. +- Users can create workbench containers directly in the cluster, rather than locally on their workstations. +- Admins can provide standard workbench images for their organization with required packages pre-installed. +- Access control is managed by Kubeflow's RBAC, enabling easier workbench sharing across the organization. ## Next steps -- Get started with Kubeflow Notebooks using the [quickstart guide](/docs/components/notebooks/quickstart-guide/). +- Get started with Kubeflow Workbenches using the [quickstart guide](/docs/components/notebooks/quickstart-guide/). - Learn how to create your own [container images](/docs/components/notebooks/container-images/). diff --git a/content/en/docs/components/notebooks/quickstart-guide.md b/content/en/docs/components/notebooks/quickstart-guide.md index 3117b3774e..cb23a524a7 100644 --- a/content/en/docs/components/notebooks/quickstart-guide.md +++ b/content/en/docs/components/notebooks/quickstart-guide.md @@ -1,6 +1,6 @@ +++ title = "Quickstart Guide" -description = "Getting started with Kubeflow Notebooks" +description = "Getting started with Kubeflow Workbenches" weight = 10 +++ @@ -9,10 +9,10 @@ weight = 10 1. Install Kubeflow by following [Getting Started - Installing Kubeflow](/docs/started/installing-kubeflow/). 2. Open the Kubeflow [Central Dashboard](/docs/components/central-dash/) in your browser. -3. Click __"Notebooks"__ in the left-hand panel. -4. Click __"New Server"__ to create a new notebook server. -5. Specify the configs for your notebook server. -6. Click __"CONNECT"__ once the notebook has been provisioned +3. Click __"Workbenches"__ in the left-hand panel. +4. Click __"New Workbench"__ to create a new workbench server. +5. Specify the configs for your workbench server. +6. Click __"CONNECT"__ once the workbench has been provisioned ## Detailed Steps @@ -23,39 +23,39 @@ weight = 10 - Choose the namespace that corresponds to your Kubeflow Profile. (See the page on [multi-user isolation](/docs/components/multi-tenancy/) for more information about Profiles.) - Selecting a Kubeflow namespace -3. Click __"Notebook Servers"__ in the left-hand panel: +3. Click __"Workbenches"__ in the left-hand panel: - Opening notebooks from the Kubeflow UI -4. Click __"New Server"__ on the __"Notebook Servers"__ page: +4. Click __"New Workbench"__ on the __"Workbenches"__ page: - The Kubeflow notebook servers page -5. Enter a __"Name"__ for your notebook server. +5. Enter a __"Name"__ for your workbench server. - The name can include letters and numbers, but no spaces. - - For example, `my-first-notebook`. + - For example, `my-first-workbench`. - Form for adding a Kubeflow notebook server -6. Select a Docker __"Image"__ for your notebook server +6. Select a Docker __"Image"__ for your workbench server - __Custom image__: If you select the custom option, you must specify a Docker image in the form `registry/image:tag`. (See the guide on [container images](/docs/components/notebooks/container-images/).) - __Standard image__: Click the __"Image"__ dropdown menu to see the list of available images. (You can choose from the list configured by your Kubeflow administrator) -7. Specify the amount of __"CPU"__ that your notebook server will request. +7. Specify the amount of __"CPU"__ that your workbench server will request. -8. Specify the amount of __"RAM"__ that your notebook server will request. +8. Specify the amount of __"RAM"__ that your workbench server will request. 9. Specify a __"workspace volume"__ to be mounted as a PVC Volume on your home folder. @@ -85,8 +85,8 @@ weight = 10 secretName: gcp-secret ``` -12. *(Optional)* Specify any __"GPUs"__ that your notebook server will request. - - Kubeflow uses "limits" in Pod requests to provision GPUs onto the notebook Pods +12. *(Optional)* Specify any __"GPUs"__ that your workbench server will request. + - Kubeflow uses "limits" in Pod requests to provision GPUs onto the workbench Pods (Details about scheduling GPUs can be found in the [Kubernetes Documentation](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/).) 13. *(Optional)* Specify the setting for __"enable shared memory"__. @@ -94,16 +94,16 @@ weight = 10 - Currently, there is no implementation in Kubernetes to activate shared memory. - As a workaround, Kubeflow mounts an empty directory volume at `/dev/shm`. -14. Click __"LAUNCH"__ to create a new Notebook CRD with your specified settings. - - You should see an entry for your new notebook server on the __"Notebook Servers"__ page +14. Click __"LAUNCH"__ to create a new Workbench CRD with your specified settings. + - You should see an entry for your new workbench server on the __"Workbenches"__ page - There should be a spinning indicator in the __"Status"__ column. - - It can take a few minutes for kubernetes to provision the notebook server pod. + - It can take a few minutes for kubernetes to provision the workbench server pod. - You can check the status of your Pod by hovering your mouse cursor over the icon in the __"Status"__ column. -15. Click __"CONNECT"__ to view the web interface exposed by your notebook server. +15. Click __"CONNECT"__ to view the web interface exposed by your workbench server. - Opening notebooks from the Kubeflow UI ## Next steps diff --git a/content/en/docs/components/notebooks/submit-kubernetes.md b/content/en/docs/components/notebooks/submit-kubernetes.md index f4dd9f27f6..e70fb0cc59 100644 --- a/content/en/docs/components/notebooks/submit-kubernetes.md +++ b/content/en/docs/components/notebooks/submit-kubernetes.md @@ -1,13 +1,13 @@ +++ title = "Submit Kubernetes Resources" -description = "Submitting Kubernetes resources from a Notebook" +description = "Submitting Kubernetes resources from a Workbench" weight = 40 +++ -## Notebook Pod ServiceAccount +## Workbench Pod ServiceAccount -Kubeflow assigns the `default-editor` Kubernetes ServiceAccount to the Notebook Pods. +Kubeflow assigns the `default-editor` Kubernetes ServiceAccount to the Workbench Pods. The Kubernetes `default-editor` ServiceAccount is bound to the `kubeflow-edit` ClusterRole, which has namespace-scoped permissions to many Kubernetes resources. You can get the full list of RBAC for `ClusterRole/kubeflow-edit` using: @@ -15,9 +15,9 @@ You can get the full list of RBAC for `ClusterRole/kubeflow-edit` using: kubectl describe clusterrole kubeflow-edit ``` -## Kubectl in Notebook Pod +## Kubectl in Workbench Pod -Because every Notebook Pod has the highly-privileged `default-editor` Kubernetes ServiceAccount bound to it, you can run `kubectl` inside it without providing additional authentication. +Because every Workbench Pod has the highly-privileged `default-editor` Kubernetes ServiceAccount bound to it, you can run `kubectl` inside it without providing additional authentication. For example, the following command will create the resources defined in `test.yaml`: @@ -27,5 +27,5 @@ kubectl create -f "test.yaml" --namespace "MY_PROFILE_NAMESPACE" ## Next steps -- See the Kubeflow Notebook [quickstart guide](/docs/components/notebooks/quickstart-guide/). +- See the Kubeflow Workbench [quickstart guide](/docs/components/notebooks/quickstart-guide/). - Explore the other [components of Kubeflow](/docs/components/). diff --git a/content/en/docs/components/notebooks/troubleshooting.md b/content/en/docs/components/notebooks/troubleshooting.md index 2cb117b8eb..91581b879b 100644 --- a/content/en/docs/components/notebooks/troubleshooting.md +++ b/content/en/docs/components/notebooks/troubleshooting.md @@ -1,13 +1,13 @@ +++ title = "Troubleshooting" -description = "Problems and solutions for common problems with Kubeflow Notebooks" +description = "Problems and solutions for common problems with Kubeflow Workbenches" weight = 100 +++ -## ISSUE: notebook not starting +## ISSUE: workbench not starting -### SOLUTION: check events of Notebook +### SOLUTION: check events of Workbench Run the following command then check the `events` section to make sure that there are no errors: @@ -39,12 +39,12 @@ Run the following command to get the logs from the Pod: kubectl logs "${MY_NOTEBOOK_NAME}-0" --namespace "${MY_PROFILE_NAMESPACE}" ``` -## ISSUE: manually delete notebook +## ISSUE: manually delete workbench -### SOLUTION: use kubectl to delete Notebook resource +### SOLUTION: use kubectl to delete Workbench resource -Run the following command to delete a Notebook resource manually: +Run the following command to delete a Workbench resource manually: ```shell kubectl delete notebook "${MY_NOTEBOOK_NAME}" --namespace "${MY_PROFILE_NAMESPACE}" -``` \ No newline at end of file +``` diff --git a/content/en/docs/components/pipelines/v1/installation/overview.md b/content/en/docs/components/pipelines/v1/installation/overview.md index e0679eb8d3..2a15ef7fea 100644 --- a/content/en/docs/components/pipelines/v1/installation/overview.md +++ b/content/en/docs/components/pipelines/v1/installation/overview.md @@ -88,9 +88,9 @@ Notes on specific features : * After deployment, your Kubernetes cluster contains Kubeflow Pipelines only. It does not include the other Kubeflow components. - For example, to use a Jupyter Notebook, you must use a local notebook or a - hosted notebook in a cloud service such as the [AI Platform - Notebooks](https://cloud.google.com/ai-platform/notebooks/docs/). + For example, to use a Jupyter Notebook, you must use a local workbench or a + hosted workbench in a cloud service such as the [AI Platform + Workbenches](https://cloud.google.com/ai-platform/notebooks/docs/). * Kubeflow Pipelines multi-user support is **not available** in standalone, because multi-user support depends on other Kubeflow components. @@ -143,7 +143,7 @@ Notes on specific features * After deployment, your Kubernetes cluster includes all the [Kubeflow components](/docs/components/). For example, you can use the Jupyter notebook services - [deployed with Kubeflow](/docs/components/notebooks/) to create one or more notebook + [deployed with Kubeflow](/docs/components/notebooks/) to create one or more workbench servers in your Kubeflow cluster. * Kubeflow Pipelines multi-user support is **only available** in full Kubeflow. It supports using a single Kubeflow Pipelines control plane to orchestrate user pipeline @@ -177,7 +177,7 @@ Interfaces services * Kubeflow Pipelines UI via the **Open Pipelines Dashboard** link in the Google Cloud Console - * Kubeflow Pipelines SDK in Cloud Notebooks + * Kubeflow Pipelines SDK in Cloud Workbenches * Kubeflow Pipelines endpoint of your instance is auto-configured for you Release Schedule @@ -203,6 +203,6 @@ Notes on specific features * After deployment, your Kubernetes cluster contains Kubeflow Pipelines only. It does not include the other Kubeflow components. For example, to use a Jupyter Notebook, you can use [AI Platform - Notebooks](https://cloud.google.com/ai-platform/notebooks/docs/). + Workbenches](https://cloud.google.com/ai-platform/notebooks/docs/). * Kubeflow Pipelines multi-user support is **not available** in AI Platform Pipelines, because multi-user support depends on other Kubeflow components. diff --git a/content/en/docs/components/pipelines/v1/introduction.md b/content/en/docs/components/pipelines/v1/introduction.md index 7087ab8a0a..abc599539f 100644 --- a/content/en/docs/components/pipelines/v1/introduction.md +++ b/content/en/docs/components/pipelines/v1/introduction.md @@ -22,7 +22,7 @@ The Kubeflow Pipelines platform consists of: * A user interface (UI) for managing and tracking experiments, jobs, and runs. * An engine for scheduling multi-step ML workflows. * An SDK for defining and manipulating pipelines and components. -* Notebooks for interacting with the system using the SDK. +* Workbenches for interacting with the system using the SDK. The following are the goals of Kubeflow Pipelines: diff --git a/content/en/docs/components/pipelines/v1/overview/quickstart.md b/content/en/docs/components/pipelines/v1/overview/quickstart.md index b8948decfd..55b0a915c6 100644 --- a/content/en/docs/components/pipelines/v1/overview/quickstart.md +++ b/content/en/docs/components/pipelines/v1/overview/quickstart.md @@ -97,7 +97,7 @@ You can find the [source code for the **XGBoost - Iterative model training** dem [important concepts](/docs/pipelines/overview/concepts/) in Kubeflow Pipelines. * This page showed you how to run some of the examples supplied in the Kubeflow - Pipelines UI. Next, you may want to run a pipeline from a notebook, or compile + Pipelines UI. Next, you may want to run a pipeline from a workbench, or compile and run a sample from the code. See the guide to experimenting with [the Kubeflow Pipelines samples](/docs/components/pipelines/tutorials/build-pipeline/). * Build your own machine-learning pipelines with the [Kubeflow Pipelines diff --git a/content/en/docs/components/pipelines/v1/sdk-v2/component-development.md b/content/en/docs/components/pipelines/v1/sdk-v2/component-development.md index a49fc25771..503278a786 100644 --- a/content/en/docs/components/pipelines/v1/sdk-v2/component-development.md +++ b/content/en/docs/components/pipelines/v1/sdk-v2/component-development.md @@ -549,7 +549,7 @@ def my_pipeline(): parameter_1='5', ) -# If you run this command on a Jupyter notebook running on Kubeflow, +# If you run this command on a workbench running on Kubeflow, # you can exclude the host parameter. # client = kfp.Client() client = kfp.Client(host='') diff --git a/content/en/docs/components/pipelines/v1/sdk-v2/v2-compatibility.md b/content/en/docs/components/pipelines/v1/sdk-v2/v2-compatibility.md index ff05a827cd..1a6dd96d7e 100644 --- a/content/en/docs/components/pipelines/v1/sdk-v2/v2-compatibility.md +++ b/content/en/docs/components/pipelines/v1/sdk-v2/v2-compatibility.md @@ -21,7 +21,7 @@ mode](https://bit.ly/kfp-v2-compatible), or join the Kubeflow Pipelines communit 1. Install [Kubeflow Pipelines Standalone](/docs/components/pipelines/installation/standalone-deployment) 1.7.0 or higher. Note, support for other distributions is under development, see [Current Caveats section](#current-caveats). -2. Run the following command to install Kubeflow Pipelines SDK v1.8. If you run this command in a Jupyter notebook, restart the kernel after installing the SDK. +2. Run the following command to install Kubeflow Pipelines SDK v1.8. If you run this command in a workbench, restart the kernel after installing the SDK. ```bash pip install kfp==1.8 @@ -36,7 +36,7 @@ mode](https://bit.ly/kfp-v2-compatible), or join the Kubeflow Pipelines communit 4. Create an instance of the kfp.Client class. To find your Kubeflow Pipelines cluster’s hostname and URL scheme, open the Kubeflow Pipelines user interface in your browser. The URL of the Kubeflow Pipelines user interface is something like https://my-cluster.my-organization.com/pipelines. In this case, the host name and URL scheme are https://my-cluster.my-organization.com. ```python - # If you run this command on a Jupyter notebook running on Kubeflow, you can + # If you run this command on a workbench running on Kubeflow, you can # exclude the host parameter. # client = kfp.Client() client = kfp.Client(host='') diff --git a/content/en/docs/components/pipelines/v1/sdk/component-development.md b/content/en/docs/components/pipelines/v1/sdk/component-development.md index af523365dd..78364b2387 100644 --- a/content/en/docs/components/pipelines/v1/sdk/component-development.md +++ b/content/en/docs/components/pipelines/v1/sdk/component-development.md @@ -546,7 +546,7 @@ def my_pipeline(): parameter_1='5', ) -# If you run this command on a Jupyter notebook running on Kubeflow, +# If you run this command on a workbench running on Kubeflow, # you can exclude the host parameter. # client = kfp.Client() client = kfp.Client(host='') diff --git a/content/en/docs/components/pipelines/v1/sdk/connect-api.md b/content/en/docs/components/pipelines/v1/sdk/connect-api.md index 9e2d230407..ec1e1b4c4d 100644 --- a/content/en/docs/components/pipelines/v1/sdk/connect-api.md +++ b/content/en/docs/components/pipelines/v1/sdk/connect-api.md @@ -110,7 +110,7 @@ spec: {{% alert title="Tip" color="info" %}} * `PodDefaults` are namespaced resources, so you need to create one inside __each__ of your Kubeflow `Profile` namespaces. -* The Notebook Spawner UI will be aware of any `PodDefaults` in the user's namespace (they are selectable under the "configurations" section). +* The Workbench Spawner UI will be aware of any `PodDefaults` in the user's namespace (they are selectable under the "configurations" section). {{% /alert %}} ### RBAC Authorization @@ -140,7 +140,7 @@ subjects: {{% alert title="Tip" color="info" %}} * Review the ClusterRole called [`aggregate-to-kubeflow-pipelines-edit`](https://github.com/kubeflow/pipelines/blob/efb96135033fc6e6e55078d33814c45a98566e68/manifests/kustomize/base/installs/multi-user/view-edit-cluster-roles.yaml#L36-L99) for a list of some important `pipelines.kubeflow.org` RBAC verbs. -* Kubeflow Notebooks pods run as the `default-editor` ServiceAccount by default, so the RoleBindings for `default-editor` apply to them +* Kubeflow Workbenches pods run as the `default-editor` ServiceAccount by default, so the RoleBindings for `default-editor` apply to them and give them access to submit pipelines in their own namespace. {{% /alert %}} diff --git a/content/en/docs/components/pipelines/v1/sdk/output-viewer.md b/content/en/docs/components/pipelines/v1/sdk/output-viewer.md index 29108de88e..d796592899 100644 --- a/content/en/docs/components/pipelines/v1/sdk/output-viewer.md +++ b/content/en/docs/components/pipelines/v1/sdk/output-viewer.md @@ -753,10 +753,10 @@ The pipeline uses a number of prebuilt, reusable components, including: component](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/components/deprecated/dataflow/tfma/component.yaml) which writes out the data for the `web-app` viewer. -## Lightweight Python component Notebook example +## Lightweight Python component Workbench example For a complete example of lightweigh Python component, you can refer to -[the lightweight python component notebook example](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/samples/core/lightweight_component/lightweight_component.ipynb) to learn more about declaring output visualizations. +[the lightweight python component workbench example](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/samples/core/lightweight_component/lightweight_component.ipynb) to learn more about declaring output visualizations. ## YAML component example diff --git a/content/en/docs/components/pipelines/v1/sdk/python-based-visualizations.md b/content/en/docs/components/pipelines/v1/sdk/python-based-visualizations.md index c7ea3d32cc..606e70b0b0 100644 --- a/content/en/docs/components/pipelines/v1/sdk/python-based-visualizations.md +++ b/content/en/docs/components/pipelines/v1/sdk/python-based-visualizations.md @@ -6,7 +6,7 @@ weight = 1400 {{% alert title="Deprecated" color="warning" %}} Python based visualization is deprecated. We recommend fetching data via -Kubeflow Pipelines SDK and visualizing from your own notebook instead. +Kubeflow Pipelines SDK and visualizing from your own workbench instead. {{% /alert %}} This page describes Python based visualizations, how to create them, and how to diff --git a/content/en/docs/components/pipelines/v1/tutorials/api-pipelines.md b/content/en/docs/components/pipelines/v1/tutorials/api-pipelines.md index 102cb6af07..0d10db77a7 100644 --- a/content/en/docs/components/pipelines/v1/tutorials/api-pipelines.md +++ b/content/en/docs/components/pipelines/v1/tutorials/api-pipelines.md @@ -22,7 +22,7 @@ You also need to install [jq](https://stedolan.github.io/jq/download/), and the ## Building and running a pipeline -Follow this guide to download, compile, and run the [`sequential.py` sample pipeline](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/samples/core/sequential/sequential.py). To learn how to compile and run pipelines using the Kubeflow Pipelines SDK or a Jupyter notebook, follow the [experimenting with Kubeflow Pipelines samples tutorial](/docs/components/pipelines/tutorials/build-pipeline/). +Follow this guide to download, compile, and run the [`sequential.py` sample pipeline](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/samples/core/sequential/sequential.py). To learn how to compile and run pipelines using the Kubeflow Pipelines SDK or a workbench, follow the [experimenting with Kubeflow Pipelines samples tutorial](/docs/components/pipelines/tutorials/build-pipeline/). ``` PIPELINE_URL=https://raw.githubusercontent.com/kubeflow/pipelines/master/samples/core/sequential/sequential.py diff --git a/content/en/docs/components/pipelines/v1/tutorials/build-pipeline.md b/content/en/docs/components/pipelines/v1/tutorials/build-pipeline.md index 73477b3a75..5fb71fcb0d 100644 --- a/content/en/docs/components/pipelines/v1/tutorials/build-pipeline.md +++ b/content/en/docs/components/pipelines/v1/tutorials/build-pipeline.md @@ -1,6 +1,6 @@ +++ title = "Experiment with the Pipelines Samples" -description = "Get started with the Kubeflow Pipelines notebooks and samples" +description = "Get started with the Kubeflow Pipelines workbenches and samples" weight = 30 +++ @@ -69,17 +69,17 @@ guide to [getting started with the UI](/docs/components/pipelines/overview/quick ## Building a pipeline in a Jupyter notebook You can choose to build your pipeline in a Jupyter notebook. The -[sample notebooks](https://github.com/kubeflow/pipelines/tree/sdk/release-1.8/samples/core) +[sample workbenches](https://github.com/kubeflow/pipelines/tree/sdk/release-1.8/samples/core) walk you through the process. It's easiest to use the Jupyter services that are installed in the same cluster as the Kubeflow Pipelines system. -Note: The notebook samples don't work on Jupyter notebooks outside the same +Note: The workbench samples don't work on workbench outside the same cluster, because the Python library communicates with the Kubeflow Pipelines system through in-cluster service names. -Follow these steps to start a notebook: +Follow these steps to start a workbench: 1. Deploy Kubeflow: @@ -90,7 +90,7 @@ Follow these steps to start a notebook: * When Kubeflow is running, access the Kubeflow UI at a URL of the form `https://.endpoints..cloud.goog/`. -1. Follow the [Kubeflow notebooks setup guide](/docs/components/notebooks/setup/) to +1. Follow the [Kubeflow workbenches setup guide](/docs/components/notebooks/setup/) to create a Jupyter notebook server and open the Jupyter UI. 1. Download the sample notebooks from diff --git a/content/en/docs/distributions/azure/troubleshooting-azure.md b/content/en/docs/distributions/azure/troubleshooting-azure.md index 426b4d99f0..39e0e3cbb0 100644 --- a/content/en/docs/distributions/azure/troubleshooting-azure.md +++ b/content/en/docs/distributions/azure/troubleshooting-azure.md @@ -4,7 +4,7 @@ description = "Help diagnose and fix issues you may encounter in your Kubeflow d weight = 100 +++ -### Jupyter Notebook ‘is not a valid page’ when accessing notebook +### Jupyter Notebook ‘is not a valid page’ when accessing workbench Restarting the ambassador pods will often fix this issue: `kubectl delete pods -l service=ambassador` diff --git a/content/en/docs/distributions/ekf/_index.md b/content/en/docs/distributions/ekf/_index.md index 68b27c88b8..03561c602a 100644 --- a/content/en/docs/distributions/ekf/_index.md +++ b/content/en/docs/distributions/ekf/_index.md @@ -8,4 +8,4 @@ The Arrik - *Automation*: Orchestrate your end-to-end ML workflow with a click of a button. Start by tagging cells in Jupyter Notebooks to define pipeline steps, hyperparameter tuning, GPU usage, and metrics tracking. Click a button to create pipeline components and KFP DSL, resolve dependencies, inject data objects into each step, deploy the data science pipeline, and serve the best model. Or use the Kale SDK to do all the above with your preferred IDE. - *Reproducibility*: Snapshot pipeline code, libraries, and data for every step with Arrikto’s Rok data management platform. Roll back to any machine learning pipeline step at it’s exact execution state for easy debugging. Collaborate with other data scientists through a Git-style publish/subscribe versioning workflow. - *Portability*: Deploy and upgrade your Kubeflow environment with a proven GitOps process across all major public clouds, and on-prem infrastructure. Move ML workflows seamlessly across with Rok Registry. -- *Security*: Manage teams and user access via GitLab or any ID provider via Istio/OIDC. Isolate user ML data access within their own namespace while enabling notebook and pipeline collaboration in shared namespaces. Manage secrets and credentials securely, and efficiently. \ No newline at end of file +- *Security*: Manage teams and user access via GitLab or any ID provider via Istio/OIDC. Isolate user ML data access within their own namespace while enabling workbench and pipeline collaboration in shared namespaces. Manage secrets and credentials securely, and efficiently. \ No newline at end of file diff --git a/content/en/docs/external-add-ons/elyra/_index.md b/content/en/docs/external-add-ons/elyra/_index.md index 300cc0f81b..852aa528e3 100644 --- a/content/en/docs/external-add-ons/elyra/_index.md +++ b/content/en/docs/external-add-ons/elyra/_index.md @@ -16,4 +16,4 @@ and related properties that are all managed in the visual editor. To learn more about Elyra, visit Elyra GitHub project -To enable Elyra in your Kubeflow Environment, visit Using Elyra with the Kubeflow Notebook Server +To enable Elyra in your Kubeflow Environment, visit Using Elyra with the Kubeflow Workbench Server diff --git a/content/en/docs/external-add-ons/fairing/azure/_index.md b/content/en/docs/external-add-ons/fairing/azure/_index.md index be7e745bd5..aff372d209 100644 --- a/content/en/docs/external-add-ons/fairing/azure/_index.md +++ b/content/en/docs/external-add-ons/fairing/azure/_index.md @@ -6,7 +6,7 @@ weight = 45 This page documents how to run the [Fairing prediction example notebook][xgb-notebook] on [Azure Kubernetes Service -(AKS)][az-kubernetes] in a notebook hosted on Kubeflow. +(AKS)][az-kubernetes] in a workbench hosted on Kubeflow. ## Prerequisites @@ -102,7 +102,7 @@ Run the following commands to set up your credentials as a Kubernetes secret. * **AZ_SUBSCRIPTION:** The Azure Subscription ID of your account. You can get the Subscription ID from the `id` field in the output of `az account show`. - * **TARGET_NAMESPACE:** Specify the namespace that your Notebook Server is + * **TARGET_NAMESPACE:** Specify the namespace that your Workbench Server is in. For example, this guide recommends using `kubeflow-anonymous`. * **ACR_NAME:** The name of an ACR that the service principal can access. @@ -128,10 +128,10 @@ Run the following commands to set up your credentials as a Kubernetes secret. -p "{\"imagePullSecrets\": [{\"name\": \"acrcreds\"}]}" ``` -## Creating a Notebook Server in Kubeflow +## Creating a Workbench Server in Kubeflow -To create a notebook server, use your Web browser to access the Kubeflow -Central Dashboard and select the **Notebook Servers** panel from the menu. +To create a workbench server, use your Web browser to access the Kubeflow +Central Dashboard and select the **Workbench Servers** panel from the menu. First, select the target namespace in which you want to host the server. In the default Kubeflow installation, there should be a namespace `kubeflow-anonymous` @@ -142,16 +142,16 @@ mandatory fields. The fields with default values can all be left as they are and do not have to be modified to run the example notebook. After launching the server, wait for the **CONNECT** button to appear and click -**CONNECT** to launch your Notebook Server. It may take up to a minute for the +**CONNECT** to launch your Workbench Server. It may take up to a minute for the server to be ready for connections. ## Cloning the example notebook Clone the Kubeflow Fairing repository to download the files used in this example. -1. Connect to your notebook server, then click the new terminal option +1. Connect to your workbench server, then click the new terminal option like in the screenshot below: - Creating new terminal after connecting to notebook server + Creating new terminal after connecting to workbench server 1. Run the following command to clone the Kubeflow Fairing project: @@ -159,10 +159,10 @@ Clone the Kubeflow Fairing repository to download the files used in this example git clone https://github.com/kubeflow/fairing.git ``` - This command clones the project including the example into your notebook server. + This command clones the project including the example into your workbench server. You can now close the terminal window, and you should now see the `fairing` folder -in your notebooks server's Files tab. Navigate to the example notebooks at +in your workbench server's Files tab. Navigate to the example notebooks at `fairing/examples/prediction/xgboost-high-level-apis.ipynb`. ## Executing the notebook diff --git a/content/en/docs/external-add-ons/fairing/configure-fairing.md b/content/en/docs/external-add-ons/fairing/configure-fairing.md index 577661f0d9..f8a85d1191 100644 --- a/content/en/docs/external-add-ons/fairing/configure-fairing.md +++ b/content/en/docs/external-add-ons/fairing/configure-fairing.md @@ -32,14 +32,14 @@ and Kubeflow Fairing installed in your development environment. * If you have not installed Kubeflow Fairing, follow the [installing Kubeflow Fairing][fairing-install] guide. -## Using Kubeflow Fairing with Kubeflow notebooks +## Using Kubeflow Fairing with Kubeflow workbenches -The standard Kubeflow notebook images include Kubeflow Fairing and come +The standard Kubeflow workbench images include Kubeflow Fairing and come preconfigured to run training jobs on your Kubeflow cluster. No additional configuration is required. -If you built your Kubeflow notebook server from a custom Jupyter Docker image, -follow the instruction in this guide to configure your notebooks environment +If you built your Kubeflow workbench server from a custom Docker image, +follow the instruction in this guide to configure your workbenches environment with access to your Kubeflow environment. ## Configure Docker with access to your container image registry diff --git a/content/en/docs/external-add-ons/fairing/fairing-overview.md b/content/en/docs/external-add-ons/fairing/fairing-overview.md index b9f774a457..1a08b87a0b 100644 --- a/content/en/docs/external-add-ons/fairing/fairing-overview.md +++ b/content/en/docs/external-add-ons/fairing/fairing-overview.md @@ -51,10 +51,10 @@ The following are the goals of the [Kubeflow Fairing project][fairing-repo]: ## Next steps -* Learn how to [set up a Jupyter notebooks instance on your Kubeflow - cluster][kubeflow-notebooks]. +* Learn how to [set up a workbench instance on your Kubeflow + cluster][kubeflow-workbenches]. -[kubeflow-notebooks]: /docs/components/notebooks/setup/ +[kubeflow-workbenches]: /docs/components/notebooks/setup/ [ai-platform]: https://cloud.google.com/ml-engine/docs/ [fairing-repo]: https://github.com/kubeflow/fairing [kubeflow]: /docs/started/ diff --git a/content/en/docs/external-add-ons/fairing/gcp/configure-gcp.md b/content/en/docs/external-add-ons/fairing/gcp/configure-gcp.md index 0e0c2b9185..c0d8870224 100644 --- a/content/en/docs/external-add-ons/fairing/gcp/configure-gcp.md +++ b/content/en/docs/external-add-ons/fairing/gcp/configure-gcp.md @@ -12,14 +12,14 @@ deploy a model on Kubeflow on Google Kubernetes Engine (GKE). If you have not installed Kubeflow Fairing, follow the guide to [installing Kubeflow Fairing][fairing-install] before continuing. -## Using Kubeflow Fairing with Kubeflow notebooks +## Using Kubeflow Fairing with Kubeflow workbenches -The standard Kubeflow notebook images include Kubeflow Fairing and come +The standard Kubeflow workbench images include Kubeflow Fairing and come preconfigured to run training jobs on your Kubeflow cluster. No additional configuration is required. -If your Kubeflow notebook server was built from a custom Jupyter Docker image, -follow the instruction in this guide to configure your notebooks environment +If your Kubeflow workbench server was built from a custom Docker image, +follow the instruction in this guide to configure your workbenches environment with access to your Kubeflow environment. ## Install and configure the Google Cloud SDK diff --git a/content/en/docs/external-add-ons/fairing/gcp/tutorials/gcp-kubeflow-notebook.md b/content/en/docs/external-add-ons/fairing/gcp/tutorials/gcp-kubeflow-notebook.md index f7cc6959f3..650ce67844 100644 --- a/content/en/docs/external-add-ons/fairing/gcp/tutorials/gcp-kubeflow-notebook.md +++ b/content/en/docs/external-add-ons/fairing/gcp/tutorials/gcp-kubeflow-notebook.md @@ -1,6 +1,6 @@ +++ -title = "Train and Deploy on GCP from a Kubeflow Notebook" -description = "Use Kubeflow Fairing to train and deploy a model on Google Cloud Platform (GCP) from a notebook that is hosted on Kubeflow" +title = "Train and Deploy on GCP from a Kubeflow Workbench" +description = "Use Kubeflow Fairing to train and deploy a model on Google Cloud Platform (GCP) from a workbench that is hosted on Kubeflow" weight = 35 +++ @@ -9,22 +9,22 @@ This guide introduces you to using [Kubeflow Fairing][fairing-repo] to train and deploy a model to Kubeflow on Google Kubernetes Engine (GKE) and Google AI Platform Training. -Your Kubeflow deployment includes services for spawning and managing Jupyter -notebooks. Kubeflow Fairing is preinstalled in the Kubeflow notebooks, along +Your Kubeflow deployment includes services for spawning and managing workbenches. +Kubeflow Fairing is preinstalled in the Kubeflow workbenches, along with a number of machine learning (ML) libraries. -## Set up Kubeflow and access the Kubeflow notebook environment +## Set up Kubeflow and access the Kubeflow workbench environment -Follow the [Kubeflow notebook setup guide](/docs/components/notebooks/setup/) -to install Kubeflow, access your Kubeflow hosted notebook environment, and -create a new notebook server. +Follow the [Kubeflow workbench setup guide](/docs/components/notebooks/setup/) +to install Kubeflow, access your Kubeflow hosted workbench environment, and +create a new workbench server. When selecting a Docker image and other settings for the baseline deployment -of your notebook server, you can leave all the settings at the default value. +of your workbench server, you can leave all the settings at the default value. ## Run the example notebook -As an example, this guide uses a notebook that is hosted on Kubeflow +As an example, this guide uses a Jupyter notebook that is hosted on Kubeflow to demonstrate how to: * Train an XGBoost model in a notebook, diff --git a/content/en/docs/external-add-ons/fairing/gcp/tutorials/gcp-local-notebook.md b/content/en/docs/external-add-ons/fairing/gcp/tutorials/gcp-local-notebook.md index 1167af08bb..146d1d65b3 100644 --- a/content/en/docs/external-add-ons/fairing/gcp/tutorials/gcp-local-notebook.md +++ b/content/en/docs/external-add-ons/fairing/gcp/tutorials/gcp-local-notebook.md @@ -1,15 +1,15 @@ +++ -title = "Train and Deploy on GCP from a Local Notebook" -description = "Use Kubeflow Fairing to train and deploy a model on Google Cloud Platform (GCP) from a local notebook." +title = "Train and Deploy on GCP from a Local Workbench" +description = "Use Kubeflow Fairing to train and deploy a model on Google Cloud Platform (GCP) from a local workbench." weight = 30 +++ This guide introduces you to using Kubeflow Fairing to train and deploy a model to Kubeflow on Google Kubernetes Engine (GKE), and Google Cloud ML Engine. -As an example, this guide uses a local notebook to demonstrate how to: +As an example, this guide uses a local workbench to demonstrate how to: -* Train an XGBoost model in a local notebook, +* Train an XGBoost model in a local workbench, * Use Kubeflow Fairing to train an XGBoost model remotely on Kubeflow, * Use Kubeflow Fairing to train an XGBoost model remotely on Cloud ML Engine, * Use Kubeflow Fairing to deploy a trained model to Kubeflow, and diff --git a/content/en/docs/external-add-ons/fairing/install-fairing.md b/content/en/docs/external-add-ons/fairing/install-fairing.md index 6b082f4fc6..c95942a789 100644 --- a/content/en/docs/external-add-ons/fairing/install-fairing.md +++ b/content/en/docs/external-add-ons/fairing/install-fairing.md @@ -13,17 +13,17 @@ You can use Kubeflow Fairing to build, train, and deploy machine learning (ML) models in a hybrid cloud environment directly from Python code or a Jupyter notebook. This guide describes how to install Kubeflow Fairing in your development environment for [local development][local], or [development in a -hosted notebook][hosted]. +hosted workbench][hosted]. -## Using Kubeflow Fairing with Kubeflow notebooks +## Using Kubeflow Fairing with Kubeflow workbenches -Kubeflow notebook servers that are built from one of the standard Jupyter +Kubeflow workbench servers that are built from one of the standard Jupyter Docker images include Kubeflow Fairing and come preconfigured for using Kubeflow Fairing to run training jobs on your Kubeflow cluster. -If you use a Kubeflow notebook server that was built from a custom Jupyter +If you use a Kubeflow workbench server that was built from a custom Jupyter Docker image as your development environment, follow the instruction on -[setting up Kubeflow Fairing in a hosted notebook environment][hosted]. +[setting up Kubeflow Fairing in a hosted workbench environment][hosted]. ## Set up Kubeflow Fairing for local development @@ -138,15 +138,15 @@ configuring Kubeflow Fairing][conf]. Follow these instructions to set up Kubeflow Fairing in a hosted Jupyter notebook. -If you are using a Kubeflow notebook server that was built from one of the -standard Jupyter Docker images, your notebooks environment has been +If you are using a Kubeflow workbench server that was built from one of the +standard Jupyter Docker images, your workbenches environment has been preconfigured for training and deploying ML models with Kubeflow Fairing and no additional installation steps are required. ### Prerequisites Check the following prerequisites to verify that Kubeflow Fairing is compatible -with your hosted notebook environment. +with your hosted workbench environment. 1. In the Jupyter notebooks user interface, click **File** > **New** > **Terminal** in the menu to start a new terminal session in your notebook diff --git a/content/en/docs/external-add-ons/fairing/tutorials/other-tutorials.md b/content/en/docs/external-add-ons/fairing/tutorials/other-tutorials.md index cc35b22914..3295d6626f 100644 --- a/content/en/docs/external-add-ons/fairing/tutorials/other-tutorials.md +++ b/content/en/docs/external-add-ons/fairing/tutorials/other-tutorials.md @@ -13,10 +13,10 @@ Read the following tutorials to learn more about using Kubeflow Fairing to train and deploy on various environments such as on the Google Cloud Platform (GCP). * Learn how to [train and deploy a model on GCP from a local - notebook][gcp-local]. -* Learn how to [train and deploy a model on GCP from a notebook hosted on + workbench][gcp-local]. +* Learn how to [train and deploy a model on GCP from a workbench hosted on Kubeflow][gcp-kubeflow]. -* Learn how to [train and deploy a model on Azure from a notebook hosted on +* Learn how to [train and deploy a model on Azure from a workbench hosted on Kubeflow][azure-fairing]. [gcp-local]: /docs/external-add-ons/fairing/gcp/tutorials/gcp-local-notebook/ diff --git a/content/en/docs/external-add-ons/istio/istio-in-kubeflow.md b/content/en/docs/external-add-ons/istio/istio-in-kubeflow.md index 3a979ce336..558f7dae4e 100644 --- a/content/en/docs/external-add-ons/istio/istio-in-kubeflow.md +++ b/content/en/docs/external-add-ons/istio/istio-in-kubeflow.md @@ -58,7 +58,7 @@ Kubeflow uses Istio as a uniform way to secure, connect, and monitor microservic The following diagram illustrates how user requests interact with services in Kubeflow. It walks through the process when a user requests to create a new -notebook server via the Notebooks Servers UI accessible through the Kubeflow Central Dashboard. +workbench server via the Workbenches Servers UI accessible through the Kubeflow Central Dashboard.