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+# MLX Workshop
+
+This workshop demonstrates how a data scientist can use MLX to analyze
+a dataset, train a model, and deploy it on Kubernetes using MLX.
+
+We will make use of [Project CodeNet](https://github.com/IBM/Project_CodeNet),
+a set of approximately 14 million code samples, each of which is an intended
+solution to one of 4000 coding problems. For more information about Project
+CodeNet take a looks at the article
+[Kickstarting AI for Code: Introducing IBM’s Project CodeNet](https://research.ibm.com/blog/codenet-ai-for-code)
+
+The specific dataset, notebook, and model we will use can be found here:
+- https://ml-exchange.org/datasets/codenet-langclass/
+- https://ml-exchange.org/notebooks/project-codenet-language-classification/
+- https://ml-exchange.org/models/codenet-language-classification
+
+
+## Table of Contents
+
+- [Prerequisites](#prerequisites)
+- [Login to the MLX Web UI](#login-to-the-mlx-web-ui)
+- [Prepare the Dataset to be Mounted](#prepare-the-dataset-to-be-mounted)
+- [Run the Notebook](#run-the-notebook)
+- [Serve the Model](#serve-the-model)
+- [Conclusion](#conclusion)
+
+
+## Prerequisites
+
+For this workshop you will need access to a Kubernetes cluster with MLX deployed,
+or a local deployment of MLX on KIND (Kubernetes in Docker).
+
+- [Deploy MLX on a Kubernetes cluster](mlx-setup.md)
+- [Deploy MLX locally on KIND](install-mlx-on-kind.md)
+
+There are only minor differences in how to navigate the Kubernetes resources
+depending on the selected deployment option, with the most notable difference
+being the host and port of the MLX (UI) server.
+
+For this workshop we assume a single-user local MLX deployment on `localhost`
+port `3000` as described in the [MLX on KIND](install-mlx-on-kind.md) deployment
+guide.
+
+
+## Login to the MLX Web UI
+
+Open the MLX Web UI in your browser and login as the admin user:
+
+```
+http://localhost:3000/login
+```
+
+Once you are logged in, you should see the **Settings** menu item at the
+bottom of the left-hand navigation.
+
+
+## Prepare the Dataset to be Mounted
+
+For this workshop we will use a small subset of the
+[CodeNet dataset](https://developer.ibm.com/exchanges/data/all/project-codenet/)
+containing about
+[2200 random samples across 10 languages](https://dax-cdn.cdn.appdomain.cloud/dax-project-codenet/1.0.0/Project_CodeNet_LangClass.tar.gz)
+to train a language classification model.
+
+![Datasets Featured Page](./images/workshop/Datasets-featured-page.png)
+
+Click on the **Datasets** menu item and select the **Project CodeNet Language Classifier**
+dataset.
+
+* The **DESCRIPTION** tab shows the details of the dataset
+* The **DATASET YAML** tab shows the metadata required by MLX, i.e. for
+  creating a PVC
+* The **LAUNCH** tab allows users to create a Persistent Volume Claim (PVC)
+  containing the dataset
+* The **RELATED ASSETS** tab shows other assets that make use of this dataset
+
+![Datasets Launch Screen](./images/workshop/Datasets-launch.png)
+
+We will click on the **LAUNCH** tab to start a pipeline run which will download
+the dataset and create the PVC. 
+* Enter a **Run Name** of your choice or leave the default value
+* Enter the **Namespace** for the PVC to be created in. The namespace will vary 
+  depending on your MLX deployment. For single-user deployments of MLX, like MLX 
+  on KIND, enter `kubeflow` (for multi-user deployments enter `mlx`)
+* Click **SUBMIT** to start the pipeline run
+
+![Datasets Run Graph](./images/workshop/Datasets-run-graph.png)
+
+Now the Kubeflow Pipeline run view should appear, and get updated as the pipeline
+execution progresses. You can click on a task in the pipeline graph and follow
+the **Logs**. 
+
+![Datasets Run Output](./images/workshop/Datasets-output-PVC-name.png)
+
+Once the pipeline is finished, click on the second task of
+the pipeline and select the **Input/Output** tab.
+Under the **Output Parameters**, **dataset-status**, find the **name** of the PVC
+that was created: "`codenet-langclass`", which should be the same as the ID of
+the dataset. Select and Copy it to the clipboard. We will need that in the next step.
+
+
+## Run the Notebook
+
+We will use the **Project CodeNet Language Classification** notebook to train a
+Keras model that should be able to detect the language of a given snippet of
+source code.
+
+![Notebooks Featured Page](./images/workshop/Notebooks-featured-page.png)
+
+Click on the **Notebooks** menu item and select the **Project CodeNet Language Classification**
+notebook.
+
+* The **DETAILS** tab shows the details of the notebook
+* The **LAUNCH** tab allows users to run the notebook in a pipeline
+* The **YAML DEFINITION** tab shows the metadata required by MLX to run the
+  notebook in a pipeline
+* The **NOTEBOOK CODE** tab displays the notebook source code
+
+![Notebooks Launch Screen](./images/workshop/Notebooks-launch.png)
+
+Click on the **LAUNCH** tab to start a pipeline run with this notebook
+* Enter a **Run Name** of your choice or leave the default value
+* Enter the **Dataset PVC** name from the Dataset run output: "`codenet-langclass`"
+* Enter a **Mount Path** to mount the PVC to the Kubernetes Pod that is running
+  the notebook: "`/tmp/data`"
+* Click **SUBMIT** to start the pipeline run
+
+![Notebooks Run Graph](./images/workshop/Notebooks-run-graph.png)
+
+Now the Kubeflow Pipeline run view should appear, and get updated as the pipeline
+execution progresses. You can click on the task in the pipeline graph and follow
+the **Logs**.
+
+![Notebooks Run Logs](./images/workshop/Notebooks-run-logs.png)
+
+The pipeline run will generate an HTML version of the notebook containing the
+input and newly generated output cells of the notebook. Once the pipeline is finished,
+scroll to the end of the **Logs** and find the line that reads
+`PUT /mlpipeline/notebooks/project-codenet-language-classification/Project_CodeNet_LangClass.html`
+i.e.:
+```
+[D 02:30:16.327] http://10.96.180.205:9000 "PUT /mlpipeline/notebooks/project-codenet-language-classification/Project_CodeNet_LangClass.html HTTP/1.1" 200
+```
+This is where the notebook HTML was stored in the Minio object storage.
+
+To download the notebook output we require access to Minio from our local browser.
+For MLX on KIND we can achieve that by forwarding the Minio service port:
+
+```Bash
+kubectl port-forward -n kubeflow svc/minio-service 9000:9000
+```
+
+Now we should be able to download the notebook by pasting the URL in our
+browser's address bar:
+
+```
+http://localhost:9000/mlpipeline/notebooks/project-codenet-language-classification/Project_CodeNet_LangClass.html
+```
+
+Open the notebook in a browser tab and scroll close to the bottom to find out
+about the accuracy of our trained model and compare it to the accuracy achieved
+when the original notebook was created.
+
+![Notebooks Language Classification Test](./images/workshop/Notebooks-language-classification-test.png)
+
+
+## Serve the Model
+
+We have created a containerized version of the **CodeNet Language Classification**
+model that can be deployed on Kubernetes to allow inferencing via `curl`, or,
+directly from a web browser using a simple Swagger UI to the inferencing service.  
+
+![Models Featured Page](./images/workshop/Models-featured-page.png)
+
+Click on the **Models** menu item and select the **CodeNet Language Classification**
+model.
+
+* The **DESCRIPTION** tab shows the details of the model with links to the original
+  dataset
+* The **LAUNCH** tab allows users to (train or) serve the model on Kubernetes
+* The **YAML DEFINITION** tab shows the metadata required by MLX
+* The **SAMPLE SERVING CODE** tab displays a generated sample pipeline to serve
+  the model
+
+![Models Launch Tab](./images/workshop/Models-launch.png)
+
+Click on the **LAUNCH** tab to start the pipeline run to serve the model
+* The **Launch Type** is `Serving`
+* The **Platform** is `Kubernetes`
+* Enter a **Run Name** or leave the default
+* Click **SUBMIT** to start the pipeline run
+
+![Models Run Output](./images/workshop/Models-run-output.png)
+
+Now the Kubeflow Pipeline run view should appear, and get updated as the pipeline
+execution progresses. You can click on the task in the pipeline graph and follow
+the **Logs**. Once the second step of the pipeline is completed, you can find the
+name of the deployed model "`codenet-language-classification`" and the URL where
+the deployment can be accessed.
+
+For MLX on KIND we need to make the port available on localhost by running the
+following command:
+
+```Bash
+kubectl port-forward svc/codenet-language-classification 5000:5000
+```
+
+Now we should be able to access the UI page for the deployed model by pasting
+`localhost:5000` into our browser's address bar:
+
+```
+http://localhost:5000/
+```
+
+Expand the first section called **model** and click on **POST /model/predict**
+and the click on the **Try it out** button on the very right of the **Parameters**
+line.
+
+![Models Swagger UI](./images/workshop/Models-Swagger-UI.png)
+
+In order to test the classification model, we need to provide a source code file
+in any of the 10 supported languages like C/C++, Java, JavaScript or Python.
+You could either use a source file from one of your local projects or download
+the [CodeNet Sample Dataset for Language Classification](https://dax-cdn.cdn.appdomain.cloud/dax-project-codenet/1.0.0/Project_CodeNet_LangClass.tar.gz)
+and extract it to a local folder.
+
+On the model prediction input dialog, click **Browse** and select a source
+file
+
+![Open Source File](./images/workshop/Models-predict-open-source-file.png)
+
+Now click **Execute** to get the model's prediction.
+
+The Response should look similar to this:
+
+**Response body**
+
+    {
+      "status": "ok",
+      "predictions": [
+        {
+          "language": "Haskell",
+          "probability": 0.9999926090240479
+        }
+      ]
+    }
+
+## Conclusion
+
+In this workshop we demonstrated how to use MLX to download a dataset into
+a Persistent Volume Claim on Kubernetes and how to mount that dataset to a pod
+running a notebook. We also showed how MLX can be used to deploy pretrained
+containerized models on Kubernetes.
+
+For comments, suggestions or bug reports, please do to 
+https://github.com/machine-learning-exchange/mlx/issues