Update links with kubeflow/examples as base

american-express-default-kaggle-competition/README.md

@@ -1,13 +1,13 @@
 # Objective
-In this example we are going to convert this generic [notebook](https://github.com/josepholaide/examples/blob/master/american-express-default-kaggle-competition/american-express-default-prediction-orig.ipynb) 
+In this example we are going to convert this generic [notebook](./american-express-default-prediction-orig.ipynb)
 based on the [Kaggle American Express - Default Prediction](https://www.kaggle.com/competitions/amex-default-prediction) competition into a Kubeflow pipeline.
 
 The objective of this task is to correctly predict credit default using a large scale industrial dataset from American Express
 
 # Testing Environment
 
 Environment:
-| Name        | version           | 
+| Name        | version           |
 | ------------- |:-------------:|
 | Kubeflow      | v1.4   |
 | kfp           | 1.8.11 |
@@ -20,39 +20,39 @@ Environment:
 
 1. Vanilla KFP Pipeline: Kubeflow lightweight component method
 
-   To get started, visit the Kubeflow Pipelines [documentation](https://www.kubeflow.org/docs/components/pipelines/sdk/) 
-   to get acquainted with what pipelines are, its components, pipeline metrics and how to pass data between components in a pipeline. 
-   There are different ways to build out a pipeline component as mentioned [here](https://www.kubeflow.org/docs/components/pipelines/sdk/build-pipeline/#building-pipeline-components). 
+   To get started, visit the Kubeflow Pipelines [documentation](https://www.kubeflow.org/docs/components/pipelines/sdk/)
+   to get acquainted with what pipelines are, its components, pipeline metrics and how to pass data between components in a pipeline.
+   There are different ways to build out a pipeline component as mentioned [here](https://www.kubeflow.org/docs/components/pipelines/sdk/build-pipeline/#building-pipeline-components).
    In the following example, we are going to use the lightweight python functions based components for building our Kubeflow pipeline.
 
 2. Kale KFP Pipeline
 
-   To get started, visit Kale's [documentation](https://docs.arrikto.com/user/kale/index.html) to get acquainted with the 
-   Kale user interface (UI) from a Jupyter Notebook, [notebook cell annotation](https://docs.arrikto.com/user/kale/jupyterlab/annotate.html) 
+   To get started, visit Kale's [documentation](https://docs.arrikto.com/user/kale/index.html) to get acquainted with the
+   Kale user interface (UI) from a Jupyter Notebook, [notebook cell annotation](https://docs.arrikto.com/user/kale/jupyterlab/annotate.html)
    and how to create a machine learning pipeline using Kale.
    In the following example, we are going to use the Kale JupyterLab Extension to build our Kubeflow pipeline.
-   
+
 ## Section 2: Prepare environment for data download
 
 1. Open your Kubeflow Cluster, create a Notebook Server and connect to it.
    * Fill in your Notebook server name and leave the rest as default.
    * Launch Notebook Server.
 
 2. Download the American Express dataset using Kaggle's API. To do this, do the following:
-   
+
    * Login to Kaggle and click on your user profile picture.
    * Click on ‘Account’.
-   
+
    <p align=center>
-   <img src="https://github.com/josepholaide/examples/blob/master/american-express-default-kaggle-competition/images/kaggle-account.PNG?raw=true" alt="kaggle-click-account"/>
+   <img src="./images/kaggle-account.PNG?raw=true" alt="kaggle-click-account"/>
    </p>
-   
+
    * Under ‘Account’, navigate to the ‘API’ section.
-   
+
    <p align=center>
-   <img src="https://github.com/josepholaide/examples/blob/master/jpx-tokyo-stock-exchange-kaggle-competition/images/kaggle-create-new-api-token.PNG?raw=true" alt="kaggle-create-new-api-token"/>
+   <img src="../jpx-tokyo-stock-exchange-kaggle-competition/images/kaggle-create-new-api-token.PNG?raw=true" alt="kaggle-create-new-api-token"/>
    </p>
-   
+
    * Click ‘Create New API token’.
    * After creating a new API token, a kaggle.json file is automatically downloaded, and the json file contains the ‘api-key’ and ‘username’ needed to download the dataset.
     * Create a Kubernetes secret to handle the sensitive API credentials and to prevent you from passing your credentials in plain text to the pipeline notebook.
@@ -81,17 +81,17 @@ Environment:
        secretName: kaggle-secret
     ```
    * Apply the pod YAML file
-   `kubectl apply -f kaggle_pod.yaml` 
+   `kubectl apply -f kaggle_pod.yaml`
    * After successfully deploying the PodDefault, create a new Notebook Server and add the `kaggle-secret` configuration to the new Notebook Server
      that runs the Kale or KFP pipeline.
    <p align=center>
-   <img src="https://github.com/josepholaide/examples/blob/master/jpx-tokyo-stock-exchange-kaggle-competition/images/notebook-ui-kaggle-config.png?raw=true" alt="notebook-ui-kaggle-config"/>
+   <img src="../jpx-tokyo-stock-exchange-kaggle-competition/images/notebook-ui-kaggle-config.png?raw=true" alt="notebook-ui-kaggle-config"/>
    </p>
 
 ## Section 3: Vanilla KFP Pipeline
 
 ### Kubeflow lightweight component method
-Here, a python function is created to carry out a certain task and the python function is passed inside a kfp component method [`create_component_from_func`](https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.create_component_from_func). 
+Here, a python function is created to carry out a certain task and the python function is passed inside a kfp component method [`create_component_from_func`](https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.create_component_from_func).
 
 The different components used in this example are:
 
@@ -102,42 +102,42 @@ The different components used in this example are:
 - Functions
 
 ## Kubeflow pipelines
-A Kubeflow pipeline connects all components together, to create a directed acyclic graph (DAG). The kfp [`dsl.pipeline`](https://www.kubeflow.org/docs/components/pipelines/sdk/sdk-overview/) decorator was used to create a pipeline function. 
+A Kubeflow pipeline connects all components together, to create a directed acyclic graph (DAG). The kfp [`dsl.pipeline`](https://www.kubeflow.org/docs/components/pipelines/sdk/sdk-overview/) decorator was used to create a pipeline function.
 The [`kfp.dsl.VolumeOp`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.VolumeOp) method was used to create a [`PersistentVolumeClaim`](https://kubernetes.io/docs/concepts/storage/persistent-volumes/) that helps request for data storage. This storage is used to pass data between components in the pipeline.
 
 Finally, the  [`create_run_from_pipeline_func`](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.client.html) from the KFP SDK Client was used to submit pipeline directly from pipeline function
 
 ## To create pipeline using Vanilla KFP
-   
-1. Open your Kubeflow Cluster and do the following: 
-  
-     * Create a new Notebook Server 
+
+1. Open your Kubeflow Cluster and do the following:
+
+     * Create a new Notebook Server
 
      * Set the CPU specification to 8 and RAM to 16 GB
    <p align=center>
-   <img src="https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/images/cpu-ram-spec.PNG?raw=true" alt="cpu/ram spec"/>
+   <img src="../G-research-crypto-forecasting-kaggle-competition/images/cpu-ram-spec.PNG?raw=true" alt="cpu/ram spec"/>
    </p>
 
      * Add the `kaggle-secret` configuration to the new Notebook Server.
 
 2. Create a new Terminal and clone this repo. After cloning, navigate to this directory.
 
-3. Open the [american-express-default-prediction-kfp](https://github.com/josepholaide/examples/blob/master/american-express-default-kaggle-competition/american-express-default-prediction-kfp.ipynb) notebook 
- 
+3. Open the [american-express-default-prediction-kfp](./american-express-default-prediction-kfp.ipynb) notebook
+
 4. Run the american-express-default-prediction notebook from start to finish
 
 5. View run details immediately after submitting pipeline.
 
 ### View Pipeline
 
 <p align=center>
-<img src="https://github.com/josepholaide/examples/blob/master/american-express-default-kaggle-competition/images/kfp-pipeline.PNG?raw=true" alt="kubeflow pipeline"/>
+<img src="./images/kfp-pipeline.PNG?raw=true" alt="kubeflow pipeline"/>
  </p>
- 
+
  ### View Pipeline Metric
 
 <p align=center>
-<img src="https://github.com/josepholaide/examples/blob/master/american-express-default-kaggle-competition/images/kfp-pipeline-metrics.PNG?raw=true" alt="kubeflow pipeline metrics"/>
+<img src="./images/kfp-pipeline-metrics.PNG?raw=true" alt="kubeflow pipeline metrics"/>
  </p>
 
 
@@ -146,35 +146,35 @@ Finally, the  [`create_run_from_pipeline_func`](https://kubeflow-pipelines.readt
 To create a KFP pipeline using the Kale JupyterLab extension
 
 
-1. Open your Kubeflow Cluster and do the following: 
-  
-     * Create a new Notebook Server 
+1. Open your Kubeflow Cluster and do the following:
+
+     * Create a new Notebook Server
 
      * Set the CPU specification to 8 and RAM to 16 GB
    <p align=center>
-   <img src="https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/images/cpu-ram-spec.PNG?raw=true" alt="cpu/ram spec"/>
+   <img src="../G-research-crypto-forecasting-kaggle-competition/images/cpu-ram-spec.PNG?raw=true" alt="cpu/ram spec"/>
    </p>
 
-     * Increase the Workspace Volume to 32Gi 
+     * Increase the Workspace Volume to 32Gi
 
    <p align=center>
-   <img src="https://github.com/josepholaide/examples/blob/master/G-research-crypto-forecasting-kaggle-competition/images/kale-volume.PNG?raw=true" alt="kale volume"/>
+   <img src="../G-research-crypto-forecasting-kaggle-competition/images/kale-volume.PNG?raw=true" alt="kale volume"/>
    </p>
 
      * Add the `kaggle-secret` configuration to the new Notebook Server.
 
 2. Create a new Terminal and clone this repo. After cloning, navigate to this directory.
 
-3. Launch the [american-express-default-prediction-orig.ipynb](https://github.com/josepholaide/examples/blob/master/american-express-default-kaggle-competition/american-express-default-prediction-orig.ipynb) Notebook
+3. Launch the [american-express-default-prediction-orig.ipynb](./american-express-default-prediction-orig.ipynb) Notebook
 
 4. Install the requirements.txt file. After installation, restart the kernel.
 
-5. Enable the Kale extension in JupyterLab 
+5. Enable the Kale extension in JupyterLab
 
-6. Ensure the notebook cells are annotated with the necessary Kale tags just as it is in the [american-express-default-prediction-kale.ipynb](https://github.com/josepholaide/examples/blob/master/american-express-default-kaggle-competition/american-express-default-prediction-kale.ipynb) Notebook
+6. Ensure the notebook cells are annotated with the necessary Kale tags just as it is in the [american-express-default-prediction-kale.ipynb](./american-express-default-prediction-kale.ipynb) Notebook
 
    To fully understand the different Kale tags available, visit Kale [documentation](https://docs.arrikto.com/user/kale/jupyterlab/cell-types.html?highlight=pipeline%20metrics#annotate-pipeline-step-cells)
-   
+
    The following Kale tags were used in this example:
 
    * Imports
@@ -183,15 +183,15 @@ To create a KFP pipeline using the Kale JupyterLab extension
    * Pipeline Metrics
    * Pipeline Step
    * Skip Cell
-   
+
    With the use of Kale tags we define the following:
 
    * Pipeline parameters are assigned using the "pipeline parameters" tag
    * The necessary libraries that need to be used throughout the Pipeline are passed through the "imports" tag
    * Notebook cells are assigned to specific Pipeline components (download data, load data, etc.) using the "pipeline step" tag
    * Cell dependencies are defined between the different pipeline steps with the "depends on" flag
    * Pipeline metrics are assigned using the "pipeline metrics" tag
-   
+
    The pipeline steps created in this example:
 
    * Download data
@@ -203,23 +203,23 @@ To create a KFP pipeline using the Kale JupyterLab extension
 7. Compile and run the Notebook by hitting the "Compile & Run" in Kale's left panel
 
 <p align=center>
-<img src="https://github.com/josepholaide/examples/blob/master/american-express-default-kaggle-competition/images/kale-deployment-panel.PNG?raw=true" alt="kale-deployment-panel"/>
+<img src="./images/kale-deployment-panel.PNG?raw=true" alt="kale-deployment-panel"/>
  </p>
 
 ### View Pipeline
 
 View Pipeline by clicking "View" in Kale's left panel
 
 <p align=center>
-<img src="https://github.com/josepholaide/examples/blob/master/american-express-default-kaggle-competition/images/view-pipeline.PNG?raw=true" alt="view-pipeline"/>
+<img src="./images/view-pipeline.PNG?raw=true" alt="view-pipeline"/>
  </p>
- 
+
 <p align=center>
-<img src="https://github.com/josepholaide/examples/blob/master/american-express-default-kaggle-competition/images/kale-pipeline.PNG?raw=true" alt="kale-pipeline"/>
+<img src="./images/kale-pipeline.PNG?raw=true" alt="kale-pipeline"/>
  </p>
 
  ### View Pipeline Metric
 
 <p align=center>
-<img src="https://github.com/josepholaide/examples/blob/master/american-express-default-kaggle-competition/images/kale-pipeline-metrics.PNG?raw=true" alt="kale-metrics"/>
+<img src="./images/kale-pipeline-metrics.PNG?raw=true" alt="kale-metrics"/>
  </p>

american-express-default-kaggle-competition/american-express-default-prediction-kale.ipynb

@@ -7,7 +7,7 @@
    },
    "source": [
     "# 🪙 American Express - Default Prediction Competition Kale Pipeline\n",
-    "![](https://github.com/josepholaide/examples/blob/amex/american-express-default-kaggle-competition/images/background.jpg?raw=true)\n",
+    "![](./images/background.jpg?raw=true)\n",
     "\n",
     "---\n",
     "\n",
@@ -188,7 +188,7 @@
     "    zip_ref.extractall(data_path)\n",
     "    \n",
     "# download kaggle's Amex-credit-prediction train_labels.zip\n",
-    "download_link = \"https://github.com/josepholaide/examples/blob/amex/american-express-default-kaggle-competition/data/train_labels.zip?raw=true\"\n",
+    "download_link = \"https://github.com/kubeflow/examples/blob/master/american-express-default-kaggle-competition/data/train_labels.zip?raw=true\"\n",
     "wget.download(download_link, f'{data_path}/train_labels.zip')\n",
     "\n",
     "# extract Amex-credit-prediction.zip to data_path\n",

american-express-default-kaggle-competition/american-express-default-prediction-kfp.ipynb

@@ -7,7 +7,7 @@
    },
    "source": [
     "# 🪙 American Express - Default Prediction Competition Vanilla KFP Pipeline\n",
-    "![](https://github.com/josepholaide/examples/blob/amex/american-express-default-kaggle-competition/images/background.jpg?raw=true)\n",
+    "![](./images/background.jpg?raw=true)\n",
     "\n",
     "---\n",
     "\n",
@@ -156,7 +156,7 @@
     "        zip_ref.extractall(data_path)\n",
     "\n",
     "    # download kaggle's Amex-credit-prediction train_labels.zip\n",
-    "    download_link = \"https://github.com/josepholaide/examples/blob/amex/american-express-default-kaggle-competition/data/train_labels.zip?raw=true\"\n",
+    "    download_link = \"https://github.com/kubeflow/examples/blob/master/american-express-default-kaggle-competition/data/train_labels.zip?raw=true\"\n",
     "    \n",
     "    wget.download(download_link, f'{data_path}/train_labels.zip')\n",
     "\n",

american-express-default-kaggle-competition/american-express-default-prediction-orig.ipynb

@@ -7,7 +7,7 @@
    },
    "source": [
     "# 🪙 American Express - Default Prediction Competition Original Notebook\n",
-    "![](https://github.com/josepholaide/examples/blob/amex/american-express-default-kaggle-competition/images/background.jpg?raw=true)\n",
+    "![](./images/background.jpg?raw=true)\n",
     "\n",
     "---\n",
     "\n",
@@ -210,7 +210,7 @@
     "    zip_ref.extractall(data_path)\n",
     "    \n",
     "# download kaggle's Amex-credit-prediction train_labels.zip\n",
-    "download_link = \"https://github.com/josepholaide/examples/blob/amex/american-express-default-kaggle-competition/data/train_labels.zip?raw=true\"\n",
+    "download_link = \"https://github.com/kubeflow/examples/blob/master/american-express-default-kaggle-competition/data/train_labels.zip?raw=true\"\n",
     "wget.download(download_link, f'{data_path}/train_labels.zip')\n",
     "\n",
     "# extract Amex-credit-prediction.zip to data_path\n",