From 7feff4282764c5664d8513f0345c24347b859ab3 Mon Sep 17 00:00:00 2001
From: Sarah Maddox <sarahmaddox@users.noreply.github.com>
Date: Wed, 28 Nov 2018 05:13:06 +1100
Subject: [PATCH 1/7] Added the Kubeflow Pipelines docs (#302)

* Added pipelines overview.

* Added pipelines to components and created menu structure.

* Added the pipelines concepts page.

* Added the pipelines deployment guide.

* Added guide to building a pipeline.

* Added guide to pipelines UI.

* Added guide to building your own components.

* Added content from 'components' page and various small clarifications.

* Added lightweight Python components.

* Added the guide to output viewers.

* Added samples and notebooks.

* Clarified various titles.
---
 config.toml                                   |    8 +-
 content/docs/guides/components/mpi.md         |    2 +-
 content/docs/guides/components/mxnet.md       |    2 +-
 content/docs/guides/components/pipelines.md   |   26 +
 content/docs/guides/components/pytorch.md     |    2 +-
 .../docs/guides/pipelines/build-component.md  |  174 ++
 .../docs/guides/pipelines/build-pipeline.md   |  144 ++
 .../pipelines/deploy-pipelines-service.md     |  197 ++
 .../lightweight-python-components.md          |   63 +
 .../docs/guides/pipelines/output-viewer.md    |  141 ++
 .../guides/pipelines/pipelines-concepts.md    |   83 +
 .../guides/pipelines/pipelines-overview.md    |  105 +
 .../guides/pipelines/pipelines-samples.md     |  149 ++
 content/docs/guides/pipelines/pipelines-ui.md |  148 ++
 content/docs/images/cm.png                    |  Bin 0 -> 8638 bytes
 content/docs/images/job.png                   |  Bin 0 -> 29389 bytes
 content/docs/images/metric.png                | 2017 +++++++++++++++++
 content/docs/images/predict.png               |  Bin 0 -> 35022 bytes
 content/docs/images/roc.png                   |  Bin 0 -> 22385 bytes
 content/docs/images/run.png                   |  Bin 0 -> 53339 bytes
 20 files changed, 3257 insertions(+), 4 deletions(-)
 create mode 100644 content/docs/guides/components/pipelines.md
 create mode 100644 content/docs/guides/pipelines/build-component.md
 create mode 100644 content/docs/guides/pipelines/build-pipeline.md
 create mode 100644 content/docs/guides/pipelines/deploy-pipelines-service.md
 create mode 100644 content/docs/guides/pipelines/lightweight-python-components.md
 create mode 100644 content/docs/guides/pipelines/output-viewer.md
 create mode 100644 content/docs/guides/pipelines/pipelines-concepts.md
 create mode 100644 content/docs/guides/pipelines/pipelines-overview.md
 create mode 100644 content/docs/guides/pipelines/pipelines-samples.md
 create mode 100644 content/docs/guides/pipelines/pipelines-ui.md
 create mode 100644 content/docs/images/cm.png
 create mode 100644 content/docs/images/job.png
 create mode 100644 content/docs/images/metric.png
 create mode 100644 content/docs/images/predict.png
 create mode 100644 content/docs/images/roc.png
 create mode 100644 content/docs/images/run.png

diff --git a/config.toml b/config.toml
index 49643d4ee1..02848fe725 100644
--- a/config.toml
+++ b/config.toml
@@ -73,8 +73,14 @@ Paginate = 20
   identifier = "components"
   url = "/docs/guides/components/"
 [[menu.docs]]
-  name = "GKE"
+  name = "Pipelines"
   weight = 5
   parent = "guides"
+  identifier = "pipelines"
+  url = "/docs/guides/pipelines/"
+[[menu.docs]]
+  name = "GKE"
+  weight = 6
+  parent = "guides"
   identifier = "gke"
   url = "/docs/guides/gke/"
diff --git a/content/docs/guides/components/mpi.md b/content/docs/guides/components/mpi.md
index cb896c29df..7ed8082862 100644
--- a/content/docs/guides/components/mpi.md
+++ b/content/docs/guides/components/mpi.md
@@ -7,7 +7,7 @@ bref= "This guide will walk you through using MPI for training"
 [menu]
 [menu.docs]
   parent = "components"
-  weight = 30
+  weight = 25
 +++
 
 ## Installing MPI Operator
diff --git a/content/docs/guides/components/mxnet.md b/content/docs/guides/components/mxnet.md
index 9d988e4821..0b6cc4dd0c 100644
--- a/content/docs/guides/components/mxnet.md
+++ b/content/docs/guides/components/mxnet.md
@@ -7,7 +7,7 @@ bref= "This guide will walk you through using MXNet with Kubeflow"
 [menu]
 [menu.docs]
   parent = "components"
-  weight = 30
+  weight = 25
 +++
 
 ## Installing MXNet Operator
diff --git a/content/docs/guides/components/pipelines.md b/content/docs/guides/components/pipelines.md
new file mode 100644
index 0000000000..775a34abe6
--- /dev/null
+++ b/content/docs/guides/components/pipelines.md
@@ -0,0 +1,26 @@
++++
+title = "Pipelines"
+description = "Introduction to Kubeflow Pipelines"
+weight = 10
+toc = true
+[menu]
+[menu.docs]
+  parent = "components"
+  weight = 35
++++
+
+Kubeflow Pipelines is a platform for building and deploying portable and
+scalable end-to-end ML workflows, based on containers.
+
+The Kubeflow Pipelines platform has the following goals:
+
+* End-to-end orchestration: enabling and simplifying the orchestration of
+  machine learning pipelines.
+* Easy experimentation: making it easy for you to try numerous ideas and 
+  techniques and manage your various trials/experiments.
+* Easy re-use: enabling you to re-use components and pipelines to quickly 
+  cobble together end-to-end solutions, without having to rebuild each time.
+
+  Read more in the 
+  [pipelines section](/docs/guides/pipelines/pipelines-overview)
+  of the documentation.
\ No newline at end of file
diff --git a/content/docs/guides/components/pytorch.md b/content/docs/guides/components/pytorch.md
index 96463c70de..445eb402a8 100644
--- a/content/docs/guides/components/pytorch.md
+++ b/content/docs/guides/components/pytorch.md
@@ -7,7 +7,7 @@ bref= "This guide will walk you through using PyTorch with Kubeflow"
 [menu]
 [menu.docs]
   parent = "components"
-  weight = 30
+  weight = 35
 +++
 
 ## Installing PyTorch Operator
diff --git a/content/docs/guides/pipelines/build-component.md b/content/docs/guides/pipelines/build-component.md
new file mode 100644
index 0000000000..b275acdaa3
--- /dev/null
+++ b/content/docs/guides/pipelines/build-component.md
@@ -0,0 +1,174 @@
++++
+title = "Build Your Own Pipeline Components"
+description = "Building your own components for Kubeflow Pipelines."
+weight = 3
+toc = true
+
+[menu.docs]
+  parent = "pipelines"
+  weight = 6
++++
+This page is for advanced users. It describes how to build your own pipeline 
+components. For an easier start, try 
+[building a pipeline with the provided samples](/docs/guides/pipelines/build-pipeline).
+
+## Overview of pipeline components
+
+Kubeflow Pipelines components are implementations of pipeline tasks. Each task 
+takes one or more 
+[artifacts](/docs/guides/pipelines/pipelines-concepts#step-output-artifacts) as
+input and may produce one or more
+[artifacts](/docs/guides/pipelines/pipelines-concepts#step-output-artifacts) as 
+output.
+
+Each task usually includes two parts:
+
+``Client code``
+  The code that talks to endpoints to submit jobs. For example, code to talk to 
+  the Google Dataproc API to submit a Spark job.
+
+``Runtime code``
+  The code that does the actual job and usually runs in the cluster. For 
+  example, Spark code that transforms raw data into preprocessed data.
+
+Note the naming convention for client code and runtime code&mdash;for a task 
+named "mytask":
+
+* The `mytask.py` program contains the client code.
+* The `mytask` directory contains all the runtime code.
+
+A component consists of an interface (inputs/outputs), the implementation 
+(a Docker container image and command-line arguments) and metadata 
+(name, description).
+
+Components can be instantiated inside the `pipeline` function to create tasks.
+
+There are multiple ways to author components:
+
+* Wrap an existing Docker container image using `ContainerOp`, as described 
+  below.
+* Create a 
+  [lightweight python component](/docs/guides/pipelines/lightweight-python-components) 
+  from a Python function
+* Build a new Docker container image from a Python function.
+
+## Example: XGBoost DataProc components
+
+* [Set up cluster](https://github.com/kubeflow/pipelines/blob/master/components/dataproc/xgboost/create_cluster.py)
+* [Analyze](https://github.com/kubeflow/pipelines/blob/master/components/dataproc/xgboost/analyze.py)
+* [Transform](https://github.com/kubeflow/pipelines/blob/master/components/dataproc/xgboost/transform.py)
+* [Train (distributed)](https://github.com/kubeflow/pipelines/blob/master/components/dataproc/xgboost/train.py)
+* [Delete cluster](https://github.com/kubeflow/pipelines/blob/master/components/dataproc/xgboost/delete_cluster.py)
+
+## Requirements for building a component
+
+Install [Docker](https://www.docker.com/get-docker).
+
+## Step One: Create a container for each component
+
+In most cases, you need to create your own container image that includes your 
+program. See the  
+[container building examples](https://github.com/kubeflow/pipelines/blob/master/components). 
+(In the directory, go to any subdirectory and then go to the `containers` directory.)
+
+If your component creates some outputs to be fed as inputs to the downstream 
+components, each output must be a string and must be written to a separate local 
+text file by the container image. For example, if a trainer component needs to 
+output the trained model path, it writes the path into a  local file 
+`/output.txt`. In the Python class (in step three), you have the chance to 
+specify how to map the content  of local files to component outputs.
+
+<!---[TODO]: Add how to produce UI metadata.--->
+
+## Step Two: Create a Python class for your component
+
+The Python classes describe the interactions with the Docker container image 
+created in step one. For example, a component to create confusion matrix data 
+from prediction results looks like this:
+
+```python
+class ConfusionMatrixOp(kfp.dsl.ContainerOp):
+
+  def __init__(self, name, predictions, output_path):
+    super(ConfusionMatrixOp, self).__init__(
+      name=name,
+      image='gcr.io/project-id/ml-pipeline-local-confusion-matrix:v1',
+      command=['python', '/ml/confusion_matrix.py'],
+      arguments=[
+        '--output', '%s/{{workflow.name}}/confusionmatrix' % output_path,
+        '--predictions', predictions
+     ],
+     file_outputs={'label': '/output.txt'})
+
+```
+
+Note:
+
+* Each component needs to inherit from `kfp.dsl.ContainerOp`.
+* If you already defined ENTRYPOINT in the container image, you don’t need to 
+  provide `command` unless you want to override it.
+* In the init arguments, there can be Python native types (such as str, int) and 
+  `kfp.dsl.PipelineParam` types. Each `kfp.dsl.PipelineParam` represents a 
+  parameter whose value is usually only known at run time. It might be a 
+  pipeline  parameter whose value is provided at pipeline run time by user, or 
+  it can be an output from an upstream component. 
+  In the above case, `predictions` and `output_path` are `kfp.dsl.PipelineParam` types.
+* Although the value of each PipelineParam is only available at run time, you 
+  can still use the parameters inline in the  argument (note the “%s”). This 
+  means at run time the argument contains the value of the param inline.
+* `file_outputs` lists a map between labels and local file paths. In the above 
+  case, the content of `/output.txt` is gathered as a string output of the 
+  operator. To reference the output in code:
+
+    ```python
+    op = ConfusionMatrixOp(...)
+    op.outputs['label']
+    ```
+
+If there is only one output then you can also do `op.output`.
+
+## Step Three: Create your workflow as a Python function
+
+Each pipeline is identified as a Python function. For example:
+
+```python
+@kfp.dsl.pipeline(
+  name='TFX Trainer',
+  description='A trainer that does end-to-end training for TFX models.'
+)
+def train(
+    output_path,
+    train_data=kfp.dsl.PipelineParam('train-data',
+        value='gs://ml-pipeline-playground/tfx/taxi-cab-classification/train.csv'),
+    eval_data=kfp.dsl.PipelineParam('eval-data',
+        value='gs://ml-pipeline-playground/tfx/taxi-cab-classification/eval.csv'),
+    schema=kfp.dsl.PipelineParam('schema',
+        value='gs://ml-pipeline-playground/tfx/taxi-cab-classification/schema.json'),
+    target=kfp.dsl.PipelineParam('target', value='tips'),
+    learning_rate=kfp.dsl.PipelineParam('learning-rate', value=0.1),
+    hidden_layer_size=kfp.dsl.PipelineParam('hidden-layer-size', value='100,50'),
+    steps=kfp.dsl.PipelineParam('steps', value=1000),
+    slice_columns=kfp.dsl.PipelineParam('slice-columns', value='trip_start_hour'),
+    true_class=kfp.dsl.PipelineParam('true-class', value='true'),
+    need_analysis=kfp.dsl.PipelineParam('need-analysis', value='true'),
+)
+```
+
+Note:
+
+* **@kfp.dsl.pipeline** is a required decoration including `name` and 
+  `description` properties.
+* Input arguments show up as pipeline parameters in the Kubeflow Pipelines UI. 
+  As a Python rule, positional  args go first and keyword args go next.
+* Each function argument is of type `kfp.dsl.PipelineParam`. The default values 
+  should all be of that type. The default values show up in the Kubeflow 
+  Pipelines UI but can be overwritten.
+
+
+See [an example](https://github.com/kubeflow/pipelines/blob/master/samples/xgboost-spark/xgboost-training-cm.py).
+
+## Lightweight Python components
+
+You can also build lightweight components from Python functions. See the guide 
+to 
+[lightweight python components](/docs/guides/pipelines/lightweight-python-components).
diff --git a/content/docs/guides/pipelines/build-pipeline.md b/content/docs/guides/pipelines/build-pipeline.md
new file mode 100644
index 0000000000..0be06219f4
--- /dev/null
+++ b/content/docs/guides/pipelines/build-pipeline.md
@@ -0,0 +1,144 @@
++++
+title = "Build a Pipeline"
+description = "Deploy the Kubeflow Pipelines service"
+weight = 3
+toc = true
+bref = "This page describes how to author pipelines and components, and submit them to the Kubeflow Pipelines system to run."
+
+[menu.docs]
+  parent = "pipelines"
+  weight = 4
++++
+
+## Using a notebook
+
+You can choose to build your pipeline in a Jupyter notebook.
+
+We suggest that you use the JupyterHub that is installed in the same cluster as 
+the pipeline system. Follow these steps to start a notebook:
+
+* Follow the [instructions](/docs/guides/pipelines/deploy-pipelines-service) to 
+  deploy a pipeline cluster and run a `kubectl` proxy to connect to the cluster. 
+  You should see the Kubeflow Pipelines UI.
+
+* Click **Notebooks** in the left-hend menu. If this is the first time you've 
+  visited JupyterHub, you need to sign in with any username. Password can be 
+  blank. Then click the **Spawn** button to create a new instance. After a few 
+  minutes, the Jupyter UI opens. You can switch to the JupyterLab UI by changing 
+  `/tree` to `/lab` in the URL. 
+
+* Download the sample notebooks from 
+  https://github.com/kubeflow/pipelines/tree/master/samples/notebooks. 
+  
+* Upload these notebooks from the Jupyter UI. In Jupyter, go to the tree view 
+  and find the **upload** button in the top right-hand area of the screen.
+
+* Open the uploaded notebooks and make sure you are on Python 3. The Python 
+  version is at the top right-hand corner in the Jupyter notebook view. You can 
+  run the notebooks now.
+
+Note: The notebook samples don't work on Jupyter notebooks outside the same cluster, 
+because the Python library communicates with the Kubeflow Pipelines system 
+through in-cluster service names.
+
+The following notebooks are available:
+
+* [KubeFlow pipeline using TFX OSS components](https://github.com/kubeflow/pipelines/blob/master/samples/notebooks/KubeFlow%20Pipeline%20Using%20TFX%20OSS%20Components.ipynb): 
+  This notebook demonstrates building a machine learning pipeline based on
+  [TensorFlow Extended (TFX)](https://www.tensorflow.org/tfx/) components. 
+  The pipeline includes a TFDV step to infer the schema, a TFT preprocessor, a 
+  TensorFlow trainer, a TFMA analyzer, and a model deployer which deploys the 
+  trained model to `tf-serving` in the same cluster. The notebook also 
+  demonstrates how to build a component based on Python 3 inside the notebook 
+  including building a docker container.
+
+* [Lightweight python components](https://github.com/kubeflow/pipelines/blob/master/samples/notebooks/Lightweight%20Python%20components%20-%20basics.ipynb): 
+  This notebook demonstrates building simple Python components based on Python 3
+  and using  them in a pipeline with fast iterations. Going this route, building
+  a component does not require building a docker container so it is faster, but 
+  the container image may not be self contained because the source code is not 
+  built into the container.
+
+By following the notebooks you learn how to build pipelines and components with 
+the Kubeflow Pipelines SDK (a Python DSL).
+
+## Using the command line
+
+Instead of using a notebook, you can choose to set up things yourself using the 
+command line. But currently you can't use the Python SDK to submit 
+pipelines to a cluster, and you can't build container images using the SDK.
+The DSL compiler works as usual.
+
+**Python 3.5 or above is required**. If you don't have Python 3 set up, we 
+suggest the following steps to install 
+[Miniconda](https://conda.io/miniconda.html).
+ 
+* In a Debian/Ubuntu/[Cloud shell](https://console.cloud.google.com/cloudshell) 
+  environment:   
+
+    ```bash
+    apt-get update; apt-get install -y wget bzip2
+    wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
+    bash Miniconda3-latest-Linux-x86_64.sh
+    ```
+
+* In a Windows environment, download the 
+  [installer](https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86_64.exe) 
+  and  make sure you select the "*Add Miniconda to my PATH environment variable*" 
+  option during the installation.
+
+* In a Mac environment, download the 
+  [installer](https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh) 
+  and run the following command:
+
+    ```bash
+    bash Miniconda3-latest-MacOSX-x86_64.sh
+    ```
+
+Create a clean Python 3 environment:
+ 
+```bash
+conda create --name mlpipeline python=3.6
+source activate mlpipeline
+```
+ 
+If the `conda` command is not found, be sure to add the Miniconda path:
+ 
+```bash
+export PATH=MINICONDA_PATH/bin:$PATH
+```
+ 
+### Install the Kubeflow Pipelines SDK
+
+Run the following to install the Kubeflow Pipelines SDK:
+
+```bash
+pip3 install https://storage.googleapis.com/ml-pipeline/release/0.1.2/kfp.tar.gz --upgrade
+```
+
+After successful installation the command `dsl-compile` should be added to your 
+PATH.
+
+### Compile the samples
+
+The pipelines are written in Python, but they must be compiled to an 
+intermediate representation before submitting to the Kubeflow Pipelines service:
+
+```bash
+dsl-compile --py [path/to/py/file] --output [path/to/output/tar.gz]
+```
+
+For example:
+
+```bash
+dsl-compile --py [ML_REPO_DIRECTORY]/samples/basic/sequential.py --output [ML_REPO_DIRECTORY]/samples/basic/sequential.tar.gz
+```
+
+### Deploy the samples
+
+Upload the generated `.tar.gz` file through the Kubeflow Pipelines UI.
+
+## Advanced: Build your own components
+
+See how to 
+[build your own pipeline components](/docs/guides/pipelines/build-component).
diff --git a/content/docs/guides/pipelines/deploy-pipelines-service.md b/content/docs/guides/pipelines/deploy-pipelines-service.md
new file mode 100644
index 0000000000..575eda0753
--- /dev/null
+++ b/content/docs/guides/pipelines/deploy-pipelines-service.md
@@ -0,0 +1,197 @@
++++
+title = "Set Up and Deploy the Pipelines Service"
+description = "Deploy the Kubeflow Pipelines service"
+weight = 3
+toc = true
+bref = "This page guides you through the steps to deploy Kubeflow, including the Kubeflow Pipelines service."
+
+[menu.docs]
+  parent = "pipelines"
+  weight = 3
++++
+## Deploying Kubeflow Pipelines on a local Minikube cluster
+
+Install [Docker](https://docs.docker.com/install/), socat 
+(`sudo apt-get install socat` - needed for port forwarding) and 
+[Minikube](https://github.com/kubernetes/minikube#installation) (or 
+[install Minikube without VM](https://github.com/kubernetes/minikube#linux-continuous-integration-without-vm-support)).
+
+Then continue to [deploy Kubeflow Pipelines](#deploy-kubeflow-pipelines)
+as described below.
+
+## Deploying Kubeflow Pipelines on GKE
+
+You can use [Cloud Shell](https://cloud.google.com/shell/docs/quickstart) to run 
+all the commands in this guide. 
+
+Alternatively, if you prefer to install and interact with 
+Kubernetes Engine (GKE) from your local  machine, make sure you have 
+[gcloud CLI](https://cloud.google.com/sdk/) and 
+[kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/#download-as-part-of-the-google-cloud-sdk) 
+installed locally.
+
+Follow the 
+[GCP  guide](https://cloud.google.com/resource-manager/docs/creating-managing-projects) 
+to create a GCP project.
+
+Enable the GKE API on 
+[the GCP Console](https://console.developers.google.com/apis/enabled). You can 
+find more details about enabling 
+[billing](https://cloud.google.com/billing/docs/how-to/modify-project?#enable-billing), 
+as well as activating the 
+[GKE API](https://cloud.google.com/kubernetes-engine/docs/quickstart#before-you-begin).
+
+We recommend that you use Cloud Shell from the GCP Console to run the following 
+commands. Cloud Shell starts with an environment already logged in to your 
+account and set to the currently selected project. The following two commands 
+are required only in a workstation shell environment; you don't need to run
+these commands if you're using Cloud Shell:
+
+```bash
+gcloud auth login
+gcloud config set project [your-project-id]
+```
+
+You need a GKE cluster to run Kubeflow Pipelines. To start a new GKE cluster, 
+first set a default compute zone (`us-central1-a` in this case):
+
+```bash
+gcloud config set compute/zone us-central1-a
+```
+
+Then start a GKE cluster:
+
+```bash
+# Specify your cluster name
+CLUSTER_NAME=[YOUR-CLUSTER-NAME]
+gcloud container clusters create $CLUSTER_NAME \
+  --zone us-central1-a \
+  --scopes cloud-platform \
+  --enable-cloud-logging \
+  --enable-cloud-monitoring \
+  --machine-type n1-standard-2 \
+  --num-nodes 4
+```
+
+The above example uses the `cloud-platform` scope so that the cluster can 
+invoke GCP APIs. You can find all the options for creating a cluster in the 
+[GKE reference guide](https://cloud.google.com/sdk/gcloud/reference/container/clusters/create). 
+
+Next, grant your user account permission to create new cluster roles. This step 
+is necessary because installing Kubeflow Pipelines includes installing a few 
+[clusterroles](https://github.com/kubeflow/pipelines/search?utf8=%E2%9C%93&q=clusterrole+path%3Aml-pipeline%2Fml-pipeline&type=). 
+
+```bash
+kubectl create clusterrolebinding ml-pipeline-admin-binding --clusterrole=cluster-admin --user=$(gcloud config get-value account)
+```
+ 
+## Deploy Kubeflow Pipelines
+
+Go to the 
+[release page](https://github.com/kubeflow/pipelines/releases) to find a version 
+of the pipelines library. Deploy Kubeflow Pipelines to your cluster.
+
+For example:
+
+```bash
+PIPELINE_VERSION=0.1.2
+kubectl create -f https://storage.googleapis.com/ml-pipeline/release/$PIPELINE_VERSION/bootstrapper.yaml
+```
+
+By running `kubectl get job`, you should see a job created that deploys 
+Kubeflow Pipelines along with all dependencies in the cluster. Wait for the 
+number of successful job runs to reach 1:
+
+```
+NAME                      DESIRED   SUCCESSFUL   AGE
+deploy-ml-pipeline-wjqwt  1         1            9m
+```
+
+You can check the deployment log for any failures:
+
+```bash
+kubectl logs $(kubectl get pods -l job-name=[JOB_NAME] -o jsonpath='{.items[0].metadata.name}')
+```
+
+By default, the Kubeflow Pipelines service is deployed with usage collection 
+turned on. 
+It uses [Spartakus](https://github.com/kubernetes-incubator/spartakus) which 
+does not report any personal identifiable information (PII).
+
+When deployment is successful, forward a local port to visit the Kubeflow 
+Pipelines UI dashboard:
+
+```bash
+export NAMESPACE=kubeflow
+kubectl port-forward -n ${NAMESPACE} $(kubectl get pods -n ${NAMESPACE} --selector=service=ambassador -o jsonpath='{.items[0].metadata.name}') 8080:80
+```
+
+Open your browser and point to 
+[localhost:8080/pipeline](http://localhost:8080/pipeline).
+
+## Run your first pipeline
+
+Click through to the **Pipelines** section in the UI, create a new experiment 
+and run a sample pipeline. 
+For the project name parameter, use your GCP project name. 
+
+To build your own pipelines, see the 
+[guide to building a pipeline](/docs/guides/pipelines/build-pipeline).
+
+## Disable usage reporting
+
+If you want to turn off the usage report, you can download the bootstrapper 
+file and change the arguments to the deployment job.
+
+For example, download bootstrapper:
+
+```bash
+PIPELINE_VERSION=0.0.42
+curl https://storage.googleapis.com/ml-pipeline/release/$PIPELINE_VERSION/bootstrapper.yaml --output bootstrapper.yaml
+```
+
+Update the argument in the file:
+
+```
+        args: [
+          ... 
+          # uncomment following line
+          "--report_usage", "false",
+          ...
+        ]
+```
+
+Create a job using the updated YAML by running the following command:
+
+```bash
+kubectl create -f bootstrapper.yaml
+```
+
+## Uninstall
+
+To uninstall Kubeflow Pipelines, download the bootstrapper file and change the 
+arguments to the deployment job.
+
+For example, download bootstrapper:
+
+```bash
+PIPELINE_VERSION=0.0.42
+curl https://storage.googleapis.com/ml-pipeline/release/$PIPELINE_VERSION/bootstrapper.yaml --output bootstrapper.yaml
+```
+
+Update the argument in the file:
+
+```
+        args: [
+          ... 
+          # uncomment following line
+          "--uninstall",
+          ...
+        ]
+```
+
+Create a job using the updated YAML by running the following command:
+
+```bash
+kubectl create -f bootstrapper.yaml
+```
diff --git a/content/docs/guides/pipelines/lightweight-python-components.md b/content/docs/guides/pipelines/lightweight-python-components.md
new file mode 100644
index 0000000000..aa0ade7571
--- /dev/null
+++ b/content/docs/guides/pipelines/lightweight-python-components.md
@@ -0,0 +1,63 @@
++++
+title = "Build Lightweight Python Components"
+description = "Building your own lightweight pipelines components from Python."
+weight = 3
+toc = true
+
+[menu.docs]
+  parent = "pipelines"
+  weight = 7
++++
+Lightweight Python components do not require you to build a new container image
+for every code change. They're intended for fast iteration in a notebook 
+environment.
+
+Advantages over container components:
+
+* Faster iteration: No need to build new container image after every change 
+  (building images takes some time).
+* Easier authoring: Components can be created in a local environment. Docker and 
+  Kubernetes are not required.
+
+## Building a lightweight Python component
+
+To build a component, define a stand-alone Python function and then call 
+`kfp.components.func_to_container_op(func)` to convert the function to a 
+component that can be used in a pipeline.
+
+There are several requirements for the component function:
+
+* The function must be stand-alone.
+
+  * It should not use any code declared outside the function definition.
+  * Any imports should be added inside the main component function.
+  * Any helper functions should also be defined inside the main component 
+    function.
+
+* The function can only import packages that are available in the base image.
+
+  * If you need to import a package that's not available in the default base 
+    image you can try to find a container image that already includes the 
+    required packages. (As a workaround you can use the `subprocess` module 
+    to run `pip install` for the required package.)
+
+* If the function operates on numbers, the parameters must have type hints. 
+  Supported types are `int`, `float`, `bool`. All other arguments are passed as 
+  strings.
+* To build a component with multiple output values, use Python's 
+  [typing.NamedTuple](https://docs.python.org/3/library/typing.html#typing.NamedTuple) 
+  type hint syntax: 
+  
+    ```
+    NamedTuple('MyFunctionOutputs', [('output_name_1', type), ('output_name_2', float)])
+    ``` 
+    
+    The `NamedTuple` class is already imported, so that it can be used in the 
+    function declaration.
+
+## Tutorials
+
+See the notebook on 
+[lightweight Python component basics](https://github.com/kubeflow/pipelines/blob/master/samples/notebooks/Lightweight%20Python%20components%20-%20basics.ipynb) 
+for an example of creating lightweight Python components and using them in a 
+pipeline.
\ No newline at end of file
diff --git a/content/docs/guides/pipelines/output-viewer.md b/content/docs/guides/pipelines/output-viewer.md
new file mode 100644
index 0000000000..68bbb79baf
--- /dev/null
+++ b/content/docs/guides/pipelines/output-viewer.md
@@ -0,0 +1,141 @@
++++
+title = "Use an Output Viewer"
+description = "Using output viewers for pipelines components."
+weight = 3
+toc = true
+
+[menu.docs]
+  parent = "pipelines"
+  weight = 8
++++
+The Kubeflow Pipelines UI has built-in support for several types of 
+visualizations, in order to provide rich performance evaluation and comparison. 
+Components can use these visualizations by writing a JSON file 
+to their local filesystem at any point during their execution. 
+
+## Metadata for the output viewers
+
+The pipeline component must write a JSON file specifying metadata for the
+output viewers. The file name must be `/metadata.json`, and the file
+must be written to the root level of the container filesystem.
+
+The JSON specifies an array of outputs, each of which describes metadata for an 
+output viewer. The JSON structure looks like this:
+
+```
+{
+  "version": 1,
+  "outputs": [
+    {
+      "type": "confusion_matrix",
+      "format": "csv",
+      "source": "dir1/matrix.csv",
+      "schema": "dir1/schema.json",
+      "predicted_col": "column1",
+      "target_col": "column2"
+    },
+    {
+      ...
+    }
+  ]
+}
+```
+
+If the component writes such a file to its container filesystem, Kubeflow 
+Pipelines extracts the file, and the UI uses the file to generate the 
+specified viewer(s). The metadata specifies where the artifact data should be 
+loaded from, and then the UI loads the data **into memory** and renders it. 
+It's important to keep this data at a level that's manageable by the UI, for 
+example by running a sampling step before exporting the file as an artifact.
+
+These are the metadata fields that you can specify:
+
+| Field name      | Description |
+| -------------   | ------------- |
+| `format`        | Specifies the format of the artifact data, default is 'csv'. *NOTE* The only format supported as of now is 'csv'. |
+| `header`        | A list of strings that are used as the header of the artifact data. |
+| `labels`        | A list of strings that are used to label artifact columns/rows. |
+| `predicted_col` | Name of the predicted column. |
+| `schema`        | A list of {type, name} objects that specify the schema of the artifact data. |
+| `source`        | Full path to data. This can contain wildcards '*', in which case the data is concatenated before it's displayed by the UI. |
+| `storage`       | Storage provider service name, default is 'gcs'. |
+| `target_col`    | Name of the target column. |
+| `type`          | Name of the viewer, one of the ones below. |
+
+Below are the supported viewer types and the required metadata fields for each
+type:
+
+## Confusion matrix
+
+**Metadata fields:**
+
+- `source`
+- `labels`
+- `schema`
+- `format`
+
+Plots a confusion matrix visualization using the data from the given source 
+path, and the schema to be able to parse the data. Labels provide the names of 
+the classes to be plotted on the x and y axes.
+
+## ROC curve
+
+**Metadata fields:**
+
+- `source`
+- `format`
+- `schema`
+
+Plots a ROC curve using the data from the given source path. It assumes the 
+schema includes three columns with the following names: 
+
+* fpr
+* tpr
+* thresholds
+
+Hovering on the ROC curve shows the threshold value used for the cursor's 
+closest fpr and tpr values.
+
+## Table
+
+**Metadata fields:**
+
+- `source`
+- `header`
+- `format`
+
+Builds an HTML table out of the data at the given source path, where the 
+`header` field specifies what shows up in the first row of the table. The table 
+supports pagination.
+
+## Tensorboard
+
+**Metadata Fields:**
+
+- `source`
+
+Adds a "Start Tensorboard" button to the output page. Clicking this button 
+starts a Tensorboard Pod in the Kubernetes cluster, and switches the button to
+ "Open Tensorboard." Clicking this button again opens up the Tensorboard 
+ interface in a new tab, pointing it to the logdir data specified in the 
+ `source` field.
+
+It's important to point out that Tensorboard instances are not completely 
+managed by the Kubeflow Pipelines UI. The "Start Tensorboard" is only a 
+convenience feature to avoid interrupting the user's workflow when looking at 
+pipeline runs. The user is responsible for recycling or deleting those Pods 
+separately using their Kubernetes management tools.
+
+## Web app
+
+**Metadata fields:**
+
+- `source`
+
+In order to provide more flexibility rendering custom output, 
+this viewer supports specifying an HTML file that is created by the component 
+and is rendered in the outputs page as is. It's important to note that this file 
+must be self-contained, with no references to other files in the filesystem. It 
+can still have absolute references to files on the web, however. Content running 
+inside this web app is isolated in an iframe, and cannot communicate with the 
+Kubeflow Pipelines UI.
diff --git a/content/docs/guides/pipelines/pipelines-concepts.md b/content/docs/guides/pipelines/pipelines-concepts.md
new file mode 100644
index 0000000000..88e02b2c06
--- /dev/null
+++ b/content/docs/guides/pipelines/pipelines-concepts.md
@@ -0,0 +1,83 @@
++++
+title = "Concepts in Kubeflow Pipelines"
+description = "Concepts Relevant to Kubeflow Pipelines"
+weight = 3
+toc = true
+bref = "This page describes the most important concepts used within the Kubeflow Pipelines service."
+
+[menu.docs]
+  parent = "pipelines"
+  weight = 2
++++
+
+## Pipeline
+
+A description of a machine learning (ML) workflow, including all of its 
+different components, and how they come together in the form of a graph, as well 
+as a list of the parameters. This is the main shareable artifact in the Kubeflow 
+Pipelines service. You create and edit a pipeline separately from the pipelines
+ UI, although you can upload and list pipelines on the UI.
+
+## Pipeline component
+
+A building block in the pipeline template; self-contained user code that 
+performs one step in the pipeline, such as preprocessing, transformation, 
+training, etc. A component must be packaged as a 
+[Docker image](https://docs.docker.com/get-started/). See the guide to
+[building your own components](/docs/guides/pipelines/build-component).
+
+## Experiment
+
+A workspace where you can try different configurations of your pipelines. You 
+can use experiments to organize your runs to logical groups. Experiments can 
+contain arbitrary runs, and you can add 
+[recurring runs](#recurring-run-config) there as well.
+
+## Run
+
+A single execution of a pipeline. Runs comprise an immutable log of all e
+xperiments that you attempt, and are designed to be self-contained to allow for 
+reproducibility. You can track the progress of a run by looking at its details 
+page, where you can see its runtime graph, as well as output artifacts and logs 
+for each of its steps.
+
+## Recurring run config
+
+A copy of the pipeline with all fields (parameters) specified, plus a 
+[run trigger](#run-trigger). You can start a recurring run inside any 
+experiment, and it will periodically start a new copy of the run config. You 
+can enable/disable the recurring run from the UI.
+
+## Run trigger
+
+You select one of multiple types of triggers to tell the system when a 
+recurring run config spawns a new run:
+
+* Periodic: for an interval-based scheduling of runs (for example: every 2 hours 
+  or every 45 minutes).
+* Cron: for specifying cron semantics for scheduling runs. The UI also has an 
+  option for you to manually enter a cron expression.
+
+## Step
+
+An execution of one of the components in the pipeline. The relationship of a 
+step to its component is much like that of a run to its pipeline: an 
+instantiation relationship. In a complex pipeline, components can execute 
+multiple times in loops, or conditionally after resolving an if/else like clause 
+in the pipeline code.
+
+## Step output artifacts
+
+Artifacts are outputs emitted by the pipeline's steps, which the Kubeflow 
+pipelines UI understands, and can render as rich visualizations. It’s useful for 
+pipeline components to include artifacts so that you can provide for performance 
+evaluation, quick decision making for the run, or comparison across different 
+runs. Artifacts also make it possible to understand how the pipeline’s different 
+components work. This can range from a plain textual view of the data to rich 
+interactive visualizations.
+
+## Back end
+
+A REST API server supports the front end. For user data stored in external 
+services (for example, Google Cloud Storage), the front end makes requests 
+directly to those services using their client libraries.
\ No newline at end of file
diff --git a/content/docs/guides/pipelines/pipelines-overview.md b/content/docs/guides/pipelines/pipelines-overview.md
new file mode 100644
index 0000000000..07545c873a
--- /dev/null
+++ b/content/docs/guides/pipelines/pipelines-overview.md
@@ -0,0 +1,105 @@
++++
+title = "Overview of Kubeflow Pipelines"
+description = "Overview of Kubeflow Pipelines"
+weight = 3
+toc = true
+bref = "Kubeflow Pipelines is a platform for building and deploying portable and scalable end-to-end ML workflows, based on containers."
+aliases = ["/docs/guides/pipelines/"]
+
+[menu.docs]
+  parent = "pipelines"
+  weight = 1
++++
+
+## What is Kubeflow Pipelines?
+
+The Kubeflow Pipelines platform consists of:
+
+* User interface for managing and tracking experiments, jobs, and runs
+* Engine for scheduling multi-step ML workflows
+* SDK for defining and manipulating pipelines and components
+* Notebooks for interacting with the system using the SDK
+
+The following are the goals of Kubeflow Pipelines:
+
+* End-to-end orchestration: enabling and simplifying the orchestration of
+  machine learning pipelines.
+* Easy experimentation: making it easy for you to try numerous ideas and 
+  techniques and manage your various trials/experiments.
+* Easy re-use: enabling you to re-use components and pipelines to quickly 
+  cobble together end-to-end solutions, without having to rebuild each time.
+
+
+## Getting started
+
+Follow these guides to deploy Kubeflow Pipelines and build your first pipeline:
+
+* [Deploy the Kubeflow Pipelines service to GKE](https://github.com/kubeflow/pipelines/wiki/Deploy-the-Kubeflow-Pipelines-Service).
+* [Build a pipeline and run the pipeline samples](https://github.com/kubeflow/pipelines/wiki/Build-a-Pipeline).
+
+## The Python code to represent a pipeline workflow graph
+
+Below is an example of the Python code that defines a pipeline:
+
+```python
+
+@dsl.pipeline(
+  name='XGBoost Trainer',
+  description='A trainer that does end-to-end distributed training for XGBoost models.'
+)
+def xgb_train_pipeline(
+    output,
+    project,
+    region='us-central1',
+    train_data='gs://ml-pipeline-playground/sfpd/train.csv',
+    eval_data='gs://ml-pipeline-playground/sfpd/eval.csv',
+    schema='gs://ml-pipeline-playground/sfpd/schema.json',
+    target='resolution',
+    rounds=200,
+    workers=2,
+    true_label='ACTION',
+):
+  delete_cluster_op = DeleteClusterOp('delete-cluster', project, region)
+  with dsl.ExitHandler(exit_op=delete_cluster_op):
+    create_cluster_op = CreateClusterOp('create-cluster', project, region, output)
+
+    analyze_op = AnalyzeOp('analyze', project, region, create_cluster_op.output,
+                           schema, train_data,
+                           '%s/{{workflow.name}}/analysis' % output)
+
+    transform_op = TransformOp('transform', project, region,
+                               create_cluster_op.output, train_data, eval_data,
+                               target, analyze_op.output,
+                               '%s/{{workflow.name}}/transform' % output)
+
+    train_op = TrainerOp('train', project, region, create_cluster_op.output,
+                         transform_op.outputs['train'],transform_op.outputs['eval'],
+                         target, analyze_op.output, workers,
+                         rounds, '%s/{{workflow.name}}/model' % output)
+
+    predict_op = PredictOp('predict', project, region, create_cluster_op.output,
+                           transform_op.outputs['eval'], train_op.output, target,
+                           analyze_op.output,
+                           '%s/{{workflow.name}}/predict' % output)
+
+    cm_op = ConfusionMatrixOp('confusion-matrix',
+                              predict_op.output,
+                              '%s/{{workflow.name}}/confusionmatrix' % output)
+
+    roc_op = RocOp('roc', predict_op.output, true_label,
+                   '%s/{{workflow.name}}/roc' % output)
+```
+
+## The above pipeline after you've uploaded it
+
+![Job](/docs/images/job.png)
+
+## The runtime execution graph of the pipeline
+
+![Graph](/docs/images/run.png)
+
+## Outputs from the pipeline
+
+![Prediction Output](/docs/images/predict.png)
+![Confusion Matrix Output](/docs/images/cm.png)
+![ROC Output](/docs/images/roc.png)
diff --git a/content/docs/guides/pipelines/pipelines-samples.md b/content/docs/guides/pipelines/pipelines-samples.md
new file mode 100644
index 0000000000..d3cbe8ae91
--- /dev/null
+++ b/content/docs/guides/pipelines/pipelines-samples.md
@@ -0,0 +1,149 @@
++++
+title = "Pipeline Samples and Notebooks"
+description = "Using the samples and notebooks for pipelines components."
+weight = 3
+toc = true
+
+[menu.docs]
+  parent = "pipelines"
+  weight = 9
++++
+This page describes how to use the Kubeflow Pipelines 
+[notebooks]( https://github.com/kubeflow/pipelines/tree/master/samples/notebooks)
+and [samples](https://github.com/kubeflow/pipelines/tree/master/samples)
+to create a pipeline for deployment in Kubeflow.
+
+## Using a notebook
+
+You can choose to build your pipeline in a Jupyter notebook.
+
+We suggest that you use the JupyterHub that is installed in the same cluster as 
+the pipeline system. Follow these steps to start a notebook:
+
+* Follow the [instructions](/docs/guides/pipelines/deploy-pipelines-service) to 
+  deploy a pipeline cluster and run a `kubectl` proxy to connect to the cluster. 
+  You should see the Kubeflow Pipelines UI.
+
+* Click **Notebooks** in the left-hend menu. If this is the first time you've 
+  visited JupyterHub, you need to sign in with any username. Password can be 
+  blank. Then click the **Spawn** button to create a new instance. After a few 
+  minutes, the Jupyter UI opens. You can switch to the JupyterLab UI by changing 
+  `/tree` to `/lab` in the URL. 
+
+* Download the sample notebooks from 
+  https://github.com/kubeflow/pipelines/tree/master/samples/notebooks. 
+  
+* Upload these notebooks from the Jupyter UI. In Jupyter, go to the tree view 
+  and find the **upload** button in the top right-hand area of the screen.
+
+* Open the uploaded notebooks and make sure you are on Python 3. The Python 
+  version is at the top right-hand corner in the Jupyter notebook view. You can 
+  run the notebooks now.
+
+Note: The notebook samples don't work on Jupyter notebooks outside the cluster, 
+because the Python library communicates with the Kubeflow Pipelines system 
+through in-cluster service names.
+
+The following notebooks are available:
+
+* [KubeFlow pipeline using TFX OSS components](https://github.com/kubeflow/pipelines/blob/master/samples/notebooks/KubeFlow%20Pipeline%20Using%20TFX%20OSS%20Components.ipynb): 
+  This notebook demonstrates building a machine learning pipeline based on
+  [TensorFlow Extended (TFX)](https://www.tensorflow.org/tfx/) components. 
+  The pipeline includes a TFDV step to infer the schema, a TFT preprocessor, a 
+  TensorFlow trainer, a TFMA analyzer, and a model deployer which deploys the 
+  trained model to `tf-serving` in the same cluster. The notebook also 
+  demonstrates how to build a component based on Python 3 inside the notebook 
+  including building a docker container.
+
+* [Lightweight python components](https://github.com/kubeflow/pipelines/blob/master/samples/notebooks/Lightweight%20Python%20components%20-%20basics.ipynb): 
+  This notebook demonstrates building simple Python components based on Python 3
+  and using  them in a pipeline with fast iterations. Going this route, building
+  a component does not require building a docker container so it is faster, but 
+  the container image may not be self contained because the source code is not 
+  built into the container.
+
+By following the notebooks you learn how to build pipelines and components with 
+the Kubeflow Pipelines SDK (a Python DSL).
+
+## Using the samples
+
+Instead of using a notebook, you can choose to set up things yourself using the 
+command line. But currently you can't use the Python SDK to submit 
+pipelines to a cluster, and you can't build container images using the SDK.
+The DSL compiler works as usual.
+
+### Set up Python
+
+**Python 3.5 or above is required**. If you don't have Python 3 set up, we 
+suggest the following steps to install 
+[Miniconda](https://conda.io/miniconda.html).
+ 
+* In a Debian/Ubuntu/[Cloud shell](https://console.cloud.google.com/cloudshell) 
+  environment:   
+
+    ```bash
+    apt-get update; apt-get install -y wget bzip2
+    wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
+    bash Miniconda3-latest-Linux-x86_64.sh
+    ```
+
+* In a Windows environment, download the 
+  [installer](https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86_64.exe) 
+  and  make sure you select the "*Add Miniconda to my PATH environment variable*" 
+  option during the installation.
+
+* In a Mac environment, download the 
+  [installer](https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh) 
+  and run the following command:
+
+    ```bash
+    bash Miniconda3-latest-MacOSX-x86_64.sh
+    ```
+
+Create a clean Python 3 environment:
+ 
+```bash
+conda create --name mlpipeline python=3.6
+source activate mlpipeline
+```
+ 
+If the `conda` command is not found, be sure to add Miniconda to your path:
+ 
+```bash
+export PATH=MINICONDA_PATH/bin:$PATH
+```
+
+### Install the Kubeflow Pipelines SDK
+
+Run the following to install the Kubeflow Pipelines SDK:
+
+```bash
+pip3 install https://storage.googleapis.com/ml-pipeline/release/0.1.2/kfp.tar.gz --upgrade
+```
+
+After successful installation the command `dsl-compile` should be added to your 
+PATH.
+
+### Compile the samples
+
+The pipelines are written in Python, but they must be compiled to an 
+intermediate representation before submitting to the Kubeflow Pipelines service:
+
+```bash
+dsl-compile --py [path/to/py/file] --output [path/to/output/tar.gz]
+```
+
+For example:
+
+```bash
+dsl-compile --py [ML_REPO_DIRECTORY]/samples/basic/sequential.py --output [ML_REPO_DIRECTORY]/samples/basic/sequential.tar.gz
+```
+
+### Deploy the samples
+
+Upload the generated `.tar.gz` file through the Kubeflow Pipelines UI.
+
+## Advanced: Build your own components
+
+See how to 
+[build your own pipeline components](/docs/guides/pipelines/build-component).
diff --git a/content/docs/guides/pipelines/pipelines-ui.md b/content/docs/guides/pipelines/pipelines-ui.md
new file mode 100644
index 0000000000..a372e71024
--- /dev/null
+++ b/content/docs/guides/pipelines/pipelines-ui.md
@@ -0,0 +1,148 @@
++++
+title = "Using the Pipelines UI"
+description = "Using the Kubeflow Pipelines UI"
+weight = 3
+toc = true
+
+[menu.docs]
+  parent = "pipelines"
+  weight = 5
++++
+You can use the Kubeflow Pipelines UI to complete tasks like creating, listing, 
+and managing different resources, and viewing and comparing the outputs of 
+experiments. This guide discusses the different operations you can perform using 
+the UI.
+
+## List available pipelines
+
+The landing page of the UI is a list of all available pipelines, with management 
+controls such as uploading new pipelines, deleting existing ones, or creating an 
+experiment out of a selected pipeline.
+
+## Create an experiment
+
+From a pipeline's details page, click **Create experiment**. This takes you to 
+the new experiment page, where you can specify the experiment's name and 
+description. 
+
+## Create a run inside an experiment
+
+On the experiment's details page, click **Create new run**. 
+Fill out the run's name and description, pick which pipeline it will execute.
+
+The next section is the pipeline's parameters form. This form is generated 
+automatically based on the selected pipeline. You can try out different 
+pipelines using the pipeline picker dialog. 
+
+After you fill in the parameters (some are filled in automatically, if the 
+pipeline author provides default values), click the **Create** button to start 
+the run. This takes you back to the experiment's details page.
+
+## Create recurring runs
+
+In the experiment's details page, you can also click the 
+**Create recurring run** button. This takes you through a similar flow as 
+creating a run, but here you can choose the kind of trigger that should be used 
+to spawn the runs. You can specify **Maximum concurrent runs** to limit the 
+number of runs launched in parallel. This can be helpful if the pipeline is 
+expected to run for a long period of time, and is triggered to run frequently.
+
+## List runs and recurring runs
+
+The experiment's details page shows all of the runs created or generated in it 
+by default. The page also has a widget to show the number of active recurring 
+runs in this experiment, and you can click the **manage** button to see a list 
+of recurring run configs, enable and disable them, or see any config's details.
+
+## List experiments
+
+This page offers two views into the runs scheduled in the cluster:
+
+1. All experiments: This shows a list of experiments, as well as the status of 
+   each experiment's last five runs. You can expand the experiment to see the 
+   list of the most recent five runs inline, or click its name to go to its 
+   details page, where you can see all of its runs.
+1. Show all runs: This is a flat list of all runs in the system, showing each 
+   run's status, run duration, associated pipeline and experiment if any, and 
+   its start time.
+
+## Examine a run
+
+Clicking a run in the run list (either inside an experiment, or in the top-level 
+flat list of runs) takes you to the **Run Details** page in the 
+Kubeflow Pipelines UI.
+
+A run is a single execution of a pipeline. You can examine the configuration, 
+status, outputs, execution graph, and logs on the run details page.
+
+The run details page shows the following information:
+
+* A toolbar with a breadcrumb and some action buttons.
+* Two tabs, **Graph** and **Config**.
+* A visualization of the execution graph of the run.
+
+### The toolbar
+
+The breadcrumb at the top of the page shows the following information:
+ **Experiments > [experiment name] > [run name]**, with an icon to the left of 
+ the run name indicating its status. For example, 
+ _Succeeded_, _Running_, _Failed_. Each of these elements of the breadcrumb is 
+ clickable and can be used to navigate.
+
+The **Clone** button takes you to the **Create new run** form with the same 
+pipeline selected and all of the parameters set to the same values as the run 
+you are looking at. You can edit everything, but only need to fill in the 
+**run name** and make sure a pipeline is selected before deploying.
+
+The **Refresh** button retrieves the latest information from the back end, 
+updating all elements on the page.
+
+### Exploring the run
+
+There are two main views in the run details page: the **Graph** view, and the
+**Config** view.
+
+### The graph
+
+Pipelines generally consist of a number of components, and the graph shows the 
+steps that this run has executed so far with arrows indicating parent/child 
+relationships. The graph is viewable as soon as the run begins. You can update 
+it using the **Refresh** button at the top of the page. Each node within the 
+graph corresponds to a step within the pipeline and is labeled accordingly.
+
+At the top right of each node is an icon indicating its status. For example, 
+_Succeeded_, _Running_, _Failed_, or _Skipped_. A node can be skipped when its 
+parent contains a conditional and is indicated by a gray triangle pointing 
+towards a vertical line.
+
+Clicking on a node highlights it and opens a resizable panel from the right of 
+the page displaying the name of the run and containing its own three tabs: 
+**Artifacts**, **Input/Output**, and **Logs**.
+
+### Artifacts
+
+Artifacts are any 
+[viewers](docs/guides/pipelines/output-viewers) associated with a given 
+component. There are many kinds of viewers including: 
+
+* TensorBoard instances (accessible via link)
+* A confusion matrix
+* An ROC curve
+* A simple table
+* User-defined HTML
+* And more
+
+### Input/output
+
+This simple view includes the inputs and outputs of the node, if any.
+
+### Logs
+
+This tab displays the Kubernetes pod logs associated with the selected component 
+and may be useful for debugging.
+
+## The config
+
+This is a main view within the run details page and consists of some basic 
+information about the run such as its status, creation time, start time, finish 
+time, duration, and the pipeline parameters it was run with.
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+              <span class="select-menu-item-text css-truncate-target js-select-menu-filter-text">
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+               href="/kubeflow/pipelines/blob/IronPan-patch-3/docs/images/metric.png"
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From 9c5d2d253b129efa5986ba4d607ed4799becfd6e Mon Sep 17 00:00:00 2001
From: Andrey <andrey.velichkevich@gmail.com>
Date: Tue, 27 Nov 2018 15:38:45 -0800
Subject: [PATCH 2/7] Fix link to upgrade ks (#311)

---
 content/docs/guides/upgrade.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/docs/guides/upgrade.md b/content/docs/guides/upgrade.md
index b4d6926563..ad21e56866 100644
--- a/content/docs/guides/upgrade.md
+++ b/content/docs/guides/upgrade.md
@@ -16,7 +16,7 @@ Updating your deployments is a two step process
 1. Updating your ksonnet application
 
   1. We recommend checking your app into source control to back it up before proceeding
-  1. Use the script (upgrade_ks_app.py)[https://github.com/kubeflow/kubeflow/tree/{{< params "githubbranch" >}}/scripts)
+  1. Use the script [upgrade_ks_app.py](https://github.com/kubeflow/kubeflow/tree/{{< params "githubbranch" >}}/scripts)
      to update your ksonnet app with the current version for the Kubeflow packages
   1. Note: ksonnet is working on support for this see https://github.com/ksonnet/ksonnet/issues/237
   

From 2af7dcdeed81654abce3cfa7b417b50c008e1cf8 Mon Sep 17 00:00:00 2001
From: Jiaxin Shan <seedjeffwan@gmail.com>
Date: Tue, 27 Nov 2018 16:26:36 -0800
Subject: [PATCH 3/7] Remove outdated TODO item (#306)

---
 content/docs/guides/components/tfserving_new.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/content/docs/guides/components/tfserving_new.md b/content/docs/guides/components/tfserving_new.md
index 4e0fb9dd95..266bc82065 100644
--- a/content/docs/guides/components/tfserving_new.md
+++ b/content/docs/guides/components/tfserving_new.md
@@ -61,8 +61,6 @@ See [doc](https://cloud.google.com/docs/authentication/) for more detail.
 
 *S3*
 
-TODO(https://github.com/kubeflow/kubeflow/issues/1309): fix aws part
-
 To use S3, generate a different prototype
 ```
 ks generate tf-serving-aws ${MODEL_COMPONENT} --name=${MODEL_NAME}

From 6f64a22188a1e8d7622d33989675ff3ae6934aec Mon Sep 17 00:00:00 2001
From: Jeremy Lewi <jeremy+github@lewi.us>
Date: Tue, 27 Nov 2018 19:27:21 -0800
Subject: [PATCH 4/7] Update the slack invite link since the existing link
 expired. (#314)

Fix #39.
---
 content/docs/about/community.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/docs/about/community.md b/content/docs/about/community.md
index 9afbf1d5c5..feb5361c09 100644
--- a/content/docs/about/community.md
+++ b/content/docs/about/community.md
@@ -52,7 +52,7 @@ mailing list.
 
 Summary:
 
-* [Invitation to our Slack channel](https://join.slack.com/t/kubeflow/shared_invite/enQtMjgyMzMxNDgyMTQ5LWUwMTIxNmZlZTk2NGU0MmFiNDE4YWJiMzFiOGNkZGZjZmRlNTExNmUwMmQ2NzMwYzk5YzQxOWQyODBlZGY2OTg)
+* [Invitation to our Slack channel](https://join.slack.com/t/kubeflow/shared_invite/enQtNDg5MTM4NTQyNjczLWUyZGI1ZmExZWExYWY4YzlkOWI4NjljNjJhZjhjMjEwNGFjNmVkNjg2NTg4M2I0ZTM5NDExZWI5YTIyMzVmNzM)
 * [Twitter](http://twitter.com/kubeflow)
 * [Mailing list: kubeflow-discuss](https://groups.google.com/forum/#!forum/kubeflow-discuss)
 

From 1ead4f3d7fd162a505df4e5e981b09f98b07f14b Mon Sep 17 00:00:00 2001
From: Sarah Maddox <sarahmaddox@users.noreply.github.com>
Date: Thu, 29 Nov 2018 05:36:34 +1100
Subject: [PATCH 5/7] Fixed links to point to website docs. (#313)

---
 content/docs/guides/pipelines/pipelines-overview.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/content/docs/guides/pipelines/pipelines-overview.md b/content/docs/guides/pipelines/pipelines-overview.md
index 07545c873a..099db81445 100644
--- a/content/docs/guides/pipelines/pipelines-overview.md
+++ b/content/docs/guides/pipelines/pipelines-overview.md
@@ -34,8 +34,8 @@ The following are the goals of Kubeflow Pipelines:
 
 Follow these guides to deploy Kubeflow Pipelines and build your first pipeline:
 
-* [Deploy the Kubeflow Pipelines service to GKE](https://github.com/kubeflow/pipelines/wiki/Deploy-the-Kubeflow-Pipelines-Service).
-* [Build a pipeline and run the pipeline samples](https://github.com/kubeflow/pipelines/wiki/Build-a-Pipeline).
+* [Deploy the Kubeflow Pipelines service](/docs/guides/pipelines/deploy-pipelines-service).
+* [Build a pipeline and run the pipeline samples](/docs/guides/pipelines/build-pipeline).
 
 ## The Python code to represent a pipeline workflow graph
 

From 6e33e607c84514d6510af7b56224ccb601d65b29 Mon Sep 17 00:00:00 2001
From: Sarah Maddox <sarahmaddox@users.noreply.github.com>
Date: Thu, 29 Nov 2018 05:36:40 +1100
Subject: [PATCH 6/7] Clarifies relationship between tf-operator and TFJob.
 (#315)

---
 content/docs/guides/components/tftraining.md | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/content/docs/guides/components/tftraining.md b/content/docs/guides/components/tftraining.md
index 9635f77acf..2ae682558c 100644
--- a/content/docs/guides/components/tftraining.md
+++ b/content/docs/guides/components/tftraining.md
@@ -1,10 +1,10 @@
 +++
 title = "TensorFlow Training (TFJob)"
-linkTitle = "TensorFlow Training"
+linkTitle = "TensorFlow Training (TFJob)"
 description = ""
 weight = 10
 toc = true
-bref= "Training using TensorFlow"
+bref= "This page describes TFJob for training a machine learning model with TensorFlow."
 [menu]
 [menu.docs]
   parent = "components"
@@ -13,8 +13,10 @@ bref= "Training using TensorFlow"
 
 ## What is TFJob?
 
-TFJob is a Kubernetes [custom resource](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) that makes it easy to
-run TensorFlow training jobs on Kubernetes.
+TFJob is a Kubernetes 
+[custom resource](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) that you can use to run TensorFlow training jobs on Kubernetes. The Kubeflow
+implementation of TFJob is in
+[`tf-operator`](https://github.com/kubeflow/tf-operator).
 
 A TFJob is a resource with a simple YAML representation illustrated below.
 

From 9d3594904642d2b0d9cce85c065443c7d271f803 Mon Sep 17 00:00:00 2001
From: David Sabater Dinter <dsdinter@users.noreply.github.com>
Date: Wed, 28 Nov 2018 18:57:12 +0000
Subject: [PATCH 7/7] Add Pytorch Serving documentation in website (#316)

* Add David Sabater as approver and reviewer

* Add Pytorch Serving instructions using Seldon
This is to implement [#1117](https://github.com/kubeflow/kubeflow/issues/1117)
---
 OWNERS                                        |  2 +
 .../docs/guides/components/pytorchserving.md  | 79 +++++++++++++++++++
 2 files changed, 81 insertions(+)
 create mode 100644 content/docs/guides/components/pytorchserving.md

diff --git a/OWNERS b/OWNERS
index 3510795d53..b74a9e058c 100644
--- a/OWNERS
+++ b/OWNERS
@@ -11,6 +11,7 @@ approvers:
   - sarahmaddox
   - texasmichelle
   - willingc
+  - dsdinter
 reviewers:
   - abhi-g
   - aronchick
@@ -25,3 +26,4 @@ reviewers:
   - sarahmaddox
   - texasmichelle
   - willingc
+  - dsdinter
diff --git a/content/docs/guides/components/pytorchserving.md b/content/docs/guides/components/pytorchserving.md
new file mode 100644
index 0000000000..7bc46a3502
--- /dev/null
+++ b/content/docs/guides/components/pytorchserving.md
@@ -0,0 +1,79 @@
++++
+title = "PyTorch Serving"
+description = "Instructions for serving a PyTorch model with Seldon"
+weight = 10
+toc = true
+bref= "This guide will walk you through serving a PyTorch trained model in Kubeflow"
+[menu]
+[menu.docs]
+  parent = "components"
+  weight = 35
++++
+
+## Serving a model
+
+We use [seldon-core](https://github.com/SeldonIO/seldon-core) component deployed following [these](/docs/guides/components/seldon/) instructions to serve the model.
+
+See also this [Example module](https://github.com/kubeflow/examples/blob/master/pytorch_mnist/serving/seldon-wrapper/mnistddpserving.py) which contains the code to wrap the model with Seldon. 
+
+We will wrap this class into a seldon-core microservice which we can then deploy as a REST or GRPC API server.
+
+##  Building a model server
+
+We use the public model server image `gcr.io/kubeflow-examples/mnistddpserving` as an example
+
+  * This server loads the model from the mount point /mnt/kubeflow-gcfs and includes the supporting assets baked into the container image
+  * So you can just run this image to get a pre-trained model from the shared persistent disk
+  * Serving your own model using this server, exposing predict service as GRPC API
+
+## Building your own model server
+
+You can use the below command to build your own image to wrap your model, also check [this](https://github.com/kubeflow/examples/blob/master/pytorch_mnist/serving/seldon-wrapper/build_image.sh) 
+script example that calls the docker Seldon wrapper to build our server image, exposing the predict service as GRPC API.
+```
+docker run -v $(pwd):/my_model seldonio/core-python-wrapper:0.7 /my_model mnistddpserving 0.1 gcr.io --image-name=kubeflow-examples/mnistddpserving --grpc
+```
+
+You can then push the image by running `gcloud docker -- push gcr.io/kubeflow-examples/mnistddpserving:0.1`.
+
+You can find more details about wrapping a model with seldon-core [here](https://github.com/SeldonIO/seldon-core/blob/master/docs/wrappers/python.md)
+
+## Deploying the model to your Kubeflow cluster
+
+We need to have seldon component deployed, you can deploy the model once trained using a pre-defined ksonnet component, similar to [this](https://github.com/kubeflow/examples/blob/master/pytorch_mnist/ks_app/components/serving_model.jsonnet) example.
+We need to setup our own environment `${KF_ENV}` (e.g., 'default') and modify the Ksonnet component 
+[parameters](https://github.com/kubeflow/examples/blob/master/pytorch_mnist/ks_app/components/params.libsonnet) to use your specific image.
+
+```bash
+cd ks_app
+ks env add ${KF_ENV}
+ks apply ${KF_ENV} -c serving_model
+```
+
+## Testing model server
+
+Seldon Core component uses ambassador to route it's requests to our model server. To send requests to the model, you can port-forward the ambassador container locally:
+
+```
+kubectl port-forward $(kubectl get pods -n ${NAMESPACE} -l service=ambassador -o jsonpath='{.items[0].metadata.name}') -n ${NAMESPACE} 8080:80
+```
+
+And send a request, for our example we know is not a torch MNIST image, so it will return an error 500
+
+```
+curl -X POST -H 'Content-Type: application/json' -d '{"data":{"int":"8"}}' http://localhost:8080/seldon/mnist-classifier/api/v0.1/predictions
+```
+
+We should receive an error response as the model server is expecting a 1x786 vector representing a torch image, this will be sufficient to confirm the server model is up and running
+(This is to avoid having to send manually a vector of 786 pixels, you can interact properly with the model using a web interface if you follow all the 
+[instructions](https://github.com/kubeflow/examples/tree/master/pytorch_mnist) in the example)
+
+```
+{
+"timestamp":1540899355053,
+"status":500,"error":"Internal Server Error",
+"exception":"io.grpc.StatusRuntimeException",
+"message":"UNKNOWN: Exception calling application: tensor is not a torch image.",
+"path":"/api/v0.1/predictions"
+}
+```