add chainer to the website. (#200)

* add chainer job guide to the website.

* add chainer to multi-framework section.

* adjust weight in Component section.

* added what chainer is.

* remove not so helpful cat command and embed manifest contents instead.

content/docs/guides/components/chainer.md

@@ -0,0 +1,149 @@
++++
+title = "Chainer Training"
+description = "Instructions for using Chainer for training"
+weight = 10
+toc = true
+bref= "This guide will walk you through using Chainer for training"
+[menu]
+[menu.docs]
+  parent = "components"
+  weight = 4
++++
+
+## What is Chainer?
+
+[Chainer](https://chainer.org/) is a powerful, flexible and intuitive deep learning framework.
+
+- Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.
+- Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures.
+- Forward computation can include any control flow statements of Python without lacking the ability of backpropagation. It makes code intuitive and easy to debug.
+
+[ChainerMN](https://github.com/chainer/chainermn) is an additional package for Chainer, a flexible deep learning framework. ChainerMN enables multi-node distributed deep learning with the following features:
+
+- Scalable --- it makes full use of the latest technologies such as NVIDIA NCCL and CUDA-Aware MPI,
+- Flexible --- even dynamic neural networks can be trained in parallel thanks to Chainer's flexibility, and
+- Easy --- minimal changes to existing user code are required.
+
+[This blog post](https://chainer.org/general/2017/02/08/Performance-of-Distributed-Deep-Learning-Using-ChainerMN.html) provides a benchmark results using up to 128 GPUs.
+
+## Installing Chainer Operator
+
+If you haven't already done so please follow the [Getting Started Guide](/docs/started/getting-started/) to deploy Kubeflow.
+
+An **alpha** version of [Chainer](https://chainer.org/) support was introduced with Kubeflow 0.3.0. You must be using a version of Kubeflow newer than 0.3.0.
+
+## Verify that Chainer support is included in your Kubeflow deployment
+
+Check that the Chainer Job custom resource is installed
+
+```shell
+kubectl get crd
+```
+
+The output should include `chainerjobs.kubeflow.org`
+
+```
+NAME                                       AGE
+...
+chainerjobs.kubeflow.org                   4d
+...
+```
+
+If it is not included you can add it as follows
+
+```shells
+cd ${KSONNET_APP}
+ks pkg install kubeflow/chainer-job
+ks generate chainer-operator chainer-operator
+ks apply ${ENVIRONMENT} -c chainer-operator
+```
+
+## Creating a Chainer Job
+
+You can create an Chainer Job by defining an ChainerJob config file.  First, please create a file `example-job-mn.yaml` like below:
+
+```yaml
+apiVersion: kubeflow.org/v1alpha1
+kind: ChainerJob
+metadata:
+  name: example-job-mn
+spec:
+  backend: mpi
+  master:
+    mpiConfig:
+      slots: 1 
+    activeDeadlineSeconds: 6000
+    backoffLimit: 60
+    template:
+      spec:
+        containers:
+        - name: chainer
+          image: everpeace/chainermn:1.3.0
+          command:
+          - sh
+          - -c
+          - |
+            mpiexec -n 3 -N 1 --allow-run-as-root --display-map  --mca mpi_cuda_support 0 \
+            python3 /train_mnist.py -e 2 -b 1000 -u 100
+  workerSets:
+    ws0:
+      replicas: 2
+      mpiConfig:
+        slots: 1
+      template:
+        spec:
+          containers:
+          - name: chainer
+            image: everpeace/chainermn:1.3.0
+            command:
+            - sh
+            - -c
+            - |
+              while true; do sleep 1 & wait; done
+```
+
+See [examples/chainerjob-reference.yaml](https://github.com/kubeflow/chainer-operator/blob/master/examples/chainerjob-reference.yaml) for definitions of each attributes. You may change the config file based on your requirements. By default, the example job is distributed learning with 3 nodes (1 master, 2 workers).
+
+Deploy the ChainerJob resource to start training:
+
+```shell
+kubectl create -f example-job-mn.yaml
+```
+
+You should now be able to see the created pods which consist of the chainer job.
+
+```
+kubectl get pods -l chainerjob.kubeflow.org/name=example-job-mn
+```
+
+The training should run only for 2 epochs and takes within a few minutes even on cpu only cluster. Logs can be inspected to see its training progress.
+
+```
+PODNAME=$(kubectl get pods -l chainerjob.kubeflow.org/name=example-job-mn,chainerjob.kubeflow.org/role=master -o name)
+kubectl logs -f ${PODNAME}
+```
+
+## Monitoring an Chainer Job
+
+```shell
+kubectl get -o yaml chainerjobs example-job-mn
+```
+
+See the status section to monitor the job status. Here is sample output when the job is successfully completed.
+
+```yaml
+apiVersion: kubeflow.org/v1alpha1
+kind: ChainerJob
+metadata:
+  name: example-job-mn
+...
+status:
+  completionTime: 2018-09-01T16:42:35Z
+  conditions:
+  - lastProbeTime: 2018-09-01T16:42:35Z
+    lastTransitionTime: 2018-09-01T16:42:35Z
+    status: "True"
+    type: Complete
+  startTime: 2018-09-01T16:34:04Z
+  succeeded: 1
+```

themes/kf/layouts/index.html

@@ -63,7 +63,7 @@ <h4>Model serving</h4>
 				</div>
 				<div class="text">
 					<h4>Multi-framework</h4>
-					<p>Our development plans go beyond TensorFlow, and we are working hard to include PyTorch, MXNet, and more. We also integrate with Ambassador for ingress and Pachyderm for managing your data science pipelines.</p>
+					<p>Our development plans go beyond TensorFlow, and we are working hard to include PyTorch, MXNet, Chainer, and more. We also integrate with Ambassador for ingress and Pachyderm for managing your data science pipelines.</p>
 				</div>
 			</div>
 

themes/kf/sass/src/index.html

@@ -169,7 +169,7 @@ <h4>Model serving</h4>
 				</div>
 				<div class="text">
 					<h4>Multi-framework</h4>
-					<p>Our development plans go beyond TensorFlow, and we are working hard to include PyTorch, MXNet, and more. We also integrate with Ambassador for ingress and Pachyderm for managing your data science pipelines.</p>
+					<p>Our development plans go beyond TensorFlow, and we are working hard to include PyTorch, MXNet, Chainer, and more. We also integrate with Ambassador for ingress and Pachyderm for managing your data science pipelines.</p>
 				</div>
 			</div>