Remove chainer docs (#1516)

* Update chainer.md

* Update chainer.md

* Update chainer.md

* Added brevity and links to Kubeflow 0.6 docs

* Added specific link to Chainer page

Co-authored-by: Sarah Maddox <sarahmaddox@users.noreply.github.com>

Author: 2 people

content/docs/components/training/chainer.md

@@ -1,148 +1,8 @@
 +++
 title = "Chainer Training"
-description = "Instructions for using Chainer for training"
+description = "See Kubeflow [v0.6 docs](https://v0-6.kubeflow.org/docs/components/training/chainer/) for instructions on using Chainer for training"
 weight = 4
 toc = true
 +++
 
-This guide walks you through using Chainer for training your model.
-
-## 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
-
-_This section has not yet been converted to kustomize, please refer to [kubeflow/manifests/issues/232](https://github.com/kubeflow/manifests/issues/232)._
-
-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
-```
+[Chainer](https://github.com/kubeflow/chainer-operator) is not supported in Kubeflow versions greater than 0.6.