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+.. _dlrs:
+
+Deep Learning Reference Stack
+#############################
+
+This tutorial shows you how to run benchmarking workloads in |CL-ATTR| using
+TensorFlow\* and Kubeflow with the Deep Learning Reference Stack.
+
+The Deep Learning Reference Stack is available in two versions.
+The first is `Eigen`_, which includes `TensorFlow`_ optimized for Intel®
+architecture. The second is `Intel MKL-DNN`_, which includes the TensorFlow
+framework optimized using Intel® Math Kernel Library for Deep Neural
+Networks (Intel® MKL-DNN) primitives.
+
+.. contents:: :local:
+   :depth: 1
+
+Release notes
+=============
+
+View current `release notes`_ for the Deep Learning Reference Stack.
+
+.. note::
+
+   Performance test numbers in the Deep Learning Reference Stack were obtained using `runc` as the runtime.
+
+Prerequisites
+=============
+
+* |CL| installed on host system. If not installed, :ref:`bare-metal-install`
+* `containers-basic` bundle
+* `cloud-native-basic` bundle
+
+In |CL|, `containers-basic` provides Docker\*, which is required for
+TensorFlow benchmarking. Use the :command:`swupd` utility to check if
+`containers-basic` and `cloud-native-basic` are present:
+
+.. code-block:: bash
+
+   sudo swupd bundle-list
+
+If you need to install the `containers-basic` or `cloud-native-basic`, enter:
+
+.. code-block:: bash
+
+   sudo swupd bundle-add containers-basic cloud-native-basic
+
+To ensure that kubernetes is correctly installed and configured,
+:ref:`kubernetes`.
+
+We have validated these steps against the following software package
+versions:
+
+* |CL| 26240--lowest version permissible.
+* Docker 18.06.1
+* Kubernetes 1.11.3
+* Go 1.11.12
+
+TensorFlow single and multi-node benchmarks
+============================================
+
+This section describes running the `TensorFlow benchmarks`_ in single node.
+For multi-node testing, replicate these steps for each node. These steps
+provide a template to run other benchmarks, provided that they can invoke
+TensorFlow.
+
+#. Download and run either the `Eigen`_ or the `Intel MKL-DNN`_ docker image
+   from `Docker Hub`_.
+
+   .. note::
+
+      You will enter the following commands in the running container.
+
+      Replace <docker_name> with the name of the image.
+
+#. Clone the benchmark repository:
+
+   .. code-block:: bash
+
+      docker exec -t <docker_name> bash -c ‘git clone http://github.com/tensorflow/benchmarks -b cnn_tf_v1.11_compatible’
+
+#. Next, execute the benchmark script to run the benchmark.
+
+   .. code-block:: bash
+
+      docker exec -i <docker_name> bash -c ‘python benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --device=cpu --model=resnet50 --data_format=NWHC ’.
+
+      .. note::
+
+         You can replace the model with one of your choice supported by the
+         TensorFlow benchmarks.
+
+Kubeflow multi-node benchmarks
+==============================
+
+The benchmark workload will run in a Kubernetes cluster. We will use
+`Kubeflow`_ for the Machine Learning workload deployment on three nodes.
+
+Kubernetes setup
+****************
+
+Follow the instructions in the :ref:`kubernetes` tutorial to get set up on
+|CL|. The kubernetes community also has
+`instructions for creating a cluster`_.
+
+Kubernetes networking
+*********************
+
+We used `flannel`_ as the network provider for these tests. If you are
+comfortable with another network layer, refer to the Kubernetes
+`networking documentation`_ for setup.
+
+Images
+******
+
+We need to add `launcher.py` to our docker image to include the Deep
+Learning Reference Stack and put the benchmarks repo in the correct
+location. From the docker image, run the following:
+
+.. code-block:: bash
+
+   mkdir -p /opt
+   git clone https://github.com/tensorflow/benchmarks.git /opt/tf-benchmarks
+   cp launcher.py /opt
+   chmod u+x /opt/*
+
+Your entry point now becomes "/opt/launcher.py".
+
+This will build an image which can be consumed directly by TFJob from
+kubeflow. We are working to create these images as part of our release
+cycle.
+
+ksonnet\*
+*********
+
+Kubeflow uses ksonnet* to manage deployments, so we need to install that before setting up Kubeflow. On |CL|, follow these steps:
+
+.. code-block:: bash
+
+   swupd bundle-add go-basic-dev
+   export GOPATH=$HOME/go
+   export PATH=$PATH:$GOPATH/bin
+   go get github.com/ksonnet/ksonnet
+   cd $GOPATH/src/github.com/ksonnet/ksonnet
+   make install
+
+After the ksonnet installation is complete, ensure that binary `ks` is
+accessible across the environment.
+
+Kubeflow
+********
+
+Once you have Kubernetes running on your nodes, you can setup `Kubeflow`_ by
+following these instructions from their `quick start guide`_.
+
+.. code-block:: bash
+
+   export KUBEFLOW_SRC=$HOME/kflow
+   export KUBEFLOW_TAG=”v0.3.2”
+   export KFAPP=”kflow_app”
+   export K8S_NAMESPACE=”kubeflow”
+
+   mkdir ${KUBEFLOW_SRC}
+   cd ${KUBEFLOW_SRC}
+   ks init ${KFAPP}
+   cd ${KFAPP}
+   ks registry add kubeflow github.com/kubeflow/kubeflow/tree/${KUBEFLOW_TAG}/kubeflow
+   ks pkg install kubeflow/core
+
+Now you have all the required kubeflow packages, and you can deploy the primary one for our purposes: tf-job-operator.
+
+.. code-block:: bash
+
+   ks env rm default
+   kubectl create namespace ${K8S_NAMESPACE}
+   ks env add default --namespace "${K8S_NAMESPACE}"
+   ks generate tf-job-operator tf-job-operator
+   ks apply default -c tf-job-operator
+
+This creates the CustomResourceDefinition(CRD) endpoint to launch a TFJob.
+
+Run a TFJob
+===========
+
+#. Select this link for the `ksonnet registries for deploying TFJobs`_.
+
+   #. Install the TFJob componets as follows:
+
+      .. code-block:: bash
+
+         ks registry add dlaas-tfjob github.com/clearlinux/dockerfiles/tree/master/stacks/dlaas/kubeflow/dlaas-tfjob
+
+         ks pkg install dlaas-tfjob/dlaas-bench
+
+#. Next, generate Kubernetes manifests for the workloads and apply them to
+   create and run them using these commands
+
+   .. code-block:: bash
+
+      ks generate dlaas-resnet50 dlaasresnet50 --name=dlaasresnet50
+      ks generate dlaas-alexnet dlaasalexnet --name=dlaasalexnet
+      ks apply default -c dlaasresnet50
+      ks apply default -c dlaasalexnet
+
+This will replicate and deploy three test setups in your Kubernetes cluster.
+
+Results
+=======
+You need to parse the logs of the Kubernetes pod to get the performance
+numbers. The pods will still be around post completion and will be in
+‘Completed’ state. You can get the logs from any of the pods to inspect the
+benchmark results. More information about `Kubernetes logging`_ is available from the Kubernetes community.
+
+.. _TensorFlow: https://www.tensorflow.org/
+.. _Kubeflow: https://www.kubeflow.org/
+.. _Docker Hub: https://hub.docker.com/
+.. _TensorFlow benchmarks: https://www.tensorflow.org/guide/performance/benchmarks
+.. _instructions for creating a cluster: https://kubernetes.io/docs/setup/independent/create-cluster-kubeadm/
+.. _flannel: https://github.com/coreos/flannel
+.. _networking documentation: https://kubernetes.io/docs/setup/independent/create-cluster-kubeadm/#pod-network
+.. _quick start guide: https://www.kubeflow.org/docs/started/getting-started/
+
+.. _Eigen: https://hub.docker.com/r/clearlinux/stacks-dlaas-oss/
+.. _Intel MKL-DNN: https://hub.docker.com/r/clearlinux/stacks-dlaas-mkl/
+
+.. _release notes: https://github.com/clearlinux/dockerfiles/tree/master/stacks/dlaas
+
+.. _Clear Linux Docker Hub page: https://hub.docker.com/u/clearlinux/
+
+.. _ksonnet registries for deploying TFJobs: https://github.com/clearlinux/dockerfiles/tree/master/stacks/dlaas/kubeflow/dlaas-tfjob
+
+.. _Kubernetes logging: https://kubernetes.io/docs/concepts/cluster-administration/logging/