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