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Update for DLRS V5 release (#962)
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Beth Dean
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michael vincerra
michael vincerra
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7b21fb9159
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e90885986f
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@@ -20,18 +20,15 @@ customized solutions, and enables you to quickly prototype and deploy Deep
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Learning workloads. Use this guide to run benchmarking workloads on your
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solution.
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The Deep Learning Reference Stack is available in the following versions:
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The latest release of the Deep Learning Reference Stack (`DLRS V5.0`_ ) supports the following features:
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* `Intel MKL-DNN-VNNI`_, which is optimized using Intel® Math Kernel Library
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for Deep Neural Networks (Intel® MKL-DNN) primitives and introduces support
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for Intel® AVX-512 Vector Neural Network Instructions (VNNI).
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* `Intel MKL-DNN`_, which includes the TensorFlow framework optimized using
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Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)
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primitives.
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* `Eigen`_, which includes `TensorFlow`_ optimized for Intel® architecture.
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* `PyTorch with OpenBLAS`_, which includes PyTorch with OpenBlas.
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* `PyTorch with Intel MKL-DNN`_, which includes PyTorch optimized using Intel®
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Math Kernel Library (Intel® MKL) and Intel MKL-DNN.
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* TensorFlow* 1.15 and TensorFlow* 2.0, an end-to-end open source platform for machine learning (ML).
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* PyTorch* 1.3, an open source machine learning framework that accelerates the path from research prototyping to production deployment.
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* PyTorch Lightning* which is a lightweight wrapper for PyTorch designed to help researchers set up all the boilerplate state-of-the-art training.
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* Transformers* , a state-of-the-art Natural Language Processing (NLP) for TensorFlow 2.0 and PyTorch.
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* Intel® OpenVINO™ model server version 2019_R3, delivering improved neural network performance on Intel processors, helping unlock cost-effective, real-time vision applications.
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* Intel Deep Learning Boost (DL Boost) with AVX-512 Vector Neural Network Instruction (Intel AVX-512 VNNI) designed to accelerate deep neural network-based algorithms.
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* Deep Learning Compilers (TVM* 0.6), an end-to-end compiler stack.
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.. important::
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@@ -41,9 +38,14 @@ The Deep Learning Reference Stack is available in the following versions:
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* Intel® AVX-512 images require an Intel® Xeon® Scalable Platform
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* VNNI requires a 2nd generation Intel® Xeon® Scalable Platform
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Releases
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********
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Refer to the `Deep Learning Reference Stack website`_ for information and download links for the different versions and offerings of the stack.
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* `DLRS V5.0`_ release announcement.
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* `DLRS V4.0`_ release announcement, including benchmark results.
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* `DLRS V3.0`_ release announcement, including benchmark results.
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* `DLRS V2.0`_ including PyTorch benchmark results.
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@@ -60,7 +62,7 @@ Releases
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Version compatibility
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=====================
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We validated the steps in this guide against the following software package versions:
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We validated the steps in this guide against the following software package versions, unless otherwise stated:
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* |CL| 26240 (Minimum supported version)
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* Docker 18.06.1
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@@ -373,7 +375,7 @@ Submitting PyTorch Jobs
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=======================
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We provide `DLRS PytorchJob`_ examples that use the Deep Learning Reference Stack as the base image for creating the container(s) that will run training workloads in your Kubernetes cluster.
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Select one form the list below:
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Using Kubeflow Seldon and OpenVINO* with the Deep Learning Reference Stack
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@@ -465,6 +467,7 @@ Pre-requisites
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dlrs-seldon/helm/seldon-model-server
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Using the Intel® OpenVINO Model Optimizer
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*****************************************
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@@ -616,6 +619,138 @@ This example walks through the basic instructions for using the inference engine
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Using Seldon and OpenVINO* model server with the Deep Learning Reference Stack
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******************************************************************************
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`Seldon Core`_ is an open source platform for deploying machine learning models on a Kubernetes cluster. In this section we will walk through using a Seldon server with OpenVINO to serve a model.
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Pre-requisites
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==============
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* A running :ref:`kubernetes` cluster
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* An existing Kubeflow deployment
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* Helm
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* A pre-trained model
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Please refer to:
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* :ref:`kubernetes`
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* `Getting Started with Kubeflow`_
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* `Installing Helm`_
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.. note::
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This document was validated with Kubernetes v1.14.8, Kubeflow v0.7, and Helm v3.0.1
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Prepare the model
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=================
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There are several methods to add a model to a Seldon server; we will cover two of them. First a model will be stored in a persistent volume by creating a persistent volume claim and a pod, then copying the model into the pod. Second, a model will be built directly into the base image. Adding a model to a volume is perhaps more traditional in Kubernetes, but some cloud providers have access rules that disallow a private cluster, and adding the model to the image avoids the issue in that scenario.
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Mount pre-trained models into a persistent volume
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-------------------------------------------------
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We will create a small pod to get the model into a volume.
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#. Apply all PV manifests to the cluster
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.. code-block:: bash
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kubectl apply -f storage/pv-volume.yaml
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kubectl apply -f storage/model-store-pvc.yaml
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kubectl apply -f storage/pv-pod.yaml
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#. Use :command:`kubectl cp` to move the model into the pod, and therefore into the volume
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.. code-block:: bash
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kubectl cp ./<your model file> pv-pod:/home
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#. In the running container, fetch your pre-trained models and save them in the :file:`/opt/ml` directory path.
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.. code-block:: bash
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root@hostpath-pvc:/# cd /opt/ml
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root@hostpath-pvc:/# # Copy your models here
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root@hostpath-pvc:/# # exit
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Add the pre-trained model to the image
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--------------------------------------
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A custom DLRS image is provided to serve OpenVINO through Seldon. Add a curl command to download your publicly hosted model and save it in :file:`/opt/ml` in the container filesystem. For example, if you have a model on GCP, use this command:
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.. code-block:: bash
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curl -o "[SAVE_TO_LOCATION]" \
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"https://storage.googleapis.com/storage/v1/b/[BUCKET_NAME]/o/[OBJECT_NAME]?alt=media"
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Prepare the DLRS image
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======================
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A base image with Seldon and the OpenVINO inference engine should be created using the :file:`Dockerfile_openvino_base` dockerfile.
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.. code-block:: bash
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cd docker
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docker build -f Dockerfile_openvino_base -t dlrs_openvino_base .
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cd ..
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Deploy the model server
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=======================
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Now you're ready to deploy the model server using the Helm chart provided.
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.. code-block:: bash
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cd helm
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helm install dlrs-seldon seldon-model-server \
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--namespace kubeflow \
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--set openvino.image=dlrs_openvino_base \
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--set openvino.model.path=/opt/ml \
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--set openvino.model.name=<model_name> \
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--set openvino.model.input=data \
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--set openvino.model.output=prob
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This will create your SeldonDeployment
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Extended example with Seldon using Source to Image
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==================================================
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`Source to Image (s2i)`_ is a tool to create docker images from source code.
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#. Install source to image (s2i)
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.. code-block:: bash
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cd ${SRC-DIR}
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wget https://github.com/openshift/source-to-image/releases/download/v1.1.14/source-to-image-v1.1.14-874754de-linux-amd64.tar.gz
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tar xf source-to-image-v1.1.14-874754de-linux-amd64.tar.gz
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mv s2i ${BIN_DIR}/s2i && ln -s s2i ${BIN_DIR}/sti
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#. Clone the seldon-core repository
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.. code-block:: bash
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git clone https://github.com/SeldonIO/seldon-core.git ${SRC_DIR}/seldon-core
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#. Create the new image
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Using the DLRS image created above, you can build another image for deploying the Image Transformer component that consumes imagenet classificatin models.
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.. code-block:: bash
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cd ${SRC_DIR}/seldon-core/examples/models/openvino_imagenet_ensemble/resources/transformer/
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s2i -E environment_grpc . dlrs_openvino_base:0.1 imagenet_transformer:0.1
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Use this newly created image for deploying the Image Transformer component of the `OpenVino Imagenet Pipelines`_ example from Seldon.
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Use Jupyter Notebook
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********************
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@@ -829,7 +964,7 @@ Related topics
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.. _flannel: https://github.com/coreos/flannel
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.. _Getting Started with Kubeflow: https://www.kubeflow.org/docs/started/getting-started/
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.. _Getting Started with Kubeflow: https://github.intel.com/verticals/usecases/blob/56717f4642ecd958dc93bbc361c551dfc578d3ed/kubeflow/README.md#getting-started-with-kubeflow
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.. _Eigen: https://hub.docker.com/r/clearlinux/stacks-dlrs-oss/
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@@ -845,6 +980,8 @@ Related topics
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.. _DLRS V4.0: https://clearlinux.org/news-blogs/deep-learning-reference-stack-v4
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.. _DLRS V5.0: https://clearlinux.org/blogs-news/deep-learning-reference-stack-v50-now-available
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.. _dlrs-tfjob: https://github.com/clearlinux/dockerfiles/tree/master/stacks/dlrs/kubeflow/dlrs-tfjob
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.. _Logging Architecture: https://kubernetes.io/docs/concepts/cluster-administration/logging/
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@@ -899,3 +1036,11 @@ Related topics
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.. _DLRS TFJob: https://github.com/clearlinux/dockerfiles/tree/master/stacks/dlrs/kubeflow/dlrs-tfjob
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.. _DLRS PytorchJob: https://github.com/clearlinux/dockerfiles/tree/master/stacks/dlrs/kubeflow/dlrs-pytorchjob
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.. _Installing Helm: https://helm.sh/docs/intro/install/
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.. _OpenVino Imagenet Pipelines: https://docs.seldon.io/projects/seldon-core/en/stable/examples/openvino_ensemble.html
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.. _Source to Image (s2i): https://docs.seldon.io/projects/seldon-core/en/latest/wrappers/s2i.html
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.. _Deep Learning Reference Stack website: https://clearlinux.org/stacks/deep-learning
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