| (window.webpackJsonp=window.webpackJsonp||[]).push([[42],{109:function(e,t,n){"use strict";n.r(t),n.d(t,"frontMatter",(function(){return o})),n.d(t,"metadata",(function(){return c})),n.d(t,"toc",(function(){return l})),n.d(t,"default",(function(){return b}));var r=n(3),a=n(7),i=(n(0),n(193)),o={title:"Quickstart"},c={unversionedId:"gettingStarted/quickstart",id:"gettingStarted/quickstart",isDocsHomePage:!1,title:"Quickstart",description:"\x3c!--",source:"@site/docs/gettingStarted/quickstart.md",slug:"/gettingStarted/quickstart",permalink:"/docs/next/gettingStarted/quickstart",editUrl:"https://github.com/apache/submarine/edit/master/website/docs/gettingStarted/quickstart.md",version:"current",sidebar:"docs",next:{title:"Jupyter Notebook",permalink:"/docs/next/gettingStarted/notebook"}},l=[{value:"Installation",id:"installation",children:[{value:"Prepare a Kubernetes cluster",id:"prepare-a-kubernetes-cluster",children:[]},{value:"Launch submarine in the cluster",id:"launch-submarine-in-the-cluster",children:[]},{value:"Ensure submarine is ready",id:"ensure-submarine-is-ready",children:[]},{value:"Connect to workbench",id:"connect-to-workbench",children:[]}]},{value:"Example: Submit a mnist distributed example",id:"example-submit-a-mnist-distributed-example",children:[{value:"1. Write a python script for distributed training",id:"1-write-a-python-script-for-distributed-training",children:[]},{value:"2. Prepare an environment compatible with the training",id:"2-prepare-an-environment-compatible-with-the-training",children:[]},{value:"3. Submit the experiment",id:"3-submit-the-experiment",children:[]},{value:"4. Monitor the process",id:"4-monitor-the-process",children:[]},{value:"5. Serve the model (In development)",id:"5-serve-the-model-in-development",children:[]}]}],s={toc:l};function b(e){var t=e.components,o=Object(a.a)(e,["components"]);return Object(i.b)("wrapper",Object(r.a)({},s,o,{components:t,mdxType:"MDXLayout"}),Object(i.b)("p",null,"This document gives you a quick view on the basic usage of Submarine platform. You can finish each step of ML model lifecycle on the platform without messing up with the troublesome environment problems."),Object(i.b)("h2",{id:"installation"},"Installation"),Object(i.b)("h3",{id:"prepare-a-kubernetes-cluster"},"Prepare a Kubernetes cluster"),Object(i.b)("ol",null,Object(i.b)("li",{parentName:"ol"},"Prerequisite")),Object(i.b)("ul",null,Object(i.b)("li",{parentName:"ul"},"Check ",Object(i.b)("a",{parentName:"li",href:"/docs/next/devDocs/Dependencies"},"dependency page")," for the compatible version"),Object(i.b)("li",{parentName:"ul"},Object(i.b)("a",{parentName:"li",href:"https://kubernetes.io/docs/tasks/tools/install-kubectl/"},"kubectl")),Object(i.b)("li",{parentName:"ul"},Object(i.b)("a",{parentName:"li",href:"https://helm.sh/docs/intro/install/"},"helm")," (Helm v3 is minimum requirement.)"),Object(i.b)("li",{parentName:"ul"},Object(i.b)("a",{parentName:"li",href:"https://minikube.sigs.k8s.io/docs/start/"},"minikube"),".")),Object(i.b)("ol",{start:2},Object(i.b)("li",{parentName:"ol"},"Start minikube cluster")),Object(i.b)("pre",null,Object(i.b)("code",{parentName:"pre"},"minikube start --vm-driver=docker --cpus 8 --memory 4096 --kubernetes-version v1.21.2\n")),Object(i.b)("h3",{id:"launch-submarine-in-the-cluster"},"Launch submarine in the cluster"),Object(i.b)("ol",null,Object(i.b)("li",{parentName:"ol"},"Clone the project")),Object(i.b)("pre",null,Object(i.b)("code",{parentName:"pre"},"git clone https://github.com/apache/submarine.git\n")),Object(i.b)("ol",{start:2},Object(i.b)("li",{parentName:"ol"},"Install the submarine operator and dependencies by helm chart")),Object(i.b)("pre",null,Object(i.b)("code",{parentName:"pre"},"cd submarine\nhelm install submarine ./helm-charts/submarine\n")),Object(i.b)("ol",{start:3},Object(i.b)("li",{parentName:"ol"},"Create a Submarine custom resource and the operator will create the submarine server, database, etc. for us.")),Object(i.b)("pre",null,Object(i.b)("code",{parentName:"pre"},"kubectl apply -f submarine-cloud-v2/artifacts/examples/example-submarine.yaml\n")),Object(i.b)("h3",{id:"ensure-submarine-is-ready"},"Ensure submarine is ready"),Object(i.b)("ol",null,Object(i.b)("li",{parentName:"ol"},"Use kubectl to query the status of pods")),Object(i.b)("pre",null,Object(i.b)("code",{parentName:"pre"},"kubectl get pods\n")),Object(i.b)("ol",{start:2},Object(i.b)("li",{parentName:"ol"},"Make sure each pod is ",Object(i.b)("inlineCode",{parentName:"li"},"Running"))),Object(i.b)("pre",null,Object(i.b)("code",{parentName:"pre"},"NAME READY STATUS RESTARTS AGE\nnotebook-controller-deployment-5d4f5f874c-mnbc8 1/1 Running 0 61m\npytorch-operator-844c866d54-xm8nl 1/1 Running 2 61m\nsubmarine-database-85bd68dbc5-qggtm 1/1 Running 0 11m\nsubmarine-minio-76465444f6-hdgdp 1/1 Running 0 11m\nsubmarine-mlflow-75f86d8f4d-rj2z7 1/1 Running 0 11m\nsubmarine-operator-5dd79cdf86-gpm2p 1/1 Running 0 61m\nsubmarine-server-68985b767-vjdvx 1/1 Running 0 11m\nsubmarine-tensorboard-5df8499fd4-vnklf 1/1 Running 0 11m\nsubmarine-traefik-7cbcfd4bd9-wbf8b 1/1 Running 0 61m\ntf-job-operator-6bb69fd44-zmlmr 1/1 Running 1 61m\n")),Object(i.b)("h3",{id:"connect-to-workbench"},"Connect to workbench"),Object(i.b)("ol",null,Object(i.b)("li",{parentName:"ol"},Object(i.b)("p",{parentName:"li"},"Exposing service"),Object(i.b)("pre",{parentName:"li"},Object(i.b)("code",{parentName:"pre"},"# Method 1 -- use minikube ip\nminikube ip # you'll get the IP address of minikube, ex: 192.168.49.2\n\n# Method 2 -- use port-forwarding\nkubectl port-forward --address 0.0.0.0 service/submarine-traefik 32080:80\n"))),Object(i.b)("li",{parentName:"ol"},Object(i.b)("p",{parentName:"li"},"View workbench\nIf you use method 1, go to ",Object(i.b)("inlineCode",{parentName:"p"},"http://{minikube ip}:32080"),". For example, ",Object(i.b)("inlineCode",{parentName:"p"},"http://192.168.49.2:32080"),". If you use method 2, go to ",Object(i.b)("inlineCode",{parentName:"p"},"http://0.0.0.0:32080"),".\n",Object(i.b)("img",{src:n(217).default})))),Object(i.b)("h2",{id:"example-submit-a-mnist-distributed-example"},"Example: Submit a mnist distributed example"),Object(i.b)("p",null,"We put the code of this example ",Object(i.b)("a",{parentName:"p",href:"https://github.com/apache/submarine/tree/master/dev-support/examples/quickstart"},"here"),". ",Object(i.b)("inlineCode",{parentName:"p"},"train.py")," is our training script, and ",Object(i.b)("inlineCode",{parentName:"p"},"build.sh")," is the script to build a docker image."),Object(i.b)("h3",{id:"1-write-a-python-script-for-distributed-training"},"1. Write a python script for distributed training"),Object(i.b)("p",null,"Take a simple mnist tensorflow script as an example. We choose ",Object(i.b)("inlineCode",{parentName:"p"},"MultiWorkerMirroredStrategy")," as our distributed strategy."),Object(i.b)("pre",null,Object(i.b)("code",{parentName:"pre",className:"language-python"},"\"\"\"\n./dev-support/examples/quickstart/train.py\nReference: https://github.com/kubeflow/tf-operator/blob/master/examples/v1/distribution_strategy/keras-API/multi_worker_strategy-with-keras.py\n\"\"\"\n\nimport tensorflow_datasets as tfds\nimport tensorflow as tf\nfrom tensorflow.keras import layers, models\nimport submarine\n\ndef make_datasets_unbatched():\n BUFFER_SIZE = 10000\n\n # Scaling MNIST data from (0, 255] to (0., 1.]\n def scale(image, label):\n image = tf.cast(image, tf.float32)\n image /= 255\n return image, label\n\n datasets, _ = tfds.load(name='mnist', with_info=True, as_supervised=True)\n\n return datasets['train'].map(scale).cache().shuffle(BUFFER_SIZE)\n\n\ndef build_and_compile_cnn_model():\n model = models.Sequential()\n model.add(\n layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n model.add(layers.MaxPooling2D((2, 2)))\n model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n model.add(layers.MaxPooling2D((2, 2)))\n model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n model.add(layers.Flatten())\n model.add(layers.Dense(64, activation='relu'))\n model.add(layers.Dense(10, activation='softmax'))\n\n model.summary()\n\n model.compile(optimizer='adam',\n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n\n return model\n\ndef main():\n strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(\n communication=tf.distribute.experimental.CollectiveCommunication.AUTO)\n\n BATCH_SIZE_PER_REPLICA = 4\n BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync\n\n with strategy.scope():\n ds_train = make_datasets_unbatched().batch(BATCH_SIZE).repeat()\n options = tf.data.Options()\n options.experimental_distribute.auto_shard_policy = \\\n tf.data.experimental.AutoShardPolicy.DATA\n ds_train = ds_train.with_options(options)\n # Model building/compiling need to be within `strategy.scope()`.\n multi_worker_model = build_and_compile_cnn_model()\n\n class MyCallback(tf.keras.callbacks.Callback):\n def on_epoch_end(self, epoch, logs=None):\n # monitor the loss and accuracy\n print(logs)\n submarine.log_metrics({\"loss\": logs[\"loss\"], \"accuracy\": logs[\"accuracy\"]}, epoch)\n\n multi_worker_model.fit(ds_train, epochs=10, steps_per_epoch=70, callbacks=[MyCallback()])\n\n\nif __name__ == '__main__':\n main()\n")),Object(i.b)("h3",{id:"2-prepare-an-environment-compatible-with-the-training"},"2. Prepare an environment compatible with the training"),Object(i.b)("p",null,"Build a docker image equipped with the requirement of the environment."),Object(i.b)("pre",null,Object(i.b)("code",{parentName:"pre",className:"language-bash"},"eval $(minikube docker-env)\n./dev-support/examples/quickstart/build.sh\n")),Object(i.b)("h3",{id:"3-submit-the-experiment"},"3. Submit the experiment"),Object(i.b)("ol",null,Object(i.b)("li",{parentName:"ol"},Object(i.b)("p",{parentName:"li"},"Open submarine workbench and click ",Object(i.b)("inlineCode",{parentName:"p"},"+ New Experiment"))),Object(i.b)("li",{parentName:"ol"},Object(i.b)("p",{parentName:"li"},"Choose ",Object(i.b)("inlineCode",{parentName:"p"},"Define your experiment"))),Object(i.b)("li",{parentName:"ol"},Object(i.b)("p",{parentName:"li"},"Fill the form accordingly. Here we set 3 workers."),Object(i.b)("ol",{parentName:"li"},Object(i.b)("li",{parentName:"ol"},"Step 1\n",Object(i.b)("img",{src:n(270).default})),Object(i.b)("li",{parentName:"ol"},"Step 2\n",Object(i.b)("img",{src:n(271).default})),Object(i.b)("li",{parentName:"ol"},"Step 3\n",Object(i.b)("img",{src:n(272).default})),Object(i.b)("li",{parentName:"ol"},"The experiment is successfully submitted\n",Object(i.b)("img",{src:n(273).default}))))),Object(i.b)("h3",{id:"4-monitor-the-process"},"4. Monitor the process"),Object(i.b)("ol",null,Object(i.b)("li",{parentName:"ol"},"In our code, we use ",Object(i.b)("inlineCode",{parentName:"li"},"submarine")," from ",Object(i.b)("inlineCode",{parentName:"li"},"submarine-sdk")," to record the metrics. To see the result, click corresponding experiment with name ",Object(i.b)("inlineCode",{parentName:"li"},"mnist-example")," in the workbench."),Object(i.b)("li",{parentName:"ol"},"To see the metrics of each worker, you can select a worker from the left top list.")),Object(i.b)("p",null,Object(i.b)("img",{src:n(274).default})),Object(i.b)("h3",{id:"5-serve-the-model-in-development"},"5. 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