Merge pull request #359 from cliveseldon/mlflow_example

Mlflow Example
SeldonIO · Jan 6, 2019 · 3b37b4a · 3b37b4a
2 parents bf74240 + d31c2d2
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diff --git a/examples/models/mlflow_model/Makefile b/examples/models/mlflow_model/Makefile
@@ -0,0 +1,7 @@
+
+
+.PHONY: clean
+clean:
+	rm -rf mlflow
+	rm -rf mlruns
+	rm -rf __pycache__
diff --git a/examples/models/mlflow_model/MyMlflowModel.py b/examples/models/mlflow_model/MyMlflowModel.py
@@ -0,0 +1,17 @@
+from mlflow import pyfunc
+import os
+import pandas as pd
+
+class MyMlflowModel(object):
+
+    def __init__(self):
+        self.pyfunc_model = pyfunc.load_pyfunc("mlruns/0/"+next(os.walk('mlruns/0'))[1][0]+"/artifacts/model")
+
+    def predict(self,X,features_names):
+        if not features_names is None and len(features_names)>0:
+            df = pd.DataFrame(data=X,columns=features_names)
+        else:
+            df = pd.DataFrame(data=X)
+        return self.pyfunc_model.predict(df)
+
+
diff --git a/examples/models/mlflow_model/contract.json b/examples/models/mlflow_model/contract.json
@@ -0,0 +1,80 @@
+{
+    "features":[
+	{
+	    "name":"alcohol",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,12]
+	},
+	{
+	    "name":"chlorides",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,1]
+	},
+	{
+	    "name":"citric acid",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,1]
+	},
+	{
+	    "name":"density",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,1]
+	},
+	{
+	    "name":"fixed acidity",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,1]
+	},
+	{
+	    "name":"free sulphur dioxide",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,1]
+	},
+	{
+	    "name":"pH",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,7]
+	},
+	{
+	    "name":"residual sugar",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,1]
+	},
+	{
+	    "name":"sulphates",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,1]
+	},
+	{
+	    "name":"total sulphur dioxide",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,100]
+	},
+	{
+	    "name":"volatile acidity",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,1]
+	}
+    ],
+    "targets":[
+	{
+	    "name":"class",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,1]
+	}
+    ]
+}
+
+
diff --git a/examples/models/mlflow_model/deployment.json b/examples/models/mlflow_model/deployment.json
@@ -0,0 +1,46 @@
+{
+    "apiVersion": "machinelearning.seldon.io/v1alpha2",
+    "kind": "SeldonDeployment",
+    "metadata": {
+        "labels": {
+            "app": "seldon"
+        },
+        "name": "mlflow-example"
+    },
+    "spec": {
+        "name": "mlflow-dep",
+        "oauth_key": "oauth-key",
+        "oauth_secret": "oauth-secret",
+        "predictors": [
+            {
+                "componentSpecs": [{
+                    "spec": {
+                        "containers": [
+                            {
+                                "image": "mlflow_model:0.1",
+                                "imagePullPolicy": "IfNotPresent",
+                                "name": "model",
+                                "resources": {
+                                    "requests": {
+                                        "memory": "1Mi"
+                                    }
+                                }
+                            }
+                        ],
+                        "terminationGracePeriodSeconds": 20
+                    }
+                }],
+                "graph": {
+                    "children": [],
+                    "name": "model",
+                    "endpoint": {
+                        "type" : "REST"
+                    },
+                    "type": "MODEL"
+                },
+                "name": "mlflow-pred",
+                "replicas": 1
+            }
+        ]
+    }
+}
diff --git a/examples/models/mlflow_model/environment_rest b/examples/models/mlflow_model/environment_rest
@@ -0,0 +1,4 @@
+MODEL_NAME=MyMlflowModel
+API_TYPE=REST
+SERVICE_TYPE=MODEL
+PERSISTENCE=0
diff --git a/examples/models/mlflow_model/mlflow.ipynb b/examples/models/mlflow_model/mlflow.ipynb
@@ -0,0 +1,228 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run MLFlow Model in Seldon Core\n",
+    "\n",
+    "This notebook shows how you can easily train a model using [MLFlow](https://mlflow.org/) and serve requests within Seldon Core on Kubernetes.\n",
+    "\n",
+    "Dependencies\n",
+    "\n",
+    "  * ```pip install seldon-core```\n",
+    "  * ```pip install mlflow```\n",
+    "  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train Example MlFlow Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/mlflow/mlflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python mlflow/examples/sklearn_elasticnet_wine/train.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test Inference Locally"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pygmentize MyMlflowModel.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!s2i build -E environment_rest . seldonio/seldon-core-s2i-python3:0.5-SNAPSHOT mlflow_model:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker run --name \"mlflow_model\" -d --rm -p 5000:5000 mlflow_model:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!curl -H \"Content-Type: application/x-www-form-urlencoded\" -g 0.0.0.0:5000/predict -d 'json={\"data\":{\"names\":[\"alcohol\", \"chlorides\", \"citric acid\", \"density\", \"fixed acidity\", \"free sulfur dioxide\", \"pH\", \"residual sugar\", \"sulphates\", \"total sulfur dioxide\", \"volatile acidity\"],\"ndarray\":[[12.8, 0.029, 0.48, 0.98, 6.2, 29, 3.33, 1.2, 0.39, 75, 0.66]]}}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!curl -H \"Content-Type: application/x-www-form-urlencoded\" -g 0.0.0.0:5000/predict -d 'json={\"data\":{\"ndarray\":[[12.8, 0.029, 0.48, 0.98, 6.2, 29, 3.33, 1.2, 0.39, 75, 0.66]]}}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!docker rm mlflow_model --force"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test Inference on Minikube\n",
+    "\n",
+    "**Due to a [minikube/s2i issue](https://github.com/SeldonIO/seldon-core/issues/253) you will need [s2i >= 1.1.13](https://github.com/openshift/source-to-image/releases/tag/v1.1.13)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube start --memory 4096"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl create clusterrolebinding kube-system-cluster-admin --clusterrole=cluster-admin --serviceaccount=kube-system:default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl rollout status deploy/tiller-deploy -n kube-system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!helm install ../../../helm-charts/seldon-core-crd --name seldon-core-crd  --set usage_metrics.enabled=true\n",
+    "!helm install ../../../helm-charts/seldon-core --name seldon-core "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!eval $(minikube docker-env) && s2i build -E environment_rest . seldonio/seldon-core-s2i-python3:0.5-SNAPSHOT mlflow_model:0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl create -f deployment.json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!kubectl get seldondeployments mlflow-example -o jsonpath='{.status}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon-core-api-tester contract.json \\\n",
+    "    `minikube ip` `kubectl get svc -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].spec.ports[0].nodePort}'` \\\n",
+    "    --oauth-key oauth-key --oauth-secret oauth-secret -p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!minikube delete"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/models/mlflow_model/requirements.txt b/examples/models/mlflow_model/requirements.txt
@@ -0,0 +1,3 @@
+mlflow
+sklearn
+pandas
-Original file line number
+Diff line change
@@ -0,0 +1,3 @@
+    mlflow
+    sklearn
+    pandas