Added mlflow server

servers/mlflowserver/Makefile

@@ -0,0 +1,21 @@
+VERSION=0.1
+IMAGE_BASE=seldonio/mlflowserver
+
+build_rest:
+	s2i build -E environment_rest ./mlflowserver seldonio/seldon-core-s2i-python37:0.11-SNAPSHOT ${IMAGE_BASE}_rest:${VERSION}
+
+push_rest:
+	docker push ${IMAGE_BASE}_rest:${VERSION}
+
+build_grpc:
+	s2i build -E environment_grpc ./mlflowserver seldonio/seldon-core-s2i-python37:0.11-SNAPSHOT ${IMAGE_BASE}_grpc:${VERSION}
+
+push_grpc:
+	docker push ${IMAGE_BASE}_grpc:${VERSION}
+
+
+.PHONY: push_all
+push_all: push_rest push_grpc
+
+.PHONY: build_all
+build_all: build_rest build_grpc

servers/mlflowserver/environment_grpc

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+MODEL_NAME=MLFlowServer
+API_TYPE=GRPC
+SERVICE_TYPE=MODEL
+PERSISTENCE=0

servers/mlflowserver/environment_rest

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+MODEL_NAME=MLFlowServer
+API_TYPE=REST
+SERVICE_TYPE=MODEL
+PERSISTENCE=0

servers/mlflowserver/mlflowserver/MLFlowServer.py

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+from mlflow import pyfunc
+import seldon_core
+from seldon_core.user_model import SeldonComponent
+from typing import Dict, List, Union, Iterable
+import numpy as np
+import os
+import logging
+import requests
+import pandas as pd
+
+log = logging.getLogger()
+
+class MLFlowServer(SeldonComponent):
+
+    def __init__(self, model_uri: str):
+        super().__init__()
+        log.info(f"Creating MLFLow server with URI: {model_uri}")
+        self.model_uri = model_uri
+        self.ready = False
+
+    def load(self):
+        log.info("Loading model")
+        self._model = pyfunc.load_model(self.model_uri)
+        self.ready = True
+
+    def predict(
+                self,
+                X: np.ndarray,
+                feature_names: Iterable[str] = [],
+                meta: Dict = None
+            ) -> Union[np.ndarray, List, Dict, str, bytes]:
+
+        log.info(f"Requesting prediction with: {X}")
+        if not self.ready:
+            self.load()
+            # TODO: Make sure this doesn't get called from here, but 
+            #   from the actual python wrapper. Raise exception instead
+            #raise requests.HTTPError("Model not loaded yet")
+        if not feature_names is None and len(feature_names)>0:
+            df = pd.DataFrame(data=X, columns=feature_names)
+        else:
+            df = pd.DataFrame(data=X)
+        result = self._model.predict(df)
+        log.info(f"Prediction result: {result}")
+        return result
+

servers/mlflowserver/mlflowserver/requirements.txt

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+mlflow
+sklearn
+pandas

servers/mlflowserver/test/.gitignore

@@ -0,0 +1,2 @@
+mlflow
+mlruns

servers/mlflowserver/test/contract.json

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+{
+    "features":[
+	{
+	    "name":"fixed_acidity",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[4,8]
+	},
+	{
+	    "name":"volatile_acidity",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[1,10]
+	},
+	{
+	    "name":"citric_acid",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,3]
+	},
+	{
+	    "name":"residual_sugar",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,3]
+	},
+	{
+	    "name":"chlorides",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,3]
+	},
+	{
+	    "name":"free_sulfur",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,3]
+	},
+	{
+	    "name":"total_sulfur",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,3]
+	},
+	{
+	    "name":"density",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,3]
+	},
+	{
+	    "name":"ph",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,3]
+	},
+	{
+	    "name":"sulphates",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,3]
+	},
+	{
+	    "name":"alcohol",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,3]
+	}
+    ],
+    "targets":[
+	{
+	    "name":"class",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,10],
+	    "repeat": 1
+	}
+    ]
+}
+
+