Update MLflow in MLFLOW_SERVER

.gitignore

@@ -100,6 +100,7 @@ examples/models/r_mnist/t10k-labels-idx1-ubyte
 examples/models/r_mnist/train-images-idx3-ubyte
 examples/models/r_mnist/train-labels-idx1-ubyte
 examples/models/sklearn_iris_fbs/IrisClassifier.sav
+examples/models/mlflow_server_ab_test_ambassador/mlruns
 
 # Pickle files
 *.pickle
@@ -219,4 +220,4 @@ examples/models/autoscaling/autoscaling_example.py
 *.log
 
 # vscode local history
-.history/
+.history/

examples/models/mlflow_server_ab_test_ambassador/mlflow_server_ab_test_ambassador.ipynb

@@ -374,8 +374,8 @@
    "outputs": [],
    "source": [
     "!curl -X POST -H 'Content-Type: application/json' \\\n",
-    "    -d \"{'data': {'names': [], 'ndarray': [[7.0, 0.27, 0.36, 20.7, 0.045, 45.0, 170.0, 1.001, 3.0, 0.45, 8.8]]}}\" \\\n",
-    "    http://localhost:80/seldon/seldon/mlflow-deployment/api/v0.1/predictions"
+    "    -d '{\"data\": {\"names\": [], \"ndarray\": [[7.0, 0.27, 0.36, 20.7, 0.045, 45.0, 170.0, 1.001, 3.0, 0.45, 8.8]]}}' \\\n",
+    "    http://localhost:8003/seldon/seldon/mlflow-deployment/api/v0.1/predictions"
    ]
   },
   {
@@ -550,6 +550,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "sc = SeldonClient(\n",
+    "    gateway=\"ambassador\", \n",
+    "    namespace=\"seldon\",\n",
+    "    deployment_name='wines-classifier'\n",
+    ")\n",
+    "\n",
     "def _get_reward(y, y_pred):\n",
     "    if y == y_pred:\n",
     "        return 500    \n",
@@ -568,7 +574,7 @@
     "        data=X,\n",
     "        names=feature_names)\n",
     "    \n",
-    "    y_pred = r.response.data.tensor.values\n",
+    "    y_pred = r.response['data']['tensor']['values']\n",
     "    reward = _get_reward(y, y_pred)\n",
     "    sc.feedback(\n",
     "        deployment_name=\"mlflow-deployment\",\n",
@@ -625,7 +631,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.8"
   },
   "varInspector": {
    "cols": {

servers/mlflowserver/mlflowserver/MLFlowServer.py

@@ -1,21 +1,22 @@
-import numpy as np
+import yaml
+import os
 import logging
 import requests
+import numpy as np
 import pandas as pd
+
 from mlflow import pyfunc
 from seldon_core import Storage
 from seldon_core.user_model import SeldonComponent
-from typing import Dict, List, Union, Iterable
-import yaml
-import os
+from typing import Dict, List, Union
 
 logger = logging.getLogger()
 
 MLFLOW_SERVER = "model"
 
 
 class MLFlowServer(SeldonComponent):
-    def __init__(self, model_uri: str, xtype: str = 'ndarray'):
+    def __init__(self, model_uri: str, xtype: str = "ndarray"):
         super().__init__()
         logger.info(f"Creating MLFLow server with URI {model_uri}")
         logger.info(f"xtype: {xtype}")
@@ -30,9 +31,9 @@ def load(self):
         self.ready = True
 
     def predict(
-        self, X: np.ndarray, feature_names: Iterable[str] = [], meta: Dict = None
+        self, X: np.ndarray, feature_names: List[str] = [], meta: Dict = None
     ) -> Union[np.ndarray, List, Dict, str, bytes]:
-        logger.info(f"Requesting prediction with: {X}")
+        logger.debug(f"Requesting prediction with: {X}")
 
         if not self.ready:
             raise requests.HTTPError("Model not loaded yet")
@@ -45,7 +46,8 @@ def predict(
             else:
                 df = pd.DataFrame(data=X)
             result = self._model.predict(df)
-        logger.info(f"Prediction result: {result}")
+
+        logger.debug(f"Prediction result: {result}")
         return result
 
     def init_metadata(self):

servers/mlflowserver/mlflowserver/requirements.txt

@@ -1,7 +1,7 @@
-pyyaml==5.1.2
-requests==2.22.0
-mlflow>=1.4.0,<1.9.0
-pandas
+pyyaml<5.4.0
+requests<2.25.0
+mlflow<1.12.0
+pandas<1.2.0
 
 # local seldon-core inside the s2i image
 /microservice/python