Merge pull request #4 from MLOPsStudyGroup/scripts_master

updating deployment and update scripts

Author: guipleite

metadata.yaml

@@ -1,5 +1,7 @@
-project_name: Rain_aus
-project_version: v0.3
-model_type: scikit-learn_0.23
 author: guipleite
 datetime_creted: 29/03/2021_13:46:23:802394723
+deployment_uid: 59562979-4bb8-4cc4-85f0-fb7a280b8b3c
+model_type: scikit-learn_0.23
+model_uid: 3d5e76dc-a4a3-43a9-ab54-debbdbdc8673
+project_name: Rain_aus
+project_version: v0.3

src/scripts/Pipelines/model.joblib

@@ -5,6 +5,12 @@
 import joblib
 from ibm_watson_machine_learning import APIClient
 
+"""
+    Usage:
+        python3 model_deploy_pipeline.py ./pickle_model ../path/to/project/ ../credentials.yaml
+
+"""
+
 MODEL_PATH = os.path.abspath(sys.argv[1])
 PROJ_PATH = os.path.abspath(sys.argv[2])
 CRED_PATH = os.path.abspath(sys.argv[3])
@@ -60,3 +66,11 @@
     artifact_uid=model_uid,
     meta_props=deployment_props,
 )
+
+deployment_uid = client.deployments.get_uid(deployment)
+
+metadata["model_uid"] = model_uid
+metadata["deployment_uid"] = deployment_uid
+
+f = open(META_PATH, "w+")
+yaml.dump(metadata, f, allow_unicode=True)

src/scripts/Pipelines/model_deploy_pipeline.py

@@ -6,6 +6,13 @@
 from sklearn.metrics import confusion_matrix, accuracy_score
 from sklearn.model_selection import cross_val_score
 from ibm_watson_machine_learning import APIClient
+from sklearn.model_selection import train_test_split
+
+"""
+    Usage:
+        python3 model_deployed_validate_pipeline.py path/to/data.csv  ../../credentials.yaml path/to/project/
+
+"""
 
 DATA_PATH = os.path.abspath(sys.argv[1])
 CRED_PATH = os.path.abspath(sys.argv[2])
@@ -20,26 +27,42 @@ def main():
         except yaml.YAMLError as exc:
             print(exc)
 
+    with open(META_PATH) as stream:
+        try:
+            metadata = yaml.safe_load(stream)
+        except yaml.YAMLError as exc:
+            print(exc)
+
     data = pd.read_csv(DATA_PATH)
 
     X = data.iloc[:, :-1]
     y = data[data.columns[-1]]
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.3, random_state=0
+    )
 
     wml_credentials = {"url": credentials["url"], "apikey": credentials["apikey"]}
 
     client = APIClient(wml_credentials)
     client.spaces.list()
 
     SPACE_ID = credentials["space_id"]
-    DEPLOYMENT_UID = input("DEPLOYMENT UID: ")
 
-    client.set.default_space(SPACE_ID)
+    if "deployment_uid" in metadata.keys():
+        DEPLOYMENT_UID = metadata["deployment_uid"]
+        print("\nExtracting DEPLOYMENT UID from metadata file\n")
 
-    # deployment_uid = client.deployments.get_uid(DEPLOYMENT_UID)
+    else:
+        DEPLOYMENT_UID = input("DEPLOYMENT UID: ")
+
+    client.set.default_space(SPACE_ID)
 
     payload = {
         "input_data": [
-            {"fields": X.columns.to_numpy().tolist(), "values": X.to_numpy().tolist()}
+            {
+                "fields": X.columns.to_numpy().tolist(),
+                "values": X_test.to_numpy().tolist(),
+            }
         ]
     }
     result = client.deployments.score(DEPLOYMENT_UID, payload)
@@ -53,7 +76,7 @@ def comb_eval(y, y_pred):
 
         return {"cm": cm, "acc": acc}
 
-    eval = comb_eval(y, y_pred_values)
+    eval = comb_eval(y_test, y_pred_values)
     print(eval)
 
     return eval

src/scripts/Pipelines/model_deployed_validate_pipeline.py

@@ -1,49 +1,63 @@
 import sys
 import os
+import time
 import yaml
 from ibm_watson_machine_learning import APIClient
 
-# MODEL_PATH = os.path.abspath(sys.argv[1])
-CRED_PATH = os.path.abspath(sys.argv[1])
-# PROJ_PATH = os.path.abspath(sys.argv[3])
-# META_PATH = PROJ_PATH+"/metadata.yaml"
+"""
+    Usage:
+        python3 model_reploy_pipeline.py ../path/to/project/ ../credentials.yaml
+
+"""
+
+PROJ_PATH = os.path.abspath(sys.argv[1])
+CRED_PATH = os.path.abspath(sys.argv[2])
+META_PATH = PROJ_PATH + "/metadata.yaml"
 
 with open(CRED_PATH) as stream:
     try:
         credentials = yaml.safe_load(stream)
     except yaml.YAMLError as exc:
         print(exc)
 
+with open(META_PATH) as stream:
+    try:
+        metadata = yaml.safe_load(stream)
+    except yaml.YAMLError as exc:
+        print(exc)
 
 wml_credentials = {"url": credentials["url"], "apikey": credentials["apikey"]}
 
 client = APIClient(wml_credentials)
 client.spaces.list()
 
 SPACE_ID = credentials["space_id"]
-MODEL_GUID = input("MODEL GUID: ")
-DEPLOYMENT_UID = input("DEPLOYMENT UID: ")
+
+if "deployment_uid" in metadata.keys():
+    MODEL_GUID = metadata["model_uid"]
+    DEPLOYMENT_UID = metadata["deployment_uid"]
+    print("\nExtracting DEPLOYMENT UID and MODEL GUID from metadata file\n")
+
+else:
+    MODEL_GUID = input("MODEL GUID: ")
+    DEPLOYMENT_UID = input("DEPLOYMENT UID: ")
 
 client.set.default_space(SPACE_ID)
-MODEL_GUID = "b065a8c6-01ec-461b-96cc-c3abdcc35405"
-DEPLOYMENT_UID = "560eed08-7d74-4ed8-8429-8d26b88cd8a3"
 
 client.repository.list_models_revisions(MODEL_GUID)
 
 MODEL_VERSION = input("MODEL VERSION: ")
 
-metadata = {
+meta = {
     client.deployments.ConfigurationMetaNames.ASSET: {
         "id": MODEL_GUID,
         "rev": MODEL_VERSION,
     }
 }
 updated_deployment = client.deployments.update(
-    deployment_uid=DEPLOYMENT_UID, changes=metadata
+    deployment_uid=DEPLOYMENT_UID, changes=meta
 )
 
-import time
-
 status = None
 while status not in ["ready", "failed"]:
     print(".", end=" ")
@@ -52,3 +66,4 @@
     status = deployment_details["entity"]["status"].get("state")
 
 print("\nDeployment update finished with status: ", status)
+# print(deployment_details)

src/scripts/Pipelines/model_evalute.py

@@ -3,29 +3,49 @@
 import yaml
 from ibm_watson_machine_learning import APIClient
 
-CRED_PATH = os.path.abspath(sys.argv[1])
+"""
+    Usage:
+        python3 model_update_deployment_pipeline.py ../path/to/project/ ../credentials.yaml
+
+"""
+
+PROJ_PATH = os.path.abspath(sys.argv[1])
+CRED_PATH = os.path.abspath(sys.argv[2])
+META_PATH = PROJ_PATH + "/metadata.yaml"
 
 with open(CRED_PATH) as stream:
     try:
         credentials = yaml.safe_load(stream)
     except yaml.YAMLError as exc:
         print(exc)
 
+with open(META_PATH) as stream:
+    try:
+        metadata = yaml.safe_load(stream)
+    except yaml.YAMLError as exc:
+        print(exc)
 
 wml_credentials = {"url": credentials["url"], "apikey": credentials["apikey"]}
 
 client = APIClient(wml_credentials)
 client.spaces.list()
 
 SPACE_ID = credentials["space_id"]
-MODEL_ID = input("MODEL ID: ")
-DEPLOYMENT_ID = input("DEPLOYMENT ID: ")
+
+if "deployment_uid" in metadata.keys():
+    MODEL_GUID = metadata["model_uid"]
+    DEPLOYMENT_UID = metadata["deployment_uid"]
+    print("\nExtracting DEPLOYMENT UID and MODEL GUID from metadata file\n")
+
+else:
+    MODEL_GUID = input("MODEL GUID: ")
+    DEPLOYMENT_UID = input("DEPLOYMENT UID: ")
 
 client.set.default_space(SPACE_ID)
 
-change_meta = {client.deployments.ConfigurationMetaNames.ASSET: {"id": MODEL_ID}}
+change_meta = {client.deployments.ConfigurationMetaNames.ASSET: {"id": MODEL_GUID}}
 
 print("Alterando o deploy abaixo: ")
-print(client.deployments.get_details(DEPLOYMENT_ID))
+print(client.deployments.get_details(DEPLOYMENT_UID))
 
-client.deployments.update(DEPLOYMENT_ID, change_meta)
+client.deployments.update(DEPLOYMENT_UID, change_meta)

src/scripts/Pipelines/model_redeploy_pipeline.py

@@ -1,11 +1,18 @@
 import sys
 import os
 import yaml
+import joblib
 from ibm_watson_machine_learning import APIClient
 
+"""
+    Usage:
+        python3 model_update_pipeline.py ./pickle_model ../path/to/project/ ../credentials.yaml
+
+"""
+
 MODEL_PATH = os.path.abspath(sys.argv[1])
-CRED_PATH = os.path.abspath(sys.argv[2])
-PROJ_PATH = os.path.abspath(sys.argv[3])
+PROJ_PATH = os.path.abspath(sys.argv[2])
+CRED_PATH = os.path.abspath(sys.argv[3])
 META_PATH = PROJ_PATH + "/metadata.yaml"
 
 with open(CRED_PATH) as stream:
@@ -20,22 +27,31 @@
     except yaml.YAMLError as exc:
         print(exc)
 
+with open(MODEL_PATH, "rb") as file:
+    # pickle_model = pickle.load(file)
+    pipeline = joblib.load(file)
 
 wml_credentials = {"url": credentials["url"], "apikey": credentials["apikey"]}
 
 client = APIClient(wml_credentials)
 client.spaces.list()
 
 SPACE_ID = credentials["space_id"]
-MODEL_GUID = input("MODEL GUID: ")
+
+if "model_uid" in metadata.keys():
+    MODEL_GUID = metadata["model_uid"]
+    print("\nExtracting MODEL GUID from metadata file\n")
+
+else:
+    MODEL_GUID = input("MODEL GUID: ")
 
 client.set.default_space(SPACE_ID)
 
 print("\nCreating new version")
 
 published_model = client.repository.update_model(
     model_uid=MODEL_GUID,
-    update_model=MODEL_PATH,
+    update_model=pipeline,
     updated_meta_props={
         client.repository.ModelMetaNames.NAME: metadata["project_name"]
         + "_"