Rename model references from gitguarden to secret_detection across co…

…nfigurations and pipelines

train_and_deploy/README.md

@@ -7,7 +7,7 @@ classification datasets provided by the scikit-learn library. The project was
 generated from the [E2E Batch ZenML project template](https://github.com/zenml-io/template-e2e-batch)
 with the following properties:
 - Project name: ZenML E2E project
-- Technical Name: gitguarden
+- Technical Name: secret_detection
 - Version: `0.0.1`
 - Licensed with apache to ZenML GmbH<>
 - Deployment environment: `staging`

train_and_deploy/configs/deploy_production.yaml

@@ -33,7 +33,7 @@ steps:
 
 # configuration of the Model Control Plane
 model:
-  name: gitguarden
+  name: secret_detection
   version: staging
 
 # pipeline level extra configurations

train_and_deploy/configs/deployer_config.yaml

@@ -33,7 +33,7 @@ steps:
 
 # configuration of the Model Control Plane
 model:
-  name: gitguarden
+  name: secret_detection
   version: staging
 
 # pipeline level extra configurations

train_and_deploy/configs/inference_config.yaml

@@ -32,7 +32,7 @@ steps:
 
 # configuration of the Model Control Plane
 model:
-  name: gitguarden
+  name: secret_detection
   version: staging
 
 # pipeline level extra configurations

train_and_deploy/configs/train_config.yaml

@@ -28,19 +28,19 @@ settings:
 steps:
   model_trainer:
     parameters:
-      name: gitguarden
+      name: secret_detection
   promote_with_metric_compare:
     parameters:
-      mlflow_model_name: gitguarden
+      mlflow_model_name: secret_detection
   notify_on_success:
     parameters:
       notify_on_success: False
 
 # configuration of the Model Control Plane
 model:
-  name: gitguarden
+  name: secret_detection
   license: apache
-  description: gitguarden E2E Batch Use Case
+  description: secret_detection E2E Batch Use Case
   audience: All ZenML users
   use_cases: |
     The ZenML E2E project project demonstrates how the most important steps of 

train_and_deploy/gitguarden.yaml

@@ -0,0 +1,35 @@
+components:
+  artifact_store:
+    configuration:
+      path: s3://zenml-dev
+    flavor: s3
+    name: s3
+    type: artifact_store
+  container_registry:
+    configuration:
+      uri: 339712793861.dkr.ecr.eu-central-1.amazonaws.com
+    flavor: aws
+    name: ecr
+    type: container_registry
+  data_validator:
+    configuration: {}
+    flavor: evidently
+    name: evidently_data_validator
+    type: data_validator
+  image_builder:
+    configuration: {}
+    flavor: local
+    name: local_builder
+    type: image_builder
+  model_deployer:
+    configuration: {}
+    flavor: bentoml
+    name: jayesh_bento
+    type: model_deployer
+  orchestrator:
+    configuration: {}
+    flavor: local
+    name: default
+    type: orchestrator
+stack_name: secret_detection
+zenml_version: 0.70.0

train_and_deploy/pipelines/__init__.py

@@ -16,7 +16,7 @@
 #
 
 
-from .batch_inference import gitguarden_batch_inference
-from .training import gitguarden_training
-from .local_deployment import gitguarden_local_deployment
-from .deploy_production import gitguarden_production_deployment
+from .batch_inference import secret_detection_batch_inference
+from .training import secret_detection_training
+from .local_deployment import secret_detection_local_deployment
+from .deploy_production import secret_detection_production_deployment

train_and_deploy/pipelines/batch_inference.py

@@ -33,7 +33,7 @@
 
 
 @pipeline(on_failure=notify_on_failure)
-def gitguarden_batch_inference(
+def secret_detection_batch_inference(
     target_env: str,
 ):
     """

train_and_deploy/pipelines/deploy_production.py

@@ -21,7 +21,7 @@
 
 
 @pipeline(on_failure=notify_on_failure, enable_cache=False)
-def gitguarden_production_deployment(
+def secret_detection_production_deployment(
     target_env: str,
 ):
     """Model deployment pipeline.

train_and_deploy/pipelines/local_deployment.py

@@ -21,7 +21,7 @@
 
 
 @pipeline(on_failure=notify_on_failure, enable_cache=False)
-def gitguarden_local_deployment(
+def secret_detection_local_deployment(
     target_env: str,
 ):
     """Model deployment pipeline.

train_and_deploy/pipelines/training.py

@@ -39,7 +39,7 @@
 
 
 @pipeline(on_failure=notify_on_failure)
-def gitguarden_training(
+def secret_detection_training(
     model_search_space: Dict[str, Any],
     target_env: str,
     test_size: float = 0.2,

train_and_deploy/run.py

@@ -21,10 +21,10 @@
 
 import click
 from pipelines import (
-    gitguarden_batch_inference,
-    gitguarden_local_deployment,
-    gitguarden_production_deployment,
-    gitguarden_training,
+    secret_detection_batch_inference,
+    secret_detection_local_deployment,
+    secret_detection_production_deployment,
+    secret_detection_training,
 )
 from zenml.logger import get_logger
 
@@ -202,9 +202,9 @@ def main(
             "train_config.yaml",
         )
         pipeline_args["run_name"] = (
-            f"gitguarden_training_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}"
+            f"secret_detection_training_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}"
         )
-        gitguarden_training.with_options(**pipeline_args)(**run_args_train)
+        secret_detection_training.with_options(**pipeline_args)(**run_args_train)
         logger.info("Training pipeline finished successfully!")
 
     if deployment:
@@ -216,9 +216,9 @@ def main(
             "deployer_config.yaml",
         )
         pipeline_args["run_name"] = (
-            f"gitguarden_local_deployment_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}"
+            f"secret_detection_local_deployment_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}"
         )
-        gitguarden_local_deployment.with_options(**pipeline_args)(**run_args_inference)
+        secret_detection_local_deployment.with_options(**pipeline_args)(**run_args_inference)
 
     if inference:
         # Execute Batch Inference Pipeline
@@ -229,9 +229,9 @@ def main(
             "inference_config.yaml",
         )
         pipeline_args["run_name"] = (
-            f"gitguarden_batch_inference_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}"
+            f"secret_detection_batch_inference_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}"
         )
-        gitguarden_batch_inference.with_options(**pipeline_args)(
+        secret_detection_batch_inference.with_options(**pipeline_args)(
             **run_args_inference
         )
     if production:
@@ -243,9 +243,9 @@ def main(
             "deploy_production.yaml",
         )
         pipeline_args["run_name"] = (
-            f"gitguarden_production_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}"
+            f"secret_detection_production_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}"
         )
-        gitguarden_production_deployment.with_options(**pipeline_args)(
+        secret_detection_production_deployment.with_options(**pipeline_args)(
             **run_args_production
         )
 

train_and_deploy/service.py

@@ -11,7 +11,7 @@ class GitGuarden:
     """
 
     # Load in the class scope to declare the model as a dependency of the service
-    iris_model = bentoml.models.get("gitguarden:latest")
+    iris_model = bentoml.models.get("secret_detection:latest")
 
     def __init__(self):
         """