Fixed the PR comments

data/mlops-stepfunctions/config/input.yaml

@@ -1,5 +1,5 @@
 app_id: aiops
-model_id: demo
+model_id: MODEL_NAME
 job_prefix: mlops
 preprocessing:
   run: true
@@ -25,7 +25,7 @@ preprocessing:
       - InputName: Code
         AppManaged: false
         S3Input:
-          S3Uri: s3://MLOPS_BUCKET/demo/scripts
+          S3Uri: s3://MLOPS_BUCKET/MODEL_NAME/scripts
           LocalPath: /opt/ml/processing/code
           S3DataType: S3Prefix
           S3InputMode: File
@@ -35,13 +35,13 @@ preprocessing:
         - OutputName: train
           AppManaged: false
           S3Output:
-            S3Uri: s3://MLOPS_BUCKET/demo/processing/train
+            S3Uri: s3://MLOPS_BUCKET/MODEL_NAME/processing/train
             LocalPath: /opt/ml/processing/train
             S3UploadMode: EndOfJob
         - OutputName: test
           AppManaged: false
           S3Output:
-            S3Uri: s3://MLOPS_BUCKET/demo/processing/test
+            S3Uri: s3://MLOPS_BUCKET/MODEL_NAME/processing/test
             LocalPath: /opt/ml/processing/test
             S3UploadMode: EndOfJob
     StoppingCondition:
@@ -71,16 +71,16 @@ training:
         DataSource:
           S3DataSource:
             S3DataType: S3Prefix
-            S3Uri: s3://MLOPS_BUCKET/demo/processing/train
+            S3Uri: s3://MLOPS_BUCKET/MODEL_NAME/processing/train
             S3DataDistributionType: FullyReplicated
       - ChannelName: code
         DataSource:
           S3DataSource:
             S3DataType: S3Prefix
-            S3Uri: s3://MLOPS_BUCKET/demo/scripts
+            S3Uri: s3://MLOPS_BUCKET/MODEL_NAME/scripts
             S3DataDistributionType: FullyReplicated
     OutputDataConfig:
-      S3OutputPath: s3://MLOPS_BUCKET/demo/model/
+      S3OutputPath: s3://MLOPS_BUCKET/MODEL_NAME/model/
     StoppingCondition:
       MaxRuntimeInSeconds: 3600
     Tags:
@@ -106,23 +106,23 @@ evaluation:
       - InputName: input
         AppManaged: false
         S3Input:
-          S3Uri: s3://MLOPS_BUCKET/demo/model/SFN_EXECUTION_ID/output/model.tar.gz
+          S3Uri: s3://MLOPS_BUCKET/MODEL_NAME/model/SFN_EXECUTION_ID/output/model.tar.gz
           LocalPath: /opt/ml/processing/model
           S3DataType: S3Prefix
           S3InputMode: File
           S3DataDistributionType: FullyReplicated
       - InputName: Code
         AppManaged: false
         S3Input:
-          S3Uri: s3://MLOPS_BUCKET/demo/scripts
+          S3Uri: s3://MLOPS_BUCKET/MODEL_NAME/scripts
           LocalPath: /opt/ml/processing/code
           S3DataType: S3Prefix
           S3InputMode: File
           S3DataDistributionType: FullyReplicated
       - InputName: test
         AppManaged: false
         S3Input:
-          S3Uri: s3://MLOPS_BUCKET/demo/processing/test
+          S3Uri: s3://MLOPS_BUCKET/MODEL_NAME/processing/test
           LocalPath: /opt/ml/processing/test
           S3DataType: S3Prefix
           S3InputMode: File
@@ -132,7 +132,7 @@ evaluation:
         - OutputName: evaluation
           AppManaged: false
           S3Output:
-            S3Uri: s3://MLOPS_BUCKET/demo/evaluation/output
+            S3Uri: s3://MLOPS_BUCKET/MODEL_NAME/evaluation/output
             LocalPath: /opt/ml/processing/evaluation
             S3UploadMode: EndOfJob
     StoppingCondition:
@@ -153,10 +153,10 @@ CreateModel:
     VpcConfig:
     PrimaryContainer:
       Image: IMAGE_URI
-      ModelDataUrl: s3://MLOPS_BUCKET/demo/model/SFN_EXECUTION_ID/output/model.tar.gz
+      ModelDataUrl: s3://MLOPS_BUCKET/MODEL_NAME/model/SFN_EXECUTION_ID/output/model.tar.gz
       Environment:
         SAGEMAKER_PROGRAM: inference.py
-        SAGEMAKER_SUBMIT_DIRECTORY: s3://MLOPS_BUCKET/demo/scripts/source.tar.gz
+        SAGEMAKER_SUBMIT_DIRECTORY: s3://MLOPS_BUCKET/MODEL_NAME/scripts/source.tar.gz
 
     ExecutionRoleArn: SAGEMAKER_EXECUTION_ROLE
 
@@ -174,11 +174,11 @@ batchTransform:
       DataSource:
         S3DataSource:
           S3DataType: S3Prefix
-          S3Uri: s3://MLOPS_BUCKET/demo/processing/test/test_features.csv
+          S3Uri: s3://MLOPS_BUCKET/MODEL_NAME/processing/test/test_features.csv
     TransformOutput:
       Accept: text/csv
       AssembleWith: Line
-      S3OutputPath: s3://MLOPS_BUCKET/demo/batch-output/SFN_EXECUTION_ID/
+      S3OutputPath: s3://MLOPS_BUCKET/MODEL_NAME/batch-output/SFN_EXECUTION_ID/
     TransformResources:
       InstanceType: ml.m5.xlarge
       InstanceCount: 1

manifests/mlops-stepfunctions/mlops-stepfunctions.yaml

@@ -4,5 +4,11 @@ targetAccount: primary
 dataFiles:
   - filePath: data/mlops-stepfunctions/config/input.yaml
 parameters:
+  - name: model-name
+    # Replace value with the valid model name
+    value: aws
+  - name: hours
+    # Replace the value with the hours at what time the StateMachine Needs to run in UTC daily
+    value: "18"
   - name: config-file-path
     value: data/mlops-stepfunctions/config/input.yaml

modules/examples/mlops-stepfunctions/README.md

@@ -26,53 +26,47 @@ Here's a typical workflow:
 
 # Deployment Guide
 
-## Set-up the environment(s)
+See deployment steps in the [Deployment Guide](../../../DEPLOYMENT.md).
 
-1. Clone the repository and checkout a release branch using the below command:
 
-```
-git clone --origin upstream --branch release/1.4.0 https://github.com/awslabs/aiops-modules
-```
-The release version can be replaced with the version of interest.
+## Inputs/Outputs
 
-2. Move into the `aiops-modules` repository:
-```
-cd aiops-modules
-```
-3. Create and activate a Virtual environment
-```
-python3 -m venv .venv && source .venv/bin/activate
-```
-4. Install the requirements
-```
-pip install -r ./requirements.txt
-```
-5. Set environment variables
+### Input Parameters
 
-Replace the values below with your AWS account id and Administrator IAM Role.
-```
-export PRIMARY_ACCOUNT=XXXXXXXXXXXX
-export ADMIN_ROLE_ARN=arn:aws:iam::XXXXXXXXXXXX:role/XXXXX
-```
+#### Required
 
-5. Bootstrap the CDK environment (one time per region) with CDK V2. Assuming you are deploying in `us-east-1`:
-```
-cdk bootstrap aws://${PRIMARY_ACCOUNT}/us-east-1
-```
-6. Bootstrap AWS Account(s)
+- `model-name` : Model Identifier  (default it is "demo")
+- `hours`: Time in UTC hour to schedule the event to run the statemachine daily.
 
-Assuming that you will be using a single account, follow the guide [here](https://seed-farmer.readthedocs.io/en/latest/bootstrapping.html#) to bootstrap your account(s) to function as a toolchain and target account.
+## Sample manifest declaration
 
-Following is the command to bootstrap your existing account to a toolchain and target account.
+Create a manifest file under appropriate location, for example examples/manifests
 ```
-seedfarmer bootstrap toolchain --project aiops --trusted-principal ${ADMIN_ROLE_ARN} --as-target
+name: mlops-stepfunctions
+path: git::https://github.com/awslabs/aiops-modules.git//modules/examples/mlops-stepfunctions?ref=release/1.4.0&depth=1
+parameters:
+  - name: model-name
+    value: demo
+  - name: hours
+    value: "18"
 ```
 
-## Deployment
+### Module Metadata Outputs
 
-Pick the manifest to deploy. Manifests are located in `manifests/` directory. For example, to deploy this modules, run:
+- `MlOpsBucket`: Name of the Bucket where Model Artifacts are stored.
+- `SageMakerExecutionRole`: Execution Roles used by SageMaker Service.
+- `ImageUri`: Docker Image URI used by SageMaker Jobs.
+- `StateMachine`: ARN of State Machine.
+- `LambdaFunction`: ARN of Lambda function which starts the execution of State Machine.
 
-!Note: if you are deploying into a region different from `us-east-1`, change the `regionMappings` in `deployment.yaml`.
-```
-seedfarmer apply manifests/mlops-stepfunctions/deployment.yaml
+#### Output Example
+
+```yaml
+metadata: | {
+    "MlOpsBucket": "",
+    "SageMakerExecutionRole": "arn:aws:iam::123456789012:role/SageMakerExecutionRole",
+    "ImageUri": "683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-scikit-learn:1.2-1-cpu-py3",
+    "StateMachine": "arn:aws:states:us-east-1:123456789012:stateMachine:MLOpsStateMachine",
+    "LambdaFunction": "arn:aws:lambda:us-east-1:123456789012:function:MlOpsLambdaFunction",
+}
 ```

modules/examples/mlops-stepfunctions/app.py

@@ -21,8 +21,8 @@
     project_name=app_settings.seedfarmer_settings.project_name,
     deployment_name=app_settings.seedfarmer_settings.deployment_name,
     module_name=app_settings.seedfarmer_settings.module_name,
-    bucket_policy_arn=app_settings.module_settings.bucket_policy_arn,
-    permission_boundary_arn=app_settings.module_settings.permission_boundary_arn,
+    model_name=app_settings.module_settings.model_name,
+    hours=app_settings.module_settings.hours,
     env=aws_cdk.Environment(
         account=app_settings.cdk_settings.account,
         region=app_settings.cdk_settings.region,
@@ -40,6 +40,8 @@
             "MlOpsBucket": stack.mlops_assets_bucket.bucket_name,
             "SageMakerExecutionRole": stack.sagemaker_execution_role.role_arn,
             "ImageUri": image_uri,
+            "StateMachine": stack.state_machine_arn,
+            "LambdaFunction": stack.lambda_function_arn,
         }
     ),
 )

modules/examples/mlops-stepfunctions/deployspec.yaml

@@ -25,8 +25,9 @@ deploy:
         - sed -i "s/SAGEMAKER_EXECUTION_ROLE/${SAGEMAKER_EXECUTION_ROLE}/g" scripts/input.yaml
         - export IMAGE_URI=$(echo ${IMAGE_URI} | sed -e "s#/#\\\/#g")
         - sed -i "s/IMAGE_URI/${IMAGE_URI}/g" scripts/input.yaml
+        - sed -i "s/MODEL_NAME/${SEEDFARMER_PARAMETER_MODEL_NAME}/g" scripts/input.yaml
         - tar -czf scripts/source.tar.gz -C scripts inference.py
-        - aws s3 cp --recursive scripts/ s3://$MLOPS_BUCKET/demo/scripts/
+        - aws s3 cp --recursive scripts/ s3://$MLOPS_BUCKET/$SEEDFARMER_PARAMETER_MODEL_NAME/scripts/
 destroy:
   phases:
     install:

modules/examples/mlops-stepfunctions/settings.py

@@ -27,9 +27,8 @@ class ModuleSettings(CdkBaseSettings):
 
     model_config = SettingsConfigDict(env_prefix="SEEDFARMER_PARAMETER_")
 
-    bucket_policy_arn: Optional[str] = Field(default=None)
-    permission_boundary_arn: Optional[str] = Field(default=None)
-
+    model_name: str = Field(default="demo")
+    hours: str = Field(default="18")  # 6PM UTC
     tags: Optional[Dict[str, str]] = Field(default=None)
 
 

modules/examples/mlops-stepfunctions/stack.py

@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import logging
-from typing import Any, Optional
+from typing import Any
 
 import aws_cdk.aws_events as events
 import aws_cdk.aws_events_targets as events_targets
@@ -27,8 +27,8 @@ def __init__(
         project_name: str,
         deployment_name: str,
         module_name: str,
-        bucket_policy_arn: Optional[str] = None,
-        permission_boundary_arn: Optional[str] = None,
+        model_name: str,
+        hours: str,
         **kwargs: Any,
     ) -> None:
         # MLOPS Env vars
@@ -141,6 +141,7 @@ def __init__(
             state_machine_type=sfn.StateMachineType.STANDARD,
             role=sfn_exec_role,
         )
+        self.state_machine_arn = state_machine.state_machine_arn
 
         sfn_execution_for_lambda = aws_iam.PolicyDocument(
             statements=[
@@ -164,6 +165,7 @@ def __init__(
             environment={"STATE_MACHINE_ARN": state_machine.state_machine_arn},
             timeout=Duration.seconds(60),
         )
+        self.lambda_function_arn = lambda_function.function_arn
 
         lambda_role.attach_inline_policy(aws_iam.Policy(self, "SFNExecutionPolicy", document=sfn_execution_for_lambda))
         lambda_role.attach_inline_policy(aws_iam.Policy(self, "S3AccessRole", document=s3_access_statements))
@@ -175,7 +177,7 @@ def __init__(
             "MyEventRule",
             schedule=events.Schedule.cron(
                 minute="0",
-                hour="18",  # 6 PM UTC
+                hour=hours,
                 month="*",
                 week_day="*",
                 year="*",
@@ -186,7 +188,7 @@ def __init__(
         custom_input = {
             "config": {
                 "bucket": mlops_assets_bucket.bucket_name,
-                "prefix": "demo/scripts/input.yaml",
+                "prefix": f"{model_name}/scripts/input.yaml",
             }
             # Add more key-value pairs as needed
         }