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Added LLM evaluation.
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Co-authored-by: kukushking <[email protected]>
Co-authored-by: ViktorMalesevic <[email protected]>
Co-authored-by: Marco1402 <[email protected]>
Co-authored-by: omsh <[email protected]>
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7 changes: 4 additions & 3 deletions CHANGELOG.md
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Expand Up @@ -24,9 +24,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- fix inputs for `bedrock-finetuning` module not working
- add `retention-type` argument for the bucket in the `bedrock-finetuning` module
- fix broken dependencies for `examples/airflow-dags`
- use `add_dependency` to avoid deprecation warnings from CDK.
- Various typo fixes.
- Various clean-ups to the SageMaker Service Catalog templates.
- use `add_dependency` to avoid deprecation warnings from CDK
- various typo fixes
- various clean-ups to the SageMaker Service Catalog templates
- fix opensearch removal policy

## v1.2.0

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20 changes: 10 additions & 10 deletions README.md

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2 changes: 1 addition & 1 deletion manifests/fmops-qna-rag/storage-modules.yaml
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Expand Up @@ -4,7 +4,7 @@ parameters:
- name: encryption-type
value: SSE
- name: retention-type
value: RETAIN
value: DESTROY
- name: vpc-id
valueFrom:
moduleMetadata:
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Expand Up @@ -18,6 +18,12 @@ The template contains an example SageMaker Pipeline to train a model on Abalone

![Abalone with XGBoost](docs/_static/abalone-xgboost-template.png "Abalone with XGBoost Template Architecture")

#### LLM evaluate template

This project template contains SageMaker pipeline that performs LLM evaluation.

![LLM evaluate template](docs/_static/llm-evaluate.png "LLM Evaluate Template Architecture")

The template is based on basic multi-account template from [AWS Enterprise MLOps Framework](https://github.com/aws-samples/aws-enterprise-mlops-framework/blob/main/mlops-multi-account-cdk/mlops-sm-project-template/README.md#sagemaker-project-stack).

#### Batch Inference Template
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# Adding a comment here - empty files create issues with zipping https://github.com/aws/aws-cdk/issues/19012
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0

from typing import Any

import aws_cdk
from aws_cdk import Aws
from aws_cdk import aws_cloudwatch as cloudwatch
from aws_cdk import aws_codebuild as codebuild
from aws_cdk import aws_codecommit as codecommit
from aws_cdk import aws_codepipeline as codepipeline
from aws_cdk import aws_codepipeline_actions as codepipeline_actions
from aws_cdk import aws_iam as iam
from aws_cdk import aws_s3 as s3
from aws_cdk import aws_s3_assets as s3_assets
from constructs import Construct


class BuildPipelineConstruct(Construct):
def __init__(
self,
scope: Construct,
construct_id: str,
project_name: str,
project_id: str,
model_package_group_name: str,
model_bucket: s3.IBucket,
pipeline_artifact_bucket: s3.IBucket,
repo_asset: s3_assets.Asset,
**kwargs: Any,
) -> None:
super().__init__(scope, construct_id, **kwargs)

# Define resource name
sagemaker_pipeline_name = f"{project_name}-{project_id}"
sagemaker_pipeline_description = f"{project_name} Model Build Pipeline"

# Create source repo from seed bucket/key
build_app_repository = codecommit.Repository(
self,
"Build App Code Repo",
repository_name=f"{project_name}-{construct_id}",
code=codecommit.Code.from_asset(
asset=repo_asset,
branch="main",
),
)
aws_cdk.Tags.of(build_app_repository).add("sagemaker:project-id", project_id)
aws_cdk.Tags.of(build_app_repository).add("sagemaker:project-name", project_name)

sagemaker_seedcode_bucket = s3.Bucket.from_bucket_name(
self,
"SageMaker Seedcode Bucket",
f"sagemaker-servicecatalog-seedcode-{Aws.REGION}",
)

codebuild_role = iam.Role(
self,
"CodeBuild Role",
assumed_by=iam.ServicePrincipal("codebuild.amazonaws.com"),
path="/service-role/",
)

sagemaker_execution_role = iam.Role(
self,
"SageMaker Execution Role",
assumed_by=iam.ServicePrincipal("sagemaker.amazonaws.com"),
path="/service-role/",
)

# Create a policy statement for SM and ECR pull
sagemaker_policy = iam.Policy(
self,
"SageMaker Policy",
document=iam.PolicyDocument(
statements=[
iam.PolicyStatement(
actions=[
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:PutLogEvents",
],
resources=["*"],
),
iam.PolicyStatement(
actions=[
"ecr:BatchCheckLayerAvailability",
"ecr:BatchGetImage",
"ecr:Describe*",
"ecr:GetAuthorizationToken",
"ecr:GetDownloadUrlForLayer",
],
resources=["*"],
),
iam.PolicyStatement(
actions=[
"kms:Encrypt",
"kms:ReEncrypt*",
"kms:GenerateDataKey*",
"kms:Decrypt",
"kms:DescribeKey",
],
effect=iam.Effect.ALLOW,
resources=[f"arn:{Aws.PARTITION}:kms:{Aws.REGION}:{Aws.ACCOUNT_ID}:key/*"],
),
]
),
)

cloudwatch.Metric.grant_put_metric_data(sagemaker_policy)
model_bucket.grant_read_write(sagemaker_policy)
sagemaker_seedcode_bucket.grant_read_write(sagemaker_policy)

sagemaker_execution_role.grant_pass_role(codebuild_role)
sagemaker_execution_role.grant_pass_role(sagemaker_execution_role)

# Attach the policy
sagemaker_policy.attach_to_role(sagemaker_execution_role)
sagemaker_policy.attach_to_role(codebuild_role)

# Grant extra permissions for the SageMaker role
sagemaker_execution_role.add_to_policy(
iam.PolicyStatement(
actions=[
"sagemaker:CreateModel",
"sagemaker:DeleteModel",
"sagemaker:DescribeModel",
"sagemaker:CreateProcessingJob",
"sagemaker:DescribeProcessingJob",
"sagemaker:StopProcessingJob",
"sagemaker:CreateTrainingJob",
"sagemaker:DescribeTrainingJob",
"sagemaker:StopTrainingJob",
"sagemaker:AddTags",
"sagemaker:DeleteTags",
"sagemaker:ListTags",
],
resources=[
f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:model/*",
f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:processing-job/*",
f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:training-job/*",
],
)
)
sagemaker_execution_role.add_to_policy(
iam.PolicyStatement(
actions=[
"sagemaker:CreateModelPackageGroup",
"sagemaker:DeleteModelPackageGroup",
"sagemaker:DescribeModelPackageGroup",
"sagemaker:CreateModelPackage",
"sagemaker:DeleteModelPackage",
"sagemaker:UpdateModelPackage",
"sagemaker:DescribeModelPackage",
"sagemaker:ListModelPackages",
"sagemaker:AddTags",
"sagemaker:DeleteTags",
"sagemaker:ListTags",
],
resources=[
f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:model-package-group/"
f"{model_package_group_name}",
f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:model-package/"
f"{model_package_group_name}/*",
],
),
)

# Grant extra permissions for the CodeBuild role
codebuild_role.add_to_policy(
iam.PolicyStatement(
actions=[
"sagemaker:DescribeModelPackage",
"sagemaker:ListModelPackages",
"sagemaker:UpdateModelPackage",
"sagemaker:AddTags",
"sagemaker:DeleteTags",
"sagemaker:ListTags",
],
resources=[
f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:model-package/"
f"{model_package_group_name}/*"
],
)
)
codebuild_role.add_to_policy(
iam.PolicyStatement(
actions=[
"sagemaker:CreatePipeline",
"sagemaker:UpdatePipeline",
"sagemaker:DeletePipeline",
"sagemaker:StartPipelineExecution",
"sagemaker:StopPipelineExecution",
"sagemaker:DescribePipelineExecution",
"sagemaker:ListPipelineExecutionSteps",
"sagemaker:AddTags",
"sagemaker:DeleteTags",
"sagemaker:ListTags",
],
resources=[
f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:pipeline/"
f"{sagemaker_pipeline_name}",
f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:pipeline/"
f"{sagemaker_pipeline_name}/execution/*",
],
),
)
codebuild_role.add_to_policy(
iam.PolicyStatement(
actions=[
"sagemaker:DescribeImageVersion",
],
resources=[
f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:image-version/*",
],
)
)

# Create the CodeBuild project
sm_pipeline_build = codebuild.PipelineProject(
self,
"SM Pipeline Build",
project_name=f"{project_name}-{construct_id}",
role=codebuild_role, # figure out what actually this role would need
build_spec=codebuild.BuildSpec.from_source_filename("buildspec.yml"),
environment=codebuild.BuildEnvironment(
build_image=codebuild.LinuxBuildImage.STANDARD_5_0,
environment_variables={
"SAGEMAKER_PROJECT_NAME": codebuild.BuildEnvironmentVariable(value=project_name),
"SAGEMAKER_PROJECT_ID": codebuild.BuildEnvironmentVariable(value=project_id),
"MODEL_PACKAGE_GROUP_NAME": codebuild.BuildEnvironmentVariable(value=model_package_group_name),
"AWS_REGION": codebuild.BuildEnvironmentVariable(value=Aws.REGION),
"SAGEMAKER_PIPELINE_NAME": codebuild.BuildEnvironmentVariable(
value=sagemaker_pipeline_name,
),
"SAGEMAKER_PIPELINE_DESCRIPTION": codebuild.BuildEnvironmentVariable(
value=sagemaker_pipeline_description,
),
"SAGEMAKER_PIPELINE_ROLE_ARN": codebuild.BuildEnvironmentVariable(
value=sagemaker_execution_role.role_arn,
),
"ARTIFACT_BUCKET": codebuild.BuildEnvironmentVariable(value=model_bucket.bucket_name),
"ARTIFACT_BUCKET_KMS_ID": codebuild.BuildEnvironmentVariable(
value=model_bucket.encryption_key.key_id # type: ignore[union-attr]
),
},
),
)

source_artifact = codepipeline.Artifact(artifact_name="GitSource")

build_pipeline = codepipeline.Pipeline(
self,
"Pipeline",
pipeline_name=f"{project_name}-{construct_id}",
artifact_bucket=pipeline_artifact_bucket,
)

# add a source stage
source_stage = build_pipeline.add_stage(stage_name="Source")
source_stage.add_action(
codepipeline_actions.CodeCommitSourceAction(
action_name="Source",
output=source_artifact,
repository=build_app_repository,
branch="main",
)
)

# add a build stage
build_stage = build_pipeline.add_stage(stage_name="Build")
build_stage.add_action(
codepipeline_actions.CodeBuildAction(
action_name="SMPipeline",
input=source_artifact,
project=sm_pipeline_build,
)
)
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