Release v3.0.1

.github/workflows/code.release.branch.yml

@@ -26,7 +26,7 @@ jobs:
           RELEASE_TAG=${{ github.event.inputs.release_tag }}
           git checkout -b release/${{ github.event.inputs.release_tag }}
           echo "$( jq --arg version ${RELEASE_TAG:1} '.version = $version' package.json )" > package.json
-          sed -E -i "" -e "s/version = \"[0-9\.].+\"/version = \"${RELEASE_TAG:1}\"/g" lisa-sdk/pyproject.toml
+          sed -E -i -e "s/version = \"[0-9\.].+\"/version = \"${RELEASE_TAG:1}\"/g" lisa-sdk/pyproject.toml
           echo ${RELEASE_TAG:1} > VERSION
           git commit -a -m "Updating version for release ${{ github.event.inputs.release_tag }}"
           git push origin release/${{ github.event.inputs.release_tag }}

CHANGELOG.md

@@ -1,3 +1,25 @@
+# v3.0.1
+## Bug fixes
+- Updated our Lambda admin validation to work for no-auth if user has the admin secret token. This applies to model management APIs.
+- State machine for create model was not reporting failed status
+- Delete state machine could not delete models that weren't stored in LiteLLM DB
+
+## Enhancements
+- Added units to the create model wizard to help with clarity
+- Increased default timeouts to 10 minutes to enable large documentation processing without errors
+- Updated ALB and Target group names to be lower cased by default to prevent networking issues
+
+## Coming Soon
+- 3.1.0 will expand support for model management. Administrators will be able to modify, activate, and deactivate models through the UI or APIs. The following release we will continue to ease deployment steps for customers through a new deployment wizard and updated documentation.
+
+## Acknowledgements
+* @petermuller
+* @estohlmann
+* @dustins
+
+**Full Changelog**: https://github.com/awslabs/LISA/compare/v3.0.0...v3.0.1
+
+
 # v3.0.0
 ## Key Features
 ### Model Management Administration

README.md

@@ -659,6 +659,10 @@ curl -s -H "Authorization: Bearer <admin_token>" -X GET https://<apigw_endpoint>
 
 LISA provides the `/models` endpoint for creating both ECS and LiteLLM-hosted models. Depending on the request payload, infrastructure will be created or bypassed (e.g., for LiteLLM-only models).
 
+This API accepts the same model definition parameters that were accepted in the V2 model definitions within the config.yaml file with one notable difference: the `containerConfig.baseImage.path` field is
+now a path relative to the `lib/serve/ecs-model` directory, instead of from its original path relative to the repository root. This means that if the path used to be `lib/serve/ecs-model/textgen/tgi`, then
+it will now be `textgen/tgi` for the CreateModel API. For vLLM models, the `path` will be `vllm`, and for TEI, it will be `embedding/tei`.
+
 #### Request Example:
 
 ```

VERSION

@@ -1 +1 @@
-3.0.0
+3.0.1

ecs_model_deployer/src/lib/ecsCluster.ts

@@ -109,6 +109,11 @@ export class ECSCluster extends Construct {
             ],
         });
 
+        new CfnOutput(this, 'autoScalingGroup', {
+            key: 'autoScalingGroup',
+            value: autoScalingGroup.autoScalingGroupName,
+        });
+
         const environment = ecsConfig.environment;
         const volumes: Volume[] = [];
         const mountPoints: MountPoint[] = [];
@@ -272,10 +277,11 @@ export class ECSCluster extends Construct {
         const loadBalancer = new ApplicationLoadBalancer(this, createCdkId([ecsConfig.identifier, 'ALB']), {
             deletionProtection: config.removalPolicy !== RemovalPolicy.DESTROY,
             internetFacing: false,
-            loadBalancerName: createCdkId([config.deploymentName, ecsConfig.identifier], 32, 2),
+            loadBalancerName: createCdkId([config.deploymentName, ecsConfig.identifier], 32, 2).toLowerCase(),
             dropInvalidHeaderFields: true,
             securityGroup,
             vpc,
+            idleTimeout: Duration.seconds(600)
         });
 
         // Add listener
@@ -294,7 +300,7 @@ export class ECSCluster extends Construct {
         // Add targets
         const loadBalancerHealthCheckConfig = ecsConfig.loadBalancerConfig.healthCheckConfig;
         const targetGroup = listener.addTargets(createCdkId([ecsConfig.identifier, 'TgtGrp']), {
-            targetGroupName: createCdkId([config.deploymentName, ecsConfig.identifier], 32, 2),
+            targetGroupName: createCdkId([config.deploymentName, ecsConfig.identifier], 32, 2).toLowerCase(),
             healthCheck: {
                 path: loadBalancerHealthCheckConfig.path,
                 interval: Duration.seconds(loadBalancerHealthCheckConfig.interval),

lambda/authorizer/lambda_functions.py

@@ -16,15 +16,20 @@
 import logging
 import os
 import ssl
+from functools import cache
 from typing import Any, Dict
 
+import boto3
 import create_env_variables  # noqa: F401
 import jwt
 import requests
-from utilities.common_functions import authorization_wrapper, get_id_token
+from botocore.exceptions import ClientError
+from utilities.common_functions import authorization_wrapper, get_id_token, retry_config
 
 logger = logging.getLogger(__name__)
 
+secrets_manager = boto3.client("secretsmanager", region_name=os.environ["AWS_REGION"], config=retry_config)
+
 
 @authorization_wrapper
 def lambda_handler(event: Dict[str, Any], context) -> Dict[str, Any]:  # type: ignore [no-untyped-def]
@@ -48,6 +53,11 @@ def lambda_handler(event: Dict[str, Any], context) -> Dict[str, Any]:  # type: i
 
     deny_policy = generate_policy(effect="Deny", resource=event["methodArn"])
 
+    if id_token in get_management_tokens():
+        allow_policy = generate_policy(effect="Allow", resource=event["methodArn"], username="lisa-management-token")
+        logger.debug(f"Generated policy: {allow_policy}")
+        return allow_policy
+
     if jwt_data := id_token_is_valid(id_token=id_token, client_id=client_id, authority=authority):
         is_admin_user = is_admin(jwt_data, admin_group, jwt_groups_property)
         allow_policy = generate_policy(effect="Allow", resource=event["methodArn"], username=jwt_data["sub"])
@@ -134,3 +144,22 @@ def is_admin(jwt_data: dict[str, Any], admin_group: str, jwt_groups_property: st
         else:
             return False
     return admin_group in current_node
+
+
+@cache
+def get_management_tokens() -> list[str]:
+    """Return secret management tokens if they exist."""
+    secret_tokens: list[str] = []
+    secret_id = os.environ.get("MANAGEMENT_KEY_NAME")
+
+    try:
+        secret_tokens.append(
+            secrets_manager.get_secret_value(SecretId=secret_id, VersionStage="AWSCURRENT")["SecretString"]
+        )
+        secret_tokens.append(
+            secrets_manager.get_secret_value(SecretId=secret_id, VersionStage="AWSPREVIOUS")["SecretString"]
+        )
+    except ClientError as e:
+        logger.warn(f"Unable to fetch {secret_id}. {e.response['Error']['Code']}: {e.response['Error']['Message']}")
+
+    return secret_tokens

lambda/models/domain_objects.py

@@ -18,7 +18,7 @@
 from typing import Annotated, List, Optional, Union
 
 from pydantic import BaseModel
-from pydantic.functional_validators import AfterValidator
+from pydantic.functional_validators import AfterValidator, field_validator
 from utilities.validators import validate_instance_type
 
 
@@ -87,7 +87,7 @@ class LoadBalancerConfig(BaseModel):
 
 
 class AutoScalingConfig(BaseModel):
-    """Autoscaling configuration."""
+    """Autoscaling configuration upon model creation."""
 
     minCapacity: int
     maxCapacity: int
@@ -96,6 +96,14 @@ class AutoScalingConfig(BaseModel):
     metricConfig: MetricConfig
 
 
+class AutoScalingInstanceConfig(BaseModel):
+    """Autoscaling instance count configuration upon model update."""
+
+    minCapacity: Optional[int] = None
+    maxCapacity: Optional[int] = None
+    desiredCapacity: Optional[int] = None
+
+
 class ContainerHealthCheckConfig(BaseModel):
     """Health check configuration for a container."""
 
@@ -180,16 +188,24 @@ class GetModelResponse(ApiResponseBase):
 class UpdateModelRequest(BaseModel):
     """Request object when updating a model."""
 
-    autoScalingConfig: Optional[AutoScalingConfig] = None
-    containerConfig: Optional[ContainerConfig] = None
-    inferenceContainer: Optional[InferenceContainer] = None
-    instanceType: Optional[Annotated[str, AfterValidator(validate_instance_type)]] = None
-    loadBalancerConfig: Optional[LoadBalancerConfig] = None
-    modelId: str
-    modelName: Optional[str] = None
+    autoScalingInstanceConfig: Optional[AutoScalingInstanceConfig] = None
+    enabled: Optional[bool] = None
     modelType: Optional[ModelType] = None
     streaming: Optional[bool] = None
 
+    @field_validator("autoScalingInstanceConfig")  # type: ignore
+    @classmethod
+    def validate_autoscaling_instance_config(cls, config: AutoScalingInstanceConfig) -> AutoScalingInstanceConfig:
+        """Validate that the AutoScaling instance config has at least one positive value."""
+        if not config:
+            raise ValueError("The autoScalingInstanceConfig must not be null if defined in request payload.")
+        config_fields = (config.minCapacity, config.maxCapacity, config.desiredCapacity)
+        if all((field is None for field in config_fields)):
+            raise ValueError("At least one option of autoScalingInstanceConfig must be defined.")
+        if any((isinstance(field, int) and field < 0 for field in config_fields)):
+            raise ValueError("All autoScalingInstanceConfig fields must be >= 0.")
+        return config
+
 
 class UpdateModelResponse(ApiResponseBase):
     """Response object when updating a model."""

lambda/models/exception/__init__.py

@@ -15,6 +15,9 @@
 """Exception definitions for model management APIs."""
 
 
+# LiteLLM errors
+
+
 class ModelNotFoundError(LookupError):
     """Error to raise when a specified model cannot be found in the database."""
 
@@ -25,3 +28,24 @@ class ModelAlreadyExistsError(LookupError):
     """Error to raise when a specified model already exists in the database."""
 
     pass
+
+
+# State machine exceptions
+
+
+class MaxPollsExceededException(Exception):
+    """Exception to indicate that polling for a state timed out."""
+
+    pass
+
+
+class StackFailedToCreateException(Exception):
+    """Exception to indicate that the CDK for creating a model stack failed."""
+
+    pass
+
+
+class UnexpectedCloudFormationStateException(Exception):
+    """Exception to indicate that the CloudFormation stack has transitioned to a non-healthy state."""
+
+    pass

lambda/models/lambda_functions.py

@@ -123,7 +123,7 @@ def _create_dummy_model(model_name: str, model_type: ModelType, model_status: Mo
             ),
             sharedMemorySize=2048,
             healthCheckConfig=ContainerHealthCheckConfig(
-                command=["CMD-SHELL", "exit 0"], Interval=10, StartPeriod=30, Timeout=5, Retries=5
+                command=["CMD-SHELL", "exit 0"], interval=10, startPeriod=30, timeout=5, retries=5
             ),
             environment={
                 "MAX_CONCURRENT_REQUESTS": "128",
@@ -177,7 +177,7 @@ async def update_model(
 ) -> UpdateModelResponse:
     """Endpoint to update a model."""
     # TODO add service to update model
-    model = _create_dummy_model("model_name", ModelType.TEXTGEN, ModelStatus.UPDATING)
+    model = _create_dummy_model(model_id, ModelType.TEXTGEN, ModelStatus.UPDATING)
     return UpdateModelResponse(model=model)
 
 

lambda/models/state_machine/create_model.py

@@ -24,6 +24,11 @@
 from botocore.config import Config
 from models.clients.litellm_client import LiteLLMClient
 from models.domain_objects import CreateModelRequest, ModelStatus
+from models.exception import (
+    MaxPollsExceededException,
+    StackFailedToCreateException,
+    UnexpectedCloudFormationStateException,
+)
 from utilities.common_functions import get_cert_path, get_rest_api_container_endpoint, retry_config
 
 lambdaConfig = Config(connect_timeout=60, read_timeout=600, retries={"max_attempts": 1})
@@ -113,8 +118,13 @@ def handle_poll_docker_image_available(event: Dict[str, Any], context: Any) -> D
         output_dict["image_info"]["remaining_polls"] -= 1
         if output_dict["image_info"]["remaining_polls"] <= 0:
             ec2Client.terminate_instances(InstanceIds=[event["image_info"]["instance_id"]])
-            raise Exception(
-                "Maximum number of ECR poll attempts reached. Something went wrong building the docker image."
+            raise MaxPollsExceededException(
+                json.dumps(
+                    {
+                        "error": "Max number of ECR polls reached. Docker Image was not replicated successfully.",
+                        "event": event,
+                    }
+                )
             )
         return output_dict
 
@@ -153,7 +163,17 @@ def camelize_object(o):  # type: ignore[no-untyped-def]
 
     payload = response["Payload"].read()
     payload = json.loads(payload)
-    stack_name = payload.get("stackName")
+    stack_name = payload.get("stackName", None)
+
+    if not stack_name:
+        raise StackFailedToCreateException(
+            json.dumps(
+                {
+                    "error": "Failed to create Model CloudFormation Stack. Please validate model parameters are valid.",
+                    "event": event,
+                }
+            )
+        )
 
     response = cfnClient.describe_stacks(StackName=stack_name)
     stack_arn = response["Stacks"][0]["StackId"]
@@ -192,10 +212,24 @@ def handle_poll_create_stack(event: Dict[str, Any], context: Any) -> Dict[str, A
         output_dict["continue_polling_stack"] = True
         output_dict["remaining_polls_stack"] -= 1
         if output_dict["remaining_polls_stack"] <= 0:
-            raise Exception("Maximum number of CloudFormation polls reached")
+            raise MaxPollsExceededException(
+                json.dumps(
+                    {
+                        "error": "Max number of CloudFormation polls reached.",
+                        "event": event,
+                    }
+                )
+            )
         return output_dict
     else:
-        raise Exception(f"Stack in unexpected state: {stackStatus}")
+        raise UnexpectedCloudFormationStateException(
+            json.dumps(
+                {
+                    "error": f"Stack entered unexpected state: {stackStatus}",
+                    "event": event,
+                }
+            )
+        )
 
 
 def handle_add_model_to_litellm(event: Dict[str, Any], context: Any) -> Dict[str, Any]:
@@ -234,3 +268,38 @@ def handle_add_model_to_litellm(event: Dict[str, Any], context: Any) -> Dict[str
     )
 
     return output_dict
+
+
+def handle_failure(event: Dict[str, Any], context: Any) -> Dict[str, Any]:
+    """
+    Handle failures from state machine.
+
+    Possible causes of failures would be:
+    1. Docker Image failed to replicate into ECR in expected amount of time
+    2. CloudFormation Stack creation failed from parameter validation.
+    3. CloudFormation Stack creation failed from taking too long to stand up.
+
+    Expectation of this function is to terminate the EC2 instance if it is still running, and to set the model status
+    to Failed. Cleaning up the CloudFormation stack, if it still exists, will happen in the DeleteModel API.
+    """
+    error_dict = json.loads(  # error from SFN is json payload on top of json payload we add to the exception
+        json.loads(event["Cause"])["errorMessage"]
+    )
+    error_reason = error_dict["error"]
+    original_event = error_dict["event"]
+
+    # terminate EC2 instance if we have one recorded
+    if "image_info" in original_event and "instance_id" in original_event["image_info"]:
+        ec2Client.terminate_instances(InstanceIds=[original_event["image_info"]["instance_id"]])
+
+    # set model as Failed in DDB, so it shows as such in the UI. adds error reason as well.
+    model_table.update_item(
+        Key={"model_id": original_event["modelId"]},
+        UpdateExpression="SET model_status = :ms, last_modified_date = :lm, failure_reason = :fr",
+        ExpressionAttributeValues={
+            ":ms": ModelStatus.FAILED,
+            ":lm": int(datetime.utcnow().timestamp()),
+            ":fr": error_reason,
+        },
+    )
+    return event

lambda/models/state_machine/delete_model.py

@@ -62,7 +62,7 @@ def handle_set_model_to_deleting(event: Dict[str, Any], context: Any) -> Dict[st
     if not item:
         raise RuntimeError(f"Requested model '{model_id}' was not found in DynamoDB table.")
     output_dict[CFN_STACK_ARN] = item.get(CFN_STACK_ARN, None)
-    output_dict[LITELLM_ID] = item[LITELLM_ID]
+    output_dict[LITELLM_ID] = item.get(LITELLM_ID, None)
 
     ddb_table.update_item(
         Key=model_key,
@@ -77,7 +77,8 @@ def handle_set_model_to_deleting(event: Dict[str, Any], context: Any) -> Dict[st
 
 def handle_delete_from_litellm(event: Dict[str, Any], context: Any) -> Dict[str, Any]:
     """Delete model reference from LiteLLM."""
-    litellm_client.delete_model(identifier=event[LITELLM_ID])
+    if event[LITELLM_ID]:  # if non-null ID
+        litellm_client.delete_model(identifier=event[LITELLM_ID])
     return event