I want to deploy an MLflow image to an AWS Sagemaker endpoint that contains a machine learning model. I executed the following code, which I found in this blog post.
import mlflow.sagemaker as mfs
run_id = run_id # the model you want to deploy - this run_id was saved when we trained our model
region = "us-east-1" # region of your account
aws_id = "XXXXXXXXXXX" # from the aws-cli output
arn = "arn:aws:iam::XXXXXXXXXXX:role/your-role"
app_name = "iris-rf-1"
model_uri = "mlruns/%s/%s/artifacts/random-forest-model" % (experiment_id,run_id) # edit this path based on your working directory
image_url = aws_id + ".dkr.ecr." + region + ".amazonaws.com/mlflow-pyfunc:1.2.0" # change to your mlflow version
mfs.deploy(app_name=app_name,
model_uri=model_uri,
region_name=region,
mode="create",
execution_role_arn=arn,
image_url=image_url)
But I got the following error. I checked all policies and permissions attached to the IAM role. They all comply with what the error message complains about. I don't know what to do next. I'd appreciate your help. Thanks.
botocore.exceptions.ClientError: An error occurred (ValidationException) when calling the CreateModel operation: Could not access model data at https://s3.amazonaws.com/mlflow-sagemaker-us-east-1-xxx/mlflow-xgb-demo-model-eqktjeoit5mxhmjn-abpanw/model.tar.gz. Please ensure that the role "arn:aws:iam::xxx:role/mlflow-sagemaker-dev" exists and that its trust relationship policy allows the action "sts:AssumeRole" for the service principal "sagemaker.amazonaws.com". Also ensure that the role has "s3:GetObject" permissions and that the object is located in us-east-1.
