francesco-camussoni-ueno opened a new issue, #43552:
URL: https://github.com/apache/airflow/issues/43552
### Apache Airflow Provider(s)
amazon
### Versions of Apache Airflow Providers
8.2.0
### Apache Airflow version
2.6.3
### Operating System
mw1.small
### Deployment
Official Apache Airflow Helm Chart
### Deployment details
_No response_
### What happened
I have this task related to a tuning job on a dag:
`tuning_dict = {"task_id": "tuning", "config":
{"HyperParameterTuningJobConfig": {"ParameterRanges":
{"CategoricalParameterRanges": [{"Name": "max_features", "Values": ["sqrt",
"log2"]}, {"Name": "criterion", "Values": ["gini", "entropy", "log_loss"]}],
"ContinuousParameterRanges": [{"Name": "ccp_alpha", "MinValue": "0.0",
"MaxValue": "0.02"}], "IntegerParameterRanges": [{"Name": "min_samples_leaf",
"MinValue": "2", "MaxValue": "15"}, {"Name": "n_estimators", "MinValue": "50",
"MaxValue": "500"}]}, "HyperParameterTuningJobObjective": {"Name":
"validation:accuracy", "Type": "Maximize"}, "Strategy": "Bayesian",
"RandomSeed": 123}, ...`
The key ContinuousParameterRanges contains some hyperparameters for mi
tunning job that are casted as a string. This is a must based on the
TuningOperator:
https://github.com/apache/airflow/blob/providers-amazon/3.4.0/airflow/providers/amazon/aws/example_dags/example_sagemaker.py
(line 202).
But I'm seeing that they are converted to float in the case of
ContinuousParameterRanges or to int in the case of IntegerParameterRanges
because of this bunch of code:
https://github.com/apache/airflow/blob/providers-amazon/8.20.0/airflow/providers/amazon/aws/operators/sagemaker.py
(line 99 or function parse_config_integers/parse_integers)
So when I execute the dag I get this kind of erros:
```
Invalid type for parameter
HyperParameterTuningJobConfig.ParameterRanges.ContinuousParameterRanges[0].MinValue,
value: 0.0, type: <class 'float'>, valid types: <class 'str'>
Invalid type for parameter
HyperParameterTuningJobConfig.ParameterRanges.ContinuousParameterRanges[0].MaxValue,
value: 0.02, type: <class 'float'>, valid types: <class 'str'>
Invalid type for parameter
HyperParameterTuningJobConfig.ParameterRanges.IntegerParameterRanges[0].MinValue,
value: 2, type: <class 'int'>, valid types: <class 'str'>
Invalid type for parameter
HyperParameterTuningJobConfig.ParameterRanges.IntegerParameterRanges[0].MaxValue,
value: 15, type: <class 'int'>, valid types: <class 'str'>
Invalid type for parameter
HyperParameterTuningJobConfig.ParameterRanges.IntegerParameterRanges[1].MinValue,
value: 50, type: <class 'int'>, valid types: <class 'str'>
Invalid type for parameter
HyperParameterTuningJobConfig.ParameterRanges.IntegerParameterRanges[1].MaxValue,
value: 500, type: <class 'int'>, valid types: <class 'str'>
```
Any help?
### What you think should happen instead
I think that those parameters don't have to be converted as float or string
### How to reproduce
Generate a dag with this task:
'tuning_dict = {"task_id": "tuning", "config":
{"HyperParameterTuningJobConfig": {"ParameterRanges":
{"CategoricalParameterRanges": [{"Name": "max_features", "Values": ["sqrt",
"log2"]}, {"Name": "criterion", "Values": ["gini", "entropy", "log_loss"]}],
"ContinuousParameterRanges": [{"Name": "ccp_alpha", "MinValue": "0.0",
"MaxValue": "0.02"}], "IntegerParameterRanges": [{"Name": "min_samples_leaf",
"MinValue": "2", "MaxValue": "15"}, {"Name": "n_estimators", "MinValue": "50",
"MaxValue": "500"}]}, "HyperParameterTuningJobObjective": {"Name":
"validation:accuracy", "Type": "Maximize"}, "Strategy": "Bayesian",
"RandomSeed": 123}, "ResourceLimits": {"MaxNumberOfTrainingJobs": 10,
"MaxParallelTrainingJobs": 4, "MaxRuntimeInSeconds": 7200}, "Tags": [{"Key":
"USER", "Value": "[email protected]"}, {"Key": "TRIBU", "Value":
"Central Data"}, {"Key": "SQUAD", "Value": "Personalization and Relevance"},
{"Key": "ONLINE_OR_BATCH", "Value": "batch"}, {"Key": "PREDICTION_TYPE", "Va
lue": "clasificacion binaria"}, {"Key": "VERSION_DESCRIPTION", "Value":
"Version inicial"}, {"Key": "DESCRIPTION", "Value": "Desarrollo de deployment
de pipeline de entrenamiento"}], "HyperParameterTuningJobName":
"mlpipeline-training-tuning", "TrainingJobDefinition":
{"AlgorithmSpecification": {"TrainingImage": "<Training_image>",
"TrainingInputMode": "File", "MetricDefinitions": [{"Name":
"validation:accuracy", "Regex": "validation-accuracy=(.*?);"}, {"Name":
"validation:recall", "Regex": "validation-recall=(.*?);"}, {"Name":
"validation:precision", "Regex": "validation-precision=(.*?);"}]},
"InputDataConfig": [{"ChannelName": "ingestion", "DataSource": {"S3DataSource":
{"S3DataType": "S3Prefix", "S3Uri": "<BUCKET>", "S3DataDistributionType":
"FullyReplicated"}}}], "OutputDataConfig": {"S3OutputPath":
"s3://pr-ueno-prod-sagemaker/ml-projects/mlpipeline/training_pipeline/tuning/output"},
"ResourceConfig": {"InstanceType": "ml.m5.large", "InstanceCount": 1,
"VolumeSizeInGB": 10}, "S
toppingCondition": {"MaxRuntimeInSeconds": 7200}, "RoleArn": "<ROLE>",
"StaticHyperParameters": {}}}}`
### Anything else
_No response_
### Are you willing to submit PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of
Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
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