Can not compile an XGBoost 1.0 model #1616

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1. ajaykarpur commented on Jun 23, 2020

   

   ajaykarpur

   on Jun 23, 2020

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   Hi @codypenta, thanks for bringing this to our attention. Can you please provide the following information?

   • The version of the SageMaker Python SDK you're using. (You can check this using sagemaker.__version__.)
   • The previous version of XGBoost that you used to successfully compile a model.
   • Any other information from the bug report issue template that you think may be relevant.
2. NukaCody commented on Jun 23, 2020

   

   NukaCody

   on Jun 23, 2020

   Last edited by NukaCody

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   Hope this helps:
   Sagemaker Version: 1.60.0
   Python Version: 3

   What's worked previously

   container = get_image_uri(region, 'xgboost', repo_version='0.90-2')

   compiled_model = xgb.compile_model(
       target_instance_family = "ml_m5",
       role = sagemaker.get_execution_role(),
       input_shape = {'data': [1, 64]},
       output_path = f"{xgb.output_path}/compiled",
       framework = "xgboost",
       framework_version = "0.90-2"
   )

   I've tried 1.0 and 1.0-1 for framework version but they both returned the same error
3. metrizable

   added

   type: question

   on Jul 7, 2020
4. NukaCody commented on Jul 8, 2020

   

   NukaCody

   on Jul 8, 2020

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   Update:

   I tried taking the model artifiact produced from:

   container = get_image_uri(region, 'xgboost', repo_version='1.0-1')
   
   xgb_estimator = Estimator(
       container,
       role = sage_exec,
       sagemaker_session = session,
       input_mode = 'File',
       output_path = f"s3://{bucket}{prefix}/output",
       
       train_instance_count = 2,
       train_instance_type = 'ml.m5.2xlarge',
       
       train_use_spot_instances = True,
       train_max_run = 300,
       train_max_wait = 600,
   )
   
   xgb_estimator.fit(s3_data)

   And manually compiling it through the console. The same error exist. ClientError: InputConfiguration: Invalid XGBoost model. Please make sure the framework you select is correct. local variable 'model_objects' referenced before assignment
5. laurenyu

   added

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   on Jul 23, 2020
6. akozd commented on Oct 28, 2020

   

   akozd

   on Oct 28, 2020

   Last edited by akozd

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   Same issue for me here. Error I'm seeing for 1.0-1:

   ClientError: InputConfiguration: Treelite failed to convert XGBoost model. Please make sure the framework you select is correct. std::bad_alloc

   boto3 call:

   response = sm.create_compilation_job(
       CompilationJobName=job_name,
       RoleArn=get_execution_role(),
       InputConfig={
           'S3Uri': model_path,
           'DataInputConfig': '{"data":[1,10]}',
           'Framework': 'XGBOOST'
       },
       OutputConfig={
           'S3OutputLocation': output_path,
           'TargetDevice': 'ml_m5'
       },
       StoppingCondition={
           'MaxRuntimeInSeconds':3600,
           'MaxWaitTimeInSeconds': 3600
       }
   )

   👍React with 👍1Reacted by mattmaws
7. coverton-usgs commented on Feb 27, 2021

   

   coverton-usgs

   on Feb 27, 2021

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   I'm not sure if this relates but,...

   I'm encountered the same error with sagemaker 2.26.0 when I accidentally loaded:
   retrieve(framework="xgboost", region=region, version='latest')
   which is discouraged according to the AWS documentation

   when loading a specific container:
   retrieve(framework="xgboost", region=region, version='1.2-1')

   it worked just fine
8. bveeramani

   closed this as completedon May 20, 2021
9. aws

   locked and limited conversation to collaborators on May 20, 2021