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The model consumed more memory than was appropriated for it. Maximum allowed memory for the model is 2560 MB. Please check your model for issues. RRS feed

  • Question

  • While Building the model i used Cross Validation tehniq with 10-folds.

    Error Message: The model consumed more memory than was appropriated for it. Maximum allowed memory for the model is 2560 MB. Please check your model for issues.
    Site Path: /workspaces/23e6f3cd2fec448b82c640d098b9d492/webservices/f8975be448754588989e5343de0cf771/endpoints/default/test/rrs
    Activity ID: 82e53138-b3b9-4a94-8695-0b8152c505ac
    Request ID: bd4ab1a3-157f-44a7-9fed-7a58f17f3c47
    Workspace ID: 23e6f3cd2fec448b82c640d098b9d492
    Workspace Type: PaidStandard
    User Role: Owner
    Tenant ID: cb8d0f5e-295e-44f1-8cab-184ae827c864

    Thursday, October 24, 2019 10:04 AM

All replies

  • Hi,

    The error indicates that the model exceeded the memory quota assigned to it. Did you receive this error when training, deploying, or testing a webservice? Also, what type of model are you building? Thanks.

    Regards,

    GiftA-MSFT.

    If a post helps to resolve your issue, please click “Mark as Answer” and/or “Vote as helpful”. By marking a post as Answered and/or Helpful, you help others find the answer faster.  Thanks.

    Friday, October 25, 2019 12:48 AM
    Moderator
  • While Building the Model, I not receiving any error. This error received while deploying the model.

    Model details :

    • Sample : Partition and Sample using 5 folds
    • Model-1 : Decision Forest with Cross Validate Model
    • Model-2 : Liner Regression with Cross Validate Model

    Please let me know any additional information needed


    Narendra Prasad K

    Tuesday, October 29, 2019 9:15 AM
  • Hi,

    I’m unable to reproduce this issue. How large is your data? Can you try using a smaller data sample and deploy each model to determine if error persists? Furthermore, is your experiment similar to the following example? If so, can you try deploying it to determine if error persists? Also, what parameters did you specify for Decision Forest Regression module? Perhaps you can try reducing number of trees or adjusting other parameters to determine whether error persists? Let me know whether any of these steps helped. Thanks.


    Regards,

    GiftA-MSFT.

    If a post helps to resolve your issue, please click “Mark as Answer” and/or “Vote as helpful”. By marking a post as Answered and/or Helpful, you help others find the answer faster.  Thanks.

    Tuesday, November 5, 2019 3:07 AM
    Moderator