tags

[pjfalbe]

I have successfully written a command line script based on your notebook and matched the results. Now I am trying to use a save container model created on GCP. I see several difference from your saved model. If I do this on your model I get the following results

saved_model_cli show --dir $PWD --tag_set serve --signature_def serving_default --list_ops

The MetaGraph with tag set ['serve'] contains the following ops: {'Pack', 'VarHandleOp', 'Identity', 'NoOp', 'Mul', 'BiasAdd', 'AssignVariableOp', 'ReadVariableOp', 'AddV2', 'StatefulPartitionedCall', 'Select', 'Shape', 'Softmax', 'StringJoin', 'DepthwiseConv2dNative', 'Reshape', 'Const', 'AvgPool', 'Relu6', 'MergeV2Checkpoints', 'Placeholder', 'Sub', 'Conv2D', 'ShardedFilename', 'RestoreV2', 'StaticRegexFullMatch', 'SaveV2', 'Squeeze', 'FusedBatchNormV3'}
The given SavedModel SignatureDef contains the following input(s):
  inputs['inputs'] tensor_info:
      dtype: DT_FLOAT
      shape: (-1, 224, 224, 3)
      name: serving_default_inputs:0
The given SavedModel SignatureDef contains the following output(s):
  outputs['logits'] tensor_info:
      dtype: DT_FLOAT
      shape: (-1, 1001)
      name: StatefulPartitionedCall:0
Method name is: tensorflow/serving/predict

If I do on the GCP container saved model I get

The MetaGraph with tag set ['serve'] contains the following ops: {'Placeholder', 'Tile', 'Enter', 'LogicalAnd', 'Relu', 'Cast', 'TensorArrayReadV3', 'DepthwiseConv2dNative', 'TensorArrayWriteV3', 'Conv2D', 'NoOp', 'TensorArrayV3', 'LoopCond', 'ExpandDims', 'Slice', 'Const', 'Softmax', 'Shape', 'Rsqrt', 'FakeQuantWithMinMaxVars', 'ResizeBilinear', 'TensorArrayScatterV3', 'MatMul', 'Switch', 'Squeeze', 'Reshape', 'AddV2', 'Range', 'Merge', 'Mean', 'Exit', 'Identity', 'Mul', 'BiasAdd', 'Sub', 'Less', 'Pack', 'TensorArrayGatherV3', 'DecodeJpeg', 'NextIteration', 'RealDiv', 'StridedSlice'}
The given SavedModel SignatureDef contains the following input(s):
  inputs['image_bytes'] tensor_info:
      dtype: DT_STRING
      shape: (-1)
      name: Placeholder:0
  inputs['key'] tensor_info:
      dtype: DT_STRING
      shape: (-1)
      name: Placeholder_1:0
The given SavedModel SignatureDef contains the following output(s):
  outputs['key'] tensor_info:
      dtype: DT_STRING
      shape: (-1)
      name: Identity:0
  outputs['labels'] tensor_info:
      dtype: DT_STRING
      shape: (-1, 2)
      name: Tile:0
  outputs['scores'] tensor_info:
      dtype: DT_FLOAT
      shape: (-1, 2)
      name: scores:0
Method name is: tensorflow/serving/predict

I don't get a preferred size and I don't see a tag StatefulPartitionedCall which I think is making these calls die

my %ops = (
    in  => $graph->OperationByName('serving_default_inputs'),
    out => $graph->OperationByName('StatefulPartitionedCall'),
);

Any ideas?

[zmughal]

OK, so it looks like the input names are:

• image_bytes and
• key

and the output names are

• key,
• labels, and
• scores.

I currently do not have any code that tests the DT_STRING data type though, so this won't work quite yet. I didn't have a simple model to try it out on so that is why I left out support at the time. What is the underlying the model (that is, what was the model that this derives from so I can see the model documentation)? I would like to add an example for that so you can get something working faster.

From searching around, I believe that the data that goes into image_bytes will need to be something like JPEG and the model will decode the data from that, but without the documentation, I can't try to reproduce it myself.

[pjfalbe]

it is a vertex ai doing a auto ml is that what you mean? I could send the saved model to you.

[zmughal]

it is a vertex ai doing a auto ml is that what you mean?

What I mean: is there a model that this is a fine-tuning of or is this directly one of the models that Google provides without any fine-tuning? I just need the name of the pre-trained foundation/base model or architecture that this is based on and I should be able to download it myself from Google.

I could send the saved model to you.

Yeah, that could work if necessary, but I would prefer something openly available from Google so that I can create another notebook around it.

[pjfalbe]

I will see if I can get the model name

[pjfalbe]

from Data Scientist "This is not a Google-model but one we created for you. This was not a model provided by Google." so I don't wouldn't be an open available model

[mohawk2]

@pjfalbe Would you be able to find an openly available model that has inputs of DT_STRING and share its name/link here?

[mohawk2]

The best I could find is a tutorial on creating signatures: https://mlflow.org/docs/latest/model/notebooks/signature_examples.html

I'd expect a trivial model wouldn't be hard to just create, allowing testing of this.

[pjfalbe]

Sathis, do you know the answer for this?

…

On Tue, Nov 19, 2024 at 12:17 AM mohawk2 ***@***.***> wrote: The best I could find is a tutorial on creating signatures: https://mlflow.org/docs/latest/model/notebooks/signature_examples.html <https://mlflow.org/docs/latest/model/notebooks/signature_examples.html> I'd expect a trivial model wouldn't be hard to just create, allowing testing of this. — Reply to this email directly, view it on GitHub <#22 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ABUZI7GV2UAHSP5FGJPPHZD2BLJXZAVCNFSM6AAAAABSBH6R5CVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDIOBUG44TGNJSGE> . You are receiving this because you were mentioned.Message ID: ***@***.*** com>

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