[Tensorflow Serving] Issue : Tensor :0, specified in either feed_devices or fetch_devices was not found in the Graph

[Maxime Poulain]

Hello ! 

I just posted an issue regarding tensorflow-serving :  https://stackoverflow.com/questions/61057392/issue-with-tensorflow-serving-tensor-0-specified-in-either-feed-devices-or-f

but you can see my problem in the following text :  

So, I have an issue using tensorflow serving with `SparseTensor` inputs.

I'm using :

 - Tensorflow 2.1.0

 - latest tensorflow-serving docker image

I have the following code to export model for serving :

```

def serving_input_fn():

    """Input function for serving"""

    def _sparse_to_dense_features_tensor(feature_indices, features_values):

        indices = feature_indices.indices

        indices = tf.transpose(indices)

        indices = tf.stack([indices[0], feature_indices.values])

        indices = tf.transpose(indices)

        return tf.sparse.to_dense(tf.SparseTensor(indices=indices, values=features_values.values,

                               dense_shape=np.array([1, 2112])))

    

    feature_spec = {'features_indices' : tf.io.VarLenFeature(tf.int64),

                        'features_values' : tf.io.VarLenFeature(tf.float32)

                   }

    

    raw_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)

    raw_reatures = raw_input_fn().features

    feature_tensor = _sparse_to_dense_features_tensor(raw_reatures['features_indices'], raw_reatures['features_values'])

    return tf.estimator.export.ServingInputReceiver(feature_tensor, raw_reatures)

exported_model_dir = 'exported_model'

classifier.export_saved_model(exported_model_dir, serving_input_fn)

```

When I'm trying to predict using the REST API like following : 

```

curl -d "{\"inputs\":{\"features_values\": [10.0, 12.0], \"features_indices\": [3, 4]}}" \

  -X POST http://localhost:8501/v1/models/my_model:predict

```

I'm getting this issue : 

```

{ "error": "Tensor :0, specified in either feed_devices or fetch_devices was not found in the Graph" }

```

I have found some insights here : https://github.com/tensorflow/serving/issues/1100

This issue seems to come from the fact that `there is no name assigned to the SparseTensor` but there is no clear answer to deal with this problem... 

I don't know if I made a mistake, I would be happy if someone has a solution ! 

Thanks,

Maxime

[Irene Giannoumis]

+Pedram Pejman to see if he can help with your serving issue

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[Pedram Pejman]

can you paste the output of `saved_model_cli show --dir <your savedmodel> --tag_set serve --signature_def <serving_default or whatever name you gave your signaturedef>`

More info on saved_model_cli: https://www.tensorflow.org/guide/saved_model

On Monday, April 6, 2020 at 8:41:39 AM UTC-7, Irene wrote:

+Pedram Pejman to see if he can help with your serving issue

To unsubscribe from this group and stop receiving emails from it, send an email to t...@tensorflow.org.

[Maxime Poulain]

Hello, 

Thanks, to help me ! 

I hope it's you want,  the output of `saved_model_cli show --dir <your savedmodel> --tag_set serve --signature_def <serving_default or whatever name you gave your signaturedef>` is the following :

The given SavedModel SignatureDef contains the following input(s):
  inputs['features_indices'] tensor_info:
      dtype: DT_INT64
      shape: (-1, -1)
      name: 
  inputs['features_values'] tensor_info:
      dtype: DT_FLOAT
      shape: (-1, -1)
      name: 
The given SavedModel SignatureDef contains the following output(s):
  outputs['class_ids'] tensor_info:
      dtype: DT_FLOAT
      shape: (1, 1827)
      name: Cast:0
Method name is: tensorflow/serving/predict

Maybe this can also help you, I have these informations when I'm doing "curl -X GET http://localhost:8501/v1/models/my_model/metadata" : 

{
"model_spec":{
 "name": "my_model",
 "signature_name": "",
 "version": "1586165717"
}
,
"metadata": {"signature_def": {
 "signature_def": {
  "serving_default": {
   "inputs": {
    "features_values": {
     "coo_sparse": {
      "values_tensor_name": "ParseExample/ParseExampleV2:3",
      "indices_tensor_name": "ParseExample/ParseExampleV2:1",
      "dense_shape_tensor_name": "ParseExample/ParseExampleV2:5"
     },
     "dtype": "DT_FLOAT",
     "tensor_shape": {
      "dim": [
       {
        "size": "-1",
        "name": ""
       },
       {
        "size": "-1",
        "name": ""
       }
      ],
      "unknown_rank": false
     }
    },
    "features_indices": {
     "coo_sparse": {
      "values_tensor_name": "ParseExample/ParseExampleV2:2",
      "indices_tensor_name": "ParseExample/ParseExampleV2:0",
      "dense_shape_tensor_name": "ParseExample/ParseExampleV2:4"
     },
     "dtype": "DT_INT64",
     "tensor_shape": {
      "dim": [
       {
        "size": "-1",
        "name": ""
       },
       {
        "size": "-1",
        "name": ""
       }
      ],
      "unknown_rank": false
     }
    }
   },
   "outputs": {
    "class_ids": {
     "dtype": "DT_FLOAT",
     "tensor_shape": {
      "dim": [
       {
        "size": "1",
        "name": ""
       },
       {
        "size": "1827",
        "name": ""
       }
      ],
      "unknown_rank": false
     },
     "name": "Cast:0"
    }
   },
   "method_name": "tensorflow/serving/predict"
  }
 }
}
}
}

Thanks,

Maxime

[Maxime Poulain]

Hello again !

Is there any one that can help me to handle this issue ?
Nobody is using Tensorflow Serving with SparseTensor ? Is it possible to do that ?

Thanks,

Maxime