I ran the example tutorial (link below) for creating a tfx pipeline using native keras based on the iris data set. I was able to create and save a Tensorflow model.
https://github.com/tensorflow/tfx/blob/master/tfx/examples/iris/iris_pipeline_native_keras.py
I'm trying to query the trained model using the predict API Tensorflow serving with Docker. I can't figure out the post API request for it.
I tried following command but it didn't work.
curl -d '{"instances": [7.7,3.0,6.1,2.3]}' \
-X POST http://localhost:8501/v1/models/iris_example:predict
The model's metadata is as follows
{
"model_spec": {
"name": "iris_cpu",
"signature_name": "",
"version": "1595575737"
},
"metadata": {
"signature_def": {
"signature_def": {
"serving_default": {
"inputs": {
"examples": {
"dtype": "DT_STRING",
"tensor_shape": {
"dim": [],
"unknown_rank": true
},
"name": "serving_default_examples:0"
}
},
"outputs": {
"output_0": {
"dtype": "DT_FLOAT",
"tensor_shape": {
"dim": [
{
"size": "-1",
"name": ""
},
{
"size": "3",
"name": ""
}
],
"unknown_rank": false
},
"name": "StatefulPartitionedCall:0"
}
},
"method_name": "tensorflow/serving/predict"
},
"__saved_model_init_op": {
"inputs": {},
"outputs": {
"__saved_model_init_op": {
"dtype": "DT_INVALID",
"tensor_shape": {
"dim": [],
"unknown_rank": true
},
"name": "NoOp"
}
},
"method_name": ""
}
}
}
}
}
output from running saved_model_cli command
!saved_model_cli show --dir {export_path} --all
output:
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
signature_def['__saved_model_init_op']:
The given SavedModel SignatureDef contains the following input(s):
The given SavedModel SignatureDef contains the following output(s):
outputs['__saved_model_init_op'] tensor_info:
dtype: DT_INVALID
shape: unknown_rank
name: NoOp
Method name is:
signature_def['serving_default']:
The given SavedModel SignatureDef contains the following input(s):
inputs['examples'] tensor_info:
dtype: DT_STRING
shape: (-1)
name: serving_default_examples:0
The given SavedModel SignatureDef contains the following output(s):
outputs['output_0'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 3)
name: StatefulPartitionedCall:0
Method name is: tensorflow/serving/predict
WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
2020-07-24 13:41:05.819053: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-07-24 13:41:05.836906: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fac87ae5560 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-07-24 13:41:05.836934: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
Defined Functions:
Function Name: '__call__'
Option #1
Callable with:
Argument #1
DType: list
Value: [TensorSpec(shape=(None, 1), dtype=tf.float32, name='sepal_length_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='sepal_width_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='petal_length_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='petal_width_xf')]
Argument #2
DType: bool
Value: False
Argument #3
DType: NoneType
Value: None
Option #2
Callable with:
Argument #1
DType: list
Value: [TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/0'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/1'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/2'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/3')]
Argument #2
DType: bool
Value: False
Argument #3
DType: NoneType
Value: None
Option #3
Callable with:
Argument #1
DType: list
Value: [TensorSpec(shape=(None, 1), dtype=tf.float32, name='sepal_length_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='sepal_width_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='petal_length_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='petal_width_xf')]
Argument #2
DType: bool
Value: True
Argument #3
DType: NoneType
Value: None
Option #4
Callable with:
Argument #1
DType: list
Value: [TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/0'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/1'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/2'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/3')]
Argument #2
DType: bool
Value: True
Argument #3
DType: NoneType
Value: None
Function Name: '_default_save_signature'
Option #1
Callable with:
Argument #1
DType: list
Value: [TensorSpec(shape=(None, 1), dtype=tf.float32, name='sepal_length_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='sepal_width_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='petal_length_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='petal_width_xf')]
Function Name: 'call_and_return_all_conditional_losses'
Option #1
Callable with:
Argument #1
DType: list
Value: [TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/0'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/1'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/2'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/3')]
Argument #2
DType: bool
Value: True
Argument #3
DType: NoneType
Value: None
Option #2
Callable with:
Argument #1
DType: list
Value: [TensorSpec(shape=(None, 1), dtype=tf.float32, name='sepal_length_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='sepal_width_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='petal_length_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='petal_width_xf')]
Argument #2
DType: bool
Value: False
Argument #3
DType: NoneType
Value: None
Option #3
Callable with:
Argument #1
DType: list
Value: [TensorSpec(shape=(None, 1), dtype=tf.float32, name='sepal_length_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='sepal_width_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='petal_length_xf'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='petal_width_xf')]
Argument #2
DType: bool
Value: True
Argument #3
DType: NoneType
Value: None
Option #4
Callable with:
Argument #1
DType: list
Value: [TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/0'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/1'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/2'), TensorSpec(shape=(None, 1), dtype=tf.float32, name='inputs/3')]
Argument #2
DType: bool
Value: False
Argument #3
DType: NoneType
Value: None
!saved_model_cli show --dir {export_path} --tag_set serve --signature_def serving_default
output:
The given SavedModel SignatureDef contains the following input(s):
inputs['examples'] tensor_info:
dtype: DT_STRING
shape: (-1)
name: serving_default_examples:0
The given SavedModel SignatureDef contains the following output(s):
outputs['output_0'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 3)
name: StatefulPartitionedCall:0
Method name is: tensorflow/serving/predict
What is the correct POST API call to get the predictions from this model?
Thank you in advance
