Add metadata to tensorflow serving api call - tensorflow-serving

Question

Is it possible to add metadata to a tensorflow serving servable, such that this metadata is also populated in the response from the servable?
If I have a servable with the file structure:
my_servable/
1541778457/
variables/
saved_model.pb
For example:
```
outputs {
key: "classes"
value {
dtype: DT_STRING
tensor_shape {
dim {
size: 8
}
}
string_val: "a"
string_val: "b"
string_val: "c"
string_val: "d"
string_val: "e"
string_val: "f"
string_val: "g"
string_val: "h"
}
}
outputs {
key: "scores"
value {
dtype: DT_FLOAT
tensor_shape {
dim {
size: 1
}
dim {
size: 8
}
}
float_val: 1.212528104588273e-06
float_val: 5.094948463124638e-08
float_val: 0.0009737954242154956
float_val: 0.9988483190536499
float_val: 3.245145592245535e-07
float_val: 0.00010837535955943167
float_val: 4.101086960872635e-05
float_val: 2.676981057447847e-05
}
}
model_spec {
name: "my_model"
version {
value: 1541778457
}
signature_name: "prediction"
}
If I have something like a git hash or unique identifier for the code that generated this servable like f6ca434910504532a0d50dfd12f22d4c, is it possible to get this data in the client request?
Ideally something like:
```
outputs {
key: "classes"
value {
dtype: DT_STRING
tensor_shape {
dim {
size: 8
}
}
string_val: "a"
string_val: "b"
string_val: "c"
string_val: "d"
string_val: "e"
string_val: "f"
string_val: "g"
string_val: "h"
}
}
outputs {
key: "scores"
value {
dtype: DT_FLOAT
tensor_shape {
dim {
size: 1
}
dim {
size: 8
}
}
float_val: 1.212528104588273e-06
float_val: 5.094948463124638e-08
float_val: 0.0009737954242154956
float_val: 0.9988483190536499
float_val: 3.245145592245535e-07
float_val: 0.00010837535955943167
float_val: 4.101086960872635e-05
float_val: 2.676981057447847e-05
}
}
model_spec {
name: "my_model"
version {
value: 1541778457
}
hash {
value: f6ca434910504532a0d50dfd12f22d4c
}
signature_name: "prediction"
}
I tried changing the directory from 1541778457 to the hash, but this gave:
W tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:268] No versions of servable default found under base path

Answer 1

I suppose you could approach this problem in a couple of ways. If you want the idea to change the folder name to work, remember that folder name in this case describe your model version which I think must be an integer. I would therefore assume that you would need to convert your hash to either binary or decimal and then convert it back when you receive it.
A better solution in my opinion would be if you were able to change your model and add a variable containing your hash. And add it to the models signature_def. In python that would look something like:
// create your field
hash = tf.placeholder("f6ca434910504532a0d50dfd12f22d4c",tf.string, name="HASH")
// build tensor
hash_info = tf.saved_model.utils.build_tensor_info(hash)
// add hash_info in your output in signature_def
// then you should be able to receive that data in your request