parrondo / deeptrading-tfserving-python

Tensorflow serving client implementation for trading.

deeptrading-tfserving-python

Tensorflow serving client

This is a tensorflow serving client implementation from https://github.com/epigramai/tfserving-python-predict-client.

You can find here a Tensorflow client examples to trade.

The predict client is meant to be used with a model served by TensorFlow Serving. Because tfserving model server runs a gRPC service, it cannot be requested by just sending a normal HTTP request. The predict_client package is a grcp client that can request the service.

Read the author's blog posts about TensorFlow Serving:

Part 1 and Part 2

and also my post serie: Deeptrading with tensorflow specially: https://todotrader.com/deep-trading-with-tensorflow-viii/

Install

conda create --name myenv

$ conda activate myenv

(myenv)$ pip install git+https://github.com/parrondo/deeptrading-tfserving-python.git

If you need a model server

There is one here https://github.com/bitnami/bitnami-docker-tensorflow-serving

How to use

Check out the examples.

Example 1 use fixed enter data and request a response from the serving model.

Example 2 generates random data simulating some Forex enter data and request a response from the serving model.

predict_client.prod_client ProdClient

def init(self, host, model_name, model_version):

• host: the host (e.g. '0.0.0.0:8500')
• model_name: your model name, e.g. '07_First_Forex_Prediction'
• model_version: model version, e.g. 1.

ProdClient.predict(self, request_data, request_timeout=10):

• request_data: A list of input tensors, see the example.
• request_timeout: timeout sent to the grcp stub

from predict_client.prod_client import ProdClient

client = ProdClient('localhost:9000', 'mnist', 1)

client.predict(request_data)

The predict function returns a dictionary with keys and values for each output tensor. The values in the dictionary will have the same shapes as the output tensor's shape. If an error occurs, predict will return an empty dict.

predict_client.inmemory_client InMemoryClient

def init(self, model_path):

• model_path

InMemoryClient.predict(self, request_data, request_timeout=None):

• request_data and request_timeout same as ProdClient, except request_timeout not used in this client.

from predict_client.inmemory_client import InMemoryClient

client = InMemoryClient('path/to/model.pb')

client.predict(request_data)

The predict function returns a dictionary with keys and values for each output tensor. The values in the dictionary will have the same shapes as the output tensor's shape. If an error occurs, predict will return an empty dict.

predict_client.mock_client MockClient

def init(self, mock_response):

• mock_response

MockClient.predict(self, request_data, request_timeout=None):

• request_data and request_timeout same as ProdClient, except request_timeout not used in mock client.

from predict_client.mock_client import MockClient

client = MockClient(mock_response)

client.predict(request_data)

The mock client predict function simply returns the mock response.

Development

Generate python code from .proto files

Caution, this is not always trivial!

pip install grpcio-tools python -m grpc_tools.protoc -I protos/ --python_out=predict_client/pbs --grpc_python_out=predict_client/pbs protos/*