tfserver 0.1a2

Tensor Flow Model Server

==========================================
Tensorflow gRPC and RESTful API Server
==========================================


Tensorflow Server
----------------------

Save this script to app.py

..code: python

import tfserver
import skitai
import dnn
import tensorflow as tf

pref = skitai.pref ()

pref.debug = True
pref.use_reloader = True

tf.reset_default_graph()
net = dnn.make_mlp_network(tf, phase_train=False)

pref.config.tf_config = tf.ConfigProto(
gpu_options=tf.GPUOptions (per_process_gpu_memory_fraction = 0.2),
log_device_placement = False
)
pref.config.tf_model_dir = "./exported/2"
pref.config.tf_predict_op = net ["pred"]
pref.config.tf_x= net ["x"]

skitai.mount ("/", tfserver, pref = pref)
skitai.run (port = 5000)


gRPC Client
--------------

Using grpc,

..code: python

from tfserver import cli
from tensorflow.python.framework import tensor_util

stub = cli.Proxy ("localhost", 5000)
x = np.array ([1.0, 2.0])

resp = stub.predict (
'model_name',
'signature_name',
tensor_util.make_tensor_proto(x.astype('float32'), shape=x.shape)
)
resp.y
>> [-1.5, 1.6]


Using aquests,

..code: python

from tfserver import predict_pb2, cli
import aquests
from tensorflow.python.framework import tensor_util

def print_result (resp):
cli.Response (resp.data).y
>> [-1.5, 1.6]

stub = aquests.grpc ("http://localhost:5000", callback = print_result)
x = np.array ([1.0, 2.0])

request = predict_pb2.PredictRequest()
request.model_spec.name = 'model_name'
request.model_spec.signature_name = 'signature_name'
request.inputs ["x"].CopyFrom(tensor_util.make_tensor_proto(fftseq.astype('float32'), shape=fftseq.shape))
stub.Predict (request, 10.0)


REST API
----------

Using requests,

..code: python

import requests

api = requests.session ()
resp = api.post (
"http://localhost:5000/predict",
json.dumps ({"x": getone ().astype ("float32").tolist()}),
headers = {"Content-Type": "application/json"}
)
data = json.loads (resp.text)
data ["y"]
>> [-1.5, 1.6]

Another,

..code: python

from aquests.lib import siesta

x = np.array ([1.0, 2.0])

api = siesta.API ("http://localhost:5000")
resp = api.predict ().post ({"x": x.astype ("float32").tolist()})
resp.data.y
>> [-1.5, 1.6]