Welcome to simple-tensorflow-serving’s documentation!

Introduction

_images/simple_tensorflow_serving_introduction.jpeg

Simple TensorFlow Serving is the generic and easy-to-use serving service for machine learning models.

  • [x] Support distributed TensorFlow models
  • [x] Support the general RESTful/HTTP APIs
  • [x] Support inference with accelerated GPU
  • [x] Support curl and other command-line tools
  • [x] Support clients in any programming language
  • [x] Support code-gen client by models without coding
  • [x] Support inference with raw file for image models
  • [x] Support statistical metrics for verbose requests
  • [x] Support serving multiple models at the same time
  • [x] Support dynamic online and offline for model versions
  • [x] Support loading new custom op for TensorFlow models
  • [x] Support secure authentication with configurable basic auth
  • [x] Support multiple models of TensorFlow/MXNet/PyTorch/Caffe2/CNTK/ONNX/H2o/Scikit-learn/XGBoost/PMML

_images/dashboard.png

Installation

Pip

Install the server with pip.

pip install simple_tensorflow_serving

Source

Install from source code.

git clone https://github.com/tobegit3hub/simple_tensorflow_serving

cd ./simple_tensorflow_serving/

python ./setup.py install

Bazel

Install with bazel.

git clone https://github.com/tobegit3hub/simple_tensorflow_serving

cd ./simple_tensorflow_serving/

bazel build simple_tensorflow_serving:server

Docker

Deploy with docker image.

docker run -d -p 8500:8500 tobegit3hub/simple_tensorflow_serving

docker run -d -p 8500:8500 tobegit3hub/simple_tensorflow_serving:latest-gpu

docker run -d -p 8500:8500 tobegit3hub/simple_tensorflow_serving:latest-hdfs

docker run -d -p 8500:8500 tobegit3hub/simple_tensorflow_serving:latest-py34

Docker Compose

Deploy with docker-compose.

wget https://raw.githubusercontent.com/tobegit3hub/simple_tensorflow_serving/master/docker-compose.yml

docker-compose up -d

Kubernetes

Deploy in Kubernetes cluster.

wget https://raw.githubusercontent.com/tobegit3hub/simple_tensorflow_serving/master/simple_tensorflow_serving.yaml

kubectl create -f ./simple_tensorflow_serving.yaml

Quick Start

Train or download the TensorFlow SavedModel.

import tensorflow as tf

export_dir = "./model/1"
input_keys_placeholder = tf.placeholder(
    tf.int32, shape=[None, 1], name="input_keys")
output_keys = tf.identity(input_keys_placeholder, name="output_keys")

session = tf.Session()
tf.saved_model.simple_save(
    session,
    export_dir,
    inputs={"keys": input_keys_placeholder},
    outputs={"keys": output_keys})

This script will export the model in ./model.

_images/savedmodel_files.png

Start serving to load the model.

simple_tensorflow_serving --model_base_path="./model"

Check out the dashboard in http://127.0.0.1:8500 in web browser.

_images/dashboard.pngdashboard

Generate the clients for testing without coding.

curl http://localhost:8500/v1/models/default/gen_client?language=python > client.py

python ./client.py

_images/gen_python_code.png

Advanced Usage

Multiple Models

It supports serve multiple models and multiple versions of these models. You can run the server with this configuration.

{
  "model_config_list": [
    {
      "name": "tensorflow_template_application_model",
      "base_path": "./models/tensorflow_template_application_model/",
      "platform": "tensorflow"
    }, {
      "name": "deep_image_model",
      "base_path": "./models/deep_image_model/",
      "platform": "tensorflow"
    }, {
       "name": "mxnet_mlp_model",
       "base_path": "./models/mxnet_mlp/mx_mlp",
       "platform": "mxnet"
    }
  ]
}

simple_tensorflow_serving --model_config_file="./examples/model_config_file.json"

_images/dashboard_multiple_models.png

GPU Acceleration

If you want to use GPU, try with the docker image with GPU tag and put cuda files in /usr/cuda_files/.

export CUDA_SO="-v /usr/cuda_files/:/usr/cuda_files/"
export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
export LIBRARY_ENV="-e LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/cuda_files"

docker run -it -p 8500:8500 $CUDA_SO $DEVICES $LIBRARY_ENV tobegit3hub/simple_tensorflow_serving:latest-gpu

You can set session config and gpu options in command-line parameter or the model config file.

simple_tensorflow_serving --model_base_path="./models/tensorflow_template_application_model" --session_config='{"log_device_placement": true, "allow_soft_placement": true, "allow_growth": true, "per_process_gpu_memory_fraction": 0.5}'

{
  "model_config_list": [
    {
      "name": "default",
      "base_path": "./models/tensorflow_template_application_model/",
      "platform": "tensorflow",
      "session_config": {
        "log_device_placement": true,
        "allow_soft_placement": true,
        "allow_growth": true,
        "per_process_gpu_memory_fraction": 0.5
      }
    }
  ]
}

Here is the benchmark of CPU and GPU inference and y-coordinate is the latency(the lower the better).

_images/cpu_gpu_benchmark.png

Generated Client

You can generate the test json data for the online models.

curl http://localhost:8500/v1/models/default/gen_json

Or generate clients in different languages(Bash, Python, Golang, JavaScript etc.) for your model without writing any code.

curl http://localhost:8500/v1/models/default/gen_client?language=python > client.py
curl http://localhost:8500/v1/models/default/gen_client?language=bash > client.sh
curl http://localhost:8500/v1/models/default/gen_client?language=golang > client.go
curl http://localhost:8500/v1/models/default/gen_client?language=javascript > client.js

The generated code should look like these which can be test immediately.

#!/usr/bin/env python

import requests

def main():
  endpoint = "http://127.0.0.1:8500"

  input_data = {"keys": [[1.0], [1.0]], "features": [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]}
  result = requests.post(endpoint, json=input_data)
  print(result.json())

if __name__ == "__main__":
  main()

Image Model

For image models, we can request with the raw image files instead of constructing array data.

Now start serving the image model like deep_image_model.

simple_tensorflow_serving --model_base_path="./models/deep_image_model/"

Then request with the raw image file which has the same shape of your model.

curl -X POST -F 'image=@./images/mew.jpg' -F "model_version=1" 127.0.0.1:8500

Custom Op

If your models rely on new TensorFlow custom op, you can run the server while loading the so files.

simple_tensorflow_serving --model_base_path="./model/" --custom_op_paths="./foo_op/"

Please check out the complete example in ./examples/custom_op/.

Authentication

For enterprises, we can enable basic auth for all the APIs and any anonymous request is denied.

Now start the server with the configured username and password.

./server.py --model_base_path="./models/tensorflow_template_application_model/" --enable_auth=True --auth_username="admin" --auth_password="admin"

If you are using the Web dashboard, just type your certification. If you are using clients, give the username and password within the request.

curl -u admin:admin -H "Content-Type: application/json" -X POST -d '{"data": {"keys": [[11.0], [2.0]], "features": [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]]}}' http://127.0.0.1:8500

endpoint = "http://127.0.0.1:8500"
input_data = {
  "data": {
      "keys": [[11.0], [2.0]],
      "features": [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]]
  }
}
auth = requests.auth.HTTPBasicAuth("admin", "admin")
result = requests.post(endpoint, json=input_data, auth=auth)

TSL/SSL

It supports TSL/SSL and you can generate the self-signed secret files for testing.

openssl req -x509 -newkey rsa:4096 -nodes -out /tmp/secret.pem -keyout /tmp/secret.key -days 365

Then run the server with certification files.

simple_tensorflow_serving --enable_ssl=True --secret_pem=/tmp/secret.pem --secret_key=/tmp/secret.key --model_base_path="./models/tensorflow_template_application_model"

API

RESTful API

The most import API is inference for the loaded models.

Endpoint: /
Method: POST
JSON: {
        "model_name": "default",
        "model_version": 1,
        "data": {
            "keys": [[11.0], [2.0]],
            "features": [[1, 1, 1, 1, 1, 1, 1, 1, 1],
                         [1, 1, 1, 1, 1, 1, 1, 1, 1]]
        }
      }
Response: {
            "keys": [[1], [1]]
          }

Python Example

You can easily choose the specified model and version for inference.

import requests

endpoint = "http://127.0.0.1:8500"
input_data = {
  "model_name": "default",
  "model_version": 1,
  "data": {
      "keys": [[11.0], [2.0]],
      "features": [[1, 1, 1, 1, 1, 1, 1, 1, 1],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1]]
  }
}
result = requests.post(endpoint, json=input_data)

Models

TensorFlow

For TensorFlow models, you can load with commands and configuration like these.

simple_tensorflow_serving --model_base_path="./models/tensorflow_template_application_model" --model_platform="tensorflow"

endpoint = "http://127.0.0.1:8500"
input_data = {
  "model_name": "default",
  "model_version": 1,
  "data": {
      "data": [[12.0, 2.0]]
  }
}
result = requests.post(endpoint, json=input_data)
print(result.text)

MXNET

For MXNet models, you can load with commands and configuration like these.

simple_tensorflow_serving --model_base_path="./models/mxnet_mlp/mx_mlp" --model_platform="mxnet"

endpoint = "http://127.0.0.1:8500"
input_data = {
  "model_name": "default",
  "model_version": 1,
  "data": {
      "data": [[12.0, 2.0]]
  }
}
result = requests.post(endpoint, json=input_data)
print(result.text)

ONNX

For ONNX models, you can load with commands and configuration like these.

simple_tensorflow_serving --model_base_path="./models/onnx_mnist_model/onnx_model.proto" --model_platform="onnx"

endpoint = "http://127.0.0.1:8500"
input_data = {
  "model_name": "default",
  "model_version": 1,
  "data": {
      "data": [[...]]
  }
}
result = requests.post(endpoint, json=input_data)
print(result.text)

Scikit-learn

For Scikit-learn models, you can load with commands and configuration like these.

simple_tensorflow_serving --model_base_path="./models/scikitlearn_iris/model.joblib" --model_platform="scikitlearn"

simple_tensorflow_serving --model_base_path="./models/scikitlearn_iris/model.pkl" --model_platform="scikitlearn"

endpoint = "http://127.0.0.1:8500"
input_data = {
  "model_name": "default",
  "model_version": 1,
  "data": {
      "data": [[...]]
  }
}
result = requests.post(endpoint, json=input_data)
print(result.text)

XGBoost

For XGBoost models, you can load with commands and configuration like these.

simple_tensorflow_serving --model_base_path="./models/xgboost_iris/model.bst" --model_platform="xgboost"

simple_tensorflow_serving --model_base_path="./models/xgboost_iris/model.joblib" --model_platform="xgboost"

simple_tensorflow_serving --model_base_path="./models/xgboost_iris/model.pkl" --model_platform="xgboost"

endpoint = "http://127.0.0.1:8500"
input_data = {
  "model_name": "default",
  "model_version": 1,
  "data": {
      "data": [[...]]
  }
}
result = requests.post(endpoint, json=input_data)
print(result.text)

PMML

For PMML models, you can load with commands and configuration like these. This relies on Openscoring and Openscoring-Python to load the models.

java -jar ./third_party/openscoring/openscoring-server-executable-1.4-SNAPSHOT.jar

simple_tensorflow_serving --model_base_path="./models/pmml_iris/DecisionTreeIris.pmml" --model_platform="pmml"

endpoint = "http://127.0.0.1:8500"
input_data = {
  "model_name": "default",
  "model_version": 1,
  "data": {
      "data": [[...]]
  }
}
result = requests.post(endpoint, json=input_data)
print(result.text)

H2o

For H2o models, you can load with commands and configuration like these.

# Start H2o server with "java -jar h2o.jar"

simple_tensorflow_serving --model_base_path="./models/h2o_prostate_model/GLM_model_python_1525255083960_17" --model_platform="h2o"

endpoint = "http://127.0.0.1:8500"
input_data = {
  "model_name": "default",
  "model_version": 1,
  "data": {
      "data": [[...]]
  }
}
result = requests.post(endpoint, json=input_data)
print(result.text)

Clients

Bash

Here is the example client in Bash.

curl -H "Content-Type: application/json" -X POST -d '{"data": {"keys": [[1.0], [2.0]], "features": [[10, 10, 10, 8, 6, 1, 8, 9, 1], [6, 2, 1, 1, 1, 1, 7, 1, 1]]}}' http://127.0.0.1:8500

Python

Here is the example client in Python.

endpoint = "http://127.0.0.1:8500"
payload = {"data": {"keys": [[11.0], [2.0]], "features": [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]]}}

result = requests.post(endpoint, json=payload)

Golang

endpoint := "http://127.0.0.1:8500"
dataByte := []byte(`{"data": {"keys": [[11.0], [2.0]], "features": [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]]}}`)
var dataInterface map[string]interface{}
json.Unmarshal(dataByte, &dataInterface)
dataJson, _ := json.Marshal(dataInterface)

resp, err := http.Post(endpoint, "application/json", bytes.NewBuffer(dataJson))

Ruby

Here is the example client in Ruby.

endpoint = "http://127.0.0.1:8500"
uri = URI.parse(endpoint)
header = {"Content-Type" => "application/json"}
input_data = {"data" => {"keys"=> [[11.0], [2.0]], "features"=> [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]]}}
http = Net::HTTP.new(uri.host, uri.port)
request = Net::HTTP::Post.new(uri.request_uri, header)
request.body = input_data.to_json

response = http.request(request)

JavaScript

Here is the example client in JavaScript.

var options = {
    uri: "http://127.0.0.1:8500",
    method: "POST",
    json: {"data": {"keys": [[11.0], [2.0]], "features": [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]]}}
};

request(options, function (error, response, body) {});

PHP

Here is the example client in PHP.

$endpoint = "127.0.0.1:8500";
$inputData = array(
    "keys" => [[11.0], [2.0]],
    "features" => [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]],
);
$jsonData = array(
    "data" => $inputData,
);
$ch = curl_init($endpoint);
curl_setopt_array($ch, array(
    CURLOPT_POST => TRUE,
    CURLOPT_RETURNTRANSFER => TRUE,
    CURLOPT_HTTPHEADER => array(
        "Content-Type: application/json"
    ),
    CURLOPT_POSTFIELDS => json_encode($jsonData)
));

$response = curl_exec($ch);

Erlang

Here is the example client in Erlang.

ssl:start(),
application:start(inets),
httpc:request(post,
  {"http://127.0.0.1:8500", [],
  "application/json",
  "{\"data\": {\"keys\": [[11.0], [2.0]], \"features\": [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]]}}"
  }, [], []).

Lua

Here is the example client in Lua.

local endpoint = "http://127.0.0.1:8500"
keys_array = {}
keys_array[1] = {1.0}
keys_array[2] = {2.0}
features_array = {}
features_array[1] = {1, 1, 1, 1, 1, 1, 1, 1, 1}
features_array[2] = {1, 1, 1, 1, 1, 1, 1, 1, 1}
local input_data = {
    ["keys"] = keys_array,
    ["features"] = features_array,
}
local json_data = {
    ["data"] = input_data
}
request_body = json:encode (json_data)
local response_body = {}

local res, code, response_headers = http.request{
    url = endpoint,
    method = "POST", 
    headers = 
      {
          ["Content-Type"] = "application/json";
          ["Content-Length"] = #request_body;
      },
      source = ltn12.source.string(request_body),
      sink = ltn12.sink.table(response_body),
}

Perl

Here is the example client in Perl.

my $endpoint = "http://127.0.0.1:8500";
my $json = '{"data": {"keys": [[11.0], [2.0]], "features": [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]]}}';
my $req = HTTP::Request->new( 'POST', $endpoint );
$req->header( 'Content-Type' => 'application/json' );
$req->content( $json );
$ua = LWP::UserAgent->new;

$response = $ua->request($req);

R

Here is the example client in R.

endpoint <- "http://127.0.0.1:8500"
body <- list(data = list(a = 1), keys = 1)
json_data <- list(
  data = list(
    keys = list(list(1.0), list(2.0)), features = list(list(1, 1, 1, 1, 1, 1, 1, 1, 1), list(1, 1, 1, 1, 1, 1, 1, 1, 1))
  )
)

r <- POST(endpoint, body = json_data, encode = "json")
stop_for_status(r)
content(r, "parsed", "text/html")

Postman

Here is the example with Postman.

_images/postman.png

Image Model

Introduction

Simple TensorFlow Serving has extra support for image models. You can deploy the image models easily and make inferences by uploading the image files in web browser or using form-data. The best practice is accepting base64 strings as input of model signature like this.

inputs {
  key: "images"
  value {
    name: "model_input_b64_images:0"
    dtype: DT_STRING
    tensor_shape {
      dim {
        size: -1
      }
    }
  }
}

Export Image Model

Model images should be standard TensorFlow SavedModel as well. We do not use [batch_size, r, g, b] or [batch_size, r, b, g] as signature input because it is not compatible with arbitrary image files. We can accept the base64 strings as input, then decode and resize the tensor for the required model input.

# Define model
def inference(input):
  weights = tf.get_variable(
      "weights", [784, 10], initializer=tf.random_normal_initializer())
  bias = tf.get_variable(
      "bias", [10], initializer=tf.random_normal_initializer())
  logits = tf.matmul(input, weights) + bias
 
  return logits
 
 
# Define op for model signature
tf.get_variable_scope().reuse_variables()
 
model_base64_placeholder = tf.placeholder(
    shape=[None], dtype=tf.string, name="model_input_b64_images")
model_base64_string = tf.decode_base64(model_base64_placeholder)
model_base64_input = tf.map_fn(lambda x: tf.image.resize_images(tf.image.decode_jpeg(x, channels=1), [28, 28]), model_base64_string, dtype=tf.float32)
model_base64_reshape_input = tf.reshape(model_base64_input, [-1, 28 * 28])
model_logits = inference(model_base64_reshape_input)
model_predict_softmax = tf.nn.softmax(model_logits)
model_predict = tf.argmax(model_predict_softmax, 1)
 
 
# Export model
export_dir = "./model/1"
tf.saved_model.simple_save(
    sess,
    export_dir,
    inputs={"images": model_base64_placeholder},
    outputs={
        "predict": model_predict,
        "probability": model_predict_softmax
    })

Inference With Uploaded Files

Now we can start Simple TensorFlow Serving and load the image models easily. Take the deep_image_model for example.

git clone https://github.com/tobegit3hub/simple_tensorflow_serving

cd ./simple_tensorflow_serving/models/

simple_tensorflow_serving --model_base_path="./deep_image_model"

Then you can choose the local image file to make inference.

_images/image_inference.png

Inference with Python Client

If you want to make inferences with Python client. You can encode the image file with the base64 library.

import requests
import base64

def main():
  image_string = base64.urlsafe_b64encode(open("./test.png", "rb").read())

  endpoint = "http://127.0.0.1:8500"
  json_data = {"model_name": "default", "data": {"images": [image_string]} }
  result = requests.post(endpoint, json=json_data)
  print(result.json())

if __name__ == "__main__":
  main()

Here is the exmaple data of one image’s base64 string.

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Performance

You can run SimpleTensorFlowServing with any WSGI server for better performance. We have benchmarked and compare with TensorFlow Serving. Find more details in benchmark directory.

STFS(Simple TensorFlow Serving) and TFS(TensorFlow Serving) have similar performances for different models. Vertical coordinate is inference latency(microsecond) and the less is better.

_images/benchmark_latency.jpeg

Then we test with ab with concurrent clients in CPU and GPU. TensorFlow Serving works better especially with GPUs.

_images/benchmark_concurrency.jpeg

For simplest model, each request only costs ~1.9 microseconds and one instance of Simple TensorFlow Serving can achieve 5000+ QPS. With larger batch size, it can inference more than 1M instances per second.

_images/benchmark_batch_size.jpeg

Development

Principle

  1. simple_tensorflow_serving starts the HTTP server with flask application.

  2. Load the TensorFlow models with tf.saved_model.loader Python API.

  3. Construct the feed_dict data from the JSON body of the request.

    // Method: POST, Content-Type: application/json
{
  "model_version": 1, // Optional
  "data": {
    "keys": [[1], [2]],
    "features": [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]]
  }
}

  4. Use the TensorFlow Python API to sess.run() with feed_dict data.

  5. For multiple versions supported, it starts independent thread to load models.

  6. For generated clients, it reads user’s model and render code with Jinja templates.

Debug

You can install the server with develop and test when code changes.

git clone https://github.com/tobegit3hub/simple_tensorflow_serving

cd ./simple_tensorflow_serving/

python ./setup.py develop