TFServe

TFServe is a framework designed to serve tensorflow models in a simple and easy way as an HTTP API server. It's built on top of Werkzeug.

How to install

$ pip install tfserve

After installing tfserve, install either tensorflow of tensorflow-gpu (the latter if you have GPU available).

$ pip install tensorflow

or

$ pip install tensorflow-gpu

How to use

Python API

You will need 5 parts:

1. Model: it can be a .pb file or a model directory containing ckpt files.
2. Input tensor names: name of the input tensors of the graph.
3. Output tensor names: name of the output tensors of the graph.
4. encode: python function that receives the request body data and outputs a dict mapping input tensor names to input numpy values.
5. decode: python function that receives a dict mapping output tensor names to output numpy values and returns the HTTP response.

Follow the example to learn how to combine these parts...

Example

Deploy image classification service that receives a binary jpg image and returns the class of the object found in the image alongside it's probability.

# 1. Model: trained mobilenet on ImageNet that can be downloaded from
#           https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz
MODEL_PATH = "mobilenet_v2_1.4_224/mobilenet_v2_1.4_224_frozen.pb"

# 2. Input tensor names:
INPUT_TENSORS = ["import/input:0"]

# 3. Output tensor names:
OUTPUT_TENSORS = ["import/MobilenetV2/Predictions/Softmax:0"]

# 4. encode function: Receives raw jpg image as request data. Returns dict
#                     mappint import/input:0 to numpy value.
def encode(request_data):
    with tempfile.NamedTemporaryFile(mode="wb", suffix=".jpg") as f:
        f.write(request_data)
        # Model receives 224x224 normalized RGB image.
        img = Image.open(f.name).resize((224, 224)) 
        img = np.asarray(img) / 255.

    return {INPUT_TENSORS[0]: img}

# 5. decode function: Receives `dict` mapping import/MobilenetV2/Predictions/Softmax:0 to
#                     numpy value and builds dict with for json response.
def decode(outputs):
    p = outputs[OUTPUT_TENSORS[0]] # 1001 vector with probabilities for each class.
    index = np.argmax(p)
    return {"class": index_to_class_map[index-1], "prob": float(p[index])}

That's it! Now create TFServeApp object and run it!

app = TFServeApp(MODEL_PATH, INPUT_TENSORS, OUTPUT_TENSORS, encode, decode)
app.run('127.0.0.1', 5000)  # Host and port where the server will be running

See client.py for full example.

How to consume server

The server supports only POST method to / with the input information as part of the request body.

The input will be proccessed in the encode function to produce the feed_dict object that will be passed to the graph. The graph output will be processed in the decode function and the server will return whatever the decode function returns.

CLI

tfserve also provides a CLI program with built-in encode/decode handlers:

tfserve -m PATH [-i INPUTS] [-o OUTPUTS] [-h HANDLER] [-b] [-H HOST] [-p PORT]

  -m PATH, --model PATH
                        path to pb file or directory containing checkpoint
  -i INPUTS, --inputs INPUTS
                        a comma separated list of input tensors
  -o OUTPUTS, --outputs OUTPUTS
                        a comma separated list of output tensors
  -h HANDLER, --handler HANDLER
                        encode/decode handler (deault is 'json')
  -b, --batch           process multiple inputs (default is to process
                        one input per request)
  -H HOST, --host HOST  host interface to bind to (0.0.0.0)
  -p PORT, --port PORT  port to listen on (5000)

Example

$ tfserve -m models/graph.pb -i x:0 -o out:0 -h json -H localhost

Run tfserve with models/graph.pb model that takes as input the tensor with name x:0 (dimesion: [?,5]) and outputs a tensor named out:0. The server will run on http://localhost:5000/ and will receive POST requests to /.

By using the json handler, you can provide the input data as a json object in the request body:

{
  "x:0": [1,1,3,4,5]
}

You will receive a json output object as:

{
  "out:0": 0.48
}

More information about CLI

Run:

$ tfserve --help

Help

• What if I don't know the tensor names?

You can use tfserve.helper.estimate_io_tensors(model_path) function to get a list of possible input/output tensor names. Also, you can use the CLI by running: tfserve -m [model_path] --help-model

• What if I want to run multiple inferences at the same time?

You can use batch=True when building tfserve.TFServeApp. You will then need to handle the batch dimension yourself in the encode and decode function. Also, if using the CLI, just add the --batch flag.

Limitation

It only works with one-to-one models. That is, models that need to run the graph only once to get the inference. Other architectures of inference will be supported soon. Help is appreciated!