使用 TFServing 部署 TensorFlow 模型

import os

os.environ["KERAS_BACKEND"] = "tensorflow"

import json
import shutil
import requests
import numpy as np
import tensorflow as tf
import keras
import matplotlib.pyplot as plt

model = keras.applications.MobileNet()

def preprocess(image, mean=0.5, std=0.5, shape=(224, 224)):
    """Scale, normalize and resizes images."""
    image = image / 255.0  # Scale
    image = (image - mean) / std  # Normalize
    image = tf.image.resize(image, shape)  # Resize
    return image

# Download human-readable labels for ImageNet.
imagenet_labels_url = (
    "https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt"
)
response = requests.get(imagenet_labels_url)
# Skipping background class
labels = [x for x in response.text.split("\n") if x != ""][1:]
# Convert the labels to the TensorFlow data format
tf_labels = tf.constant(labels, dtype=tf.string)


def postprocess(prediction, labels=tf_labels):
    """Convert from probs to labels."""
    indices = tf.argmax(prediction, axis=-1)  # Index with highest prediction
    label = tf.gather(params=labels, indices=indices)  # Class name
    return label

response = requests.get("https://i.imgur.com/j9xCCzn.jpeg", stream=True)

with open("banana.jpeg", "wb") as f:
    shutil.copyfileobj(response.raw, f)

sample_img = plt.imread("./banana.jpeg")
print(f"Original image shape: {sample_img.shape}")
print(f"Original image pixel range: ({sample_img.min()}, {sample_img.max()})")
plt.imshow(sample_img)
plt.show()

preprocess_img = preprocess(sample_img)
print(f"Preprocessed image shape: {preprocess_img.shape}")
print(
    f"Preprocessed image pixel range: ({preprocess_img.numpy().min()},",
    f"{preprocess_img.numpy().max()})",
)

batched_img = tf.expand_dims(preprocess_img, axis=0)
batched_img = tf.cast(batched_img, tf.float32)
print(f"Batched image shape: {batched_img.shape}")

model_outputs = model(batched_img)
print(f"Model output shape: {model_outputs.shape}")
print(f"Predicted class: {postprocess(model_outputs)}")

model_dir = "./model"
model_version = 1
model_export_path = f"{model_dir}/{model_version}"

tf.saved_model.save(
    model,
    export_dir=model_export_path,
)

print(f"SavedModel files: {os.listdir(model_export_path)}")

!saved_model_cli show --dir {model_export_path} --tag_set serve --signature_def serving_default

wget 'http://storage.googleapis.com/tensorflow-serving-apt/pool/tensorflow-model-server-universal-2.8.0/t/tensorflow-model-server-universal/tensorflow-model-server-universal_2.8.0_all.deb'
dpkg -i tensorflow-model-server-universal_2.8.0_all.deb

# Environment variable with the path to the model
os.environ["MODEL_DIR"] = f"{model_dir}"

%%bash --bg
nohup tensorflow_model_server \
  --port=8500 \
  --rest_api_port=8501 \
  --model_name=model \
  --model_base_path=$MODEL_DIR >server.log 2>&1

# We can check the logs to the server to help troubleshooting
!cat server.log

[warn] getaddrinfo: address family for nodename not supported
[evhttp_server.cc : 245] NET_LOG: Entering the event loop ...

# Now we can check if tensorflow is in the active services
!sudo lsof -i -P -n | grep LISTEN

node         7 root   21u  IPv6  19100      0t0  TCP *:8080 (LISTEN)
kernel_ma   34 root    7u  IPv4  18874      0t0  TCP 172.28.0.12:6000 (LISTEN)
colab-fil   63 root    5u  IPv4  17975      0t0  TCP *:3453 (LISTEN)
colab-fil   63 root    6u  IPv6  17976      0t0  TCP *:3453 (LISTEN)
jupyter-n   81 root    6u  IPv4  18092      0t0  TCP 172.28.0.12:9000 (LISTEN)
python3    101 root   23u  IPv4  18252      0t0  TCP 127.0.0.1:44915 (LISTEN)
python3    132 root    3u  IPv4  20548      0t0  TCP 127.0.0.1:15264 (LISTEN)
python3    132 root    4u  IPv4  20549      0t0  TCP 127.0.0.1:37977 (LISTEN)
python3    132 root    9u  IPv4  20662      0t0  TCP 127.0.0.1:40689 (LISTEN)
tensorflo 1101 root    5u  IPv4  35543      0t0  TCP *:8500 (LISTEN)
tensorflo 1101 root   12u  IPv4  35548      0t0  TCP *:8501 (LISTEN)

data = json.dumps(
    {
        "signature_name": "serving_default",
        "instances": batched_img.numpy().tolist(),
    }
)
url = "https://127.0.0.1:8501/v1/models/model:predict"


def predict_rest(json_data, url):
    json_response = requests.post(url, data=json_data)
    response = json.loads(json_response.text)
    rest_outputs = np.array(response["predictions"])
    return rest_outputs

rest_outputs = predict_rest(data, url)

print(f"REST output shape: {rest_outputs.shape}")
print(f"Predicted class: {postprocess(rest_outputs)}")

REST output shape: (1, 1000)
Predicted class: [b'banana']

import grpc

# Create a channel that will be connected to the gRPC port of the container
channel = grpc.insecure_channel("localhost:8500")

pip install -q tensorflow_serving_api

from tensorflow_serving.apis import predict_pb2, prediction_service_pb2_grpc

# Create a stub made for prediction
# This stub will be used to send the gRPCrequest to the TF Server
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)

# Get the serving_input key
loaded_model = tf.saved_model.load(model_export_path)
input_name = list(
    loaded_model.signatures["serving_default"].structured_input_signature[1].keys()
)[0]

def predict_grpc(data, input_name, stub):
    # Create a gRPC request made for prediction
    request = predict_pb2.PredictRequest()

    # Set the name of the model, for this use case it is "model"
    request.model_spec.name = "model"

    # Set which signature is used to format the gRPC query
    # here the default one "serving_default"
    request.model_spec.signature_name = "serving_default"

    # Set the input as the data
    # tf.make_tensor_proto turns a TensorFlow tensor into a Protobuf tensor
    request.inputs[input_name].CopyFrom(tf.make_tensor_proto(data.numpy().tolist()))

    # Send the gRPC request to the TF Server
    result = stub.Predict(request)
    return result


grpc_outputs = predict_grpc(batched_img, input_name, stub)
grpc_outputs = np.array([grpc_outputs.outputs['predictions'].float_val])

print(f"gRPC output shape: {grpc_outputs.shape}")
print(f"Predicted class: {postprocess(grpc_outputs)}")

gRPC output shape: (1, 1000)
Predicted class: [b'banana']

def export_model(model, labels):
    @tf.function(input_signature=[tf.TensorSpec([None, None, None, 3], tf.float32)])
    def serving_fn(image):
        processed_img = preprocess(image)
        probs = model(processed_img)
        label = postprocess(probs)
        return {"label": label}

    return serving_fn


model_sig_version = 2
model_sig_export_path = f"{model_dir}/{model_sig_version}"

tf.saved_model.save(
    model,
    export_dir=model_sig_export_path,
    signatures={"serving_default": export_model(model, labels)},
)

!saved_model_cli show --dir {model_sig_export_path} --tag_set serve --signature_def serving_default

batched_raw_img = tf.expand_dims(sample_img, axis=0)
batched_raw_img = tf.cast(batched_raw_img, tf.float32)

loaded_model = tf.saved_model.load(model_sig_export_path)
loaded_model.signatures["serving_default"](**{"image": batched_raw_img})

data = json.dumps(
    {
        "signature_name": "serving_default",
        "instances": batched_raw_img.numpy().tolist(),
    }
)
url_sig = "https://127.0.0.1:8501/v1/models/model/versions/2:predict"

print(f"REST output shape: {rest_outputs.shape}")
print(f"Predicted class: {rest_outputs}")

REST output shape: (1,)
Predicted class: ['banana']

channel = grpc.insecure_channel("localhost:8500")
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)

input_name = list(
    loaded_model.signatures["serving_default"].structured_input_signature[1].keys()
)[0]

grpc_outputs = predict_grpc(batched_raw_img, input_name, stub)
grpc_outputs = np.array([grpc_outputs.outputs['label'].string_val])

print(f"gRPC output shape: {grpc_outputs.shape}")
print(f"Predicted class: {grpc_outputs}")

gRPC output shape: (1, 1)
Predicted class: [[b'banana']]