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Delete example files.
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SangBin Cho
@@ -1,63 +0,0 @@
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# yapf: disable
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# __doc_import_begin__
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from ray import serve
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from io import BytesIO
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from PIL import Image
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import requests
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import torch
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from torchvision import transforms
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from torchvision.models import resnet18
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# __doc_import_end__
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# yapf: enable
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# __doc_define_servable_begin__
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class ImageModel:
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def __init__(self):
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self.model = resnet18(pretrained=True)
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self.preprocessor = transforms.Compose([
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transforms.Resize(224),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Lambda(lambda t: t[:3, ...]), # remove alpha channel
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])
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def __call__(self, flask_request):
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image_payload_bytes = flask_request.data
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pil_image = Image.open(BytesIO(image_payload_bytes))
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print("[1/3] Parsed image data: {}".format(pil_image))
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pil_images = [pil_image] # Our current batch size is one
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input_tensor = torch.cat(
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[self.preprocessor(i).unsqueeze(0) for i in pil_images])
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print("[2/3] Images transformed, tensor shape {}".format(
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input_tensor.shape))
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with torch.no_grad():
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output_tensor = self.model(input_tensor)
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print("[3/3] Inference done!")
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return {"class_index": int(torch.argmax(output_tensor[0]))}
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# __doc_define_servable_end__
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# __doc_deploy_begin__
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serve.init()
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serve.create_endpoint("predictor", "/image_predict", methods=["POST"])
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serve.create_backend("resnet18:v0", ImageModel)
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serve.set_traffic("predictor", {"resnet18:v0": 1})
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# __doc_deploy_end__
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# __doc_query_begin__
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ray_logo_bytes = requests.get(
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"https://github.com/ray-project/ray/raw/"
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"master/doc/source/images/ray_header_logo.png").content
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resp = requests.post(
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"http://localhost:8000/image_predict", data=ray_logo_bytes)
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print(resp.json())
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# Output
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# {'class_index': 463}
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# __doc_query_end__
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@@ -1,87 +0,0 @@
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# yapf: disable
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# __doc_import_begin__
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from ray import serve
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import pickle
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import json
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import numpy as np
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import requests
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from sklearn.datasets import load_iris
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.metrics import mean_squared_error
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# __doc_import_end__
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# yapf: enable
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# __doc_train_model_begin__
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# Load data
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data, target, target_names, description, feature_names, _ = load_iris().values(
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)
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# Instantiate model
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model = GradientBoostingClassifier()
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# Training and validation split
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np.random.shuffle(data), np.random.shuffle(target)
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train_x, train_y = data[:100], target[:100]
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val_x, val_y = data[100:], target[100:]
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# Train and evaluate models
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model.fit(train_x, train_y)
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print("MSE:", mean_squared_error(model.predict(val_x), val_y))
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# Save the model and label to file
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with open("/tmp/iris_model_logistic_regression.pkl", "wb") as f:
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pickle.dump(model, f)
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with open("/tmp/iris_labels.json", "w") as f:
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json.dump(target_names.tolist(), f)
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# __doc_train_model_end__
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# __doc_define_servable_begin__
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class BoostingModel:
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def __init__(self):
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with open("/tmp/iris_model_logistic_regression.pkl", "rb") as f:
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self.model = pickle.load(f)
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with open("/tmp/iris_labels.json") as f:
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self.label_list = json.load(f)
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def __call__(self, flask_request):
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payload = flask_request.json
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print("Worker: received flask request with data", payload)
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input_vector = [
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payload["sepal length"],
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payload["sepal width"],
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payload["petal length"],
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payload["petal width"],
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]
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prediction = self.model.predict([input_vector])[0]
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human_name = self.label_list[prediction]
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return {"result": human_name}
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# __doc_define_servable_end__
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# __doc_deploy_begin__
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serve.init()
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serve.create_endpoint("iris_classifier", "/regressor")
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serve.create_backend("lr:v1", BoostingModel)
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serve.set_traffic("iris_classifier", {"lr:v1": 1})
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# __doc_deploy_end__
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# __doc_query_begin__
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sample_request_input = {
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"sepal length": 1.2,
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"sepal width": 1.0,
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"petal length": 1.1,
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"petal width": 0.9,
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}
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response = requests.get(
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"http://localhost:8000/regressor", json=sample_request_input)
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print(response.text)
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# Result:
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# {
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# "result": "versicolor"
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# }
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# __doc_query_end__
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@@ -1,86 +0,0 @@
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# yapf: disable
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# __doc_import_begin__
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from ray import serve
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import os
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import numpy as np
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import requests
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# __doc_import_end__
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# yapf: enable
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# __doc_train_model_begin__
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TRAINED_MODEL_PATH = "/tmp/mnist_model.h5"
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def train_and_save_model():
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import tensorflow as tf
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# Load mnist dataset
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mnist = tf.keras.datasets.mnist
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_train, x_test = x_train / 255.0, x_test / 255.0
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# Train a simple neural net model
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model = tf.keras.models.Sequential([
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tf.keras.layers.Flatten(input_shape=(28, 28)),
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tf.keras.layers.Dense(128, activation="relu"),
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tf.keras.layers.Dropout(0.2),
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tf.keras.layers.Dense(10)
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])
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loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
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model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
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model.fit(x_train, y_train, epochs=1)
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model.evaluate(x_test, y_test, verbose=2)
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model.summary()
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# Save the model in h5 format in local file system
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model.save(TRAINED_MODEL_PATH)
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if not os.path.exists(TRAINED_MODEL_PATH):
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train_and_save_model()
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# __doc_train_model_end__
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# __doc_define_servable_begin__
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class TFMnistModel:
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def __init__(self, model_path):
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import tensorflow as tf
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self.model_path = model_path
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self.model = tf.keras.models.load_model(model_path)
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def __call__(self, flask_request):
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# Step 1: transform HTTP request -> tensorflow input
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# Here we define the request schema to be a json array.
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input_array = np.array(flask_request.json["array"])
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reshaped_array = input_array.reshape((1, 28, 28))
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# Step 2: tensorflow input -> tensorflow output
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prediction = self.model(reshaped_array)
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# Step 3: tensorflow output -> web output
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return {
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"prediction": prediction.numpy().tolist(),
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"file": self.model_path
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}
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# __doc_define_servable_end__
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# __doc_deploy_begin__
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serve.init()
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serve.create_endpoint(endpoint_name="tf_classifier", route="/mnist")
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serve.create_backend("tf:v1", TFMnistModel, "/tmp/mnist_model.h5")
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serve.set_traffic("tf_classifier", {"tf:v1": 1})
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# __doc_deploy_end__
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# __doc_query_begin__
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resp = requests.get(
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"http://localhost:8000/mnist",
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json={"array": np.random.randn(28 * 28).tolist()})
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print(resp.json())
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# {
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# "prediction": [[-1.504277229309082, ..., -6.793371200561523]],
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# "file": "/tmp/mnist_model.h5"
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# }
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# __doc_query_end__
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