ValueError：超出范围的浮点值在 Heroku 和 WSL 上不符合 JSON 标准，但在 Windows 上则不然

问题描述 投票：0回答：2

我正在尝试使用 FastAPI 将使用 Tensorflow 创建的 CNN 模型部署到 Heroku。该应用程序在 Heroku 上运行，但在尝试进行模型预测时返回错误。运行 

heroku logs --tail
返回：

2022-02-17T03:32:12.426547+00:00 app[web.1]: [2022-02-17 03:32:12 +0000] [10] [ERROR] Exception in ASGI application
2022-02-17T03:32:12.426549+00:00 app[web.1]: Traceback (most recent call last):
2022-02-17T03:32:12.426549+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/uvicorn/protocols/http/h11_impl.py", line 373, in run_asgi
2022-02-17T03:32:12.426550+00:00 app[web.1]: result = await app(self.scope, self.receive, self.send)
2022-02-17T03:32:12.426550+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/uvicorn/middleware/proxy_headers.py", line 75, in __call__
2022-02-17T03:32:12.426551+00:00 app[web.1]: return await self.app(scope, receive, send)
2022-02-17T03:32:12.426551+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/fastapi/applications.py", line 212, in __call__
2022-02-17T03:32:12.426552+00:00 app[web.1]: await super().__call__(scope, receive, send)
2022-02-17T03:32:12.426552+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/applications.py", line 112, in __call__
2022-02-17T03:32:12.426552+00:00 app[web.1]: await self.middleware_stack(scope, receive, send)
2022-02-17T03:32:12.426553+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/middleware/errors.py", line 181, in __call__
2022-02-17T03:32:12.426553+00:00 app[web.1]: raise exc
2022-02-17T03:32:12.426554+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/middleware/errors.py", line 159, in __call__
2022-02-17T03:32:12.426554+00:00 app[web.1]: await self.app(scope, receive, _send)
2022-02-17T03:32:12.426554+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/exceptions.py", line 82, in __call__
2022-02-17T03:32:12.426554+00:00 app[web.1]: raise exc
2022-02-17T03:32:12.426555+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/exceptions.py", line 71, in __call__
2022-02-17T03:32:12.426555+00:00 app[web.1]: await self.app(scope, receive, sender)
2022-02-17T03:32:12.426555+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/routing.py", line 656, in __call__
2022-02-17T03:32:12.426555+00:00 app[web.1]: await route.handle(scope, receive, send)
2022-02-17T03:32:12.426556+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/routing.py", line 259, in handle
2022-02-17T03:32:12.426556+00:00 app[web.1]: await self.app(scope, receive, send)
2022-02-17T03:32:12.426556+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/routing.py", line 61, in app
2022-02-17T03:32:12.426556+00:00 app[web.1]: response = await func(request)
2022-02-17T03:32:12.426557+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/fastapi/routing.py", line 250, in app
2022-02-17T03:32:12.426557+00:00 app[web.1]: response = actual_response_class(response_data, **response_args)
2022-02-17T03:32:12.426557+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/responses.py", line 49, in __init__
2022-02-17T03:32:12.426558+00:00 app[web.1]: self.body = self.render(content)
2022-02-17T03:32:12.426558+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/responses.py", line 174, in render
2022-02-17T03:32:12.426558+00:00 app[web.1]: return json.dumps(
2022-02-17T03:32:12.426559+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/json/__init__.py", line 234, in dumps
2022-02-17T03:32:12.426559+00:00 app[web.1]: return cls(
2022-02-17T03:32:12.426559+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/json/encoder.py", line 199, in encode
2022-02-17T03:32:12.426560+00:00 app[web.1]: chunks = self.iterencode(o, _one_shot=True)
2022-02-17T03:32:12.426560+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/json/encoder.py", line 257, in iterencode
2022-02-17T03:32:12.426560+00:00 app[web.1]: return _iterencode(o, 0)
2022-02-17T03:32:12.426561+00:00 app[web.1]: ValueError: Out of range float values are not JSON compliant

最值得注意的是最后一行，它显示“ValueError：超出范围的浮点值不符合 JSON”。我的 procfile 如下所示：

web: gunicorn -w 2 -k uvicorn.workers.UvicornWorker main:app

尝试使用 

python -m uvicorn main:app
在 WSL 上运行服务器时，出现此错误

ERROR:    Exception in ASGI application
Traceback (most recent call last):
  File "/home/eruaro/.local/lib/python3.8/site-packages/uvicorn/protocols/http/httptools_impl.py", line 375, in run_asgi
    result = await app(self.scope, self.receive, self.send)
  File "/home/eruaro/.local/lib/python3.8/site-packages/uvicorn/middleware/proxy_headers.py", line 75, in __call__
    return await self.app(scope, receive, send)
  File "/home/eruaro/.local/lib/python3.8/site-packages/fastapi/applications.py", line 212, in __call__
    await super().__call__(scope, receive, send)
  File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/applications.py", line 112, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/middleware/errors.py", line 181, in __call__
    raise exc
  File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/middleware/errors.py", line 159, in __call__
    await self.app(scope, receive, _send)
  File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/exceptions.py", line 82, in __call__
    raise exc
  File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/exceptions.py", line 71, in __call__
    await self.app(scope, receive, sender)
  File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/routing.py", line 656, in __call__
    await route.handle(scope, receive, send)
  File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/routing.py", line 259, in handle
    await self.app(scope, receive, send)
  File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/routing.py", line 61, in app
    response = await func(request)
  File "/home/eruaro/.local/lib/python3.8/site-packages/fastapi/routing.py", line 250, in app
    response = actual_response_class(response_data, **response_args)
  File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/responses.py", line 49, in __init__
    self.body = self.render(content)
  File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/responses.py", line 174, in render
    return json.dumps(
  File "/usr/lib/python3.8/json/__init__.py", line 234, in dumps
    return cls(
  File "/usr/lib/python3.8/json/encoder.py", line 199, in encode
    chunks = self.iterencode(o, _one_shot=True)
  File "/usr/lib/python3.8/json/encoder.py", line 257, in iterencode
    return _iterencode(o, 0)
ValueError: Out of range float values are not JSON compliant

这和我在 Heroku 上得到的类似。但是，在 Windows 上，使用与我在 WSL 上使用的命令相同的命令，该应用程序可以运行。没有返回错误，我可以在服务器上进行预测。如何消除 JSON 兼容错误？

仅供参考，我的代码位于单个 

main.py
文件中：

from fastapi import FastAPI
from tensorflow.keras.models import load_model
from tensorflow.keras.utils import get_file 
from tensorflow.keras.utils import load_img 
from tensorflow.keras.utils import img_to_array
from tensorflow import expand_dims
from tensorflow.nn import softmax
from numpy import argmax
from numpy import max
from numpy import array

app = FastAPI()
model_dir = "food-vision-model.h5"
model = load_model(model_dir)

class_predictions = array([
    'apple_pie',
    'baby_back_ribs',
    'baklava',
    'beef_carpaccio',
    'beef_tartare',
    'beet_salad',
    'beignets',
    'bibimbap',
    'bread_pudding',
    'breakfast_burrito',
    'bruschetta',
    'caesar_salad',
    'cannoli',
    'caprese_salad',
    'carrot_cake',
    'ceviche',
    'cheesecake',
    'cheese_plate',
    'chicken_curry',
    'chicken_quesadilla',
    'chicken_wings',
    'chocolate_cake',
    'chocolate_mousse',
    'churros',
    'clam_chowder',
    'club_sandwich',
    'crab_cakes',
    'creme_brulee',
    'croque_madame',
    'cup_cakes',
    'deviled_eggs',
    'donuts',
    'dumplings',
    'edamame',
    'eggs_benedict',
    'escargots',
    'falafel',
    'filet_mignon',
    'fish_and_chips',
    'foie_gras',
    'french_fries',
    'french_onion_soup',
    'french_toast',
    'fried_calamari',
    'fried_rice',
    'frozen_yogurt',
    'garlic_bread',
    'gnocchi',
    'greek_salad',
    'grilled_cheese_sandwich',
    'grilled_salmon',
    'guacamole',
    'gyoza',
    'hamburger',
    'hot_and_sour_soup',
    'hot_dog',
    'huevos_rancheros',
    'hummus',
    'ice_cream',
    'lasagna',
    'lobster_bisque',
    'lobster_roll_sandwich',
    'macaroni_and_cheese',
    'macarons',
    'miso_soup',
    'mussels',
    'nachos',
    'omelette',
    'onion_rings',
    'oysters',
    'pad_thai',
    'paella',
    'pancakes',
    'panna_cotta',
    'peking_duck',
    'pho',
    'pizza',
    'pork_chop',
    'poutine',
    'prime_rib',
    'pulled_pork_sandwich',
    'ramen',
    'ravioli',
    'red_velvet_cake',
    'risotto',
    'samosa',
    'sashimi',
    'scallops',
    'seaweed_salad',
    'shrimp_and_grits',
    'spaghetti_bolognese',
    'spaghetti_carbonara',
    'spring_rolls',
    'steak',
    'strawberry_shortcake',
    'sushi',
    'tacos',
    'takoyaki',
    'tiramisu',
    'tuna_tartare',
    'waffles'
])

@app.get("/")
async def root():
    return {"message": "Welcome to the Food Vision API!"}

@app.post("/net/image/prediction/")
async def get_net_image_prediction(image_link: str = ""):
    if image_link == "":
        return {"message": "No image link provided"}
    
    img_path = get_file(
        origin = image_link
    )
    img = load_img(
        img_path, 
        target_size = (224, 224)
    )

    img_array = img_to_array(img)
    img_array = expand_dims(img_array, 0)

    pred = model.predict(img_array)
    score = softmax(pred[0])

    class_prediction = class_predictions[argmax(score)]
    model_score = round(max(score) * 100, 2)

    return {
        "model_prediction_class": class_prediction,
        "model_prediction_score": model_score
    }
python python-3.x tensorflow heroku fastapi
2个回答
0
投票

我已经解决了这个问题，解决方案是将整个应用程序docker化，然后部署到Heroku。这样，它就可以在 WSL (Linux) 上运行，并且也可以在 Heroku（使用 Linux）上扩展运行。

因此需要稍微编辑 

main.py
文件：

from fastapi import FastAPI
from tensorflow.keras.models import load_model
from tensorflow.keras.utils import get_file 
from tensorflow.keras.utils import load_img 
from tensorflow.keras.utils import img_to_array
from tensorflow import expand_dims
from tensorflow.nn import softmax
from numpy import argmax
from numpy import max
from numpy import array
from json import dumps
from uvicorn import run
import os

app = FastAPI()
model_dir = "food-vision-model.h5"
model = load_model(model_dir)

class_predictions = array([
    'apple_pie',
    'baby_back_ribs',
    'baklava',
    'beef_carpaccio',
    'beef_tartare',
    'beet_salad',
    'beignets',
    'bibimbap',
    'bread_pudding',
    'breakfast_burrito',
    'bruschetta',
    'caesar_salad',
    'cannoli',
    'caprese_salad',
    'carrot_cake',
    'ceviche',
    'cheesecake',
    'cheese_plate',
    'chicken_curry',
    'chicken_quesadilla',
    'chicken_wings',
    'chocolate_cake',
    'chocolate_mousse',
    'churros',
    'clam_chowder',
    'club_sandwich',
    'crab_cakes',
    'creme_brulee',
    'croque_madame',
    'cup_cakes',
    'deviled_eggs',
    'donuts',
    'dumplings',
    'edamame',
    'eggs_benedict',
    'escargots',
    'falafel',
    'filet_mignon',
    'fish_and_chips',
    'foie_gras',
    'french_fries',
    'french_onion_soup',
    'french_toast',
    'fried_calamari',
    'fried_rice',
    'frozen_yogurt',
    'garlic_bread',
    'gnocchi',
    'greek_salad',
    'grilled_cheese_sandwich',
    'grilled_salmon',
    'guacamole',
    'gyoza',
    'hamburger',
    'hot_and_sour_soup',
    'hot_dog',
    'huevos_rancheros',
    'hummus',
    'ice_cream',
    'lasagna',
    'lobster_bisque',
    'lobster_roll_sandwich',
    'macaroni_and_cheese',
    'macarons',
    'miso_soup',
    'mussels',
    'nachos',
    'omelette',
    'onion_rings',
    'oysters',
    'pad_thai',
    'paella',
    'pancakes',
    'panna_cotta',
    'peking_duck',
    'pho',
    'pizza',
    'pork_chop',
    'poutine',
    'prime_rib',
    'pulled_pork_sandwich',
    'ramen',
    'ravioli',
    'red_velvet_cake',
    'risotto',
    'samosa',
    'sashimi',
    'scallops',
    'seaweed_salad',
    'shrimp_and_grits',
    'spaghetti_bolognese',
    'spaghetti_carbonara',
    'spring_rolls',
    'steak',
    'strawberry_shortcake',
    'sushi',
    'tacos',
    'takoyaki',
    'tiramisu',
    'tuna_tartare',
    'waffles'
])

@app.get("/")
async def root():
    return {"message": "Welcome to the Food Vision API!"}

@app.post("/net/image/prediction/")
async def get_net_image_prediction(image_link: str = ""):
    if image_link == "":
        return {"message": "No image link provided"}
    
    img_path = get_file(
        origin = image_link
    )
    img = load_img(
        img_path, 
        target_size = (224, 224)
    )

    img_array = img_to_array(img)
    img_array = expand_dims(img_array, 0)

    pred = model.predict(img_array)
    score = softmax(pred[0])

    class_prediction = class_predictions[argmax(score)]
    model_score = round(max(score) * 100, 2)
    model_score = dumps(model_score.tolist())

    return {
        "model_prediction_class": class_prediction,
        "model_prediction_score": model_score
    }

if __name__ == "__main__":
    port = int(os.environ.get('PORT', 5000))
    run(app, host="0.0.0.0", port=port)

这里的主要区别是

os.environ.get()
的使用。我发现仅手动定义像 
port = 5000
这样的端口会在 Heroku 上返回 
R10 (boot timeout)
error。

从这里，我创建了一个包含以下内容的 Dockerfile：

FROM python:3.7.3-stretch

# Maintainer info
LABEL maintainer="[email protected]"

# Make working directories
RUN  mkdir -p  /my-directory/project
WORKDIR  /my-directory/project

# Upgrade pip with no cache
RUN pip install --no-cache-dir -U pip

# Copy application requirements file to the created working directory
COPY requirements.txt .

# Install application dependencies from the requirements file
RUN pip install -r requirements.txt

# Copy every file in the source folder to the created working directory
COPY  . .

# Run the python application
CMD ["python", "main.py"]

随着 

requirements.txt
拥有这些：

fastapi==0.73.0
gunicorn==20.1.0
numpy==1.19.5
uvicorn==0.15.0
image==1.5.33
tensorflow-cpu==2.7.0

我使用 

tensorflow-cpu
在免费帐户上绕过了 Heroku 的 slug 大小和内存限制。

从这里我可以构建 docker 镜像并将其部署到 heroku。

$ docker image build -t app-name .
$ heroku create app-name
$ heroku container:push web --app app-name
$ heroku container:release web --app food-vision-api

本地运行时，我使用了以下命令：

$ docker run -p 5000:5000 -d app-name
0
投票

我这里也遇到了类似的问题。
一个 fastapi 应用程序，正在使用 torch 上的 CNN 进行预测得分。 运行代码时，该问题非常零星地出现。 错误：

INFO uvicorn.access 10.244.3.36:41134 - "POST /api/v1/analysis HTTP/1.1" 500
ERROR uvicorn.error Exception in ASGI application
Traceback (most recent call last):
  File "/usr/local/lib/python3.11/site-packages/uvicorn/protocols/http/h11_impl.py", line 373, in run_asgi
    result = await app(self.scope, self.receive, self.send)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/uvicorn/middleware/proxy_headers.py", line 75, in __call__
    return await self.app(scope, receive, send)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/fastapi/applications.py", line 1054, in __call__
    await super().__call__(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/applications.py", line 123, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/middleware/errors.py", line 186, in __call__
    raise exc
  File "/usr/local/lib/python3.11/site-packages/starlette/middleware/errors.py", line 164, in __call__
    await self.app(scope, receive, _send)
  File "/usr/local/lib/python3.11/site-packages/starlette/middleware/cors.py", line 83, in __call__
    await self.app(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/middleware/exceptions.py", line 62, in __call__
    await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
    raise exc
  File "/usr/local/lib/python3.11/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    await app(scope, receive, sender)
  File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 758, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 778, in app
    await route.handle(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 299, in handle
    await self.app(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 79, in app
    await wrap_app_handling_exceptions(app, request)(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
    raise exc
  File "/usr/local/lib/python3.11/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    await app(scope, receive, sender)
  File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 74, in app
    response = await func(request)
               ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/fastapi/routing.py", line 326, in app
    response = actual_response_class(content, **response_args)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/starlette/responses.py", line 183, in __init__
    super().__init__(content, status_code, headers, media_type, background)
  File "/usr/local/lib/python3.11/site-packages/starlette/responses.py", line 41, in __init__
    self.body = self.render(content)
                ^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/starlette/responses.py", line 186, in render
    return json.dumps(
           ^^^^^^^^^^^
  File "/usr/local/lib/python3.11/json/__init__.py", line 238, in dumps
    **kw).encode(obj)
          ^^^^^^^^^^^
  File "/usr/local/lib/python3.11/json/encoder.py", line 200, in encode
    chunks = self.iterencode(o, _one_shot=True)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/json/encoder.py", line 258, in iterencode
    return _iterencode(o, 0)
           ^^^^^^^^^^^^^^^^^
ValueError: Out of range float values are not JSON compliant

经过大量谷歌搜索后，我在这里找到了这篇文章。在这里，我的解决方案似乎是确保作为“jsonified”响应返回的结果没有 

NaN
值。当我们尝试转换为 JSON 时，
NaN
会出现上述错误。因此我必须确保结果是 
None
而不是 
NaN
。
从上面的解决方案中我可以看出 
model_score = dumps(model_score.tolist())
已经成功了。
就我而言，我使用了 pandas 数据框 - 带有应用函数来进行评分。因此，我在评分后有一个数据框。在该数据帧上，我可以使用 
df.notnull(df)
来替换 
Nan
值，如下所示：

df["score"] = df["content"].apply(
            lambda x: context_score.get_features(text=x)).astype(object)
df = df.where(pd.notnull(df), None)

这使我的应用程序运行时没有任何 

ValueError: Out of range float values are not JSON compliant
错误。 我希望这对其他人有帮助。

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