如何屏蔽TensorFlow控制台日志？已尝试多种方法未解决

我使用Python 3.10 + TensorFlow 2.9.1开发了一款聊天机器人，每次启动运行时都会输出大量TensorFlow警告日志，必须等日志输出完毕才能正常使用。尝试了多种屏蔽方法，但均未奏效。

环境信息 #

• Python版本：3.10
• TensorFlow版本：2.9.1

项目完整代码 #

import nltk
from nltk.stem.lancaster import LancasterStemmer

stemmer = LancasterStemmer()

import numpy
import tflearn
import tensorflow
import random
import json
import pickle
import warnings


with open("intents.json") as file:
    data = json.load(file)

try:
    with open("data.pickle", "rb") as f:
        words, labels, training, output = pickle.load(f)

except Exception as e:
    words = []
    labels = []
    docs_x = []
    docs_y = []


    for intent in data["intents"]:
        for pattern in intent["patterns"]:
            wrds = nltk.word_tokenize(pattern)
            words.extend(wrds)
            docs_x.append(wrds)
            docs_y.append(intent["tag"])

        if intent["tag"] not in labels:
            labels.append(intent["tag"])

    words = [stemmer.stem(w.lower()) for w in words if w != "?"]
    words = sorted(list(set(words)))

    labels = sorted(labels)


    training = []
    output = []

    out_empty = [0 for _ in range(len(labels))]


    for x, doc in enumerate(docs_x):
        bag = []

        wrds = [stemmer.stem(w) for w in doc]

        for w in words:
            if w in wrds:
                bag.append(1)
            
            else:
                bag.append(0)
        
        output_row = out_empty[:]
        output_row[labels.index(docs_y[x])] = 1

        training.append(bag)
        output.append(output_row)

        with open("data.pickle", "wb") as f:
            pickle.dump((words, labels, training, output), f)

training = numpy.array(training)
output = numpy.array(output)

tensorflow.compat.v1.reset_default_graph()

net = tflearn.input_data(shape=[None, len(training[0])])
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
net = tflearn.regression(net)

model = tflearn.DNN(net)

try:
    model.load("model.tflearn")

except Exception as e:
    model.fit(training, output, n_epoch=2500, batch_size=8, show_metric=True)
    model.save("model.tflearn")

def bag_of_words(s, words):
    bag = [0 for _ in range(len(words))]

    s_words = nltk.word_tokenize(s)
    s_words = [stemmer.stem(word.lower()) for word in s_words]

    for se in s_words:
        for i, w in enumerate(words):
            if w == se:
                bag[i] = 1
    
    return numpy.array(bag)



def get_output(inp):
    inp = str(inp)
    responses = ""
            
    results = model.predict([bag_of_words(inp, words)])[0]
    results_index = numpy.argmax(results)
    tag = labels[results_index]

    if results[results_index] > 0.7:
            
        for tg in data["intents"]:
            if tg['tag'] == tag:
                responses = tg['responses']
        
        responses = str(random.choice(responses))
            
    else:
        responses = "I didn't get that"

    return responses


if __name__ == "__main__":
    inp = input(">>> ")
    get_out = get_output(inp)
    print(get_out)

控制台输出的警告日志 #

2022-09-26 00:49:10.472563: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2022-09-26 00:49:10.485966: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
WARNING:tensorflow:From C:\Python 310\lib\site-packages\tensorflow\python\compat\v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
curses is not supported on this machine (please install/reinstall curses for an optimal experience)
2.9.1
2022-09-26 00:50:40.291595: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2022-09-26 00:50:40.305928: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
WARNING:tensorflow:From C:\Python 310\lib\site-packages\tensorflow\python\compat\v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
curses is not supported on this machine (please install/reinstall curses for an optimal experience)
WARNING:tensorflow:From C:\Python 310\lib\site-packages\tflearn\initializations.py:164: calling TruncatedNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
2022-09-26 00:50:48.663462: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2022-09-26 00:50:48.677293: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found
2022-09-26 00:50:48.692157: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found
2022-09-26 00:50:48.707790: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cufft64_10.dll'; dlerror: cufft64_10.dll not found
2022-09-26 00:50:48.723267: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'curand64_10.dll'; dlerror: curand64_10.dll not found
2022-09-26 00:50:48.738537: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cusolver64_11.dll'; dlerror: cusolver64_11.dll not found
2022-09-26 00:50:48.754577: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cusparse64_11.dll'; dlerror: cusparse64_11.dll not found
2022-09-26 00:50:48.770482: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found
2022-09-26 00:50:48.784207: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2022-09-26 00:50:48.817031: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the 
following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-09-26 00:50:49.258725: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled

已尝试但无效的方法 #

import logging
import warnings
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 
import tensorflow

tensorflow.get_logger().setLevel('INFO')
tensorflow.compat.v1.logging.set_verbosity(tensorflow.compat.v1.logging.ERROR)
logging.getLogger('tensorflow').setLevel(logging.FATAL)
warnings.filterwarnings(action="ignore")

有效解决方案 #

问题核心是环境变量设置时机太晚，且未处理tflearn自身的日志输出。需将日志屏蔽代码放在所有TensorFlow相关库导入的最前端，同时覆盖tflearn的日志级别。

修改后的完整代码开头部分如下：

# 先设置环境变量，必须在导入tensorflow/tflearn之前执行
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # 屏蔽TensorFlow C层日志（含GPU库缺失警告）
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'  # 关闭oneDNN优化提示日志

# 屏蔽Python全局警告
import warnings
warnings.filterwarnings("ignore")

# 屏蔽tensorflow和tflearn的日志输出
import logging
logging.getLogger('tensorflow').setLevel(logging.FATAL)
logging.getLogger('tflearn').setLevel(logging.FATAL)

# 再导入TensorFlow相关库并配置日志级别
import tensorflow as tf
tf.get_logger().setLevel('ERROR')
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)

# 最后导入其他业务库
import nltk
from nltk.stem.lancaster import LancasterStemmer
import numpy
import tflearn
import random
import json
import pickle

# 后续原有代码保持不变...

关键说明 #

1. TF_CPP_MIN_LOG_LEVEL=3：彻底屏蔽TensorFlow底层C++模块的所有日志
2. TF_ENABLE_ONEDNN_OPTS=0：关闭oneDNN优化相关的提示日志
3. 日志屏蔽逻辑必须前置，确保在TensorFlow/tflearn初始化前生效
4. 新增logging.getLogger('tflearn').setLevel(logging.FATAL)，覆盖tflearn自身的警告输出

内容的提问来源于stack exchange，提问作者user20084690