2023-05-24 12:26:21.353318: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2023-05-24 12:26:23.180135: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT Number of devices: 3

Error：in ./node-modules/axios/lib/platform/index.js

在JavaScript的开发环境中，我们经常会遇到各种错误，其中之一就是“Error in ./node_modules/axios/lib/platform/index.js”。这个错误提示通常意味着在项目中使用axios库时遇到了问题。Axios是一款非常流行的基于...

解决gedit: symbol lookup error: /home/xxx/libgobject-2.0.so.0: undefined symbol: g_date_copy问题

2. 进入编辑模式（通常按'i'键）并在文件末尾添加新的路径，使得gedit优先查找/usr/lib/x86_64-linux-gnu/下的库文件： export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH 3. 保存并...

pyswarms.single.global_best: 0%| |0/502023-06-12 09:15:36.800789: I tensorflow/core/platform/cpu_feature_guard.cc:142] 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. 2023-06-12 09:15:37.280722: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9426 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3080 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6 2023-06-12 09:15:37.633600: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2) 2023-06-12 09:15:38.355549: F .\tensorflow/core/util/gpu_launch_config.h:129] Check failed: work_element_count > 0 (0 vs. 0)

这是 TensorFlow 在运行过程中出现的错误提示，其中包含了一些调试信息。具体的错误原因需要根据错误提示的上下文来确定。首先，提示中指出了 TensorFlow 二进制文件是使用 oneAPI Deep Neural Network Library ...

2023-07-13 14:44:21.978453: I tensorflow/core/platform/cpu_feature_guard.cc:142] 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. 2023-07-13 14:44:22.835121: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3475 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6 2023-07-13 14:44:22.954245: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)

1. TensorFlow二进制文件已经优化，使用了oneAPI Deep Neural Network Library (oneDNN)来执行性能关键操作，使用了AVX和AVX2指令集。如果想在其他操作中启用它们，请重新使用适当的编译器标志重新构建TensorFlow。 ...

2023-07-14 07:37:44.835091: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.

这是关于 TensorFlow 的另一条提示消息，它说明您正在使用的 TensorFlow 二进制文件已经针对可用的 CPU 指令进行了优化，以在性能关键的操作中提高效率。这意味着 TensorFlow 将尽可能地利用您的 CPU 的功能来提高...

2023-06-17 23:47:22.786162: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2023-06-17 23:47:22.786281: 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. 2023-06-17 23:47:24.419330: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found 2023-06-17 23:47:24.419809: W tensorflow/stream_executor/cuda/cuda_driver.cc:263] failed call to cuInit: UNKNOWN ERROR (303) 2023-06-17 23:47:24.426229: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: �� 2023-06-17 23:47:24.426345: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: �� 2023-06-17 23:47:24.430552: 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. Traceback (most recent call last): File "C:\Users\10290\Desktop\test\writer.py", line 20, in <module> write_reward_tb(summary_writer, rewards[i], i) File "C:\Users\10290\Desktop\test\writer.py", line 9, in write_reward_tb summary = tf.Summary(value=[tf.Summary.Value(tag='reward', simple_value=reward)]) AttributeError: module 'tensorflow' has no attribute 'Summary'分析错误原因

这个错误是由于TensorFlow 2.x版本中tf.Summary已被弃用所致。相反，你应该使用tf.summary.Summary。你需要将代码更新为： summary = tf.summary.Summary(value=[tf.summary.Summary.Value(tag='reward', ...

2023-07-12 19:42:25.035613: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2

这个警告意味着你的CPU支持一些TensorFlow没有编译使用的指令集，其中包括AVX2。尽管TensorFlow没有使用这些指令集，但它仍然可以正常运行，只是可能会稍微慢一些。如果你希望消除这个警告，可以尝试将TensorFlow...

023-07-15 13:17:14.941311: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2023-07-15 13:17:14.947250: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2d48ecfd850 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2023-07-15 13:17:14.947599: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version Traceback (most recent call last):

在这个特定的日志中，它说明了 TensorFlow 使用了 oneAPI Deep Neural Network Library (oneDNN) 来优化 CPU 的性能，并且指示了所使用的 CPU 指令集为 AVX2。接下来，您提到了一个 Traceback 错误，但是您并没有...

2023-07-15 11:01:04.939591: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2023-07-15 11:01:05.634397: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21865 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:73:00.0, compute capability: 8.6

它告诉你当前使用的 TensorFlow 二进制文件已经针对 oneAPI Deep Neural Network Library (oneDNN) 进行了优化，以在性能关键操作中使用以下 CPU 指令：AVX2、AVX512F 和 FMA。这些指令集能够提高 CPU 的计算性能。...

2023-07-13 10:56:56.568844: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2

这是 TensorFlow 在启动时打印的一条信息，它告诉你的 CPU 支持 AVX2 指令集，但当前使用的 TensorFlow 二进制文件没有编译以使用这些指令集。 AVX2（Advanced Vector Extensions 2）是一组用于向量化和并行计算的 ...

2023-05-23 22:56:05.135343: 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

这是一个 TensorFlow 的日志信息，它告诉你当前使用的 TensorFlow 二进制文件是优化过的，使用了 oneAPI Deep Neural Network Library（oneDNN）来提高在执行性能关键操作时使用的 CPU 指令集，其中包括 AVX 和 AVX2...

2023-07-13 09:55:41.026027: I tensorflow/core/platform/cpu_feature_guard.cc:142] 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. 2023-07-13 09:55:41.390014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1654 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3050 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6

这些信息是与 TensorFlow 和 GPU 设备相关的日志信息。它们表明你正在使用 TensorFlow 库，并且检测到了一个 NVIDIA GeForce RTX 3050 Laptop GPU 设备，并成功创建了一个 GPU 设备对象。这些日志信息通常是用于...

/hpcfile/users/test004/.jhjobs/1685606647.3652.1.shell: line 7: /hpcfile/jhinno/unischeduler/conf/jobstart/unisched: No such file or directory CondaValueError: prefix already exists: /share/software/anaconda3/envs/tensorflow 2023-06-01 16:04:52.498920: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA 2023-06-01 16:04:52.626381: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.

这是一个包含多个错误信息的日志，其中第一行提示...最后几行是TensorFlow的信息，提示该二进制文件没有使用CPU的某些指令，以及正在创建新的线程池。开发者需要对这些错误信息进行逐一排查，找出问题所在并进行修复。

2023-06-09 09:46:11.022252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1900] Ignoring visible gpu device (device: 0, name: GeForce GT 610, pci bus id: 0000:01:00.0, compute capability: 2.1) with Cuda compute capability 2.1. The minimum required Cuda capability is 3.5. 2023-06-09 09:46:11.022646: I tensorflow/core/platform/cpu_feature_guard.cc:151] 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.

这是 TensorFlow 在启动时输出的一些信息。其中包含了以下几个内容： 1. TensorFlow 检测到了一个 GPU 设备，但由于该设备的 CUDA 计算能力版本太低（2.1），不满足 TensorFlow 最低要求（3.5），因此被忽略。 2. ...

2023-06-11 22:30:11.292060: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.

这是 TensorFlow 在运行时输出的信息，表明 TensorFlow 的二进制文件是针对 CPU 进行优化的，并提示你可以通过重新编译 TensorFlow 来启用一些指令集，以在某些操作中获得更好的性能表现。这个信息并不是错误，...

怎么解决这个错误2023-07-13 19:20:25.085462: 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

要解决这个错误，你需要重新编译 TensorFlow，并使用适当的编译器标志来启用 AVX 和 AVX2 指令。以下是一些步骤可以尝试： 1. 确认你已经安装了适当版本的 TensorFlow 源代码。你可以从 TensorFlow 的官方 GitHub ...

023-07-13 09:22:25.024025: I tensorflow/core/platform/cpu_feature_guard.cc:142] 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. 2023-07-13 09:22:25.414554: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1654 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3050 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6 0.0 [ERROR:0@6.481] global cap.cpp:166 cv::VideoCapture::open VIDEOIO(CV_IMAGES): raised OpenCV exception: OpenCV(4.7.0) D:\a\opencv-python\opencv-python\opencv\modules\videoio\src\cap_images.cpp:253: error: (-5:Bad argument) CAP_IMAGES: can't find starting number (in the name of file): F:\新建文件夹 in function 'cv::icvExtractPattern'

这个错误提示表明在使用 OpenCV 库的 VideoCapture 函数时出现了错误。具体地，在你的代码中调用了 VideoCapture 函数，并传递了文件路径 F:\新建文件夹作为参数。然而，OpenCV 无法找到该路径中的起始数字，...

2023-07-13 09:11:11.159731: I tensorflow/core/platform/cpu_feature_guard.cc:142] 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. 2023-07-13 09:11:11.503341: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1654 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3050 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6 0.0 Traceback (most recent call last): File "C:\Users\怡\Desktop\Lanenet\copilot(1)\copilot\test.py", line 19, in <module> frame.process_video(file_path, 2,\ File "C:\Users\怡\Desktop\Lanenet\copilot(1)\copilot\frame.py", line 238, in process_video file_path,frame_w,frame_h,fps_actual,nb_frames//fps_actual ZeroDivisionError: float floor division by zero

这个错误提示表明在执行代码时出现了除零错误。具体地，在你的代码中的行 238 处，发生了一个浮点数除法操作，除数为零。这可能是由于视频的帧率（fps_actual）为零导致的。你可以检查视频的帧率是否正确，并确保其...

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Error：in ./node-modules/axios/lib/platform/index.js

解决gedit: symbol lookup error: /home/xxx/libgobject-2.0.so.0: undefined symbol: g_date_copy问题

2023-07-14 07:37:44.835091: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.

2023-07-12 19:42:25.035613: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2

2023-07-13 10:56:56.568844: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2

2023-05-23 22:56:05.135343: 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

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