问题[tensorflow](ubuntu)

我在我的 Virtual Box/Win10 主机（显卡：Nvidia M1200 + Intel xx）上安装了 Ubuntu 16.04，它运行良好，直到我尝试安装 tensorflow-gpu-1.13.0 并使用以下命令更新 Nvidia 驱动程序：

sudo add-apt-repository ppa:graphics-drivers/ppa

它似乎没有用，但无论如何我继续使用以下命令只是为了看看会发生什么

sudo apt update
sudo apt install nvidia-390

然后它在重新启动后进入登录循环，它让您登录然后立即将您注销，这样您就永远卡在登录 GUI 上。在尝试了一切之后，我终于放弃并尝试重新安装 Ubuntu。但是这次我不能再安装它，它给出了错误：

errno 5 - input/output error

我运行了内存诊断，它返回正常，检查使用的磁盘chkdsk c: /f /r，它返回正常。.iso 映像应该很好，因为它以前可以工作。但是在 Ubuntu 启动时（安装前），日志说

squashfs error: squashfs_read_data failed to read block 0x2811aa6d
squashfs error: zlib decompression failed, data probably corrupt
...

知道如何解决吗？

我安装了 Ubuntu 20.04，由于我的 CPU 不支持 AVX，我在使用最新版本的软件时遇到了很多麻烦。旧版本很难找到并且不能正常运行。

哪个以前的 Ubuntu 版本适合我的系统？我似乎要求 Python3.6、CUDA9、CudNN7、Tensorflow 低于 1.15。如果我键入例如 sudo apt install tensorflow，哪个以前的 Ubuntu 版本将默认安装这些版本。

我最近在使用 TensorBoard 时遇到了麻烦，因为每当我尝试运行 TensorBoard 服务器时，都会收到错误消息：

pkg_resources.VersionConflict: (grpcio 1.21.1 (/home/me/.local/lib/python3.7/site-packages), Requirement.parse('grpcio>=1.24.3'))

我试图运行：

(sudo) pip3 install grpcio==1.24.3

但是，运行pip3 list显示 grpcio 仍然是版本1.21.1

这让我相信我安装的 grpcio 不归 pip 所有，因此不能被 pip 删除/升级。然后我尝试在我的/usr/lib/python3.7and中删除 grpcio 的实例/usr/local/lib/python3.7/dist-packages，但 pip 仍然报告 grpcio 已安装。

我该如何解决这个冲突？

我正在按照这里的步骤。我目前处于这一步：

$ cd ~
$ mkdir installers
$ cd installers/
$ wget https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_410.48_linux
$ mv cuda_10.0.130_410.48_linux cuda_10.0.130_410.48_linux.run
$ chmod +x cuda_10.0.130_410.48_linux.run
$ sudo ./cuda_10.0.130_410.48_linux.run --override

最后一步具体。一旦我同意安装所有内容，我就会收到此错误消息：

Installing the NVIDIA display driver...
The driver installation has failed due to an unknown error. Please consult the driver installation log located at /var/log/nvidia-installer.log.

===========
= Summary =
===========

Driver:   Installation Failed
Toolkit:  Installation skipped
Samples:  Installation skipped

我已经尝试了很多方法来做到这一点，我对困难感到困惑。有人告诉我，使用 Linux Ubuntu 进行深度学习开发是可行的方法，但至少可以说我觉得这很荒谬。

我严格按照 tensorflow.org 上的说明安装了 Cuda、cudann 和 TensorFlow。在安装过程中，我让我的 ubuntu 切换到了 Nvidia 卡。验证安装后，我切换回英特尔。现在在编译我的代码时，我在终端中收到了这条消息：

2019-11-26 19:24:24.781299: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcuda.so.1
2019-11-26 19:24:24.830457: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 从 SysFS 读取的成功 NUMA 节点具有负值 (-1)，但必须至少有一个 NUMA 节点，所以返回NUMA 节点零
2019-11-26 19:24:24.831899：我 tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] 找到具有以下属性的设备 0：
名称：GeForce MX150 主要：6 次要：1 memoryClockRate(GHz)：1.5315
pciBusID: 0000:01:00.0
2019-11-26 19:24:24.983890: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcudart.so.10.0
2019-11-26 19:24:25.001409: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcublas.so.10.0
2019-11-26 19:24:25.009430: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcufft.so.10.0
2019-11-26 19:24:25.030189: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcurand.so.10.0
2019-11-26 19:24:25.048404: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcusolver.so.10.0
2019-11-26 19:24:25.067131: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcusparse.so.10.0
2019-11-26 19:24:25.095875: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcudnn.so.7
2019-11-26 19:24:25.096289: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 从 SysFS 读取的成功 NUMA 节点为负值 (-1)，但必须至少有一个 NUMA 节点，所以返回NUMA 节点零
2019-11-26 19:24:25.099040: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 从 SysFS 读取的成功 NUMA 节点具有负值 (-1)，但必须至少有一个 NUMA 节点，所以返回NUMA 节点零
2019-11-26 19:24:25.100673：我 tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] 添加可见 gpu 设备：0
2019-11-26 19:24:25.101394: I tensorflow/core/platform/cpu_feature_guard.cc:142] 您的 CPU 支持未编译此 TensorFlow 二进制文件以使用的指令：AVX2 FMA
2019-11-26 19:24:25.135574：I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU 频率：1800000000 Hz
2019-11-26 19:24:25.137917：我 tensorflow/compiler/xla/service/service.cc:168] XLA 服务 0x5619ea60e930 在平台主机上执行计算。设备：
2019-11-26 19:24:25.137997：I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor 设备（0）：主机，默认版本
2019-11-26 19:24:25.270223: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 从 SysFS 读取的成功 NUMA 节点具有负值 (-1)，但必须至少有一个 NUMA 节点，所以返回NUMA 节点零
2019-11-26 19:24:25.271459：我 tensorflow/compiler/xla/service/service.cc:168] XLA 服务 0x5619ebdb8290 在平台 CUDA 上执行计算。设备：
2019-11-26 19:24:25.271500: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor 设备 (0): GeForce MX150, Compute Capability 6.1
2019-11-26 19:24:25.271757: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 从 SysFS 读取的成功 NUMA 节点具有负值 (-1)，但必须至少有一个 NUMA 节点，所以返回NUMA 节点零
2019-11-26 19:24:25.272737：我 tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] 找到具有以下属性的设备 0：
名称：GeForce MX150 主要：6 次要：1 memoryClockRate(GHz)：1.5315
pciBusID: 0000:01:00.0
2019-11-26 19:24:25.272802: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcudart.so.10.0
2019-11-26 19:24:25.272831: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcublas.so.10.0
2019-11-26 19:24:25.272858: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcufft.so.10.0
2019-11-26 19:24:25.272882: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcurand.so.10.0
2019-11-26 19:24:25.272913: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcusolver.so.10.0
2019-11-26 19:24:25.272940: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcusparse.so.10.0
2019-11-26 19:24:25.272966: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcudnn.so.7
2019-11-26 19:24:25.273065: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 从 SysFS 读取的成功 NUMA 节点具有负值 (-1)，但必须至少有一个 NUMA 节点，所以返回NUMA 节点零
2019-11-26 19:24:25.274126: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 从 SysFS 读取的成功 NUMA 节点具有负值 (-1)，但必须至少有一个 NUMA 节点，所以返回NUMA 节点零
2019-11-26 19:24:25.275065: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] 添加可见 gpu 设备：0
2019-11-26 19:24:25.275131: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcudart.so.10.0
2019-11-26 19:24:25.277086: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] 设备互连 StreamExecutor 与强度 1 边缘矩阵：
2019-11-26 19:24:25.277112: 我 tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0
2019-11-26 19:24:25.277124: 我 tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N
2019-11-26 19:24:25.277325: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 从 SysFS 读取的成功 NUMA 节点具有负值 (-1)，但必须至少有一个 NUMA 节点，所以返回NUMA 节点零
2019-11-26 19:24:25.278329: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 从 SysFS 读取的成功 NUMA 节点为负值 (-1)，但必须至少有一个 NUMA 节点，所以返回NUMA 节点零
2019-11-26 19:24:25.279323：我 tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] 创建了 TensorFlow 设备（/job:localhost/replica:0/task:0/device:GPU:0 和 1323 MB 内存）-> 物理 GPU（设备：0，名称：GeForce MX150，pci 总线 ID：0000:01:00.0，计算能力：6.1）

那么我的 GPU 工作正常还是我需要做其他事情？感谢您的回答，但看起来我在尝试运行卷积层时遇到了一个新问题。错误说：

2019-11-29 20:59:03.481920: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcublas.so.10.0
2019-11-29 20:59:06.074691: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcudnn.so.7
2019-11-29 20:59:06.171580：E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] 无法创建 cudnn 句柄：CUDNN_STATUS_NOT_INITIALIZED
2019-11-29 20:59:06.171825: E tensorflow/stream_executor/cuda/cuda_dnn.cc:337] 驱动程序版本可能不足：418.87.1
2019-11-29 20:59:06.171902：E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] 无法创建 cudnn 句柄：CUDNN_STATUS_NOT_INITIALIZED
2019-11-29 20:59:06.171993: E tensorflow/stream_executor/cuda/cuda_dnn.cc:337] 驱动程序版本可能不足：418.87.1
2019-11-29 20:59:06.172777：W tensorflow/core/common_runtime/base_collective_executor.cc:216] BaseCollectiveExecutor::StartAbort Unknown：无法获取卷积算法。这可能是因为 cuDNN 初始化失败，因此请尝试查看上面是否打印了警告日志消息。
     [[{{节点顺序/conv2d/Conv2D}}]]
   32/50000 [.......................] - ETA：2:43:50Traceback（最近一次通话最后）：
  文件“sample3.py”，第 54 行，在
    验证数据=（测试图像，测试标签））
  文件“/home/encrypto/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py”，第 728 行，适合
    use_multiprocessing=use_multiprocessing)
  文件“/home/encrypto/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py”，第 324 行，适合
    total_epochs=epochs)
  文件“/home/encrypto/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py”，第 123 行，在 run_one_epoch
    batch_outs = execution_function（迭代器）
  文件“/home/encrypto/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py”，第 86 行，在 execution_function
    分布式函数（input_fn））
  __call__ 中的文件“/home/encrypto/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py”，第 457 行
    结果 = self._call(*args, **kwds)
  _call 中的文件“/home/encrypto/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py”，第 520 行
    return self._stateless_fn(*args, **kwds)
  __call__ 中的文件“/home/encrypto/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py”，第 1823 行
    return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
  _filtered_call 中的文件“/home/encrypto/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py”，第 1141 行
    self.captured_inputs)
  _call_flat 中的文件“/home/encrypto/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py”，第 1224 行
    ctx、args、cancellation_manager=cancellation_manager)
  调用中的文件“/home/encrypto/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py”，第 511 行
    ctx=ctx)
  文件“/home/encrypto/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/execute.py”，第 67 行，在 quick_execute
    六.raise_from(core._status_to_exception(e.code, message), None)
  文件“”，第 3 行，在 raise_from
tensorflow.python.framework.errors_impl.UnknownError：获取卷积算法失败。这可能是因为 cuDNN 初始化失败，因此请尝试查看上面是否打印了警告日志消息。
     [[节点顺序/conv2d/Conv2D（定义在/home/encrypto/venv/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py:1751）]] [Op:__inference_distributed_function_1055]

函数调用栈：
分布式函数

请告诉我如何处理这个？

我全新安装了 Ubuntu 18.04.3，我正在尝试安装 tensorflow-rocm（用于 AMD GPU）版本 1.14.0。

默认 pip3 install tensorflow-rocm 正在安装 v2.0 但我使用的代码集是在 1.14 上制作的，所以当我尝试在 v2.0 上运行相同的代码时出现一些错误，主要是因为包的移动方式。

所以我找到了 tensorflow-rocm v 1.14.0 的源代码，但是当我尝试构建它时，我遇到了一个错误。我不知道为什么。我检查了我的系统上是否安装了 rocm，并根据他们的官方网站安装了它。

我面临的错误如下：

Starting local Bazel server and connecting to it...
ERROR: Skipping '//tensorflow/tools/pip_package:build_pip_package': error loading package 'tensorflow/tools/pip_package': Encountered error while reading extension file 'rocm/build_defs.bzl': no such package '@local_config_rocm//rocm': Traceback (most recent call last):
    File "/home/heyitsabi/tensorflow-upstream/third_party/gpus/rocm_configure.bzl", line 861
        _create_local_rocm_repository(repository_ctx)
    File "/home/heyitsabi/tensorflow-upstream/third_party/gpus/rocm_configure.bzl", line 682, in _create_local_rocm_repository
        make_copy_dir_rule(repository_ctx, name = "rccl-inclu...", <2 more arguments>)
    File "/home/heyitsabi/tensorflow-upstream/third_party/gpus/cuda_configure.bzl", line 923, in make_copy_dir_rule
        _read_dir(repository_ctx, src_dir)
    File "/home/heyitsabi/tensorflow-upstream/third_party/gpus/cuda_configure.bzl", line 956, in _read_dir
        _execute(repository_ctx, ["find", src_dir, ..."], ...)
    File "/home/heyitsabi/tensorflow-upstream/third_party/gpus/cuda_configure.bzl", line 887, in _execute
        auto_configure_fail("\n".join([error_msg.strip() if ... ""]))
    File "/home/heyitsabi/tensorflow-upstream/third_party/gpus/cuda_configure.bzl", line 324, in auto_configure_fail
        fail(("\n%sCuda Configuration Error:%...)))

Cuda Configuration Error: Repository command failed
find: ‘/opt/rocm/rccl/include’: No such file or directory

WARNING: Target pattern parsing failed.
ERROR: error loading package 'tensorflow/tools/pip_package': Encountered error while reading extension file 'rocm/build_defs.bzl': no such package '@local_config_rocm//rocm': Traceback (most recent call last):
    File "/home/heyitsabi/tensorflow-upstream/third_party/gpus/rocm_configure.bzl", line 861
        _create_local_rocm_repository(repository_ctx)
    File "/home/heyitsabi/tensorflow-upstream/third_party/gpus/rocm_configure.bzl", line 682, in _create_local_rocm_repository
        make_copy_dir_rule(repository_ctx, name = "rccl-inclu...", <2 more arguments>)
    File "/home/heyitsabi/tensorflow-upstream/third_party/gpus/cuda_configure.bzl", line 923, in make_copy_dir_rule
        _read_dir(repository_ctx, src_dir)
    File "/home/heyitsabi/tensorflow-upstream/third_party/gpus/cuda_configure.bzl", line 956, in _read_dir
        _execute(repository_ctx, ["find", src_dir, ..."], ...)
    File "/home/heyitsabi/tensorflow-upstream/third_party/gpus/cuda_configure.bzl", line 887, in _execute
        auto_configure_fail("\n".join([error_msg.strip() if ... ""]))
    File "/home/heyitsabi/tensorflow-upstream/third_party/gpus/cuda_configure.bzl", line 324, in auto_configure_fail
        fail(("\n%sCuda Configuration Error:%...)))

Cuda Configuration Error: Repository command failed
find: ‘/opt/rocm/rccl/include’: No such file or directory

INFO: Elapsed time: 2.470s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (0 packages loaded)
    currently loading: tensorflow/tools/pip_package

tensorflow-rcom 1.14.0 源码站点

环境：Ubuntu 16.04/ tensorflow 1.14.0/ python3.5.3

我使用这个命令安装了 TensorFlow。

sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.7.1-cp27-none-linux_x86_64.whl

这是它的结果。

DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7. More details about Python 2 support in pip, can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support
WARNING: The directory '/home/hanbit-o/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
WARNING: The directory '/home/hanbit-o/.cache/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Collecting tensorflow==0.7.1 from https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.7.1-cp27-none-linux_x86_64.whl
  Downloading https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.7.1-cp27-none-linux_x86_64.whl (13.8MB)
Requirement already satisfied, skipping upgrade: wheel in /usr/local/lib/python2.7/dist-packages (from tensorflow==0.7.1) (0.33.4)
Requirement already satisfied, skipping upgrade: protobuf==3.0.0b2 in /usr/local/lib/python2.7/dist-packages (from tensorflow==0.7.1) (3.0.0b2)
Requirement already satisfied, skipping upgrade: six>=1.10.0 in /usr/lib/python2.7/dist-packages (from tensorflow==0.7.1) (1.10.0)
Requirement already satisfied, skipping upgrade: numpy>=1.8.2 in /home/hanbit-o/.local/lib/python2.7/site-packages (from tensorflow==0.7.1) (1.16.4)
Requirement already satisfied, skipping upgrade: setuptools in /usr/lib/python2.7/dist-packages (from protobuf==3.0.0b2->tensorflow==0.7.1) (20.7.0)
Installing collected packages: tensorflow
  Found existing installation: tensorflow 0.7.1
    Uninstalling tensorflow-0.7.1:
      Successfully uninstalled tensorflow-0.7.1
Successfully installed tensorflow-0.7.1

在这个时候有python2的警告

实际上，我想在 python3 上使用 TensorFlow，因为在 python2 上安装了 TensorFlow。提示上有很多评论。

Python 3.5.3 (default, Aug 28 2019, 20:35:32) 
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow
/home/hanbit-o/.local/lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/hanbit-o/.local/lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/hanbit-o/.local/lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/hanbit-o/.local/lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/hanbit-o/.local/lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/hanbit-o/.local/lib/python3.5/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
/home/hanbit-o/.local/lib/python3.5/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/hanbit-o/.local/lib/python3.5/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/hanbit-o/.local/lib/python3.5/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/hanbit-o/.local/lib/python3.5/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/hanbit-o/.local/lib/python3.5/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/hanbit-o/.local/lib/python3.5/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
>>> 

我试图忽略它。

>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
2019-10-09 21:40:31.902027: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-10-09 21:40:31.926393: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3398000000 Hz
2019-10-09 21:40:31.929440: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x42f31d0 executing computations on platform Host. Devices:
2019-10-09 21:40:31.929480: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
>>> sess.run(hello)
]b'Hello, TensorFlow!'
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> sess.run(a+b)
2019-10-09 21:41:28.143676: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set.  If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU.  To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
42

它是有效的（？），为什么有很多警告？