TensorFlow 安装与环境配置 https://tf.wiki/zh/basic/installation.html
如果程序报错，建议包含以下信息以帮助排除问题：
- TensorFlow 版本（请检查 TensorFlow 版本为 2.0 及以上）
import tensorflow as tf
print (tf.__version__)
- 完整的最小可运行程序代码（建议将代码在 IDE 中运行而不是交互式环境）
- 完整的报错信息（Traceback）
请问，
1，如何确定 gpu 是否比 cpu 快还是慢。
譬如 i7 和 gtx 1050 ，有必要安装 gpu 版本吗？
2，是否是要么是 cpu 来计算 要么是 gpu 来计算。要对比的话是否两个版本都要装？
1 Like
-
可以分别使用 GPU 和 CPU 运行同一个模型来选择合适的运算设备。GPU 和 CPU 的选择与模型类型也有关系，例如如果是卷积神经网络则 gtx1050 可能胜过 i7，但如果是强化学习则不一定。
-
可以简单地认为，是的。不需要两个版本都装，TensorFlow 2.1 默认安装（pip install tensorflow）就已经同时支持 CPU 和 GPU，可以使用
cpus = tf.config.list_physical_devices (device_type=‘CPU’)
tf.config.set_visible_devices (devices=cpus)
来限定只使用 CPU 进行运算。
1、tf.config.set_visible_devices (devices=cpus) 设定似乎有问题，搜索了 Tf2 官方，也没看明白这个用法。
2、一种方法，类似于当前程序的全局设定，放在 import tensorflow 之前
import os
os.environ [“CUDA_DEVICE_ORDER”] = “PCI_BUS_ID”
os.environ [“CUDA_VISIBLE_DEVICES”] = “-1”
3，另一种方法，是程序临时指定
with tf.device (’/cpu:0’):
A = tf.constant ([[1, 2], [3, 4]])
B = tf.constant ([[5, 6], [7, 8]])
C = tf.matmul (A, B)
print ©
with tf.device (’/gpu:0’):
A = tf.constant ([[1, 2], [3, 4]])
B = tf.constant ([[5, 6], [7, 8]])
C = tf.matmul (A, B)
print ©
可以参考 tf.config.set_visible_devices | TensorFlow v2.14.0 ，相关内容在 https://tf.wiki/zh/basic/tools.html#tf-config-gpu 也有介绍。一个简单的示例程序如下：
import tensorflow as tf
tf.debugging.set_log_device_placement (True) # 设置输出运算所在的设备
cpus = tf.config.list_physical_devices ('CPU') # 获取当前设备的 CPU 列表
tf.config.set_visible_devices (cpus) # 设置 TensorFlow 的可见设备范围为 cpu
A = tf.constant ([[1, 2], [3, 4]])
B = tf.constant ([[5, 6], [7, 8]])
C = tf.matmul (A, B)
print (C)
输出
2020-04-21 11:37:29.007897: I tensorflow/core/common_runtime/eager/execute.cc:573] Executing op MatMul in device /job:localhost/replica:0/task:0/device:CPU:0
tf.Tensor (
[[19 22]
[43 50]], shape=(2, 2), dtype=int32)
当然最保险的办法是新开一个 conda 虚拟环境并且
pip install tensorflow-cpu
，安装仅支持 CPU 的 TensorFlow 版本。
请问安装 cuda 时为什么会报错：(‘Connection broken: OSError ("(10054, ‘WSAECONNRESET’)")’, OSError ("(10054, ‘WSAECONNRESET’)"))
看起来可能是网络问题。如果使用 conda 安装的话建议设置镜像。
When I use pip install tensorflow
I got the feedback:
ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)
ERROR: No matching distribution found for tensorflow
But I find the command worked:
python -m pip install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.0-py3-none-any.whl
If you meet such a question, you can try it.
你这个 TensorFlow 的版本也太老啦。本教程面向 TensorFlow 2.1，如果 pip install tensorflow 出现错误（这种情况一般很少见），请检查 Python 环境设置，重新建立一个新的 conda 环境再安装，或者在搜索引擎里搜索一下报错内容。
好吧我找到了原因了
安装一直出错是因为我之前下了 Python3.7.4-32bit 版本，换成了 64bit 就没有问题了
关于 IDE 设置 的，我的路径是 /opt/anaconda3/envs/tf2/bin/python ，不知道会不会有问题……。我系统是 macOS 10.15.3 (19D76)
没有问题，我与你的一致
你好，按照这种方式在 Anaconda 环境中使用 TF2.1 会遇到在使用 Tensorboard 的 Profile 时报错的问题，报错如下：
2020-05-08 11:09:24.374761: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcupti.so.10.1'; dlerror: libcupti.so.10.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/gridview//pbs/dispatcher/lib::/usr/local/lib64:/usr/local/lib
2020-05-08 11:09:24.374801: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1307] function cupti_interface_->Subscribe ( &subscriber_, (CUpti_CallbackFunc) ApiCallback, this) failed with error CUPTI could not be loaded or symbol could not be found.
2020-05-08 11:09:24.374816: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1346] function cupti_interface_->ActivityRegisterCallbacks ( AllocCuptiActivityBuffer, FreeCuptiActivityBuffer) failed with error CUPTI could not be loaded or symbol could not be found.
另附 nvidia-smi 和 conda list 的结果：
Fri May 8 11:25:59 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 430.50 Driver Version: 430.50 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla V100-PCIE... Off | 00000000:89:00.0 Off | 0 |
| N/A 40C P0 37W / 250W | 1354MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla V100-PCIE... Off | 00000000:8A:00.0 Off | 0 |
| N/A 39C P0 38W / 250W | 320MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 Tesla V100-PCIE... Off | 00000000:8B:00.0 Off | 0 |
| N/A 39C P0 36W / 250W | 320MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 3 Tesla V100-PCIE... Off | 00000000:8C:00.0 Off | 0 |
| N/A 43C P0 49W / 250W | 18828MiB / 32510MiB | 65% Default |
+-------------------------------+----------------------+----------------------+
| 4 Tesla V100-PCIE... Off | 00000000:DA:00.0 Off | 0 |
| N/A 40C P0 46W / 250W | 18828MiB / 32510MiB | 73% Default |
+-------------------------------+----------------------+----------------------+
| 5 Tesla V100-PCIE... Off | 00000000:DB:00.0 Off | 0 |
| N/A 38C P0 39W / 250W | 320MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 6 Tesla V100-PCIE... Off | 00000000:DC:00.0 Off | 0 |
| N/A 64C P0 155W / 250W | 5789MiB / 32510MiB | 86% Default |
+-------------------------------+----------------------+----------------------+
| 7 Tesla V100-PCIE... Off | 00000000:DD:00.0 Off | 0 |
| N/A 40C P0 40W / 250W | 320MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 2234 G /usr/bin/X 22MiB |
| 0 83392 C /usr/lhy/anaconda3/envs/tf2/bin/python 363MiB |
| 0 135660 C /usr/lhy/anaconda3/envs/tf2/bin/python 955MiB |
| 1 135660 C /usr/lhy/anaconda3/envs/tf2/bin/python 307MiB |
| 2 135660 C /usr/lhy/anaconda3/envs/tf2/bin/python 307MiB |
| 3 135660 C /usr/lhy/anaconda3/envs/tf2/bin/python 307MiB |
| 3 139517 C python 4627MiB |
| 3 140633 C python 4627MiB |
| 3 142274 C python 4627MiB |
| 3 143266 C python 4627MiB |
| 4 133393 C python 4627MiB |
| 4 133906 C python 4627MiB |
| 4 134679 C python 4627MiB |
| 4 135379 C python 4627MiB |
| 4 135660 C /usr/lhy/anaconda3/envs/tf2/bin/python 307MiB |
| 5 135660 C /usr/lhy/anaconda3/envs/tf2/bin/python 307MiB |
| 6 15156 C python 5469MiB |
| 6 135660 C /usr/lhy/anaconda3/envs/tf2/bin/python 307MiB |
| 7 135660 C /usr/lhy/anaconda3/envs/tf2/bin/python 307MiB |
+-----------------------------------------------------------------------------+
# packages in environment at /usr/lhy/anaconda3/envs/tf2:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main defaults
absl-py 0.9.0 pypi_0 pypi
astor 0.8.1 pypi_0 pypi
attrs 19.3.0 pypi_0 pypi
backcall 0.1.0 py37_0 defaults
bleach 3.1.4 pypi_0 pypi
ca-certificates 2020.1.1 0 defaults
cachetools 4.1.0 pypi_0 pypi
certifi 2020.4.5.1 py37_0 defaults
chardet 3.0.4 pypi_0 pypi
cloudpickle 1.3.0 pypi_0 pypi
cudatoolkit 10.1.243 h6bb024c_0 defaults
cudnn 7.6.5 cuda10.1_0 defaults
cycler 0.10.0 pypi_0 pypi
decorator 4.4.2 py_0 defaults
defusedxml 0.6.0 pypi_0 pypi
entrypoints 0.3 pypi_0 pypi
gast 0.2.2 pypi_0 pypi
gin-config 0.1.3 pypi_0 pypi
google-auth 1.14.0 pypi_0 pypi
google-auth-oauthlib 0.4.1 pypi_0 pypi
google-pasta 0.2.0 pypi_0 pypi
grpcio 1.28.1 pypi_0 pypi
gym 0.10.11 pypi_0 pypi
h5py 2.10.0 pypi_0 pypi
idna 2.9 pypi_0 pypi
importlib-metadata 1.6.0 pypi_0 pypi
ipykernel 5.2.1 pypi_0 pypi
ipython 7.13.0 py37h5ca1d4c_0 defaults
ipython-genutils 0.2.0 pypi_0 pypi
ipython_genutils 0.2.0 py37_0 defaults
jedi 0.17.0 pypi_0 pypi
jinja2 2.11.2 pypi_0 pypi
joblib 0.14.1 pypi_0 pypi
json5 0.9.4 pypi_0 pypi
jsonschema 3.2.0 pypi_0 pypi
jupyter-client 6.1.3 pypi_0 pypi
jupyter_client 6.1.2 py_0 defaults
jupyter_core 4.6.3 py37_0 defaults
jupyterlab 2.1.0 pypi_0 pypi
jupyterlab-server 1.1.1 pypi_0 pypi
keras-applications 1.0.8 pypi_0 pypi
keras-preprocessing 1.1.0 pypi_0 pypi
kiwisolver 1.2.0 pypi_0 pypi
ld_impl_linux-64 2.33.1 h53a641e_7 defaults
libedit 3.1.20181209 hc058e9b_0 defaults
libffi 3.2.1 hd88cf55_4 defaults
libgcc-ng 9.1.0 hdf63c60_0 defaults
libsodium 1.0.16 h1bed415_0 defaults
libstdcxx-ng 9.1.0 hdf63c60_0 defaults
lightgbm 2.3.1 pypi_0 pypi
markdown 3.2.1 pypi_0 pypi
markupsafe 1.1.1 pypi_0 pypi
matplotlib 3.2.1 pypi_0 pypi
minepy 1.2.4 pypi_0 pypi
mistune 0.8.4 pypi_0 pypi
nbconvert 5.6.1 pypi_0 pypi
nbformat 5.0.6 pypi_0 pypi
ncurses 6.2 he6710b0_0 defaults
notebook 6.0.3 pypi_0 pypi
numpy 1.18.3 pypi_0 pypi
oauthlib 3.1.0 pypi_0 pypi
openssl 1.1.1g h7b6447c_0 defaults
opt-einsum 3.2.1 pypi_0 pypi
pandas 1.0.3 pypi_0 pypi
pandocfilters 1.4.2 pypi_0 pypi
parso 0.7.0 pypi_0 pypi
pexpect 4.8.0 py37_0 defaults
pickleshare 0.7.5 pypi_0 pypi
pip 20.0.2 py37_1 defaults
prometheus-client 0.7.1 pypi_0 pypi
prompt-toolkit 3.0.5 pypi_0 pypi
prompt_toolkit 3.0.4 0 defaults
protobuf 3.11.3 pypi_0 pypi
ptyprocess 0.6.0 pypi_0 pypi
pyaml 20.4.0 pypi_0 pypi
pyasn1 0.4.8 pypi_0 pypi
pyasn1-modules 0.2.8 pypi_0 pypi
pydot 1.4.1 pypi_0 pypi
pyglet 1.5.4 pypi_0 pypi
pygments 2.6.1 py_0 defaults
pyparsing 2.4.7 pypi_0 pypi
pyrsistent 0.16.0 pypi_0 pypi
python 3.7.7 hcf32534_0_cpython defaults
python-dateutil 2.8.1 py_0 defaults
python-graphviz 0.14 pypi_0 pypi
pytz 2019.3 pypi_0 pypi
pyyaml 5.3.1 pypi_0 pypi
pyzmq 19.0.0 pypi_0 pypi
readline 8.0 h7b6447c_0 defaults
requests 2.23.0 pypi_0 pypi
requests-oauthlib 1.3.0 pypi_0 pypi
rsa 4.0 pypi_0 pypi
scikit-learn 0.22.2.post1 pypi_0 pypi
scipy 1.4.1 pypi_0 pypi
send2trash 1.5.0 pypi_0 pypi
setuptools 46.1.3 py37_0 defaults
six 1.14.0 py37_0 defaults
SQLite 3.31.1 h7b6447c_0 defaults
tensorboard 2.1.1 pypi_0 pypi
tensorflow 2.1.0 pypi_0 pypi
tensorflow-addons 0.9.1 pypi_0 pypi
tensorflow-estimator 2.1.0 pypi_0 pypi
tensorflow-probability 0.9.0 pypi_0 pypi
termcolor 1.1.0 pypi_0 pypi
terminado 0.8.3 pypi_0 pypi
testpath 0.4.4 pypi_0 pypi
tf-agents 0.4.0 pypi_0 pypi
tk 8.6.8 hbc83047_0 defaults
tornado 6.0.4 py37h7b6447c_1 defaults
tqdm 4.45.0 pypi_0 pypi
traitlets 4.3.3 py37_0 defaults
typeguard 2.7.1 pypi_0 pypi
urllib3 1.25.9 pypi_0 pypi
wcwidth 0.1.9 py_0 defaults
webencodings 0.5.1 pypi_0 pypi
werkzeug 1.0.1 pypi_0 pypi
wheel 0.34.2 py37_0 defaults
wrapt 1.12.1 pypi_0 pypi
xgboost 0.80 pypi_0 pypi
xz 5.2.5 h7b6447c_0 defaults
zeromq 4.3.1 he6710b0_3 defaults
zipp 3.1.0 pypi_0 pypi
zlib 1.2.11 h7b6447c_3 defaults
ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)
ERROR: No matching distribution found for tensorflow
按你说的装不了 tensorflow
请检查你的 Python 版本是否为 64 位，参考 https://stackoverflow.com/questions/48720833/could-not-find-a-version-that-satisfies-the-requirement-tensorflow
您好！我安装的 tf 2.0，每次启动时，cuda 的加载信息如下，有的信息重复显示了两次，但是计算上没有问题，请问您知道是什么原因吗？
2020-05-22 09:09:35.094679: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-05-22 09:09:35.146396: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate (GHz): 1.531
pciBusID: 0000:02:00.0
2020-05-22 09:09:35.147109: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 1 with properties:
name: Quadro P2000 major: 6 minor: 1 memoryClockRate (GHz): 1.4805
pciBusID: 0000:01:00.0
2020-05-22 09:09:35.147377: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-05-22 09:09:35.149235: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-05-22 09:09:35.150833: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2020-05-22 09:09:35.151172: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2020-05-22 09:09:35.153255: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2020-05-22 09:09:35.154837: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2020-05-22 09:09:35.159338: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-05-22 09:09:35.162403: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0, 1
2020-05-22 09:09:35.198717: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2194750000 Hz
2020-05-22 09:09:35.201895: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x56304c7438d0 executing computations on platform Host. Devices:
2020-05-22 09:09:35.201939: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): Host, Default Version
2020-05-22 09:09:35.545476: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x56304ca88560 executing computations on platform CUDA. Devices:
2020-05-22 09:09:35.545540: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): TITAN X (Pascal), Compute Capability 6.1
2020-05-22 09:09:35.545550: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (1): Quadro P2000, Compute Capability 6.1
2020-05-22 09:09:35.547244: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate (GHz): 1.531
pciBusID: 0000:02:00.0
2020-05-22 09:09:35.548178: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 1 with properties:
name: Quadro P2000 major: 6 minor: 1 memoryClockRate (GHz): 1.4805
pciBusID: 0000:01:00.0
2020-05-22 09:09:35.548249: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-05-22 09:09:35.548280: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-05-22 09:09:35.548307: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2020-05-22 09:09:35.548333: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2020-05-22 09:09:35.548359: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2020-05-22 09:09:35.548384: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2020-05-22 09:09:35.548411: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-05-22 09:09:35.552360: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0, 1
2020-05-22 09:09:35.552423: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-05-22 09:09:35.555863: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-05-22 09:09:35.555894: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0 1
2020-05-22 09:09:35.555908: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N N
2020-05-22 09:09:35.555919: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 1: N N
2020-05-22 09:09:35.559429: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11435 MB memory) -> physical GPU (device: 0, name: TITAN X (Pascal), pci bus id: 0000:02:00.0, compute capability: 6.1)
2020-05-22 09:09:35.560428: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 4518 MB memory) -> physical GPU (device: 1, name: Quadro P2000, pci bus id: 0000:01:00.0, compute capability: 6.1)
<tf.Tensor: id=0, shape=(), dtype=int32, numpy=1>
1 Like
同问，安装完 tensorflow2.2，也是一样的，但是输出过程正常
@MingCheung @hifeng2017 TensorFlow 在运行过程中会在终端打一些 log，这个是正常的，具体使用上只要不报错就行（有 warning 的话大多是使用了过时的 api，可以根据提示做一些调整）
如果不喜欢这些 log，可以参考 https://stackoverflow.com/questions/35911252/disable-tensorflow-debugging-information 把 log 关掉。