TensorFlow安装教程
诸神缄默不语-个人CSDN博文目录
TensorFlow是学习深度学习时常用的Python神经网络框架，本文将介绍其部分版本在Linux系统使用pip进行安装的方法。
（注：TensorFlow官方推荐使用pip进行安装。）
作者使用anaconda作为管理虚拟环境的工具。以下工作都在虚拟环境中进行，对Python和Aanaconda的安装及对虚拟环境的管理本文不作赘述，后期可能会撰写相关的博文。
首先进入官网：TensorFlow
TensorFlow安装的总界面：Install TensorFlow 2
文章目录
- 1. TensorFlow 2最新版安装（本文撰写时为2.9.0）
- 2. TensorFlow 1.14 + Keras 2.3.1（安装时间：2022.8.17）
- 3. 其他本文撰写过程中使用的参考资料
1. TensorFlow 2最新版安装（本文撰写时为2.9.0）
官方安装指南：Install TensorFlow 2
用pip安装的指南：Install TensorFlow with pip
TensorFlow基础的系统环境等要求可直接在该网站上查看，已经2022年了，一般电脑都不会这么老吧。
新建anaconda虚拟环境：conda create -n envtf2 python==3.8（Python版本需要是3.7-3.10，本文以3.8为例，主要是因为我需要用3.8版本来安装另一个包）
激活虚拟环境：conda activate envtf2
如果要使用cuda，首先确定本机安装有NVIDIA GPU driver：nvidia-smi（一般都会有的吧，没有的话到得了这一步吗）
安装指定的cudatoolkit和cudnn版本：conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
有两种指定配置路径的方式：
①临时的，每次会话都需要先激活虚拟环境然后：export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
②自动在每次激活虚拟环境后执行此操作（我没有试过，我一直都用的是上面那种方式）：
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
更新pip：pip install --upgrade pip
安装TensorFlow：pip install tensorflow
检验CPU版TensorFlow是否可用：python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
（我的服务器有4张卡）
检验GPU版TensorFlow是否可用：python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
注意，以上操作是在终端上进行的，不能直接放到jupyter notebook。一个失败的例子：
在jupyter notebook上，我直接调用!export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/也不行，用os.environ['LD_LIBRARY_PATH']也不行，用$env也不行，就把我整得相当困惑。我看了一下，好像如果用jupyter notebook的话就必须要修改jupyter内核才能用，但是我修改了jupyter kernelspec list路径中的kernel.json后仍然不行。（参考自python - How to set env variable in Jupyter notebook - Stack Overflow）
其他我在网上有看到一些使用全局配置解决此问题的方法，但是我这个服务器上还需要运行别的版本的别的项目，总之不太方便用这个。一般来说我对此问题的解决方法就是不用jupyter notebook来跑TF项目。我看的这些资料可资参考：
解决TensorFlow在terminal中正常但在jupyter notebook中报错的方案 - stardsd - 博客园
Add CUDA Library Path to Jupyterhub Notebook - AIML - wiki.ucar.edu
install pytorch with jupyter - 知乎
所以jupyter notebook上要成功使用TensorFlow GPU功能的话就必须要先在命令行上激活虚拟环境，然后export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/，然后调用jupyter notebook命令打开jupyter notebook，这样就能直接正常使用了。
（注意：如果仅安装了ipykernel包，那么VSCode中可以打开notebook文件，但是无法使用jupyter notebook打开能够在浏览器中打开的网页，因此需要安装jupyterlab：pip install jupyterlab（参考Project Jupyter | Installing Jupyter）。VSCode即使在远程服务器上也可以把端口转到本地使用localhost域名在本地浏览器打开，挺方便的）
运行成功的效果：
2. TensorFlow 1.14 + Keras 2.3.1（安装时间：2022.8.17）
这个是苏神bert4keras（https://github.com/bojone/bert4keras）的配置。
见TensorFlow官网（使用 pip 安装 TensorFlow），仅TensorFlow2.2以上支持Python3.8以上，所以我需要一个Python3.7的环境。
新建anaconda虚拟环境：conda create -n envtf114 python=3.7 pip
安装GPU版TensorFlow：pip install tensorflow-gpu==1.14
试用如下代码（来自tensorflow-gpu1.14代码测试_爱听许嵩歌的博客-CSDN博客_tensorflow-gpu测试代码）：
import tensorflow as tf
with tf.device('/cpu:0'):
a = tf.constant([1.0, 2.0, 3.0], shape=[3], name='a')
b = tf.constant([1.0, 2.0, 3.0], shape=[3], name='b')
with tf.device('/gpu:2'):
c = a + b
# 注意：allow_soft_placement=True表明：计算设备可自行选择，如果没有这个参数，会报错。
# 因为不是所有的操作都可以被放在GPU上，如果强行将无法放在GPU上的操作指定到GPU上，将会报错。
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True))
# sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
sess.run(tf.global_variables_initializer())
print(sess.run(c))
报错：
Traceback (most recent call last):
File "trytf1.py", line 1, in
import tensorflow as tf
File "env_path/lib/python3.7/site-packages/tensorflow/__init__.py", line 28, in
from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
File "env_path/lib/python3.7/site-packages/tensorflow/python/__init__.py", line 52, in
from tensorflow.core.framework.graph_pb2 import *
File "env_path/lib/python3.7/site-packages/tensorflow/core/framework/graph_pb2.py", line 16, in
from tensorflow.core.framework import node_def_pb2 as tensorflow_dot_core_dot_framework_dot_node__def__pb2
File "env_path/lib/python3.7/site-packages/tensorflow/core/framework/node_def_pb2.py", line 16, in
from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
File "env_path/lib/python3.7/site-packages/tensorflow/core/framework/attr_value_pb2.py", line 16, in
from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
File "env_path/lib/python3.7/site-packages/tensorflow/core/framework/tensor_pb2.py", line 16, in
from tensorflow.core.framework import resource_handle_pb2 as tensorflow_dot_core_dot_framework_dot_resource__handle__pb2
File "env_path/lib/python3.7/site-packages/tensorflow/core/framework/resource_handle_pb2.py", line 42, in
serialized_options=None, file=DESCRIPTOR),
File "/home/wanghuijuan/anaconda3/envs/envtf114/lib/python3.7/site-packages/google/protobuf/descriptor.py", line 560, in __new__
_message.Message._CheckCalledFromGeneratedFile()
TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
1. Downgrade the protobuf package to 3.20.x or lower.
2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).
More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates
嗯虽然不知道发生了什么总之我从善如流地照着改（参考1. Downgrade the protobuf package to 3.20.x or lower._weixin_44834086的博客-CSDN博客）：
pip install protobuf==3.19.0
然后重新运行代码，这回的输出信息变成了：
env_path/lib/python3.7/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)])
env_path/lib/python3.7/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)])
env_path/lib/python3.7/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)])
env_path/lib/python3.7/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)])
env_path/lib/python3.7/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)])
env_path/lib/python3.7/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)])
env_path/lib/python3.7/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)])
env_path/lib/python3.7/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)])
env_path/lib/python3.7/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)])
env_path/lib/python3.7/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)])
env_path/lib/python3.7/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)])
env_path/lib/python3.7/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)])
WARNING:tensorflow:From trytf1.py:11: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
WARNING:tensorflow:From trytf1.py:11: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.
2022-08-17 15:27:08.829308: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
2022-08-17 15:27:08.865801: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 1800000000 Hz
2022-08-17 15:27:08.867967: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55f031fff480 executing computations on platform Host. Devices:
2022-08-17 15:27:08.868039: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): ,
2022-08-17 15:27:08.871550: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2022-08-17 15:27:09.628470: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55f0332f1040 executing computations on platform CUDA. Devices:
2022-08-17 15:27:09.628540: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): Tesla T4, Compute Capability 7.5
2022-08-17 15:27:09.628560: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (1): Tesla T4, Compute Capability 7.5
2022-08-17 15:27:09.628580: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (2): Tesla T4, Compute Capability 7.5
2022-08-17 15:27:09.628597: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (3): Tesla T4, Compute Capability 7.5
2022-08-17 15:27:09.645921: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:3b:00.0
2022-08-17 15:27:09.650885: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 1 with properties:
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:5e:00.0
2022-08-17 15:27:09.652426: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 2 with properties:
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:b1:00.0
2022-08-17 15:27:09.653863: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 3 with properties:
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:d9:00.0
2022-08-17 15:27:09.654104: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcudart.so.10.0'; dlerror: libcudart.so.10.0: cannot open shared object file: No such file or directory
2022-08-17 15:27:09.654223: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcublas.so.10.0'; dlerror: libcublas.so.10.0: cannot open shared object file: No such file or directory
2022-08-17 15:27:09.654332: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcufft.so.10.0'; dlerror: libcufft.so.10.0: cannot open shared object file: No such file or directory
2022-08-17 15:27:09.654437: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcurand.so.10.0'; dlerror: libcurand.so.10.0: cannot open shared object file: No such file or directory
2022-08-17 15:27:09.654540: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcusolver.so.10.0'; dlerror: libcusolver.so.10.0: cannot open shared object file: No such file or directory
2022-08-17 15:27:09.654661: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcusparse.so.10.0'; dlerror: libcusparse.so.10.0: cannot open shared object file: No such file or directory
2022-08-17 15:27:09.740681: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2022-08-17 15:27:09.740741: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1663] Cannot dlopen some GPU libraries. Skipping registering GPU devices...
2022-08-17 15:27:09.740824: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-08-17 15:27:09.740848: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187] 0 1 2 3
2022-08-17 15:27:09.740868: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0: N Y Y Y
2022-08-17 15:27:09.740886: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 1: Y N Y Y
2022-08-17 15:27:09.740904: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 2: Y Y N Y
2022-08-17 15:27:09.740921: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 3: Y Y Y N
Device mapping:
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:1 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:2 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:3 -> device: XLA_GPU device
2022-08-17 15:27:09.742912: I tensorflow/core/common_runtime/direct_session.cc:296] Device mapping:
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:1 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:2 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:3 -> device: XLA_GPU device
WARNING:tensorflow:From trytf1.py:13: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.
add: (Add): /job:localhost/replica:0/task:0/device:CPU:0
2022-08-17 15:27:09.746145: I tensorflow/core/common_runtime/placer.cc:54] add: (Add)/job:localhost/replica:0/task:0/device:CPU:0
init: (NoOp): /job:localhost/replica:0/task:0/device:CPU:0
2022-08-17 15:27:09.746214: I tensorflow/core/common_runtime/placer.cc:54] init: (NoOp)/job:localhost/replica:0/task:0/device:CPU:0
a: (Const): /job:localhost/replica:0/task:0/device:CPU:0
2022-08-17 15:27:09.746257: I tensorflow/core/common_runtime/placer.cc:54] a: (Const)/job:localhost/replica:0/task:0/device:CPU:0
b: (Const): /job:localhost/replica:0/task:0/device:CPU:0
2022-08-17 15:27:09.746294: I tensorflow/core/common_runtime/placer.cc:54] b: (Const)/job:localhost/replica:0/task:0/device:CPU:0
2022-08-17 15:27:09.748161: 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.
[2. 4. 6.]
其他略，总之那一堆无法打开so文件就说明cuda安装有问题，无法使用GPU。
TensorFlow版本对应GPU版本（图源https://www.tensorflow.org/install/source#gpu）：
所以首先安装所需的cudnn和cuda：conda install -c conda-forge cudatoolkit=10.0 cudnn=7.4
报了个非常诡异的bug：
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): failed
# >>>>>>>>>>>>>>>>>>>>>> ERROR REPORT <<<<<<<<<<<<<<<<<<<<<<
Traceback (most recent call last):
File "anaconda3/lib/python3.9/site-packages/urllib3/response.py", line 700, in _update_chunk_length
self.chunk_left = int(line, 16)
ValueError: invalid literal for int() with base 16: b''
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "anaconda3/lib/python3.9/site-packages/urllib3/response.py", line 441, in _error_catcher
yield
File "anaconda3/lib/python3.9/site-packages/urllib3/response.py", line 767, in read_chunked
self._update_chunk_length()
File "anaconda3/lib/python3.9/site-packages/urllib3/response.py", line 704, in _update_chunk_length
raise InvalidChunkLength(self, line)
urllib3.exceptions.InvalidChunkLength: InvalidChunkLength(got length b'', 0 bytes read)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "anaconda3/lib/python3.9/site-packages/requests/models.py", line 760, in generate
for chunk in self.raw.stream(chunk_size, decode_content=True):
File "anaconda3/lib/python3.9/site-packages/urllib3/response.py", line 575, in stream
for line in self.read_chunked(amt, decode_content=decode_content):
File "anaconda3/lib/python3.9/site-packages/urllib3/response.py", line 796, in read_chunked
self._original_response.close()
File "anaconda3/lib/python3.9/contextlib.py", line 137, in __exit__
self.gen.throw(typ, value, traceback)
File "anaconda3/lib/python3.9/site-packages/urllib3/response.py", line 458, in _error_catcher
raise ProtocolError("Connection broken: %r" % e, e)
urllib3.exceptions.ProtocolError: ("Connection broken: InvalidChunkLength(got length b'', 0 bytes read)", InvalidChunkLength(got length b'', 0 bytes read))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "anaconda3/lib/python3.9/site-packages/conda/exceptions.py", line 1114, in __call__
return func(*args, **kwargs)
File "anaconda3/lib/python3.9/site-packages/conda/cli/main.py", line 86, in main_subshell
exit_code = do_call(args, p)
File "anaconda3/lib/python3.9/site-packages/conda/cli/conda_argparse.py", line 90, in do_call
return getattr(module, func_name)(args, parser)
File "anaconda3/lib/python3.9/site-packages/conda/cli/main_install.py", line 20, in execute
install(args, parser, 'install')
File "anaconda3/lib/python3.9/site-packages/conda/cli/install.py", line 259, in install
unlink_link_transaction = solver.solve_for_transaction(
File "anaconda3/lib/python3.9/site-packages/conda/core/solve.py", line 152, in solve_for_transaction
unlink_precs, link_precs = self.solve_for_diff(update_modifier, deps_modifier,
File "anaconda3/lib/python3.9/site-packages/conda/core/solve.py", line 195, in solve_for_diff
final_precs = self.solve_final_state(update_modifier, deps_modifier, prune, ignore_pinned,
File "anaconda3/lib/python3.9/site-packages/conda/core/solve.py", line 300, in solve_final_state
ssc = self._collect_all_metadata(ssc)
File "anaconda3/lib/python3.9/site-packages/conda/common/io.py", line 86, in decorated
return f(*args, **kwds)
File "anaconda3/lib/python3.9/site-packages/conda/core/solve.py", line 463, in _collect_all_metadata
index, r = self._prepare(prepared_specs)
File "anaconda3/lib/python3.9/site-packages/conda/core/solve.py", line 1058, in _prepare
reduced_index = get_reduced_index(self.prefix, self.channels,
File "anaconda3/lib/python3.9/site-packages/conda/core/index.py", line 287, in get_reduced_index
new_records = SubdirData.query_all(spec, channels=channels, subdirs=subdirs,
File "anaconda3/lib/python3.9/site-packages/conda/core/subdir_data.py", line 139, in query_all
result = tuple(concat(executor.map(subdir_query, channel_urls)))
File "anaconda3/lib/python3.9/concurrent/futures/_base.py", line 609, in result_iterator
yield fs.pop().result()
File "anaconda3/lib/python3.9/concurrent/futures/_base.py", line 446, in result
return self.__get_result()
File "anaconda3/lib/python3.9/concurrent/futures/_base.py", line 391, in __get_result
raise self._exception
File "anaconda3/lib/python3.9/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "anaconda3/lib/python3.9/site-packages/conda/core/subdir_data.py", line 131, in
subdir_query = lambda url: tuple(SubdirData(Channel(url), repodata_fn=repodata_fn).query(
File "anaconda3/lib/python3.9/site-packages/conda/core/subdir_data.py", line 144, in query
self.load()
File "anaconda3/lib/python3.9/site-packages/conda/core/subdir_data.py", line 209, in load
_internal_state = self._load()
File "anaconda3/lib/python3.9/site-packages/conda/core/subdir_data.py", line 374, in _load
raw_repodata_str = fetch_repodata_remote_request(
File "anaconda3/lib/python3.9/site-packages/conda/core/subdir_data.py", line 700, in fetch_repodata_remote_request
resp = session.get(join_url(url, filename), headers=headers, proxies=session.proxies,
File "anaconda3/lib/python3.9/site-packages/requests/sessions.py", line 542, in get
return self.request('GET', url, **kwargs)
File "anaconda3/lib/python3.9/site-packages/requests/sessions.py", line 529, in request
resp = self.send(prep, **send_kwargs)
File "anaconda3/lib/python3.9/site-packages/requests/sessions.py", line 687, in send
r.content
File "anaconda3/lib/python3.9/site-packages/requests/models.py", line 838, in content
self._content = b''.join(self.iter_content(CONTENT_CHUNK_SIZE)) or b''
File "anaconda3/lib/python3.9/site-packages/requests/models.py", line 763, in generate
raise ChunkedEncodingError(e)
requests.exceptions.ChunkedEncodingError: ("Connection broken: InvalidChunkLength(got length b'', 0 bytes read)", InvalidChunkLength(got length b'', 0 bytes read))
`$ /home/wanghuijuan/anaconda3/bin/conda install -c conda-forge cudatoolkit=10.0 cudnn=7.4`
environment variables:
CIO_TEST=
CONDA_DEFAULT_ENV=
CONDA_EXE=anaconda3/bin/conda
CONDA_PREFIX=
CONDA_PREFIX_1=anaconda3
CONDA_PREFIX_2=
CONDA_PROMPT_MODIFIER=()
CONDA_PYTHON_EXE=anaconda3/bin/python
CONDA_ROOT=anaconda3
CONDA_SHLVL=3
CURL_CA_BUNDLE=
PATH=/anaconda3/condabin:/home/wanghuijuan/.vscode
-server/bin/6d9b74a70ca9c7733b29f0456fd8195364076dda/bin/remote-cli:/u
sr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:
/usr/local/games:/snap/bin
REQUESTS_CA_BUNDLE=
SSL_CERT_FILE=
active environment :
active env location :
shell level : 3
user config file : .condarc
populated config files :
conda version : 4.13.0
conda-build version : 3.21.8
python version : 3.9.12.final.0
virtual packages : __cuda=11.4=0
__linux=4.15.0=0
__glibc=2.27=0
__unix=0=0
__archspec=1=x86_64
base environment :anaconda3 (writable)
conda av data dir :anaconda3/etc/conda
conda av metadata url : None
channel URLs : https://conda.anaconda.org/conda-forge/linux-64
https://conda.anaconda.org/conda-forge/noarch
https://repo.anaconda.com/pkgs/main/linux-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/linux-64
https://repo.anaconda.com/pkgs/r/noarch
package cache : /home/wanghuijuan/anaconda3/pkgs
/home/wanghuijuan/.conda/pkgs
envs directories : /home/wanghuijuan/anaconda3/envs
/home/wanghuijuan/.conda/envs
platform : linux-64
user-agent : conda/4.13.0 requests/2.27.1 CPython/3.9.12 Linux/4.15.0-136-generic ubuntu/18.04.4 glibc/2.27
UID:GID : 1018:1014
netrc file : None
offline mode : False
An unexpected error has occurred. Conda has prepared the above report.
If submitted, this report will be used by core maintainers to improve
future releases of conda.
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[y/N]: y
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by running
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不知道发生了什么，总之换一种安装方式好了（参考TensorFlow-gpu安装和测试（TensorFlow-gpu1.14+Cuda10）_爱学习的小龙的博客-CSDN博客_tensorflowgpu测试）：
wget -P files/install_packages https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/linux-64/cudatoolkit-10.0.130-0.conda
conda install files/install_packages/cudatoolkit-10.0.130-0.conda
重新运行Python代码。和之前一样的输出部分就不写了，直接从不一样的地方开始：
2022-08-17 16:15:43.407219: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2022-08-17 16:15:43.409338: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2022-08-17 16:15:43.411111: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2022-08-17 16:15:43.411878: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2022-08-17 16:15:43.415478: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2022-08-17 16:15:43.418072: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2022-08-17 16:15:43.424901: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2022-08-17 16:15:43.435064: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0, 1, 2, 3
2022-08-17 16:15:43.435492: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2022-08-17 16:15:43.441476: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-08-17 16:15:43.442070: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187] 0 1 2 3
2022-08-17 16:15:43.442448: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0: N Y Y Y
2022-08-17 16:15:43.443431: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 1: Y N Y Y
2022-08-17 16:15:43.444206: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 2: Y Y N Y
2022-08-17 16:15:43.444586: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 3: Y Y Y N
2022-08-17 16:15:43.452440: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2446 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:3b:00.0, compute capability: 7.5)
2022-08-17 16:15:43.462938: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 5244 MB memory) -> physical GPU (device: 1, name: Tesla T4, pci bus id: 0000:5e:00.0, compute capability: 7.5)
2022-08-17 16:15:43.469831: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 14259 MB memory) -> physical GPU (device: 2, name: Tesla T4, pci bus id: 0000:b1:00.0, compute capability: 7.5)
2022-08-17 16:15:43.483509: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:3 with 14259 MB memory) -> physical GPU (device: 3, name: Tesla T4, pci bus id: 0000:d9:00.0, compute capability: 7.5)
Device mapping:
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:1 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:2 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:3 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla T4, pci bus id: 0000:3b:00.0, compute capability: 7.5
/job:localhost/replica:0/task:0/device:GPU:1 -> device: 1, name: Tesla T4, pci bus id: 0000:5e:00.0, compute capability: 7.5
/job:localhost/replica:0/task:0/device:GPU:2 -> device: 2, name: Tesla T4, pci bus id: 0000:b1:00.0, compute capability: 7.5
/job:localhost/replica:0/task:0/device:GPU:3 -> device: 3, name: Tesla T4, pci bus id: 0000:d9:00.0, compute capability: 7.5
2022-08-17 16:15:43.490300: I tensorflow/core/common_runtime/direct_session.cc:296] Device mapping:
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:1 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:2 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:3 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla T4, pci bus id: 0000:3b:00.0, compute capability: 7.5
/job:localhost/replica:0/task:0/device:GPU:1 -> device: 1, name: Tesla T4, pci bus id: 0000:5e:00.0, compute capability: 7.5
/job:localhost/replica:0/task:0/device:GPU:2 -> device: 2, name: Tesla T4, pci bus id: 0000:b1:00.0, compute capability: 7.5
/job:localhost/replica:0/task:0/device:GPU:3 -> device: 3, name: Tesla T4, pci bus id: 0000:d9:00.0, compute capability: 7.5
WARNING:tensorflow:From /home/wanghuijuan/whj_code1/trytf1.py:13: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.
add: (Add): /job:localhost/replica:0/task:0/device:GPU:2
2022-08-17 16:15:43.495600: I tensorflow/core/common_runtime/placer.cc:54] add: (Add)/job:localhost/replica:0/task:0/device:GPU:2
init: (NoOp): /job:localhost/replica:0/task:0/device:GPU:0
2022-08-17 16:15:43.495642: I tensorflow/core/common_runtime/placer.cc:54] init: (NoOp)/job:localhost/replica:0/task:0/device:GPU:0
a: (Const): /job:localhost/replica:0/task:0/device:CPU:0
2022-08-17 16:15:43.495664: I tensorflow/core/common_runtime/placer.cc:54] a: (Const)/job:localhost/replica:0/task:0/device:CPU:0
b: (Const): /job:localhost/replica:0/task:0/device:CPU:0
2022-08-17 16:15:43.495682: I tensorflow/core/common_runtime/placer.cc:54] b: (Const)/job:localhost/replica:0/task:0/device:CPU:0
[2. 4. 6.]
运行TensorFlow代码时需要在代码前加上这些：
import tensorflow as tf
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "2" #这里是gpu的序号，指定使用的gpu对象
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
Keras就自动安装好了。
直接用bert4keras代码来举个实际用例：
pip install bert4keras
wget -P /data/pretrained_model/chinese_L-12_H-768_A-12 https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip
unzip /data/pretrained_model/chinese_L-12_H-768_A-12/chinese_L-12_H-768_A-12.zip -d /data/pretrained_model/chinese_L-12_H-768_A-12
代码（改自https://github.com/bojone/bert4keras/blob/master/examples/basic_extract_features.py）：
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
import time
import numpy as np
from bert4keras.backend import keras
from bert4keras.models import build_transformer_model
from bert4keras.tokenizers import Tokenizer
from bert4keras.snippets import to_array
config_path = '/data/pretrained_model/chinese_L-12_H-768_A-12/chinese_L-12_H-768_A-12/bert_config.json'
checkpoint_path = '/data/pretrained_model/chinese_L-12_H-768_A-12/chinese_L-12_H-768_A-12/bert_model.ckpt'
dict_path = '/data/pretrained_model/chinese_L-12_H-768_A-12/chinese_L-12_H-768_A-12/vocab.txt'
tokenizer = Tokenizer(dict_path, do_lower_case=True) # 建立分词器
model = build_transformer_model(config_path, checkpoint_path) # 建立模型，加载权重
# 编码测试
token_ids, segment_ids = tokenizer.encode(u'语言模型')
token_ids, segment_ids = to_array([token_ids], [segment_ids])
print('\n ===== predicting =====\n')
print(model.predict([token_ids, segment_ids]))
"""
输出：
[[[-0.63251007 0.2030236 0.07936534 ... 0.49122632 -0.20493352
0.2575253 ]
[-0.7588351 0.09651865 1.0718756 ... -0.6109694 0.04312154
0.03881441]
[ 0.5477043 -0.792117 0.44435206 ... 0.42449304 0.41105673
0.08222899]
[-0.2924238 0.6052722 0.49968526 ... 0.8604137 -0.6533166
0.5369075 ]
[-0.7473459 0.49431565 0.7185162 ... 0.3848612 -0.74090636
0.39056838]
[-0.8741375 -0.21650358 1.338839 ... 0.5816864 -0.4373226
0.56181806]]]
"""
time.sleep(100)
time.sleep()命令是为了停留一下，显式用nvidia-smi命令看这个程序只在卡2上占用空间，以及具体占用了多大的GPU（占了14G，还是相当大的）
3. 其他本文撰写过程中使用的参考资料
- tensorflow 1.14指定gpu运行设置_愚昧之山绝望之谷开悟之坡的博客-CSDN博客_tensorflow指定gpu