我刚开始探索 AI,从未使用过 Tensorflow,连 Linux 对我来说都是新手。
我之前安装过NVIDIA Driver 430,它是CUDA 10.1自带的
由于 Tensorflow-gpu 1.14 不支持 CUDA 10.1,我卸载了 CUDA 10.1 并下载了 CUDA 10.0
cuda_10.0.130_410.48_linux.run
安装完成后运行
nvcc --version
nvcc:NVIDIA (R) Cuda 编译器驱动程序
版权所有 (c) 2005-2018 NVIDIA 公司
建立于 Sat_Aug_25_21:08:01_CDT_2018
Cuda编译工具,10.0版,V10.0.130
当我尝试在 Jupyter Notebook 中使用 GPU 时,代码仍然无法运行
import tensorflow as tf
with tf.device('/gpu:0'):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
with tf.Session() as sess:
print (sess.run(c))
错误:
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
~/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1355 try:
-> 1356 return fn(*args)
1357 except errors.OpError as e:
~/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1338 # Ensure any changes to the graph are reflected in the runtime.
-> 1339 self._extend_graph()
1340 return self._call_tf_sessionrun(
~/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in _extend_graph(self)
1373 with self._graph._session_run_lock(): # pylint: disable=protected-access
-> 1374 tf_session.ExtendSession(self._session)
1375
InvalidArgumentError: Cannot assign a device for operation MatMul: {{node MatMul}}was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0, /job:localhost/replica:0/task:0/device:XLA_CPU:0, /job:localhost/replica:0/task:0/device:XLA_GPU:0 ]. Make sure the device specification refers to a valid device.
[[MatMul]]
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-19-3a5be606bcc9> in <module>
6
7 with tf.Session() as sess:
----> 8 print (sess.run(c))
~/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
948 try:
949 result = self._run(None, fetches, feed_dict, options_ptr,
--> 950 run_metadata_ptr)
951 if run_metadata:
952 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
~/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1171 if final_fetches or final_targets or (handle and feed_dict_tensor):
1172 results = self._do_run(handle, final_targets, final_fetches,
-> 1173 feed_dict_tensor, options, run_metadata)
1174 else:
1175 results = []
~/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1348 if handle is None:
1349 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1350 run_metadata)
1351 else:
1352 return self._do_call(_prun_fn, handle, feeds, fetches)
~/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1368 pass
1369 message = error_interpolation.interpolate(message, self._graph)
-> 1370 raise type(e)(node_def, op, message)
1371
1372 def _extend_graph(self):
InvalidArgumentError: Cannot assign a device for operation MatMul: node MatMul (defined at <ipython-input-9-b145a02709f7>:5) was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0, /job:localhost/replica:0/task:0/device:XLA_CPU:0, /job:localhost/replica:0/task:0/device:XLA_GPU:0 ]. Make sure the device specification refers to a valid device.
[[MatMul]]
Errors may have originated from an input operation.
Input Source operations connected to node MatMul:
b (defined at <ipython-input-9-b145a02709f7>:4)
a (defined at <ipython-input-9-b145a02709f7>:3)
但是,如果我从终端用 Python 运行这段代码,它就可以工作。我可以看到输出
[[22. 28.] [49. 64.]]
最佳答案
您需要确保安装了合适的 CUDA
和 CuDNN
版本。
- 您可以使用此链接中的建议检查您的
CuDNN
版本:How to verify CuDNN installation?- 或通过运行
cat/usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
在 linux 机器上
- 或通过运行
- 您可以在此处检查您的
CUDA
版本:xcat.docsnvcc -V
- 或通过运行
nvidia-smi
- 并在此处阅读有关
xla_gpu
的信息:tensorflow xla在这里:github xla_gpu issue- xla 由 tensorflow 制作,比标准 tensorflow 更快。
- 我不确定为什么没有
CuDNN
的CUDA
会调用gpu
的xla_gpu
。 Nvidia gpus 需要 CUDA 和 CuDNN 才能与 Tensorflow 一起正常工作,因此看起来 tensorflow 正在尝试使用自己的库在 GPU 上进行计算。但是,我不太确定。
关于python - Tensorflow 不使用 GPU,发现 xla_gpu 不是 gpu,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/58189394/