Tensorflow GPU错误CUDA_ERROR_OUT_OF_MEMORY：内存不足
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【中文标题】Tensorflow GPU错误CUDA_ERROR_OUT_OF_MEMORY：内存不足【英文标题】：Tensorflow GPU error CUDA_ERROR_OUT_OF_MEMORY: out of memory 【发布时间】：2019-01-08 14:58:46 【问题描述】：我是 tensorflow 的新手，在 GPU 中运行它时遇到一些问题，在 CPU 中一切正常。
当我运行以下命令检查 tensorflow 安装时：
python -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.reduce_sum(tf.random_normal([1000, 1000])))"
我收到此错误：
2019-01-08 18:49:51.551078: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/ops/random_ops.py", line 73, in random_normal
shape_tensor = _ShapeTensor(shape)
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/ops/random_ops.py", line 44, in _ShapeTensor
return ops.convert_to_tensor(shape, dtype=dtype, name="shape")
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1050, in convert_to_tensor
as_ref=False)
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1146, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 229, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 179, in constant
t = convert_to_eager_tensor(value, ctx, dtype)
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 99, in convert_to_eager_tensor
handle = ctx._handle # pylint: disable=protected-access
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/eager/context.py", line 319, in _handle
self._initialize_handle_and_devices()
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/eager/context.py", line 267, in _initialize_handle_and_devices
self._context_handle = pywrap_tensorflow.TFE_NewContext(opts)
tensorflow.python.framework.errors_impl.InternalError: failed initializing StreamExecutor for CUDA device ordinal 0: Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 12788498432
还有下面的例子：
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
我收到此错误：
2019-01-08 18:53:07.267303: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
Traceback (most recent call last):
File "test_keras.py", line 17, in <module>
model.fit(x_train, y_train, epochs=5)
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 1639, in fit
validation_steps=validation_steps)
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 215, in fit_loop
outs = f(ins_batch)
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/keras/backend.py", line 2947, in __call__
session = get_session()
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/keras/backend.py", line 465, in get_session
_SESSION = session_module.Session(config=get_default_session_config())
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1551, in __init__
super(Session, self).__init__(target, graph, config=config)
File "/home/myUsername/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 676, in __init__
self._session = tf_session.TF_NewSessionRef(self._graph._c_graph, opts)
tensorflow.python.framework.errors_impl.InternalError: failed initializing StreamExecutor for CUDA device ordinal 0: Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 12788498432
关于如何解决这个问题的任何说明？？？？
我的系统描述是：
python3 -V
Python 3.6.7
nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176
nvidia-smi
Tue Jan 8 18:37:03 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.87 Driver Version: 390.87 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 TITAN Xp Off | 00000000:17:00.0 Off | N/A |
| 23% 31C P8 16W / 250W | 12176MiB / 12196MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 GeForce GTX 1070 Off | 00000000:65:00.0 On | N/A |
| 0% 48C P8 13W / 180W | 7768MiB / 8118MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
CuDNN 版本
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
#define CUDNN_MAJOR 7
#define CUDNN_MINOR 0
#define CUDNN_PATCHLEVEL 5
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
#include "driver_types.h"
张量流版本
python3
Python 3.6.7 (default, Oct 22 2018, 11:32:17)
[GCC 8.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> tf.__version__
'1.12.0'
【问题讨论】：
Titan Xp 的所有内存都在使用中（GTX 1070 也是如此）。您似乎已经切断了nvidia-smi 输出中显示哪些进程正在使用 GPU 的部分。在不知道机器上发生的任何其他事情的情况下，您可以： 1 重新启动。 2. 再次运行nvidia-smi，并验证 Titan Xp 内存大部分可用，3. 重试问题中的第一个命令。
【参考方案1】：
罗伯特·克罗维拉感谢您的回答。
我按照您告诉我的步骤进行操作，但我遇到了同样的问题。这就是结果。如您所见，内存使用量非常小，titan xp 为 2mb，GTX1070 为 902mb。
nvidia-smi
Wed Jan 9 10:56:55 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.87 Driver Version: 390.87 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 TITAN Xp Off | 00000000:17:00.0 Off | N/A |
| 23% 22C P8 8W / 250W | 2MiB / 12196MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 GeForce GTX 1070 Off | 00000000:65:00.0 On | N/A |
| 0% 37C P8 10W / 180W | 902MiB / 8118MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 1 1355 G /usr/lib/xorg/Xorg 40MiB |
| 1 1588 G /usr/bin/gnome-shell 81MiB |
| 1 3342 G /usr/lib/xorg/Xorg 439MiB |
| 1 3535 G /usr/bin/gnome-shell 227MiB |
| 1 8880 G ...uest-channel-token=15629967551314695332 109MiB |
| 1 26921 G /usr/bin/nvidia-settings 0MiB |
+-----------------------------------------------------------------------------+
当我安装 tensorflow 时，我按照本教程 link 进行操作，主要区别在于我在 unbuntu 18.10 中安装了 tensorflow 1.12
【讨论】：
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