TensorFlow:内部错误:Blas SGEMM 启动失败

76

当我运行sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})时,我收到了InternalError: Blas SGEMM launch failed的错误。以下是完整的错误和堆栈跟踪:

InternalErrorTraceback (most recent call last)
<ipython-input-9-a3261a02bdce> in <module>()
      1 batch_xs, batch_ys = mnist.train.next_batch(100)
----> 2 sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    338     try:
    339       result = self._run(None, fetches, feed_dict, options_ptr,
--> 340                          run_metadata_ptr)
    341       if run_metadata:
    342         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
    562     try:
    563       results = self._do_run(handle, target_list, unique_fetches,
--> 564                              feed_dict_string, options, run_metadata)
    565     finally:
    566       # The movers are no longer used. Delete them.

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
    635     if handle is None:
    636       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
--> 637                            target_list, options, run_metadata)
    638     else:
    639       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
    657       # pylint: disable=protected-access
    658       raise errors._make_specific_exception(node_def, op, error_message,
--> 659                                             e.code)
    660       # pylint: enable=protected-access
    661 

InternalError: Blas SGEMM launch failed : a.shape=(100, 784), b.shape=(784, 10), m=100, n=10, k=784
     [[Node: MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](_recv_Placeholder_0/_4, Variable/read)]]
Caused by op u'MatMul', defined at:
  File "/usr/lib/python2.7/runpy.py", line 162, in _run_module_as_main
    "__main__", fname, loader, pkg_name)
  File "/usr/lib/python2.7/runpy.py", line 72, in _run_code
    exec code in run_globals
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py", line 3, in <module>
    app.launch_new_instance()
  File "/usr/local/lib/python2.7/dist-packages/traitlets/config/application.py", line 596, in launch_instance
    app.start()
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelapp.py", line 442, in start
    ioloop.IOLoop.instance().start()
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/ioloop.py", line 162, in start
    super(ZMQIOLoop, self).start()
  File "/usr/local/lib/python2.7/dist-packages/tornado/ioloop.py", line 883, in start
    handler_func(fd_obj, events)
  File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
    self._handle_recv()
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
    self._run_callback(callback, msg)
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
    callback(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 276, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
    handler(stream, idents, msg)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 391, in execute_request
    user_expressions, allow_stdin)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/ipkernel.py", line 199, in do_execute
    shell.run_cell(code, store_history=store_history, silent=silent)
  File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2723, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2825, in run_ast_nodes
    if self.run_code(code, result):
  File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2885, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-4-d7414c4b6213>", line 4, in <module>
    y = tf.nn.softmax(tf.matmul(x, W) + b)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 1036, in matmul
    name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 911, in _mat_mul
    transpose_b=transpose_b, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 655, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2154, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1154, in __init__
    self._traceback = _extract_stack()

技术栈: EC2 g2.8xlarge 机器, Ubuntu 14.04


我怀疑这是GPU内存问题。我运行了从TensorFlow页面获取的mnist_softmax.py。在我的GTX950 PC(2GB vram)上,我遇到了这个错误。但在我的Quadro M2000M笔记本电脑(4GB vram)上,它可以正常运行。两个系统都使用Anaconda与Python 3.5和TensorFlow 1.0。 - mark jay
16个回答

0
在我的情况下,只需在单独的服务器中打开Jupyter Notebooks即可。
如果我尝试在同一台服务器上使用多个tensorflow/keras模型,则只会出现此错误。无论是打开一个笔记本电脑,执行它,然后关闭并尝试打开另一个笔记本电脑都没有关系。如果它们被加载到同一个Jupyter服务器中,则始终会发生错误。

0
在我的情况下,libcublas.so所在的网络文件系统崩溃了。节点被重新启动后一切正常。只是为数据集添加另一点。

0
重新启动我的Jupyter进程还不够,我必须重新启动计算机。

0

我在使用pytest-xdist并行运行Keras CuDNN测试时遇到了这个错误。解决方法是将它们串行运行。


0

对我来说,当我使用Keras和Tensorflow作为后端时,出现了这个错误。这是因为在Anaconda中的深度学习环境没有正确激活,结果Tensorflow也没有正确启动。我注意到这一点是因为上次我激活我的深度学习环境(称为dl)时,在Anaconda提示符中发生了变化:

(dl) C:\Users\georg\Anaconda3\envs\dl\etc\conda\activate.d>set "KERAS_BACKEND=tensorflow"

而之前只有dl。因此,为了摆脱上述错误,我关闭了jupyter笔记本和Anaconda提示符,然后重新启动了几次。


0

最近更换操作系统为Windows 10后,我遇到了这个错误,在使用Windows 7时从未遇到过。

如果我在运行另一个GPU程序时加载我的GPU Tensorflow模型,就会出现这个错误;它是作为套接字服务器加载的JCuda模型,不是很大。如果我关闭其他GPU程序,这个Tensorflow模型就可以被成功加载。

这个JCuda程序并不大,只有70M左右,相比之下,这个Tensorflow模型超过500M,要大得多。但我正在使用1080 ti,它有更多的内存。所以这可能不是内存不足的问题,而可能是Tensorflow关于操作系统或Cuda的一些棘手的内部问题。(PS:我正在使用Cuda版本8.0.44,没有下载更新版本。)


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