TensorFlow：InternalError：Blas SGEMM启动失败
当我运行sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})我得到InternalError: Blas SGEMM launch failed 。 这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()
堆栈：EC2 g2.8x大机，Ubuntu 14.04
老问题，但可能帮助别人。
尝试closures在其他进程中活动的交互式会话（如果IPython Notebook – 只是重新启动内核）。 这帮助了我！
另外，我在实验中使用这段代码closures了这个内核中的本地会话：
if 'session' in locals() and session is not None: print('Close interactive session') session.close()
运行Tensorflow Distributed时出现此错误。 您是否检查是否有任何工作人员报告了CUDA_OUT_OF_MEMORY错误？ 如果是这种情况，可能与你的体重和偏倚variables的位置有关。 例如
with tf.device("/job:paramserver/task:0/cpu:0"): W = weight_variable([input_units, num_hidden_units]) b = bias_variable([num_hidden_units])
我的环境是Python 3.5，Tensorflow 0.12和Windows 10（没有Docker）。 我在CPU和GPU上训练neural network。 我遇到了同样的错误InternalError: Blas SGEMM launch failed无论何时在GPU中训练， InternalError: Blas SGEMM launch failed 。
我找不到这个错误发生的原因，但我设法通过避免张量函数tensorflow.contrib.slim.one_hot_encoding()来在GPU中运行我的代码。 相反，我在numpy（input和输出variables）中进行单热编码操作。
以下代码重现错误和修复。 学习使用梯度下降的y = x ** 2函数是最基本的设置。
import numpy as np import tensorflow as tf import tensorflow.contrib.slim as slim def test_one_hot_encoding_using_tf(): # This function raises the "InternalError: Blas SGEMM launch failed" when run in the GPU # Initialize tf.reset_default_graph() input_size = 10 output_size = 100 input_holder = tf.placeholder(shape=[1], dtype=tf.int32, name='input') output_holder = tf.placeholder(shape=[1], dtype=tf.int32, name='output') # Define network input_oh = slim.one_hot_encoding(input_holder, input_size) output_oh = slim.one_hot_encoding(output_holder, output_size) W1 = tf.Variable(tf.random_uniform([input_size, output_size], 0, 0.01)) output_v = tf.matmul(input_oh, W1) output_v = tf.reshape(output_v, [-1]) # Define updates loss = tf.reduce_sum(tf.square(output_oh - output_v)) trainer = tf.train.GradientDescentOptimizer(learning_rate=0.1) update_model = trainer.minimize(loss) # Optimize init = tf.initialize_all_variables() steps = 1000 # Force CPU/GPU config = tf.ConfigProto( # device_count={'GPU': 0} # uncomment this line to force CPU ) # Launch the tensorflow graph with tf.Session(config=config) as sess: sess.run(init) for step_i in range(steps): # Get sample x = np.random.randint(0, 10) y = np.power(x, 2).astype('int32') # Update _, l = sess.run([update_model, loss], feed_dict={input_holder: [x], output_holder: [y]}) # Check model print('Final loss: %f' % l) def test_one_hot_encoding_no_tf(): # This function does not raise the "InternalError: Blas SGEMM launch failed" when run in the GPU def oh_encoding(label, num_classes): return np.identity(num_classes)[label:label + 1].astype('int32') # Initialize tf.reset_default_graph() input_size = 10 output_size = 100 input_holder = tf.placeholder(shape=[1, input_size], dtype=tf.float32, name='input') output_holder = tf.placeholder(shape=[1, output_size], dtype=tf.float32, name='output') # Define network W1 = tf.Variable(tf.random_uniform([input_size, output_size], 0, 0.01)) output_v = tf.matmul(input_holder, W1) output_v = tf.reshape(output_v, [-1]) # Define updates loss = tf.reduce_sum(tf.square(output_holder - output_v)) trainer = tf.train.GradientDescentOptimizer(learning_rate=0.1) update_model = trainer.minimize(loss) # Optimize init = tf.initialize_all_variables() steps = 1000 # Force CPU/GPU config = tf.ConfigProto( # device_count={'GPU': 0} # uncomment this line to force CPU ) # Launch the tensorflow graph with tf.Session(config=config) as sess: sess.run(init) for step_i in range(steps): # Get sample x = np.random.randint(0, 10) y = np.power(x, 2).astype('int32') # One hot encoding x = oh_encoding(x, 10) y = oh_encoding(y, 100) # Update _, l = sess.run([update_model, loss], feed_dict={input_holder: x, output_holder: y}) # Check model print('Final loss: %f' % l)
我遇到了这个问题，通过设置allow_soft_placement=True和gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3)来解决这个问题，它专门定义了GPU使用的内存部分。 我想这有助于避免两个张量stream程竞争GPU内存。
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3) sess = tf.Session(config=tf.ConfigProto( allow_soft_placement=True, log_device_placement=True))
也许你没有严格的释放你的GPU，如果你正在使用Linux，请尝试使用“ps -ef | grep python”来查看正在使用GPU的工作。 然后杀了他们
在我的情况下，我有2个python控制台打开，都使用keras / tensorflow。 当我closures旧的控制台（从前一天忘记），一切都开始正常工作。
所以如果你没有多个控制台/进程占用GPU，那么检查是很好的。
在我的情况下，libcublas下的文件系统。 所以被定位简单地死了。 节点重新启动，一切都很好。 只是为了给数据集添加另一个点。
