TensorFlow: InternalError: Blas SGEMM launch failed
Solution 1
Old question, but may help others.
Try to close interactive sessions active in other processes (if IPython Notebook - just restart kernels). This helped me!
Additionally, I use this code to close local sessions in this kernel during experiments:
if 'session' in locals() and session is not None:
print('Close interactive session')
session.close()
Solution 2
I encountered this problem and solved it by setting allow_soft_placement=True
and gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3)
, which specifically define the fraction of memory of GPU been used. I guess this has helped to avoid two tensorflow processes competing for the GPU memory.
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))
Solution 3
I got this error when running Tensorflow Distributed. Did you check if any of the workers were reporting CUDA_OUT_OF_MEMORY errors? If this is the case it may have to do with where you place your weight and bias variables. E.g.
with tf.device("/job:paramserver/task:0/cpu:0"):
W = weight_variable([input_units, num_hidden_units])
b = bias_variable([num_hidden_units])
Solution 4
My environment is Python 3.5, Tensorflow 0.12 and Windows 10 (no Docker). I am training neural networks in both CPU and GPU. I came across the same error InternalError: Blas SGEMM launch failed
whenever training in the GPU.
I could not find the reason why this error happens but I managed to run my code in the GPU by avoiding the tensorflow function tensorflow.contrib.slim.one_hot_encoding()
. Instead, I do the one-hot-encoding operation in numpy (input and output variables).
The following code reproduces the error and the fix. It is a minimal setup to learn the y = x ** 2
function using gradient descent.
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)
Solution 5
maybe you not free your gpu rigthly , if you are using linux,try "ps -ef | grep python" to see what jobs are using GPU. then kill them
![rafaelcosman](https://i.stack.imgur.com/I3HOb.jpg?s=256&g=1)
Comments
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rafaelcosman about 2 years
When I run
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
I getInternalError: Blas SGEMM launch failed
. Here is the full error and stack trace: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()
Stack: EC2 g2.8xlarge machine, Ubuntu 14.04
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Desperate Morty almost 3 yearsCould you specify which library has to be imported to use "session"?