TensorFlow: InternalError: Blas SGEMM launch failed

Question

When I run sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) I get InternalError: Blas SGEMM launch failed. Here is the full error and st

Answer 1

Restarting my Jupyter processes wasn't enough; I had to reboot my computer.

Answer 2

2.0 Compatible Answer: Providing 2.0 Code for erko's answer for the benefit of the Community.

session = tf.compat.v1.Session()

if 'session' in locals() and session is not None:
    print('Close interactive session')
    session.close()

Answer 3

In my case, the network filesystem under which libcublas.so was located simply died. The node was rebooted and everything was fine. Just to add another point to the dataset.

Answer 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)

Answer 5

In my case,

First, I run

conda clean --all

to clean up tarballs and unused packages.

Then, I restart IDE (Pycharm in this case) and it works well. Environment: anaconda python 3.6, windows 10 64bit. I install tensorflow-gpu by a command provided on the anaconda website.

Answer 6

I closed all other Jupyter Sessions running and this solved the problem. I think It was GPU memory issue.