ResourceExhaustedError :OOM when allocating tensor with shape []

Question

def RNN(X, weights, biases):
    X = tf.reshape(X, [-1, n_inputs])
    X_in = tf.matmul(X, weights['in']) + biases['in']
    X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units])
    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units, forget_bias=0.0, state_is_tuple=True)
    init_state = lstm_cell.zero_state(batch_size, dtype=tf.float32)
    outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, X_in, initial_state=init_state, time_major=False)

    outputs = tf.unpack(tf.transpose(outputs, [1, 0, 2]))    # states is the last outputs
    results = tf.matmul(outputs[-1], weights['out']) + biases['out']
    del outputs,final_state,lstm_cell,init_state,X,X_in
    return results

def while_loop(s,e,step):
    while s+batch_size<ran:
        batch_id=file_id[s:e]
        batch_col=label_matrix[s:e]                                             

        batch_label = csc_matrix((data, (batch_row, batch_col)), shape=(batch_size, n_classes))
        batch_label = batch_label.toarray()
        batch_xs1=tf.nn.embedding_lookup(embedding_matrix,batch_id)
        batch_xs=sess.run(batch_xs1)
        del batch_xs1
        sess.run([train_op], feed_dict={x: batch_xs,
                                        y: batch_label})

        print(step,':',sess.run(accuracy, feed_dict={x: batch_xs,y: batch_label}),sess.run(cost,feed_dict={x: batch_xs,y: batch_label}))
        if step!=0 and step % 20 == 0:
            save_path = saver.save(sess, './model/lstm_classification.ckpt',write_meta_graph=False)
            print('Save to path', save_path)

        step += 1
        s+=batch_size
        e+=batch_size
        del batch_label,batch_xs,batch_id,batch_col
        print(hp.heap())
        print(hp.heap().more)

This is my code.It keeps going this mistake 'ResourceExhaustedError:OOM when allocating tensor with shape' I used guppy.Then got this.result of guppy

Why the variable of tensorflow take so much space.

Answer 1

The problem was caused by this line in the training loop:

while s + batch_size < ran:
    # ...
    batch_xs1 = tf.nn.embedding_lookup(embedding_matrix, batch_id)

Calling the tf.nn.embedding_lookup() function adds nodes to the TensorFlow graph, and—because these are never garbage collected—doing so in a loop causes a memory leak.

The actual cause of the memory leak is probably the embedding_matrix NumPy array in the argument to tf.nn.embedding_lookup(). TensorFlow tries to be helpful and convert all NumPy arrays in the arguments to a function into tf.constant() nodes in the TensorFlow graph. However, in a loop, this will end up with multiple separate copies of the embedding_matrix copied into TensorFlow and then onto scarce GPU memory.

The simplest solution is to move the tf.nn.embedding_lookup() call outside the training loop. For example:

def while_loop(s,e,step):
  batch_id_placeholder = tf.placeholder(tf.int32)
  batch_xs1 = tf.nn.embedding_lookup(embedding_matrix, batch_id_placeholder)

  while s+batch_size<ran:
    batch_id=file_id[s:e]
    batch_col=label_matrix[s:e]                                             

    batch_label = csc_matrix((data, (batch_row, batch_col)), shape=(batch_size, n_classes))
    batch_label = batch_label.toarray()

    batch_xs=sess.run(batch_xs1, feed_dict={batch_id_placeholder: batch_id})

Answer 2

I recently had this problem with TF + Keras and previously with Darknet with yolo v3. My dataset contained very large images for the memory of my two GTX 1050s. I had to resize the images to be smaller. On average, a 1024x1024 image needs 6GB per GPU.