I want to train "standford chatbot" from here https://github.com/chiphuyen/stanford-tensorflow-tutorials/tree/master/assignments/chatbot on GPU, but it doesn't use my GPU, but all need libraries (CuNN, CUDA, tensorflow-gpu etc.) are installed I tried:
def train():
""" Train the bot """
test_buckets, data_buckets, train_buckets_scale = _get_buckets()
# in train mode, we need to create the backward path, so forwrad_only is False
model = ChatBotModel(False, config.BATCH_SIZE)
model.build_graph()
saver = tf.train.Saver(var_list=tf.trainable_variables())
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,log_device_placement=True)) as sess:
print('Start training')
sess.run(tf.global_variables_initializer())
_check_restore_parameters(sess, saver)
iteration = model.global_step.eval()
total_loss = 0
while True:
skip_step = _get_skip_step(iteration)
bucket_id = _get_random_bucket(train_buckets_scale)
encoder_inputs, decoder_inputs, decoder_masks = data.get_batch(data_buckets[bucket_id],
bucket_id,
batch_size=config.BATCH_SIZE)
start = time.time()
_, step_loss, _ = run_step(sess, model, encoder_inputs, decoder_inputs, decoder_masks, bucket_id, False)
total_loss += step_loss
iteration += 1
if iteration % skip_step == 0:
print('Итерация {}: потеря {}, время {}'.format(iteration, total_loss/skip_step, time.time() - start))
start = time.time()
total_loss = 0
saver.save(sess, os.path.join(config.CPT_PATH, 'chatbot'), global_step=model.global_step)
if iteration % (10 * skip_step) == 0:
# Run evals on development set and print their loss
_eval_test_set(sess, model, test_buckets)
start = time.time()
sys.stdout.flush()
But It always show:
InvalidArgumentError (see above for traceback): Cannot assign a device to node 'save/Const': Could not satisfy explicit device specification '/device:GPU:0' because no supported kernel for GPU devices is available.
Colocation Debug Info: Colocation group had the following types and devices: Const: CPU Identity: CPU [[Node: save/Const = Constdtype=DT_STRING, value=Tensor, _device="/device:GPU:0"]]
Are there some configuration file for tensorflow where I can specify to use only GPU or some another way (i tried "with tf.device("/gpu:0"):" and device_count={'GPU': 1}) )
