Tensorflow 不想使用 GPU

Question

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}) )

Answer 1

From your error:

Could not satisfy explicit device specification '/device:GPU:0' because no supported kernel for GPU devices is available.

That means that the 'save/Const' operation cannot be forcefully assigned to a GPU via with tf.device(): because there is no GPU implementation for it. Remove the with tf.device(): part (or put that operation outside of it) and let TF decide where to put operations (it will prefer GPU over CPU anyhow)