Multi GPU/Tower setup Tensorflow 1.2 Estimator

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I want to turn my _model_fn for Estimator into a multi GPU solution.

Is there a way to do it within the Esitmator API or do I have to explicitly code device placement and synchronization.

I know I can use tf.device('gpu:X') to place my model on GPU X. I also know I can loop over available GPU names to replicate my model across multiple GPUs. I also know I can use a single input queue for multiple GPUs.

What I do not know is which parts (optimizer, loss calculation), I can actually move to a GPU and where I have to synchronize the computation.

From the Cifar10 example I figure that I have to only synchronize the gradient.

Especially when using 

train_op = tf.contrib.layers.optimize_loss(
        loss=loss,
        global_step=tf.contrib.framework.get_global_step(),
        learning_rate=learning_rate,
        learning_rate_decay_fn=_learning_rate_decay_fn,
        optimizer=optimizer)

I cannot call optimizer.compute_gradients() or optimizer.apply_gradients() manually anymore as this is internally handled by .optimize_loss(..)

I am wondering how to average the gradients like it is done in the cifar10 example Cifar10-MultiGPU or if this is even the right approach for Estimator.

multi-gpu tensorflow python
1 Answers

Actually you can implement multi GPU in model_fn function same as before.
You can find full code in here. It is support multi threading queue reader and multi GPU to very high speed training when using estimator.

Code snippet: (GET FULL CODE)

def model_fn(features, labels, mode, params):
    # network
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=params['num_classes'],
        weight_decay=0.00004,
        is_training=(mode == tf.estimator.ModeKeys.TRAIN))

    # if predict. Provide an estimator spec for `ModeKeys.PREDICT`.
    if mode == tf.estimator.ModeKeys.PREDICT:
        logits, end_points = network_fn(features)
        return tf.estimator.EstimatorSpec(mode=mode, predictions={"output": logits})

    # Create global_step and lr
    global_step = tf.train.get_global_step()
    learning_rate = get_learning_rate("exponential", FLAGS.base_lr,
                                      global_step, decay_steps=10000)

    # Create optimizer
    optimizer = get_optimizer(FLAGS.optimizer, learning_rate)

    # Multi GPU support - need to make sure that the splits sum up to 
    # the batch size (in case the batch size is not divisible by 
    # the number of gpus. This code will put remaining samples in the
    # last gpu. E.g. for a batch size of 15 with 2 gpus, the splits 
    # will be [7, 8].
    batch_size = tf.shape(features)[0]
    split_size = batch_size // len(params['gpus_list'])
    splits = [split_size, ] * (len(params['gpus_list']) - 1)
    splits.append(batch_size - split_size * (len(params['gpus_list']) - 1))

    # Split the features and labels
    features_split = tf.split(features, splits, axis=0)
    labels_split = tf.split(labels, splits, axis=0)
    tower_grads = []
    eval_logits = []

    with tf.variable_scope(tf.get_variable_scope()):
        for i in xrange(len(params['gpus_list'])):
            with tf.device('/gpu:%d' % i):
                with tf.name_scope('%s_%d' % ("classification", i)) as scope:
                    # model and loss
                    logits, end_points = network_fn(features_split[i])
                    tf.losses.softmax_cross_entropy(labels_split[i], logits)
                    update_ops = tf.get_collection(
                        tf.GraphKeys.UPDATE_OPS, scope)
                    updates_op = tf.group(*update_ops)
                    with tf.control_dependencies([updates_op]):
                        losses = tf.get_collection(tf.GraphKeys.LOSSES, scope)
                        total_loss = tf.add_n(losses, name='total_loss')
                    # reuse var
                    tf.get_variable_scope().reuse_variables()
                    # grad compute
                    grads = optimizer.compute_gradients(total_loss)
                    tower_grads.append(grads)
                    # for eval metric ops
                    eval_logits.append(logits)

    # We must calculate the mean of each gradient. Note that this is the
    # synchronization point across all towers.
    grads = average_gradients(tower_grads)

    # Apply the gradients to adjust the shared variables.
    apply_gradient_op = optimizer.apply_gradients(
        grads, global_step=global_step)

    # Track the moving averages of all trainable variables.
    variable_averages = tf.train.ExponentialMovingAverage(0.9999, global_step)
    variables_averages_op = variable_averages.apply(tf.trainable_variables())

    # Group all updates to into a single train op.
    train_op = tf.group(apply_gradient_op, variables_averages_op)

    # Create eval metric ops
    _predictions = tf.argmax(tf.concat(eval_logits, 0), 1)
    _labels = tf.argmax(labels, 1)
    eval_metric_ops = {
        "acc": slim.metrics.streaming_accuracy(_predictions, _labels)}

    # Provide an estimator spec for `ModeKeys.EVAL` and `ModeKeys.TRAIN` modes.
    return tf.estimator.EstimatorSpec(
        mode=mode,
        loss=total_loss,
        train_op=train_op,
        eval_metric_ops=eval_metric_ops)
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