How to do distributed prediction / inferencing with Tensorflow

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I want to run distributed prediction on my GPU cluster using TF 2.0. I trained a CNN made with Keras using MirroredStrategy and saved it. I can load the model and use .predict() on it, but I was wondering if this automatically does distributed prediction using available GPUs. If not, how can I run distributed prediction to speed up inference and use all available GPU memory?

At the moment, when running many large predictions, I exceed the memory (needs 17gb) of one of my GPUs (12gb) and the inferencing fails because it runs out of memory:

Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.12GiB

but I have multiple GPUs and would like to use their memory as well. Thanks.

tensorflow python
1 Answers

I was able to piece together single-worker, multi-GPU prediction as follows (consider it a sketch - it uses plumbing code that's not generally applicable, but should give you a template to go off of):

# https://github.com/tensorflow/tensorflow/issues/37686
# https://www.tensorflow.org/tutorials/distribute/custom_training
def compute_and_write_ious_multi_gpu(path: str, filename_csv: str, include_sampled: bool):
    strategy = tf.distribute.MirroredStrategy()
    util.log('Number of devices: {}'.format(strategy.num_replicas_in_sync))
    (ds, s, n) = dataset(path, shuffle=False, repeat=False, mask_as_input=True)
    dist_ds = strategy.experimental_distribute_dataset(ds)

    def predict_step(inputs):
        images, labels = inputs
        return model(images, training=False)

    @tf.function
    def distributed_predict_step(dataset_inputs):
        per_replica_losses = strategy.run(predict_step, args=(dataset_inputs,))
        return per_replica_losses  # unwrap!?

    # https://stackoverflow.com/questions/57549448/how-to-convert-perreplica-to-tensor
    def unwrap(per_replica):  # -> list of numpy arrays
        if strategy.num_replicas_in_sync > 1:
            out = per_replica.values
        else:
            out = (per_replica,)
        return list(map(lambda x: x.numpy(), out))

    with strategy.scope():
        model = wrap_model()

    util.log(f'Starting distributed prediction for {filename_csv}')
    ious = [unwrap(distributed_predict_step(x)) for x in dist_ds]
    t = ious
    ious = [item for sublist in t for item in
            sublist]  # https://stackoverflow.com/questions/952914/how-to-make-a-flat-list-out-of-list-of-lists
    util.log(f'Distributed prediction done for {filename_csv}')
    ious = np.concatenate(ious).ravel().tolist()
    ious = round_ious(ious)
    ious = list(zip(ious, ds.all_image_paths))
    ious.sort()
    write_ious(ious, filename_csv, include_sampled)

This does distribute the load across the GPUs, but unfortunately makes very poor use of them - in my particular case the corresponding single-GPU code runs in ~12 hours, and this runs in 7.7 hours, so not even a 2x speedup despite have 8x the number of GPUs.

I think it's mostly a data feeding issue, but I don't know how to fix it. Hopefully someone else can provide some better insights?

user_7083810
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