Ishitori commented on issue #14089: Load dataset directly to vRAM URL: https://github.com/apache/incubator-mxnet/issues/14089#issuecomment-464247795 I don't think it is possible at the moment. The data always loaded first in RAM, and then you need to manually move it to the proper GPU memory with either `split_and_load()` or `as_in_context()`. If you can load the batch of 16 items in RAM, but would like to load more into GPUs before doing a forward pass, then I would: 1. Write a custom loop which would go through ImageRecordIter as many times as you need (let's say you want each GPU to work with 32 items: for 8 GPUs you need to do 8 iterations) 2. On each iteration do `split_and_load()` and concatenate the result for each GPU with the data that is already there if any. Something like this (I haven't run the code myself, so it might need adjustments): ``` import mxnet as mx from mxnet import gluon num_gpus = 8 max_items_for_ram = 16 max_items_per_gpu = 32 ctx = [mx.gpu(i) for i in range(num_gpus)] data_train = mx.io.ImageRecordIter( path_imgrec='dataset_train.rec', data_shape=(3, 256, 256), batch_size=max_items_for_ram, label_width = 22 ) gpu_items = [] iters_to_load_max_items_per_gpu = max_items_per_gpu / (max_items_for_ram / num_gpus) for iteration in range(int(iters_to_load_max_items_per_gpu)): for batch in data_train: items = gluon.utils.split_and_load(batch, ctx) if not gpu_items: gpu_items = items else: for i in range(len(ctx)): gpu_items[i] = mx.nd.concat(gpu_items[i], items[i]) ```
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