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{
  "nvidia-titan-x": {
    "devices": [
      {
        "cores": "3072",
        "memory": "12GB",
        "memory_bandwith": "336.5GB/s",
        "name": "Nvidia Titan X",
        "quantity": 1
      }

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name: "shufflenet"
#  transform_param {
#    scale: 0.017
#    mirror: false
#    crop_size: 224
#    mean_value: [103.94,116.78,123.68]
#  }
input: "data"
input_shape { 
  dim: 1

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"""
Reference:
J. Redmon. Darknet: Open source neural networks in c.
http://pjreddie.com/darknet/, 2013-2016. 5
"""

import mxnet as mx

def conv_act_layer(from_layer, name, num_filter, kernel=(3, 3), pad=(1, 1), \
    stride=(1,1), act_type="relu", use_batchnorm=True):

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Class	Pytorch	MXNet Gluon
Dataset holding arrays	torch.utils.data.TensorDataset(data_tensor, label_tensor)	gluon.data.ArrayDataset(data_array, label_array)
Data loader	torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=<function default_collate>, drop_last=False)	gluon.data.DataLoader(dataset, batch_size=None, shuffle=False, sampler=None, last_batch='keep'(discard, rollover), batch_sampler=None, batchify_fn=None, num_workers=0)
Sequentially applied sampler	torch.utils.data.sampler.SequentialSampler(data_source)	gluon.data.SequentialSampler(length)
Random order sampler	torch.utils.data.sampler.RandomSampler(data_source)	gluon.data.RandomSampler(length)

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import argparse
import mxnet as mx

parser = argparse.ArgumentParser('test')
parser.add_argument('-j', '--num-workers', default=4, type=int, dest='num_workers')
args = parser.parse_args()

dataset = mx.gluon.data.vision.MNIST()

loader = mx.gluon.data.DataLoader(dataset, 32, True, num_workers=args.num_workers)

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git clone https://github.com/zhreshold/mxnet -b model_zoo
cd mxnet/example/gluon
sudo -H pip install -U mxnet-cu90
python image_classification.py --dataseet --train-data ~/efs/users/joshuazz/data/imagenet/record/train_480_q95.rec --val-data ~/efs/users/joshuazz/data/imagenet/record/val_480_q95.rec --batch-size 64 --num-gpus 4 --epochs 120 --lr 0.1 --mode hybrid --model resnet50_v2 --log-interval 200

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try

sudo nvidia-smi --gpu-reset -i 0

or

sudo fuser -v /dev/nvidia*

sudo fuser -k /dev/nvidia*

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import mxnet as mx
from mxnet import gluon

dataset = gluon.data.vision.MNIST()
loader = gluon.data.DataLoader(dataset, 34, last_batch='rollover', num_workers=8)

ctx = [mx.gpu(i) for i in range(2)]
for e in range(10):
    for i, batch in enumerate(loader):
        data = gluon.utils.split_and_load(batch[0], ctx_list=ctx)

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import os
import argparse
import shutil
import time
import logging
import numpy as np
import mxnet as mx
from mxnet import gluon
from mxnet import autograd
from mxnet.gluon import nn

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This is a demo onnx model for super resolution.