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import mxnet as mx


class WeightedLogisticRegressionOutput(mx.operator.CustomOp):
    """

    """
    def __init__(self, beta=0.5, lower=0.3, upper=0.7):
        self._lower = lower
        self._upper = upper

zhreshold

import mxnet as mx
import numpy as np
import cv2

def rotate_ndarray(arr, degree):
  theta = np.pi / 180 * degree
  cos = np.cos(theta)
  sin = np.sin(theta)
  loc = mx.nd.array([[cos, sin, -sin, cos, 0, 0]])
  s = 200

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import argparse
import logging
import random
import time
import mxnet as mx
from mxnet import nd
from mxnet import image
from mxnet import gluon
from mxnet import autograd
import numpy as np

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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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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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try

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

or

sudo fuser -v /dev/nvidia*

sudo fuser -k /dev/nvidia*

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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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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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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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sudo apt update
sudo apt-get -y install g++ freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libglu1-mesa libglu1-mesa-dev libgfortran3
wget 'https://developer.nvidia.com/compute/cuda/9.2/Prod2/local_installers/cuda_9.2.148_396.37_linux' -O cuda92.run
wget 'https://developer.nvidia.com/compute/cuda/9.2/Prod2/patches/1/cuda_9.2.148.1_linux' -O patch1.run
sudo sh cuda92.run
sudo sh patch1.run

echo "# CUDA9.2" >> ~/.bashrc
echo "export PATH=/usr/local/cuda/bin:\$PATH" >> ~/.bashrc
echo "export LD_LIBRARY_PATH=/usr/local/cuda/lib64:\$LD_LIBRARY_PATH" >> ~/.bashrc