This is a seemingly stupid request for all post-post-doc AI researchers on here, but hear me out.
First thing that DL Newbies look for is an image inferencing example.  Inferencing is computationally cheap and has a high WOW  factor, which makes it the perfect “Hello  World” for DL.
We have the reference PyTorch ImageNet training code on Github, but we don’t have a reference ImageNet inferencing example.  If someone has an elegant, memory efficient CPU + GPU inferencing code sitting around please submit a PR and add it to the examples repo!

  
      github.com
  
  
    pytorch/examples/blob/master/imagenet/main.py 66
import argparse
import os
import shutil
import time

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models

model_names = sorted(name for name in models.__dict__
    if name.islower() and not name.startswith("__")
    and callable(models.__dict__[name]))


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Thanks!