• https://github.com/pytorch/examples
• https://github.com/bharathgs/Awesome-pytorch-list
• http://pytorch.org/tutorials/
• https://github.com/salesforce/matchbox/blob/master/examples/transformer.py
• https://github.com/rasbt/deeplearning-models

* https://github.com/szagoruyko/pytorchviz

# https://gist.github.com/avacariu/92db7b7f1fa1696279e9029f0237e2d4
#!/usr/bin/env python3.6
 
import time
import tensorflow as tf
 
summary_writer = tf.summary.FileWriter("./logdir")
 
for i in range(100_000):
    s = tf.Summary(value=[tf.Summary.Value(tag="the-value", simple_value=i/10)])
    summary_writer.add_summary(s, i)
    time.sleep(0.5)
 
# while the above is running, execute
#     tensorboard --logdir ./logdir
 
# See https://github.com/lanpa/tensorboard-pytorch/blob/master/tensorboardX/summary.py
# for different kinds of summaries you can create (e.g. histograms)

• https://github.com/lanpa/tensorboard-pytorch
• https://github.com/TeamHG-Memex/tensorboard_logger

• https://medium.com/@nicolas.metallo/deploy-your-pytorch-model-to-production-f69460192217
• pytorch + Nodejs
  • http://blog.christianperone.com/2018/10/pytorch-1-0-tracing-jit-and-libtorch-c-api-to-integrate-pytorch-into-nodejs/

col.insert_one(dict(state_dict=cloudpickle.dumps(net.state_dict()))
state_dict = cloudpickle.loads(col.find_one()['state_dict'])

loss.backward()
for p in model.parameters():
    p.grad *= C  # or whatever other operation
optimizer.step()

for param in model.parameters():
    param.requires_grad = False

• https://github.com/lutzroeder/netron

• https://github.com/uber/pyro
• https://www.kymat.io/
• https://github.com/rtqichen/torchdiffeq/tree/master/examples
• https://github.com/keunwoochoi/torchaudio-contrib

• https://github.com/facebookresearch/PyTorch-BigGraph

• https://towardsdatascience.com/how-to-pytorch-in-production-743cb6aac9d4
• https://github.com/pytorch/QNNPACK

soft_tau = 0.01
 
for target_param, param in zip(target_net.parameters(), net.parameters()):
    target_param.data.copy_(target_param.data * (1.0 - soft_tau) + param.data * soft_tau)

• https://towardsdatascience.com/soft-actor-critic-demystified-b8427df61665

• https://towardsdatascience.com/speed-up-your-algorithms-part-1-pytorch-56d8a4ae7051

• https://tvm.ai/2019/05/30/pytorch-frontend

• https://krshrimali.github.io/Announcing-PyTorch-CPP-Series/

torch.set_printoptions(linewidth=120)
torch.set_grad_enabled(True) 

• https://towardsdatascience.com/how-to-build-your-own-pytorch-neural-network-layer-from-scratch-842144d623f6

• RLkit: Reinforcement learning framework and algorithms implemented in PyTorch
  • https://github.com/vitchyr/rlkit

• https://github.com/microsoft/hummingbird

• http://www.kernel-operations.io/keops/index.html

• https://github.com/kornia/kornia