gerwang

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

	# For single-node, run this script via
	# python -m torch.distributed.launch --nproc_per_node=<ngpus this node> example.py
	#
	# For multinode, see https://pytorch.org/docs/stable/distributed.html#launch-utility
	#
	# Example showing native mixed precision tools
	# (torch.cuda.amp.GradScaler and torch.cuda.amp.autocast)
	# used along with native DistributedDataParallel to perform
	# gradient accumulation with allreduces only when stepping.
	#

	#!/bin/bash

	#
	# This script is for Arch Linux to configure XRDP for enhanced session mode
	#
	# The configuration is adapted from the Ubuntu 16.04 script.
	#
	# Script adapted from https://github.com/microsoft/linux-vm-tools
	#

	# MIT License
	#
	# Copyright (c) 2025 Alper Ahmetoglu
	#
	# Permission is hereby granted, free of charge, to any person obtaining a copy
	# of this software and associated documentation files (the "Software"), to deal
	# in the Software without restriction, including without limitation the rights
	# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	# copies of the Software, and to permit persons to whom the Software is
	# furnished to do so, subject to the following conditions:

	import torch

	def projected_gradient_descent(model, x, y, loss_fn, num_steps, step_size, step_norm, eps, eps_norm,
	clamp=(0,1), y_target=None):
	"""Performs the projected gradient descent attack on a batch of images."""
	x_adv = x.clone().detach().requires_grad_(True).to(x.device)
	targeted = y_target is not None
	num_channels = x.shape[1]

	for i in range(num_steps):


	# coding: utf-8

	# In[1]:


	import math
	import torch
	from torch.nn.parameter import Parameter
	import torch.nn.functional as F

	import tensorflow as tf


	# 1. Create and save two graphs

	# c = a*b
	g1 = tf.Graph()
	with g1.as_default():
	a = tf.placeholder(tf.float32, name='a')
	b = tf.Variable(initial_value=tf.truncated_normal((1,)), name='b')

	from contextlib import contextmanager
	import numpy as np
	import torch
	from torch import Tensor, ByteTensor
	import torch.nn.functional as F
	from torch.autograd import Variable
	import pycuda.driver
	from pycuda.gl import graphics_map_flags
	from glumpy import app, gloo, gl