mcarilli

# 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.
#

GregaVrbancic

#!/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
#

alper111

# MIT License
# 
# Copyright (c) 2024 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:

oscarknagg

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):

gngdb

guillefix

# coding: utf-8

# In[1]:


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

marta-sd

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')

victor-shepardson

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

willurd

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