r9y9

#pragma once

#include <cmath>
#include <memory>
#include <vector>
#include <cassert>

namespace sp {

/**

bastibe

# A simple limiter

from sounddevice import Stream, CallbackStop
from time import sleep
from numpy import array, random, zeros
import matplotlib.pyplot as plt

################################### Constants ##################################

fs            = 44100   # Hz

billju

/*
 Super Easy Diy Drumpad Example
 GIT: https://gist.github.com/billju/ce1337ea3c1dbb4341ce22dca1b55442
 2017 by Billju 
 Inspired by Evan Kale
 */

#include <Keyboard.h>
#include "MIDIUSB.h"

DanielArnett

This tutorial will guide you through the setup of the HTC Vive Tracker in Python 3.6 on Ubuntu 14.04.

Prerequesites

Up to date graphics drivers

x86 architecture

SteamVR requires >4GB disk space

f0k

#!/bin/bash
# Sets each CUDA device to persistence mode and sets the application clock
# and power limit to the device's maximum supported values.
# When run with "--dry-run" as first command line argument or not as superuser,
# will display the commands, otherwise it will execute them.
#
# Hint: To run this at boot time, place this script in /root and create a file
# /etc/cron.d/nvidia_boost with the following single line:
# @reboot root /root/nvidia_boost.sh >/dev/null
#

jojonki

# How to apply exponential moving average decay for variables? 
# https://discuss.pytorch.org/t/how-to-apply-exponential-moving-average-decay-for-variables/10856/2
class EMA(nn.Module):
    def __init__(self, mu):
        super(EMA, self).__init__()
        self.mu = mu
        
    def forward(self,x, last_average):
        new_average = self.mu*x + (1-self.mu)*last_average
        return new_average

carlthome

dojoteef

'''
Memory profiling utilities
'''
import gc
import inspect
import linecache
import os.path
import sys
import time
import threading

mikehamer

// An example of using the PyTorch C++ API to implement a custom forward and backward function

#include <iostream>
#include <vector>

#include <torch/torch.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/VariableTypeUtils.h>
#include <torch/csrc/autograd/functions/utils.h>

unixpickle

import torch
import torch.nn.functional as F


def maml_grad(model, inputs, outputs, lr, batch=1):
    """
    Update a model's gradient using MAML.

    The gradient will point in the direction that
    improves the total loss across all inner-loop