Chillee

import torch
import torch._inductor.config
import time

torch._inductor.config.triton.cudagraphs = False
torch.set_float32_matmul_precision('high')

def bench(f, name=None, iters=100, warmup=5, display=True, profile=False):
    for _ in range(warmup):
        f()

t-vi

from timeit import default_timer as time
import numpy as np
from numba import cuda
import os
os.environ['NUMBAPRO_LIBDEVICE']='/usr/lib/nvidia-cuda-toolkit/libdevice/'
os.environ['NUMBAPRO_NVVM']='/usr/lib/x86_64-linux-gnu/libnvvm.so.3.1.0'

import numpy
import torch
import ctypes

Kaixhin

# Collection of LSTM cells (including forget gates)
# https://en.wikipedia.org/w/index.php?title=Long_short-term_memory&oldid=784163987

import torch
from torch import nn
from torch.nn import Parameter
from torch.nn import functional as F
from torch.nn.modules.utils import _pair
from torch.autograd import Variable

szagoruyko

Using Torch in Atom

Install Atom from https://atom.io or with brew cask install atom

Then install packages (example apm install hydrogen, apm is a package manager for atom):

• hydrogen
• autocomplete-paths
• language-lua