cako

# In-Place Memory Pinning in PyTorch
# See # https://github.com/pytorch/pytorch/issues/32167#issuecomment-753551842
#
# Copyright 2023 Carlos Alberto da Costa Filho <c.dacostaf@gmail.com>
#
# 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

cako

import numpy as np
import pytest


# Test multiple dtypes at once
@pytest.mark.parametrize(
    "dtype", ["int8", "int16", "float16", "float32", "float64", "float128"]
)
def test_zero(dtype):
    # Set random seed

cako

from typing import Tuple

import numpy as np
from numpy.core.multiarray import normalize_axis_index
from numpy.typing import NDArray


def sobel(arr: NDArray, axes: Tuple[int, int] = (-2, -1)) -> NDArray:
    """Compute the Sobel filter of an image

cako

# Decorator to monitor memory of CUDA kernels
# Good read:
# https://github.com/numba/nvidia-cuda-tutorial/#session-5-memory-management

import gc

import numpy as np
from numba import cuda

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "destroyed-operations",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",

cako

using LinearMaps
using Test
function f1(source)
    dest = zeros(9)
    tmp = reshape(source, 5, 2)
    return dest
end
function f2(source)
    dest = zeros(6)
    tmp = reshape(source, 3, 3)

cako

using BenchmarkTools
using Statistics
using Plots

 _flags(A::Array) = unsafe_load(convert(Ptr{UInt16}, pointer_from_objref(A)), 9)
_isshared(A::Array) = !(_flags(A) & 0x4000 == 0x0000)

function create_and_resize_flag(n)
    a = Array{Float64}(undef, 1000)
    if !_isshared(a)