If you write a PyTorch extension utilarizing the GPU and you want to build a unit test using CMake for it you have to install several packages by yourself.
Test what CUDA is supported by your driver
#!/usr/bin/env python3 | |
from pathlib import Path | |
import argparse | |
from hurry.filesize import size | |
from pathspec import PathSpec | |
from dataclasses import dataclass | |
@dataclass |
#pragma once | |
#include <cstdint> | |
void foo(const uint64_t begin, uint64_t *result) | |
{ | |
uint64_t prev[] = {begin, 0}; | |
for (uint64_t i = 0; i < 1000000000; ++i) | |
{ | |
const auto tmp = (prev[0] + prev[1]) % 1000; |
#!/usr/bin/env python | |
import torch | |
import torch.nn as nn | |
import torch.nn.utils.rnn as rnn | |
import torch | |
import torch.optim as optim | |
from torch.utils.data import Dataset, DataLoader | |
from torch.nn import functional as F |
#!/bin/sh | |
code --install-extension DavidAnson.vscode-markdownlint | |
code --install-extension PeterJausovec.vscode-docker | |
code --install-extension avli.clojure | |
code --install-extension cssho.vscode-svgviewer | |
code --install-extension donjayamanne.githistory | |
code --install-extension eamodio.gitlens | |
code --install-extension eg2.tslint | |
code --install-extension jamesnorton.continuum |
// Linear regression with SGD | |
// Run this file with ts-node ./traditional.ts | |
import * as fs from 'fs'; | |
import {Matrix, Input} from '../../lib/tensors'; | |
// Some helper functions | |
function printVector(vector: number[], description: string) { | |
console.log(`${description}: ${vector.map(t => t.toString()).join(', ')}`); |