kastnerkyle

from __future__ import print_function
import subprocess
import shutil
import os
import stat
import time

# This script looks extremely defensive, but *should* let you rerun at
# any stage along the way. Also a lot of code repetition due to eventual support
# for "non-blob" install from something besides the magic kk_all_deps.tar.gz

kastnerkyle

from __future__ import print_function
import os
import shutil
import stat
import subprocess
import time
import numpy as np
from scipy.io import wavfile
import re
import glob

killeent

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

1. How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
2. How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
3. How does PyTorch cwrap work to generate code for Tensor methods?
4. How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.

apaszke

from graphviz import Digraph
import torch
from torch.autograd import Variable, Function

def iter_graph(root, callback):
    queue = [root]
    seen = set()
    while queue:
        fn = queue.pop()
        if fn in seen:

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

GendelfLugansk

Expo-sqlite - the right way

Why

With expo-sqlite it's not possible to execute few depending statements inside single transaction - db.transaction does not work with async/promise and tx.executeSql just enqueues sql statement but does not execute it.

Documentation

Database class has two methods - execute (to execute single statement without transaction) and transaction(cb) to execute few statements inside a transaction