Brainiarc7

import os

descriptors = list()

def getString():
    test_file = "nvidia-smi -q --gpu=0 | tail -23"
 
    try:
        p = os.popen(test_file, 'r')
        return p.read()

mohanraj-r

#!/bin/bash
# scp-speed-test.sh
# Author: Alec Jacobson alecjacobsonATgmailDOTcom
#   http://www.alecjacobson.com/weblog/?p=635
#
# Test ssh connection speed by uploading and then downloading a 10000kB test
# file (optionally user-specified size)
#
# Usage:
#   ./scp-speed-test.sh user@hostname [test file size in kBs]

gyglim

"""Simple example on how to log scalars and images to tensorboard without tensor ops.

License: BSD License 2.0
"""
__author__ = "Michael Gygli"

import tensorflow as tf
from StringIO import StringIO
import matplotlib.pyplot as plt
import numpy as np

techgaun

Installing OpenSSH 7.4 on Ubuntu 16.04

sudo apt install -y build-essential libssl-dev zlib1g-dev
wget "http://mirrors.evowise.com/pub/OpenBSD/OpenSSH/portable/openssh-7.4p1.tar.gz"
tar xfz openssh-7.4p1.tar.gz
cd openssh-7.4p1
./configure
make
sudo make install

panovr

import argparse
import os
import shutil
import time

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim

simonw

How to recover lost Python source code if it's still resident in-memory

I screwed up using git ("git checkout --" on the wrong file) and managed to delete the code I had just written... but it was still running in a process in a docker container. Here's how I got it back, using https://pypi.python.org/pypi/pyrasite/ and https://pypi.python.org/pypi/uncompyle6

Attach a shell to the docker container

Install GDB (needed by pyrasite)

apt-get update && apt-get install gdb

felixgwu

import torch
from torch import nn

__all__ = ['FCDenseNet', 'fcdensenet_tiny', 'fcdensenet56_nodrop',
           'fcdensenet56', 'fcdensenet67', 'fcdensenet103',
           'fcdensenet103_nodrop']


class DenseBlock(nn.Module):

kylehounslow

from __future__ import print_function
import requests
import json
import cv2

addr = 'http://localhost:5000'
test_url = addr + '/api/test'

# prepare headers for http request
content_type = 'image/jpeg'

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.

mkolod

.global main
.func main

main:
  LDR R0, =random_seed
  LDR R0, [R0]
  MOV R1, #num_elements
  BL _rng_loop
  LDR R0, =unordered_msg
  BL printf