magnetikonline

SSD TRIM support for Ubuntu 12.04+

Steps to add a daily SSD TRIM task to your Ubuntu system (tested with 12.04+) which will run via anacron with logging of the trim process to /var/log/fstrimall.log.

Note: if you have previously enabled TRIM support for your mounted drive(s) using the discard option within /etc/fstab you will want to remove this option and re-mount your drives first ($ mount -a).

• Install scripts

  $ sudo wget \

https://gist.github.com/magnetikonline/8190599/raw/fstrimall.sh \

qinyao-he

import os

import tensorflow as tf
import tensorflow.contrib.tensorrt as trt


def get_frozen_graph(graph_file):
  """Read Frozen Graph file from disk."""
  with tf.gfile.FastGFile(graph_file, "rb") as f:
    graph_def = tf.GraphDef()

leesheppard

Install

brew install https://gist.githubusercontent.com/leesheppard/69a283ee4af484e2029809a0f5e09932/raw/88bc116b515a456a2e1bad476b6124d28ce2f6f6/imagemagick.rb

Pin this version

To prevent upgrades you can pin this version.

brew pin imagemagick

rtv

#include <pthread.h>
#include <stdio.h>
#include <unistd.h>
#include <assert.h>


/* Compile like this:

 gcc --std=c99 -lpthread cond.c -o cond

subudeepak

WebSockets - An Introduction

WebSockets is a modern HTML5 standard which makes communication between client and server a lot more simpler than ever. We are all familiar with the technology of sockets. Sockets have been fundamental to network communication for a long time but usually the communication over the browser has been restricted. The general restrictions

• The server used to have a permanent listener while the client (aka browser) was not designated any fixed listener for a more long term connection. Hence, every communication was restricted to the client demanding and the server responding.
• This meant that unless the client requested for a particular resource, the server was unable to push such a resource to the client.
• This was detrimental since the client is then forced to check with the server at regular intervals. This meant a lot of libraries focused on optimizing asynchronous calls and identifying the response of asynchronous calls. Notably t

digiter

For each machine running xmodmap will give you the mapping and keycode, take a look first then write the script. I use ubuntu and mac keyboard. The below maps both left and right control and super.

Create an .Xmodmap file in your Linux home directory, with the following contents, then execute xmodmap .Xmodmap

clear control
clear mod4

keycode 37 = Super_L
keycode 105 = Super_R

baraldilorenzo

##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

thomwolf

def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (vocabulary size)
            top_k >0: keep only top k tokens with highest probability (top-k filtering).
            top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
    """
    assert logits.dim() == 1  # batch size 1 for now - could be updated for more but the code would be less clear
    top_k = min(top_k, logits.size(-1))  # Safety check

EncodeTS

VGG-Face model for Keras

This is the Keras model of VGG-Face.

It has been obtained through the following method:

• vgg-face-keras:directly convert the vgg-face matconvnet model to keras model
• vgg-face-keras-fc:first convert vgg-face caffe model to mxnet model,and then convert it to keras model

Details about the network architecture can be found in the following paper:

ryanfb

# Blender Python script for converting a mesh to GLB with Draco compression.
# Tested on Blender 2.82
# Usage:
#   blender --background --factory-startup --addons io_scene_gltf2 --python blender_compress_mesh.py -- -i #{source_path} -o #{out_path}
from os import path
from contextlib import redirect_stdout
from sys import argv
import argparse
import io
import bpy