andyweizhao

#!/bin/bash
## This gist contains step by step instructions to install cuda v9.0 and cudnn 7.3 in ubuntu 18.04

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

ipan

jq will sort (-S) the whole file (.) and compare STDOUT (<()) with diff

diff <(jq -S . A.json) <(jq -S . B.json)

https://stackoverflow.com/questions/31930041/using-jq-or-alternative-command-line-tools-to-diff-json-files

smitshilu

Tensorflow

System information

• OS - High Sierra 10.13
• Tensorflow - 1.4
• Xcode command line tools - 8.2 (Download from here: Xcode - Support - Apple Developer & Switch to different clang version: sudo xcode-select --switch/Library/Developer/CommandLineTools & check version: clang -v)
• Cmake - 3.7
• Bazel - 0.7.0

hollance

# Convert SE-ResNet-50 from Caffe to Keras
# Using the model from https://github.com/shicai/SENet-Caffe

import os
import numpy as np

# The caffe module needs to be on the Python path; we'll add it here explicitly.
import sys
caffe_root = "/path/to/caffe"
sys.path.insert(0, caffe_root + "python")

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:

jabooth

certik

#! /bin/bash

set -e

g++ -o test_offscreen.o -c test_offscreen.cpp -I$PYTHONHPC/include/vtk-6.0
g++ -o test_offscreen test_offscreen.o $PYTHONHPC/lib/libvtk*.so
LD_LIBRARY_PATH=$PYTHONHPC/lib/ ./test_offscreen

gsakkis

from datetime import datetime, date
from sqlalchemy.orm.query import Query

def render_query(statement, bind=None):
    """
    Generate an SQL expression string with bound parameters rendered inline
    for the given SQLAlchemy statement.

    WARNING: This method of escaping is insecure, incomplete, and for debugging
    purposes only. Executing SQL statements with inline-rendered user values is

osdf

#!/usr/bin/env python                                                           
import numpy
import sys
import timeit

try:
    import numpy.core._dotblas
    print 'FAST BLAS'
except ImportError:
    print 'slow blas'

gordonbrander

var LiveCollection = (function (_, Backbone) {
  var Collection = Backbone.Collection;
  // Define a Backbone Collection handling the details of running a "live"
  // collection. Live collections are expected to handle fetching their own
  // data, rather than being composed from separate models.
  // They typically add new models instead of resetting the collection.
  // A custom add comparison makes sure that duplicate models are not
  // added. End result: only new elements will be added, instead
  // of redrawing the whole collection.
  //