lebedov

#!/usr/bin/env python

"""
Demo of how to pass GPU memory managed by pycuda to mpi4py.

Notes
-----
This code can be used to perform peer-to-peer communication of data via
NVIDIA's GPUDirect technology if mpi4py has been built against a
CUDA-enabled MPI implementation.

casallas

These tricks require BibTool, which can be installed using homebrew.

To extract only the used fields call bibtool -x on a .aux file, e.g.

bibtool -x doc.aux -o output.bib

To delete fields that you don't want to share, e.g. notes and file locations,

bibtool -- "delete.field {annote}" -i input.bib -o output.bib

pklaus

#!/usr/bin/env python

import numpy as np
import time
import matplotlib
matplotlib.use('TKAgg')
#matplotlib.use('GTKAgg')
from matplotlib import pyplot as plt

yanlend

'''
Created on 26.02.2016
Print numpy arrays to latex tables
@author: Peter Fischer
'''

from __future__ import print_function, division


def print_to_table(avgs, stds, rows, cols,

tyuiko

import argparse

parser = argparse.ArgumentParser()
### Positional
parser.add_argument('positional1')
parser.add_argument('positional2', help="positional argument 2")
parser.add_argument('positional3', help="positional argument 3 type int", type=int)
### Optional
parser.add_argument("--optional1", help="optional argument 1")
parser.add_argument("--optional2", help="optional argument 2", action="store_true")

blole

#!/usr/bin/env python2
import os
import gym
import sys
import random
import itertools
from time import time
from copy import copy
from math import sqrt, log

wangruohui

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm

jkleint

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

kashif

Cross Entropy Method

How do we solve for the policy optimization problem which is to maximize the total reward given some parametrized policy?

Discounted future reward

To begin with, for an episode the total reward is the sum of all the rewards. If our environment is stochastic, we can never be sure if we will get the same rewards the next time we perform the same actions. Thus the more we go into the future the more the total future reward may diverge. So for that reason it is common to use the discounted future reward where the parameter discount is called the discount factor and is between 0 and 1.

A good strategy for an agent would be to always choose an action that maximizes the (discounted) future reward. In other words we want to maximize the expected reward per episode.

vadimkantorov

# Example:
# $ python argparse_dict_argument.py --env a=b --env aa=bb
# Namespace(env={'a': 'b', 'aa': 'bb'})

import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--env', action = type('', (argparse.Action, ), dict(__call__ = lambda a, p, n, v, o: getattr(n, a.dest).update(dict([v.split('=')])))), default = {}) # anonymously subclassing argparse.Action
print(parser.parse_args())