bml1g12

def worker_producer_shared_memory(np_arr_shape, shared_memory, n_frames):
    """A frame producer function that writes to shared memory"""
    mp_array, np_array = shared_memory
    for _ in range(n_frames):
        mp_array.acquire()
        np_array[:] = prepare_random_frame(np_arr_shape)  # produce a fresh array

ccj5351

import random
from itertools import chain, cycle, islice
import torch.utils.data as data

import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import time
import torch
import numpy as np

BryanCutler

from functools import partial
import multiprocessing
import os
import socket
import sys

from sklearn.preprocessing import StandardScaler

import numpy as np
import pandas as pd

wkcn

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#

jimathyp

# https://raw.githubusercontent.com/pixelb/ps_mem/master/ps_mem.py
#!/usr/bin/env python

# Try to determine how much RAM is currently being used per program.
# Note per _program_, not per process. So for example this script
# will report RAM used by all httpd process together. In detail it reports:
# sum(private RAM for program processes) + sum(Shared RAM for program processes)
# The shared RAM is problematic to calculate, and this script automatically
# selects the most accurate method available for your kernel.

yang-zhang

mjdietzx

#!/bin/bash

# install CUDA Toolkit v8.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-get update
sudo apt-get -y install cuda

tomrunia

# Initialize placeholders for feeding in to the queue
pl_queue_screens = tf.placeholder(tf.float32, shape=[config.seq_length, config.image_size, config.image_size, config.input_channels], name="queue_inputs")
pl_queue_targets = tf.placeholder(tf.uint8, shape=[config.seq_length], name="queue_targets_cnt")

#  ...

capacity = config.min_after_dequeue + 10 * (config.num_gpus*config.batch_size)
q = tf.RandomShuffleQueue(

squarism

Tabs and Windows

Function	Shortcut
New Tab	⌘ + T
Close Tab or Window	⌘ + W (same as many mac apps)
Go to Tab	⌘ + Number Key (ie: ⌘2 is 2nd tab)
Go to Split Pane by Direction	⌘ + Option + Arrow Key
Cycle iTerm Windows	⌘ + backtick (true of all mac apps and works with desktops/mission control)

lebedov

#!/usr/bin/env python

"""
Plot data received over a ZeroMQ port in real time using matplotlib.

Notes
-----
This appears to segfault when run using the WxAgg backend.
"""