tmorgan4

In the below keyboard shortcuts, I use the capital letters for reading clarity but this does not imply shift, if shift is needed, I will say shift. So ⌘ + D does not mean hold shift. ⌘ + Shift + D does of course.

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`

	demo for RTP and RTCP Buffer parse with GStreamer

	1. This example program is designed to work with the deepstream / gstreamer python API
	to calculate the latency of an rtsp transfer.
	2. The gstreamer element example is modified from NVIDIA-AI-IOT/deepstream_python_apps/
	deepstream_test1_rtsp_in_rtsp_out.py.

	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

	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

	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

	# 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
	#

	# 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.

	#!/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



	# 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(