Andrew Hundt ahundt

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

CUDA7.5

Now, CUDA did not compatible Apple clang 7.0.0 also Xcode7.0.

e.g,

> cd /Developer/NVIDIA/CUDA-7.5/samples/0_Simple/asyncAPI

> clang -v
Apple LLVM version 7.0.0 (clang-700.1.75)

Get the old VSCode icon back!! 🔥 🔥

First download the new old icon: https://cl.ly/mzTc (based on this)

You can also use the icon you want, but you need to convert it to .icns. You can use this service to convert PNG to ICNS.

Go to Applications and find VSCode, right click there and choose Get Info. Drag 'n drop the new icon.

	# Xcode 4.3.3
	Apple clang version 3.1 (tags/Apple/clang-318.0.61) (based on LLVM 3.1svn)
	Target: x86_64-apple-darwin11.4.0
	Thread model: posix

	# Xcode 4.3.2
	Apple clang version 3.1 (tags/Apple/clang-318.0.58) (based on LLVM 3.1svn)
	Target: x86_64-apple-darwin11.4.0
	Thread model: posix

	#!/usr/bin/env bash

	#################################################################################################
	# This script installls Torch7 and it's various dependencies.
	#
	# The script is adapted from:
	# https://github.com/clementfarabet/torchinstall/blob/master/install
	#
	# The above didn't quite work for me on macosx (10.8.2) so the following adjustments were made:
	# (1) install gcc 4.7.2 and set as compilers used in Torch build so open OpenMP works

	#!/bin/bash

	CUDA_LIB_DIR=/usr/local/cuda/lib
	CUDA_VERSION=7.5
	CUDA_LIBS="cublas cudart curand"

	CUDNN_LIB_DIR=/usr/local/cuda/cudnn-3/lib
	CUDNN_VERSION=7.0
	CUDNN_LIBS="cudnn"

	import tensorflow as tf
	from tensorflow.python.framework import ops
	import numpy as np

	# Define custom py_func which takes also a grad op as argument:
	def py_func(func, inp, Tout, stateful=True, name=None, grad=None):

	# Need to generate a unique name to avoid duplicates:
	rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))

	import datetime as dt

	import tensorflow as tf
	import tensorflow.contrib.slim as slim
	from tensorflow.contrib.slim.nets import resnet_v1
	import threading

	from PoseDataset import PoseDataset
	from TrainParams import TrainParams

	class weighted_categorical_crossentropy:
	def __init__(self, weights):
	self.weights = weights
	self.__name__ = 'wcentroid_loss'

	def __call__(self, y_true, y_pred):
	class0 = K.ones_like(y_pred)[:, :, :, 0] * self.weights[0]
	class1 = K.ones_like(y_pred)[:, :, :, 0] * self.weights[1]
	x = K.tf.where(y_true[:, :, :, 0] > 0, class0, class1)
	result = x * K.categorical_crossentropy(y_pred, y_true)