vtjnash

Introduction

This proposal aims to define the memory model of Julia and to provide certain guarantees in the presence of data races, both by default and through providing intrinsics to allow the user to specify the level of guarantees required. This should allow native implementation in Julia of simple system primitives (like mutexes), interoperate with native system code, and aim to give generally explainable behaviors without incurring significant performance cost. Additionally, it strives to be general-purpose and yet clear about the user's intent—particularly with respect to ensuring that an atomic-type field is accessed with proper care for synchronization.

The last two points deserve particular attention, as Julia has always provided strong reflection and generic programming capabilities that has not been seen—in this synergy combination—in any other language. Therefore, we want to be careful to observe a distinction between the asymmetries of reading vs. writing that we have felt is often not given

KristofferC

AMDGPUnative => ["ROCArrays"]
AbstractAlgebra => ["Ccluster", "IntegerTriangles"]
AbstractMCMC => ["Turing"]
AbstractTrees => ["PDFIO", "PhysOcean"]
ApproxFun => ["EffectiveWaves", "PDSampler"]
ApproxFunBase => ["ApproxFunRational", "Poltergeist", "RiemannHilbert"]
ApproxFunFourier => ["ApproxFunRational"]
ApproxFunOrthogonalPolynomials => ["ApproxFunRational"]
ArgCheck => ["ANOVA", "AltDistributions", "BAT", "ContinuousTransformations", "DarkSky", "FunctionalTables", "GoogleMaps", "HMMBase", "MultistartOptimization", "PolynomialBases", "SimpleIntegrals", "SortedVectors", "SpectralKit", "StataDTAFiles", "TransformVariables"]

fvisin

# Adapted from https://gist.github.com/batzner/7c24802dd9c5e15870b4b56e22135c96
import getopt
import sys

import tensorflow as tf

usage_str = ('python tensorflow_rename_variables.py '
             '--checkpoint_dir=path/to/dir/ --replace_from=substr '
             '--replace_to=substr --add_prefix=abc --dry_run')
find_usage_str = ('python tensorflow_rename_variables.py '

higepon

import numpy as np
import tensorflow as tf
from tensorflow.python.layers import core as layers_core

hparams = tf.contrib.training.HParams(
    batch_size=3,
    encoder_length=4,
    decoder_length=5,
    num_units=6,
    src_vocab_size=7,

braingineer

class Lambda(Layer):
    '''Used for evaluating an arbitrary Theano / TensorFlow expression
    on the output of the previous layer.

    # Examples

    ```python
        # add a x -> x^2 layer
        model.add(Lambda(lambda x: x ** 2))
    ```

0

# Anaphoric if.
macro aif(ex)
	@assert (ex.head == :if) "@aif must be applied to an if expression."

	cond = ex.args[1]
	ex.args[1] = :(convert(Bool, it))

	quote
		let it = $cond
			$ex

laurenorsini

local 192.168.2.0 # SWAP THIS NUMBER WITH YOUR RASPBERRY PI IP ADDRESS
dev tun 
proto udp #Some people prefer to use tcp. Don't change it if you don't know.
port 1194 
ca /etc/openvpn/easy-rsa/keys/ca.crt 
cert /etc/openvpn/easy-rsa/keys/Server.crt # SWAP WITH YOUR CRT NAME
key /etc/openvpn/easy-rsa/keys/Server.key # SWAP WITH YOUR KEY NAME
dh /etc/openvpn/easy-rsa/keys/dh1024.pem # If you changed to 2048, change that here!
server 10.8.0.0 255.255.255.0 
# server and remote endpoints 

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD