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"]
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 |
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 |
# 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 |
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)) | |
``` |
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, |
# 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 ' |
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