bartvm

import sys

import theano
from theano import tensor
import numpy as np


def main(length):
    # Sequential input
    x = tensor.vector('x')

bartvm

t = require 'torch'
grad = require 'autograd'

function loop(p, y, idxs)
  -- Only works if h is a derivable value as well
  x = p.x
  h = p.h
  for i = 1, x:size(1) do
    h[idxs[i]] = x[i]
  end

bartvm

import theano
from theano import tensor
import numpy as np

def main():
    x = tensor.vector('x')
    y = tensor.alloc(np.float32(0), *x.shape)

    def step(x_elem, t, prev_y):
        new_y = tensor.set_subtensor(prev_y[t], x_elem)

bartvm

from theano import tensor

from blocks.bricks import Linear, Rectifier, Softmax
from blocks.bricks.cost import CategoricalCrossEntropy
from blocks.roles import WEIGHT
from blocks.graph import ComputationGraph
from blocks.filter import VariableFilter
from blocks.initialization import IsotropicGaussian, Constant
from blocks.algorithms import GradientDescent, Scale
from blocks.log.log import TrainingLog

bartvm

cd openvpn
connections=(*.ovpn)
i=1
for filename in "${connections[@]}"; do
    extension="${filename##*.}"
    base="${filename%.*}"
    printf -v j "%02d" $((i+10))
    mkdir "$j"
    cp crl.rsa.2048.pem "$j/crl.rsa.2048.pem"
    cp ca.rsa.2048.crt "$j/ca.rsa.2048.crt"

bartvm

SOURCE=en
TARGET=fr
SCRIPTS="$(pwd)/mosesdecoder/scripts"
# Create the data directory
mkdir -p IWSLT15 && cd IWSLT15
# Download and unpack the data
# wget "https://wit3.fbk.eu/archive/2015-01/texts/$SOURCE/$TARGET/$SOURCE-$TARGET.tgz"
tar xvfz "$SOURCE-$TARGET.tgz"
cd "$SOURCE-$TARGET"
for lang in $SOURCE $TARGET; do

bartvm

Each worker is roughly of the following form:

# socket is some form of communication
def train(id, device, batch_queue, socket):
  construct_graph_and_compile()
  if id == 1:
      # This is the first process, so memory map the parameters
      m_params = memory_map(params)
      # Send them to the main thread

bartvm

DROP TABLE IF EXISTS data;
CREATE TABLE data (
  hostname TEXT,
  gpu_id TEXT,
  gpu_name TEXT,
  timestamp INTEGER,
  utilization REAL
);

bartvm

import sqlite3
import xmltodict
from datetime import datetime
from flask import g, Flask, request

app = Flask(__name__)
DATABASE = 'data.db'

# Run the following command on the hosts:
# nvidia-smi -q -x -l 60 | curl -X POST -d @- http://eos12:5000/$HOSTNAME

bartvm

import numpy as np
import timeit
from matplotlib import pyplot as plt

n = 1000
t1s, t2s = [], []
for p in np.linspace(0, 1, num=20):
    y = np.random.binomial(1, p, n)
    x = np.random.rand(n)
    t1 = timeit.timeit('np.sum(x * y)', 'import numpy as np; from __main__ import x, y', number=100000)