bartvm

bartvm

import argparse
import sqlite3
from uuid import UUID

from flask import g, json, Flask

from blocks.config import config
from blocks.log.sqlite import ANCESTORS_QUERY

app = Flask(__name__)

bartvm

th test/test_qr.lua
Running 36 tests
_____________*_*_*_*________________  ==> Done Completed 193792 asserts in 36 tests with 16 errors
--------------------------------------------------------------------------------
randomMediumQR_inPlace_torch.FloatTensor
QR = A
 TensorEQ(==) violation   val=1.5631318092346e-05, condition=1e-05
	/Users/bartvm/torch/install/share/lua/5.1/torch/Tester.lua:61: in function 'assertTensorEq'
	test/test_qr.lua:80: in function 'checkQR'

bartvm

A comparison of Theano with other deep learning frameworks, highlighting a series of low-level design choices in no particular order.

Overview

• Differentiation

Differentiation

Symbolic: Theano, CGT; Automatic: Torch, MXNet

Symbolic and automatic differentiation are often confused or used interchangeably, although their implementations are significantly different.

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)

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

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

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

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

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"