rueberger

Controller: conjure-up-localhost-e8a

Model                          Cloud/Region         Status     Machines  Access  Last connection
conjure-canonical-kubern-75b   localhost/localhost  available        12  admin   2017-12-10
conjure-canonical-kubern-ee0*  localhost/localhost  available        12  admin   17 hours ago
controller                     localhost/localhost  available         1  admin   just now

rueberger

Keybase proof

I hereby claim:

• I am rueberger on github.
• I am rueberger (https://keybase.io/rueberger) on keybase.
• I have a public key ASBO3HNq5_afMicR98MdxaEZ7sby0vVooTfsyrfZCiUw4wo

To claim this, I am signing this object:

rueberger

""" This module contains the Preprocessor abstract class
"""


from abc import ABC, abstractmethod


class Preprocessor(ABC):
    """ Abstract interface for preprocessors
    """

rueberger

[D 2017-07-27 22:25:38.734 JupyterHub app:740] Generating new cookie_secret
[I 2017-07-27 22:25:38.735 JupyterHub app:745] Writing cookie_secret to /srv/jupyterhub/jupyterhub_cookie_secret
[D 2017-07-27 22:25:38.735 JupyterHub app:796] Connecting to db: sqlite:///jupyterhub.sqlite
[W 2017-07-27 22:25:38.823 JupyterHub app:365] 
    Generating CONFIGPROXY_AUTH_TOKEN. Restarting the Hub will require restarting the proxy.
    Set CONFIGPROXY_AUTH_TOKEN env or JupyterHub.proxy_auth_token config to avoid this message.
    
[W 2017-07-27 22:25:38.838 JupyterHub app:864] No admin users, admin interface will be unavailable.
[W 2017-07-27 22:25:38.838 JupyterHub app:865] Add any administrative users to `c.Authenticator.admin_users` in config.
[I 2017-07-27 22:25:38.838 JupyterHub app:892] Not using whitelist. Any authenticated user will be allowed.

rueberger

'alembic (0.9.3)
asn1crypto (0.22.0)
backports.weakref (1.0rc1)
bkcharts (0.2)
bleach (1.5.0)
bokeh (0.12.6)
certifi (2017.4.17)
cffi (1.10.0)
chardet (3.0.2)
conda (4.3.22)

rueberger

import PIL.Image
from io import BytesIO
import IPython.display
import numpy as np

def showarray(a, fmt='png'):
    a = np.uint8(((a - np.min(a)) / np.max(a)) * 255)
    f = BytesIO()
    PIL.Image.fromarray(a).save(f, fmt)
    IPython.display.display(IPython.display.Image(data=f.getvalue()))

rueberger

import commands
import numpy as np


def fetch_gpu_status():
    """ Run nvidia-smi and parse the output
    requires Python 2 only dependency
    """

    status_code, output = commands.getstatusoutput('nvidia-smi')

rueberger

WS_N_GPUS = {
    'turagas-ws1': 2,
    'turagas-ws2': 2,
    'turagas-ws3': 2,
    'turagas-ws4': 2,
    'c04u01': 8,
    'c04u07': 8,
    'c04u12': 8,
    'c04u17': 8,
}

rueberger

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [

rueberger

I'm an undergrad in Physics interested in theoretical neuroscience and machine learning.

Some project ideas:

-Biology inspired music compression with Minimum Probability Flow learning and hopfield networks. 
Image compression has been achieved with these techniques; adapting the 
technique to music compression by mirroring the signal processing of the cochlea would be interesting. 
Teammates with strong EECS backgrounds would be desired for this project. 

I'd also be interested in using Minimum Probability Flow learning in other ways. It's a really efficient