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:
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 | |
I hereby claim:
To claim this, I am signing this object:
""" This module contains the Preprocessor abstract class | |
""" | |
from abc import ABC, abstractmethod | |
class Preprocessor(ABC): | |
""" Abstract interface for preprocessors | |
""" |
[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. |
'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) |
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())) |
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') |
WS_N_GPUS = { | |
'turagas-ws1': 2, | |
'turagas-ws2': 2, | |
'turagas-ws3': 2, | |
'turagas-ws4': 2, | |
'c04u01': 8, | |
'c04u07': 8, | |
'c04u12': 8, | |
'c04u17': 8, | |
} |
{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ |
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 |