awhitesong / XPS-15 9560 Getting Nvidia To Work on KDE Neon

Created March 16, 2017 14:32 — forked from whizzzkid/XPS-15 9560 Getting Nvidia To Work on KDE Neon

Making Nvidia Drivers + CUDA 8 + Bumblebee work together on XPS 15 Early 2017 9560 kabylake.

	# Update to 4.9 kernel do not delete the old kernel as it will be your failsafe if something happens to this one

	# Install KabyLake graphics patches
	cd /tmp;
	wget https://01.org/sites/default/files/downloads/intelr-graphics-linux/kbldmcver101.tar.bz2;
	tar xjvf kbldmcver101.tar.bz2; cd kbl_dmc_ver1_01/; sudo ./install.sh

	cd /tmp;
	wget https://01.org/sites/default/files/downloads/intelr-graphics-linux/kblgucver914.tar.gz;
	tar xvzf kblgucver914.tar.gz; cd firmware/kbl/guc/kbl_guc_ver/; sudo ./install.sh

awhitesong / git-cheat-list.md

Created February 18, 2017 06:51

Git cheat list

name of the current banch and nothing else (for automation)
```
git rev-parse --abbrev-ref HEAD
```
all commits that your branch have that are not yet in master
```
git log master..<HERE_COMES_YOUR_BRANCH_NAME>
```

awhitesong / gist:2d0b98749d8f9f792bc45427a58ec7d6

Last active April 5, 2016 21:54 — forked from evildmp/gist:3094281

Set up Django, nginx and uwsgi

This document has now been incorporated into the uWSGI documentation:

http://uwsgi-docs.readthedocs.org/en/latest/tutorials/Django_and_nginx.html

Set up Django, nginx and uwsgi

Steps with explanations to set up a server using:

awhitesong / readme.md

Created March 22, 2016 21:51 — forked from baraldilorenzo/readme.md

VGG-19 pre-trained model for Keras

##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

awhitesong / rest-server.py

Created March 21, 2016 21:58 — forked from miguelgrinberg/rest-server.py

The code from my article on building RESTful web services with Python and the Flask microframework. See the article here: http://blog.miguelgrinberg.com/post/designing-a-restful-api-with-python-and-flask

	#!flask/bin/python
	from flask import Flask, jsonify, abort, request, make_response, url_for
	from flask.ext.httpauth import HTTPBasicAuth

	app = Flask(__name__, static_url_path = "")
	auth = HTTPBasicAuth()

	@auth.get_password
	def get_password(username):
	if username == 'miguel':

awhitesong / deep_lstm.ipynb

Created February 6, 2016 22:15 — forked from xccds/deep_lstm.ipynb

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awhitesong / [Keras]Graph.ipynb

Created February 6, 2016 22:12 — forked from yusuke0519/[Keras]Graph.ipynb

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awhitesong / imdb_cnn_kim_small_embedding.py

Created February 6, 2016 22:05 — forked from entron/imdb_cnn_kim_small_embedding.py

Keras implementation of Kim's paper "Convolutional Neural Networks for Sentence Classification" with a very small embedding size.

	'''This scripts implements Kim's paper "Convolutional Neural Networks for Sentence Classification"
	with a very small embedding size (20) than the commonly used values (100 - 300) as it gives better
	result with much less parameters.

	Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_cnn.py

	Get to 0.853 test accuracy after 5 epochs. 13s/epoch on Nvidia GTX980 GPU.
	'''

	from __future__ import print_function

awhitesong / VGG_ILSVRC_19_layers_train_val.prototxt

Created February 5, 2016 15:43 — forked from graphific/VGG_ILSVRC_19_layers_train_val.prototxt

	VGG_ILSVRC_19_layers_train_val.prototxt
	name: "VGG_ILSVRC_19_layers"
	layers {
	name: "data"
	type: DATA
	include {
	phase: TRAIN
	}
	transform_param {
	crop_size: 224

awhitesong / batchgen.py

Created February 5, 2016 10:58 — forked from ebenolson/batchgen.py

	# Context manager to generate batches in the background via a process pool
	# Usage:
	#
	# def batch(seed):
	# .... # generate minibatch
	# return minibatch
	#
	# with BatchGenCM(batch) as bg:
	# minibatch = next(bg)
	# .... # do something with minibatch

Dhawal Arora awhitesong