andyljones

VAGRANTFILE_API_VERSION = "2"

Vagrant.configure(VAGRANTFILE_API_VERSION) do |config|
	config.vm.box = "debian7"
	config.vm.box_url = "https://dl.dropboxusercontent.com/s/xymcvez85i29lym/vagrant-debian-wheezy64.box"
	
	config.vm.network :forwarded_port, host: 4000, guest: 4000
	config.vm.provision :shell, :path => "bootstrap.sh"
	
	config.ssh.forward_agent = true

andyljones

# -*- coding: utf-8 -*-
"""
To use this, drop the file

'Full Results - Stack Overflow Developer Survey - 2015.csv'

from

https://drive.google.com/file/d/0Bzd_CzYvUxE5U1NSWnA2SFVKX00/view

andyljones

def work():
  print 0; yield
  print 1; yield
  print 2; yield

worker = work()
for i in range(2+1):
    next(worker)
    if i == 1: break

andyljones

# -*- coding: utf-8 -*-
"""
To use this, drop the file 

'Full Results - Stack Overflow Developer Survey - 2015.csv'

from

https://drive.google.com/file/d/0Bzd_CzYvUxE5U1NSWnA2SFVKX00/view

andyljones

import scipy as sp
from scipy.special import gammaln

def log_marginal(p, n, alpha=2):
    """Log-marginal probability of `p` positive trials and `n` negative trials from a 
    beta-binomial model with prior strength `alpha`. See 
    
    http://www.cs.ubc.ca/~murphyk/Teaching/CS340-Fall06/reading/bernoulli.pdf
    
    for details.

andyljones

EXPLOSION

We live in a strange time.

Extraordinary events keep happening that undermine the stability of our world.

Suicide bombs, waves of refugees,

Donald Trump, Vladimir Putin, even Brexit.

andyljones

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Feb  5 11:01:52 2017

@author: andyjones
"""

import scipy as sp
import matplotlib.pyplot as plt

andyljones

import requests
import pandas as pd
from io import BytesIO
import matplotlib as mpl
import matplotlib.pyplot as plt

url = 'https://www.metoffice.gov.uk/hadobs/hadcet/cetdl1772on.dat'

raw = pd.read_csv(BytesIO(requests.get(url).content), sep='\s+', header=None)
raw.columns = ['year', 'day'] + list(range(1, 13))

andyljones

"""
This is a standalone script for demonstrating some memory leaks that're troubling me. It's a torn-down version of 
the project I'm currently working on.

To run this, you'll need panda3d, pandas and tqdm. You should be able to install these with
```
pip install panda3d pandas tqdm
```
You'll **also need to enable memory tracking**. Do that by setting `track-memory-usage 1` in `panda3d.__file__`'s 
`etc/Config.prc` file. Setting it anywhere else is too late! (It's a unique setting in that way - thanks rdb!)

andyljones

"""This script should be run on a machine with at least 2 GPUs and an MPS server running. You can launch an MPS daemon with
```
nvidia-cuda-mps-control -d
```
The script first uses `test_cuda` to verify a CUDA context can be created on each GPU. It then spawns two workers; a 
'good' worker and a 'bad' worker. The workers collaborate through Pytorch's DataDistributedParallel module to calculate
the gradient for a trivial computation. The 'good' worker carries out both the forward and backward pass, while the 
bad worker carries out the forward pass and then exits. This seems to lock up the MPS server, and any subsequent 
attempts to create CUDA contexts fail by hanging eternally.