cgarciae

# client-async-sem.py

from aiohttp import ClientSession, TCPConnector
import asyncio
import sys

limit = 1000

async def fetch(url, session):
    async with session.get(url) as response:

cgarciae

# timed.sh

ulimit -n 3000 # increase to avoid "out of file descriptors" error

python server.py &

sleep 1 # Wait for server to start

/usr/bin/time --format "Memory usage: %MKB\tTime: %e seconds\tCPU usage: %P" "$@"

cgarciae

# server.py

from aiohttp import web
import asyncio
import random

async def handle(request):
    await asyncio.sleep(random.randint(0, 3))
    return web.Response(text="Hello, World!")

cgarciae

from aiohttp import ClientSession
from pypeln import io
import asyncio
import sys

async def fetch(url, session):
    async with session.get(url) as response:
        return await response.read()

async def main():

cgarciae

rsync --exclude-from=.gitignore -avz -e ssh . $1

while inotifywait -r -e modify,create,delete . ; do
    rsync --exclude-from=.gitignore -avz --delete -e ssh . $1
done 

cgarciae

cudnn install ubuntu + tensorflow

1. Install cuda
2. Export variables in ~/.bashrc or ~/.zshrc.

export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
export CUDA_HOME=/usr/local/cuda

2. Download cudnn. Note: check TensorFlow install guide for the correct version.
3. Extract cudnn, this extracts a folder named cuda, cd into it.

cgarciae

nvidia-docker with docker-compose

Setup

1. Install nvidia-docker-plugin
2. Reboot (for some reason)
3. On the folder with this docker-compose.yml file do

docker-compose up

cgarciae

defmodule Streamer do
  def async(enum) do
    Stream.resource(
      #Start
      fn ->
        origin = self
        spawn fn ->
          for x <- enum do
            send origin, {:elem, x}
          end