Alex Liberzon alexlib

To forward X11 from inside a docker container to a host running macOS

Install XQuartz: https://www.xquartz.org/
Launch XQuartz. Under the XQuartz menu, select Preferences
Go to the security tab and ensure "Allow connections from network clients" is checked.
Run xhost + ${hostname} to allow connections to the macOS host *
Setup a HOSTNAME env var export HOSTNAME=`hostname`*
Add the following to your docker-compose:

 environment:

How to Setup Automatic Uploads to Anaconda from Travis CI in 15 minutes

TL;DR: Edit .travis.yaml to install Anaconda and to run conda_upload.sh after testing. Edit meta.yaml to take in the environmental variables $VERSION and $CONDA_BLD_PATH. Create conda_upload.sh which sets the needed environmental variables, builds the tar archive, and uploads it to Anaconda. Finally edit some stuff on your Anaconda and Travis CI account so they can talk.

Intro

The following steps will detail how to automatically trigger Anaconda builds and uploads from Travis CI. This will only upload successful builds in the master branch and if there are multiple commits in a single day, it'll only keep the latest one. Both of these settings can easily be changed.

Edit .travis.yaml

First, edit .travis.yml so that it installs Anaconda.

install:

CUDA-based Deep Learning on an AWS EC2

If you're intersted in software you've probably heard about deep learning and even done some reading or played around with it. But unless you have a desktop for high-end gaming you've probably found that running all these new CUDA-based parallel-GPU computing tools is just painfully slow.

That's what happened to me. So, it's time to spin up an EC2 on AWS and use someone else's hardware. This is just a basic introduction into how I did that, from creating an AWS dev account to installing some fun Python deep learning projects on GitHub. If you follow along, you'll be in a good position to install whatever other tools you want (Caffe, for instance) and get deep.

Set up AWS Account

If you haven't already, you need to set up your Amazon AWS profile:

Effective Modern CMake

Getting Started

For a brief user-level introduction to CMake, watch C++ Weekly, Episode 78, Intro to CMake by Jason Turner. LLVM’s CMake Primer provides a good high-level introduction to the CMake syntax. Go read it now.

After that, watch Mathieu Ropert’s CppCon 2017 talk Using Modern CMake Patterns to Enforce a Good Modular Design (slides). It provides a thorough explanation of what modern CMake is and why it is so much better than “old school” CMake. The modular design ideas in this talk are based on the book [Large-Scale C++ Software Design](https://www.amazon.de/Large-Scale-Soft

	!wget https://anaconda.org/pytorch/faiss-cpu/1.2.1/download/linux-64/faiss-cpu-1.2.1-py36_cuda9.0.176_1.tar.bz2
	!tar xvjf faiss-cpu-1.2.1-py36_cuda9.0.176_1.tar.bz2
	!cp -r lib/python3.6/site-packages/* /usr/local/lib/python3.6/dist-packages/
	!pip install mkl

	import faiss

	FROM python:3.6-slim-stretch as csvbuilder

	# This one uses csvs-to-sqlite to compile the DB, and then uses datasette
	# inspect to generate inspect-data.json Compiling pandas takes way too long
	# under alpine so we use slim-stretch for this one instead.

	RUN apt-get update && apt-get install -y python3-dev gcc
	COPY *.csv csvs/
	RUN pip install csvs-to-sqlite datasette
	RUN csvs-to-sqlite csvs/names.csv data.db -f "name" -c "legislature" -c "country"

	# For Windows users# Note: <> denotes changes to be made

	#Create a conda environment
	conda create --name <environment-name> python=<version:2.7/3.5>

	#To create a requirements.txt file:
	conda list #Gives you list of packages used for the environment

	conda list -e > requirements.txt #Save all the info about packages to your folder

	/opt/bin/nvidia-smi
	wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb
	dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb 2> /dev/null
	apt-key add /var/cuda-repo-8-0-local-ga2/7fa2af80.pub
	apt-get update
	apt-get install -qq cuda gcc-5 g++-5 -y
	ln -s /usr/bin/gcc-5 /usr/local/cuda/bin/gcc
	ln -s /usr/bin/g++-5 /usr/local/cuda/bin/g++
	/usr/local/cuda/bin/nvcc --version

	# Simple Google Drive backup script with automatic authentication
	# for Google Colaboratory (Python 3)

	# Instructions:
	# 1. Run this cell and authenticate via the link and text box.
	# 2. Copy the JSON output below this cell into the `mycreds_file_contents`
	# variable. Authentication will occur automatically from now on.
	# 3. Create a new folder in Google Drive and copy the ID of this folder
	# from the URL bar to the `folder_id` variable.
	# 4. Specify the directory to be backed up in `dir_to_backup`.