markrmiller

m-radzikowski

#!/usr/bin/env bash

set -Eeuo pipefail
trap cleanup SIGINT SIGTERM ERR EXIT

script_dir=$(cd "$(dirname "${BASH_SOURCE[0]}")" &>/dev/null && pwd -P)

usage() {
  cat <<EOF
Usage: $(basename "${BASH_SOURCE[0]}") [-h] [-v] [-f] -p param_value arg1 [arg2...]

jpbarto

#!/bin/bash
#
# This script takes about 5 minutes to run and will delay the availability of
# your SageMaker notebook.
# v1.3, release 05may2020
#
# THIS SCRIPT INSTALLS:
# - THEIA IDE w/ Jupyter Server proxy
# - UPGRADES JUPYTERLAB
# - INSTALL VOILA (https://github.com/voila-dashboards/voila)

jpbarto

#!/bin/bash

set -e

sudo -u ec2-user -i <<'EOP'
## INSTALL THEIA IDE FROM SOURCE
EC2_HOME=/home/ec2-user
mkdir ${EC2_HOME}/theia && cd ${EC2_HOME}/theia
### begin by installing NVM, NodeJS v10, and Yarn
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.33.5/install.sh | bash

killeent

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

1. How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
2. How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
3. How does PyTorch cwrap work to generate code for Tensor methods?
4. How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.

exit99

find . -exec ls -dl \{\} \; | grep ".*\.py" | awk '{print $9}' | xargs 2to3 -w

dgrtwo

## helper and setup functions

wrap_party_df <- function(func) {
    function(x, object, ...) {
        n <- col_name(substitute(object))
        
        # have to create an expression since we cannot rely on local
        # variables
        args <- list(...)
        expr <- substitute(do.call(func, c(list(.[[colname]][[1]]), args)),

jarutis

# install general deps
sudo yum groupinstall "Development Tools"
sudo yum install wget unzip

# install java
cd /opt/
sudo wget --no-cookies --no-check-certificate --header "Cookie: gpw_e24=http%3A%2F%2Fwww.oracle.com%2F; oraclelicense=accept-securebackup-cookie" "http://download.oracle.com/otn-pub/java/jdk/8u60-b27/jdk-8u60-linux-x64.tar.gz"
sudo tar xzf jdk-8u60-linux-x64.tar.gz

cd /opt/jdk1.8.0_60/

soulslicer

# This script installs everything you need on an EC2 GPU Instance
# Create an Ubuntu 12.04 GPU Instance with at least 16GB of free space

# Base packages
sudo apt-get update
sudo apt-get install build-essential
sudo apt-get install gcc git cmake

# CUDA/Nvidia installations
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb

computermacgyver

#!/usr/bin/R

# Functions for R and ggplot2 to create log-scale density plots

# Main function: density.log(...)
# Produce a dataframe used to produce a density plot
# Input:
#  data: dataframe, 
#  var: variable to plot on y-axis
#  split: factor to split on