Download CMake from: https://cmake.org/download/
wget https://cmake.org/files/v3.12/cmake-3.12.3.tar.gz
tar zxvf cmake-3.*
<# | |
- BIOS of host machine also needs to be configured to allow hardware virtualization | |
- Windows 10 Pro or otherwise is needed; Windows 10 Home Edition CANNOT get WSL | |
- This gist WSLv2, but can use WSLv1 instead. I needed v1 as I run Windows 10 in a VM in Virtualbox. | |
- WSLv2 has been giving me problems in Virtualbox 6.1, but WSLv1 works properly. | |
- vbox has issues with the GUI settings when it comes to nested virtualization on certain systems, | |
so run the following if needing to give a VM this enabled setting: | |
VBoxManage modifyvm <vm-name> --nested-hw-virt on | |
#> |
wget https://cmake.org/files/v3.12/cmake-3.12.3.tar.gz
tar zxvf cmake-3.*
import graphql | |
# build_executable schema | |
# | |
# accepts schema_definition (string) and resolvers (object) in style of graphql-tools | |
# returns a schema ready for execution | |
def build_executable_schema(schema_definition, resolvers): | |
ast = graphql.parse(schema_definition) | |
schema = graphql.build_ast_schema(ast) |
This gist contains out.tex
, a tex file that adds a PDF outline ("bookmarks") to the freely available pdf file of the book
The Elements of Statistical Learning (2nd ed), by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
https://web.stanford.edu/~hastie/ElemStatLearn/
The bookmarks allow to navigate the contents of the book while reading it on a screen.
"""Script to illustrate usage of tf.estimator.Estimator in TF v1.3""" | |
import tensorflow as tf | |
from tensorflow.examples.tutorials.mnist import input_data as mnist_data | |
from tensorflow.contrib import slim | |
from tensorflow.contrib.learn import ModeKeys | |
from tensorflow.contrib.learn import learn_runner | |
# Show debugging output |
import java.time.Instant | |
import java.time.format.DateTimeFormatter | |
import java.util.concurrent.ConcurrentLinkedQueue | |
import sangria.ast._ | |
import sangria.execution._ | |
import sangria.schema.Context | |
import sangria.marshalling.queryAst._ | |
import sangria.renderer.SchemaRenderer |
In this gist I would like to describe an idea for GraphQL subscriptions. It was inspired by conversations about subscriptions in the GraphQL slack channel and different GH issues, like #89 and #411.
At the moment GraphQL allows 2 types of queries:
query
mutation
Reference implementation also adds the third type: subscription
. It does not have any semantics yet, so here I would like to propose one possible semantics interpretation and the reasoning behind it.
/* | |
* Functional Programming in JavaScript | |
* Chapter 01 | |
* Magical -run- function in support of Listing 1.1 | |
* Author: Luis Atencio | |
*/ | |
// -run- with two functions | |
var run2 = function(f, g) { | |
return function(x) { | |
return f(g(x)); |
Docker does not run natively on OSX, only Linux. Docker Machine was created to add a Linux VM environment to run Docker containers on OSX. Install using Homebrew:
brew install docker
brew install docker-machine
docker-machine create