A newbie friendly guide to configuring Vim in NixOS
Create the following file struture in /etc/nixos
/etc/nixos
|-- apps
|-- vim
|-- default.nix
|-- vimPlugins.nix
Pretty print tables summarizing properties of tensor arrays in numpy, pytorch, jax, etc. | |
Now on pip! `pip install arrgh` https://github.com/nmwsharp/arrgh |
#!/usr/bin/env python | |
import math | |
import matplotlib.pyplot as plt | |
import torch | |
import torch.nn as nn | |
from sklearn.datasets import make_moons | |
from torch import Tensor | |
from tqdm import tqdm |
# Serve nextJS app from a port through NGINX reverse proxy (HTTP) | |
# Path: /etc/nginx/sites-available/default | |
# Default server configuration for HTTP | |
server { | |
server_name www.DOMAINNAME.com DOMAINNAME.com; | |
# Serve any static assets with NGINX | |
location /_next/static { | |
alias /home/ubuntu/PROJECT_FOLDER/.next/static; |
Convert mac-os-el-capitan-pkg-to-iso.sh
from using hdiutil
and asr
to using Linux utilities.
Important: You will need about 30GB of free disk space.
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
I've tested it on Fedora 23 and Ubuntu 16.04. I'm using gcc-5.3.1, python-3.4, VS Code-1.14.0 | |
You can debug mixed Python/C++ in the same GUI. It also works for MPI applications. You can switch between the debuggers and corresponding call stacks. | |
1. Packages needed | |
1) Visual Studio Code | |
2) Extensions for VS Code: | |
"Python" from Don Jayamanne (I'm using 0.6.7) | |
This allows VS Code act as the front end to debug python. | |
This gives VS Code ability to attach to a python script that uses module "ptvsd". |
# Author: HJ van Veen <[email protected]> | |
# Description: Experiment to learn a tSNE transformer for new | |
# test data with a multi-output GBM | |
# | |
# Idea first seen at lvdmaaten.github.io/tsne | |
# > [...] it is not possible to embed test points in an existing | |
# > map [...] | |
# > A potential approach to deal with this would be to train | |
# > a multivariate regressor to predict the map location from | |
# > the input data. |