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/docker/gitea/gitea
library(shiny) | |
library(maps) | |
library(maptools) | |
library(ggplot2) | |
library(plyr) | |
library(XML) | |
# Define server logic required to plot various variables against mpg | |
shinyServer(function(input, output, session) { | |
#Setup########################################################################## | |
message("Checking for a personal library...") | |
if (!dir.exists(Sys.getenv("R_LIBS_USER"))) { | |
warning("Personal library not found, creating one...") | |
dir.create(Sys.getenv("R_LIBS_USER"), recursive = TRUE) | |
message("Registering newly created personal library...") | |
.libPaths(Sys.getenv("R_LIBS_USER")) | |
} else { |
#!/bin/bash | |
# Usage: mkproj [projectname] | |
# projectname defaults to name of current directory | |
template="Version: 1.0 | |
RestoreWorkspace: No | |
SaveWorkspace: No | |
AlwaysSaveHistory: Yes |
library(rstan) | |
library(MASS) | |
# Simulate some data | |
mu <- c(10, 5, -5) | |
Sig <- matrix(c(1, 0.7, 0.8, | |
0.7, 2, 0.2, | |
0.8, 0.2, 1.5), | |
3, 3) | |
N <- 100 |
# https://gist.github.com/noamross/73944d85cad545ae89efaa4d90b049db | |
#' Gets a file from a github repo, using the Data API blob endpoint | |
#' | |
#' This avoids the 1MB limit of the content API and uses [gh::gh] to deal with | |
#' authorization and such. See https://developer.github.com/v3/git/blobs/ | |
#' @param url the URL of the file to download via API, of the form | |
#' `:owner/:repo/blob/:path | |
#' @param ref the reference of a commit: a branch name, tag, or commit SHA | |
#' @param owner,repo,path,ref alternate way to specify the file. These will | |
#' override values in `url` |
## Rsync Filter for $HOME | |
# Universal excludes -- These apply to all subdirs | |
- [Cc]ache | |
- temp | |
- tmp | |
- octave-core | |
# Emacs temp files | |
- .#* |
[ | |
{ | |
"name": "SofleKeyboard", | |
"author": "Josef Adamcik", | |
"switchMount": "cherry" | |
}, | |
[ | |
{ | |
"y": 0.2, | |
"x": 3, |
https://zdevito.github.io/2022/08/16/memory-snapshots.html
# enable the recording of stack frame information for each allocation
import torch
torch.cuda.memory._record_memory_history(True)
from torchvision.models import resnet18
from pprint import pprint