sankalp-khare

#!/bin/bash -e
#-------------------------------------------------------------------------------------
# Summary: Executes commands to...
#   * Check/output currently installed version of gnupg
#   * Change into the brew repo/install directory
#   * Find the git commit-id of the specified version of gnupg
#   * git branch/switch to the commit-id
#   * replace the brew installed version with the specified version (and pin it)
#   * clean up the branch, switch back to master, and remove cached files
#   * Check/output the newly installed version of gnupg

jvelezmagic

# The goal of this file is to provide a FastAPI application for handling
# chat requests amd generation AI-powered responses using conversation chains.
# The application uses the LangChaing library, which includes a chatOpenAI model
# for natural language processing.

# The `StreamingConversationChain` class is responsible for creating and storing
# conversation memories and generating responses. It utilizes the `ChatOpenAI` model
# and a callback handler to stream responses as they're generated.

# The application defines a `ChatRequest` model for handling chat requests,

nymous

Logging setup for FastAPI

This logging setup configures Structlog to output pretty logs in development, and JSON log lines in production.

Then, you can use Structlog loggers or standard logging loggers, and they both will be processed by the Structlog pipeline (see the hello() endpoint for reference). That way any log generated by your dependencies will also be processed and enriched, even if they know nothing about Structlog!

Requests are assigned a correlation ID with the asgi-correlation-id middleware (either captured from incoming request or generated on the fly). All logs are linked to the correlation ID, and to the Datadog trace/span if instrumented. This data "global to the request" is stored in context vars, and automatically added to all logs produced during the request thanks to Structlog. You can add to these "global local variables" at any point in an endpoint with `structlog.contextvars.bind_contextvars(custom

alamb

// Please find the full, tested version in 
// https://github.com/influxdata/influxdb_iox/blob/fe155e15fb2ad166aee66b0458e63c24a8128dd4/query/src/exec/task.rs#L101-L118

pub struct DedicatedExecutor {
    state: Arc<Mutex<State>>,                                                                                                          
}                                                                                                                                      
                                                                                                                                       
/// Runs futures (and any `tasks` that are `tokio::task::spawned` by                                                                   
/// them) on a separate Tokio Executor                                                                                                 
struct State {                                                                                                    

minad

Polyp: Small child of the Hydra

;; The undo keybindings `C-x u` and `C-_` enter the polyp.
(defpolyp polyp-undo
  "_u_ndo  _r_edo"
  ("u" undo-fu-only-undo "C-x u" "C-_")
  ("r" undo-fu-only-redo))

mikroskeem

gccemacs on OS X

Read this first: http://akrl.sdf.org/gccemacs.html

Prerequisites

1) GCC with libgccjit enabled

For that you need to compile gcc (duh). I edited Homebrew's gcc formula:

karlicoss

(with-eval-after-load 'org-agenda
  (defun my/org-has-children ()
    (if (save-excursion (org-goto-first-child)) "▶" " ")
  )
  (add-to-list 'org-agenda-prefix-format '(
     agenda  . "%i%-3:(my/org-has-children) %-12:c%?-12t% s "
  ))
)

; Result:

sparkydogX

import os
import torch
from tqdm import tqdm
import time

# declare which gpu device to use
cuda_device = '0'

def check_mem(cuda_device):
    devices_info = os.popen('"/usr/bin/nvidia-smi" --query-gpu=memory.total,memory.used --format=csv,nounits,noheader').read().strip().split("\n")

xiaolai

How to run Jupyterlab as a desktop app on Mac OSX

In Sep, 2021, Jupyterlab Desktop App (electron) was released by Mehmet Bektas (github repo).

brew install --cask jupyterlab

kostaz

#!/bin/bash
[[ $UID == 0 ]] || { echo "run as sudo to install"; exit 1; }

REPO="https://github.com/BurntSushi/ripgrep/releases/download/"
RG_LATEST=$(curl -sSL "https://api.github.com/repos/BurntSushi/ripgrep/releases/latest" | jq --raw-output .tag_name)
RELEASE="${RG_LATEST}/ripgrep-${RG_LATEST}-x86_64-unknown-linux-musl.tar.gz"

TMPDIR=$(mktemp -d)
cd $TMPDIR
wget -O - ${REPO}${RELEASE} | tar zxf - --strip-component=1