djinn

const {
  Worker,
  isMainThread,
  parentPort,
  workerData,
  MessageChannel,
} = require("node:worker_threads");
const http = require("node:http");

if (isMainThread) {

djinn

import matplotlib.pyplot as plt
import pandas as pd
import re

def parse_gc_log(gc_log):
    # Split the input log by lines
    lines = gc_log.split("\n")

    # Prepare lists for each graph's data
    gc_timing = []

djinn

#!/bin/sh

lsb_release -a
add-apt-repository ppa:deadsnakes/ppa --yes
apt install python3.11 python3.11-venv python3.11-dev
python3.11 -m venv env
source env/bin/activate
pip install packaging wheel
pip install https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp311-cp311-linux_x86_64.whl#sha256=d4c3e9a8d31a7c0fcbb9da17c31a1917e1fac26c566a4cfbd8c9568ad7cade79
pip install  --no-build-isolation 'instructlab[cuda]' -C cmake.args="-DLLAMA_CUDA=on" -C cmake.args="-DLLAMA_NATIVE=off"

djinn

const users = [
  { id: 'user_111', email: '[email protected]' },
  { id: 'user_222', email: '[email protected]' },
  { id: 'user_333', email: '[email protected]' },
]
// Wait for all the get operations to complete and store the results.
const getResults = await Promise.all(
  users.map((user) => {
    console.log(`Getting document: ${user.id}`)
    return usersCollection.get(user.id, user)

djinn

import time
import random
from pymongo import MongoClient
from couchbase.cluster import Cluster
from couchbase.management.buckets import BucketManager
from couchbase.options import ClusterOptions
from couchbase.auth import PasswordAuthenticator
import os
import string
from tqdm import tqdm

djinn

#include <iostream>
#include <vector>
#include <random>
#include <algorithm>
#include <numeric>
#include <thread>
#include <mutex>

const int num_simulations = 10000; // Number of Monte Carlo simulations
const int num_days = 252;          // Number of trading days in a year

djinn

import numpy as np
import math

# Define the Transformer model architecture
class Transformer:
    def __init__(self, input_vocab_size, output_vocab_size, max_seq_length, d_model, num_heads, num_layers):
        self.input_vocab_size = input_vocab_size
        self.output_vocab_size = output_vocab_size
        self.max_seq_length = max_seq_length
        self.d_model = d_model

djinn

#!/bin/sh
# The cheapest CUDA instance on AWS is Graviton2 based g5g instances
# 1) Install right CUDA Drivers on the system. Whilst this script has been tested on Ubuntu 22.04. It should work on others
# 2) Install torch with GPU support. Compiling from source works the best 
# 3) Preferably Quantize the models - https://huggingface.co/docs/transformers/main/en/main_classes/quantization
#
# Author: Supreet Sethi <[email protected]>
# Web: https://www.linkedin.com/in/djinn
# 
# Step 1) is here 

djinn

#!/usr/bin/env python3
from json import dumps

test_load = """SQL statistics:
    queries performed:
        read:                            845684
        write:                           241589
        other:                           120831
        total:                           1208104
    transactions:                        60398  (1506.89 per sec.)

djinn

#!/bin/sh
# Author Supreet Sethi <[email protected]>
# Dated Thu Jul 21 13:26:06 +08 2022
# Instructions - wget <script>; chmod +x yubikey.sh; ./yubikey.sh

sudo pmset -a hibernatemode 25 && sudo pmset -a standbydelay 15
echo "Yubikey hibernation changes installed"