0xBigBoss

services:
  shovel:
    image: docker.io/indexsupply/shovel:af07
    container_name: shovel
    restart: unless-stopped
    ports:
      - "8383:80"
    env_file:
      - .env
    volumes:

0xBigBoss

def sieve_of_eratosthenes(n: int) -> list[int]:
    """
    Generate all prime numbers up to n using the Sieve of Eratosthenes algorithm.

    Args:
        n (int): Upper bound for generating prime numbers

    Returns:
        list[int]: List of all prime numbers up to n

0xBigBoss

/*
 * Example 1: POSIX Shared Memory
 * This example shows how to create, write to, and read from a POSIX shared memory segment
 */

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <fcntl.h>
#include <sys/mman.h>

0xBigBoss

#!/bin/bash

# Help function to display usage
show_help() {
  echo "Usage: $0 [OPTIONS]"
  echo
  echo "Options:"
  echo "  --image IMAGE         Docker image to use (required)"
  echo "  --address IP          Head node IP address (required)"
  echo "  --mode MODE           Either 'head' or 'worker' (required)"

0xBigBoss

# train_grpo.py
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer

# Load and prep dataset

0xBigBoss

-- Find indexes on the table
SELECT indexname, tablename
FROM pg_indexes
WHERE tablename = '' || ${table_name} || '';

-- Find foreign key relationships
SELECT
    tc.table_schema,
    tc.constraint_name,
    tc.table_name,

0xBigBoss

sudo ln -s /data2/k3s-rancher-etc/ /etc/rancher
sudo ln -s /data2/k3s/ /run/k3s
sudo ln -s /data2/k3s-kubelet/ /var/lib/kubelet
sudo ln -s /data2/k3s-rancher/ /var/lib/rancher

0xBigBoss

curl -sfL https://get.k3s.io | sh -s - \
  --bind-address=0.0.0.0 \
  --flannel-iface=zt12345678 \
  --node-ip=10.0.0.2 \
  --cluster-init

# from control plane
K3S_URL=https://10.0.0.2:6443
K3S_TOKEN=$(sudo cat /var/lib/rancher/k3s/server/node-token)

0xBigBoss

Run vLLM in Distributed Mode with Ray

Prerequisites

A docker image with the vLLM server installed.

export DOCKER_IMAGE=docker.io/fxnlabs/vllm-openai
# or use the following if you want to use the latest version

0xBigBoss

async function callDebugRpcMethod(method: string, params: any[]) {
  const response = await fetch("http://localhost:3030/rpc", {
    headers: {
      "content-type": "application/json",
    },
    referrer: "http://localhost:3000/",
    body: JSON.stringify({
      jsonrpc: "2.0",
      id: 1,
      method: method,