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# Based on younesbelkada/finetune_llama_v2.py
# Install the following libraries:
# pip install accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 scipy

from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from transformers import (

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#!/usr/bin/env bash

export PROJECT_ID=$(gcloud config get-value project)
export PROJECT_USER=$(gcloud config get-value core/account) # set current user
export PROJECT_NUMBER=$(gcloud projects describe $PROJECT_ID --format="value(projectNumber)")
export IDNS=${PROJECT_ID}.svc.id.goog # workload identity domain

export GCP_REGION="us-central1"
export GCP_ZONE="us-central1-a"

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#!/bin/bash
# Add to instance metadata with `gcloud compute instances add-metadata \
#   instance-name --metadata-from-file startup-script=idle-shutdown.sh` and reboot
# NOTE: requires `bc`, eg, sudo apt-get install bc
# Modified from https://stackoverflow.com/questions/30556920/how-can-i-automatically-kill-idle-gce-instances-based-on-cpu-usage
threshold=0.1

count=0
wait_minutes=60
while true

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# This example demonstrates running furrr code distributed on 2 AWS instances ("nodes").

# The instances have already been created.

library(future)
library(furrr)

# Two t2.micro AWS instances
# Created from http://www.louisaslett.com/RStudio_AMI/
public_ip <- c("34.205.155.182", "34.201.26.217")

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• 0.1. References
• 0.2. Other cheatsheets
• 0.3. Manage multiple gcloud config configurations
  • 0.3.1. Switch gcloud context with gcloud config
• 0.4. Credentials
• 0.5. info
• 0.6. projects
• 0.7. zones & regions
• 0.8. Organization
• 0.9. billing

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---
title: New York City Taxi & Limousine Commission (TLC) Trip Data Analysis Using Sparklyr
  and Google BigQuery
author: "Mirai Solutions"
date: 8\textsuperscript{th} January 2018
output:
  html_document:
    theme: flatly
params:
  # gcp_json_keyfile: gcp_keyfile.json

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App Install Plan

Critical

• iTerm2

  • https://iterm2.com/

• Command Line Tools

  • xcode-select –install

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""" Implementation of OKapi BM25 with sklearn's TfidfVectorizer
Distributed as CC-0 (https://creativecommons.org/publicdomain/zero/1.0/)
"""

import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy import sparse


class BM25(object):

ramnathv

Updated June 16 with latest devel

data.table's automatic indexing:

Generating some data first:

# R version 3.3.0
require(data.table) ## 1.9.7, commit 2433, github
require(dplyr) ## devel, commit 3189, github

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Effective Engineer - Notes

• By Edmond Lau
• Highly Recommended 👍
• http://www.theeffectiveengineer.com/

What's an Effective Engineer?

• They are the people who get things done. Effective Engineers produce results.

Adopt the Right Mindsets