Making Sure Everything's Ready: Your AI Setup Checklist

Building in Public: Part 2 - Is Your AI Kitchen Really Ready? 🧐

Nov 09, 2024

Hey there, Alchemists! 👋

Last time we talked about the importance of AI bias and our exciting journey ahead. Today, we're going to do something super important - we're going to make absolutely sure our AI kitchen is ready for cooking! You know how bakers check their oven temperature before making a soufflé? That's exactly what we're doing with our AI setup!

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Why Do We Need to Verify? 🤔

You might be thinking, "Come on, I installed everything, isn't that enough?" Well, let me tell you a funny story. The first time I set up my environment, everything seemed fine until I actually started training. Two hours in, I realized my GPU wasn't even being used! 😅 That's why we're going to check everything first.

Meet verify.py: Your AI Kitchen Inspector 🔍

Let's look at our verification script:

python

"""
Verify that all required packages are properly installed and configured
"""
import sys
import tensorflow as tf
import cv2
import numpy as np
import pandas as pd
from tqdm import tqdm

def verify_setup():
    """
    Verify that all required packages are properly installed and configured
    """
    print("Python version:", sys.version)
    print("\nTensorFlow version:", tf.__version__)
    print("TensorFlow Metal device:", 
tf.config.list_physical_devices('GPU'))

Think of this like your pre-flight checklist - we're checking every single thing that could affect our AI's performance!

Let's Run Some Tests! ✈️

First up, let's check our versions. It's like making sure all your ingredients aren't expired:

Now for the exciting part - testing our GPU:


print("\nTesting Metal acceleration...")
    with tf.device('/GPU:0'):
        a = tf.random.normal([1000, 1000])
        b = tf.random.normal([1000, 1000])
        c = tf.matmul(a, b)
        print("Matrix multiplication test successful")

This is like turning on all your burners to make sure they're working. If this runs quickly, your GPU is ready to rock! 🎸

Common "Uh-Oh" Moments and How to Fix Them 🔧

"GPU not found" Message

print("TensorFlow Metal device:", tf.config.list_physical_devices('GPU'))
# If this shows [], we've got a problem!

Quick Fix: Make sure you've installed tensorflow-metal and set DEVICE='metal'
Slow Matrix Multiplication If our test takes more than a few seconds, something's not right with our GPU setup. Usually this means Metal isn't properly configured.
Import Errors These are like finding out you forgot to buy a key ingredient - annoying but easily fixed!

Let's Check Memory Too! 💾

Here's a neat addition I like to make to verify.py:

print("\nChecking available memory...")
try:
    # Test with a reasonably sized matrix
    test_matrix = tf.random.normal([5000, 5000])
    print("Memory test passed!")
except:
    print("Warning: Memory constraints detected")

Your Turn: The Verification Challenge! 🎯

Try this out:

Run verify.py
Look at each output carefully
Try changing the matrix size in the GPU test
Watch your Activity Monitor while running tests

Pro Tip: Keep verify.py handy - I run it before starting any major training session. It's saved me hours of debugging!

Download Verify.py code

Quick Health Check! 🏥

Before you go, make sure:

Your GPU shows up in device list
Matrix multiplication is fast
All imports work
Memory test passes

The Ethics Corner 🎯

Even in verification,

Memory test passes

The Ethics Corner 🎯

Even in verification, we think about bias:

Are we assuming everyone has the same computing power?
How can we make our code more accessible?
What about users with different hardware?

What's Next? 🚀

Now that we know our kitchen is ready, next time we'll start cooking - I mean, training our model! Make sure everything passes these checks before then!