AI 工程从零开始

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GPU Setup & Cloud ​

Training on CPU is fine for learning. Training for real needs a GPU.

Type: Build Languages: Python Prerequisites: Phase 0, Lesson 01 Time: ~45 minutes

Learning Objectives ​

  • Verify local GPU availability using nvidia-smi and PyTorch's CUDA API
  • Configure Google Colab with a T4 GPU for free cloud-based experiments
  • Benchmark matrix multiplication on CPU vs GPU and measure the speedup
  • Estimate the largest model that fits in your VRAM using the fp16 rule of thumb

The Problem ​

Most lessons in phases 1-3 run fine on CPU. But once you start training CNNs, transformers, or LLMs (phases 4+), you need GPU acceleration. A training run that takes 8 hours on CPU takes 10 minutes on GPU.

You have three options: local GPU, cloud GPU, or Google Colab (free).

The Concept ​

Your options:

1. Local NVIDIA GPU
   Cost: $0 (you already have it)
   Setup: Install CUDA + cuDNN
   Best for: Regular use, large datasets

2. Google Colab (free tier)
   Cost: $0
   Setup: None
   Best for: Quick experiments, no GPU at home

3. Cloud GPU (Lambda, RunPod, Vast.ai)
   Cost: $0.20-2.00/hr
   Setup: SSH + install
   Best for: Serious training, large models

Build It ​

Option 1: Local NVIDIA GPU ​

Check if you have one:

bash
nvidia-smi

Install PyTorch with CUDA:

python
import torch

print(f"CUDA available: {torch.cuda.is_available()}")
print(f"CUDA version: {torch.version.cuda}")
if torch.cuda.is_available():
    print(f"GPU: {torch.cuda.get_device_name(0)}")
    print(f"Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")

Option 2: Google Colab ​

  1. Go to colab.research.google.com
  2. Runtime > Change runtime type > T4 GPU
  3. Run !nvidia-smi to verify

Upload notebooks from this course directly to Colab.

Option 3: Cloud GPU ​

For Lambda Labs, RunPod, or Vast.ai:

bash
ssh user@your-gpu-instance

pip install torch torchvision torchaudio
python -c "import torch; print(torch.cuda.get_device_name(0))"

No GPU? No problem. ​

Most lessons work on CPU. The ones that need GPU will say so and include Colab links.

python
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using: {device}")

Build It: GPU vs CPU benchmark ​

python
import torch
import time

size = 5000

a_cpu = torch.randn(size, size)
b_cpu = torch.randn(size, size)

start = time.time()
c_cpu = a_cpu @ b_cpu
cpu_time = time.time() - start
print(f"CPU: {cpu_time:.3f}s")

if torch.cuda.is_available():
    a_gpu = a_cpu.to("cuda")
    b_gpu = b_cpu.to("cuda")

    torch.cuda.synchronize()
    start = time.time()
    c_gpu = a_gpu @ b_gpu
    torch.cuda.synchronize()
    gpu_time = time.time() - start
    print(f"GPU: {gpu_time:.3f}s")
    print(f"Speedup: {cpu_time / gpu_time:.0f}x")

Exercises ​

  1. Run the benchmark above and compare CPU vs GPU times
  2. If you don't have a GPU, run it on Google Colab and compare
  3. Check how much GPU memory you have and estimate the largest model you can fit (rule of thumb: 2 bytes per parameter for fp16)

Key Terms ​

TermWhat people sayWhat it actually means
CUDA"GPU programming"NVIDIA's parallel computing platform that lets you run code on the GPU
VRAM"GPU memory"Video RAM on the GPU, separate from system RAM. Limits model size.
fp16"Half precision"16-bit floating point, uses half the memory of fp32 with minimal accuracy loss
Tensor Core"Fast matrix hardware"Specialized GPU cores for matrix multiplication, 4-8x faster than regular cores