Google Colab
EE 641: A Computational Introduction to Deep Learning
Colab runs Jupyter notebooks on Google’s machines with an attached GPU. It is a good way to train models in this course.
Tiers
| Tier | Price | Compute units / month | Notes |
|---|---|---|---|
| Free | $0 | — | T4 when available, short sessions, no guarantees |
| Pro | $9.99/mo | 100 | longer sessions, better GPU availability |
| Pro+ | $49.99/mo | 500 | background execution up to 24 h |
| Pay As You Go | $9.99 per 100 CU | — | top-up, no subscription |
Get Pro. 100 compute units per month covers everything in EE 641, including the final project. Unused units carry over for 90 days. Prices as of August 2026.
What a Compute Unit Buys
GPUs burn compute units at different rates: a T4 costs roughly 1.2–1.8 CU/hour, an A100 (40 GB) about 5.4 CU/hour. Pro’s 100 CU is therefore ~60+ T4-hours but only ~18 A100-hours. Use the T4 unless your model does not fit in its 16 GB.
Enabling the GPU
Runtime → Change runtime type → select a GPU. Verify:
import torch
print(torch.cuda.is_available(), torch.cuda.get_device_name(0))!nvidia-smiSessions Die — Checkpoint to Drive
Colab disconnects idle sessions and enforces runtime limits. Anything on the session’s local disk is lost. Mount Drive and point checkpoints there:
from google.colab import drive
drive.mount('/content/drive')
CHECKPOINT_DIR = Path('/content/drive/MyDrive/ee641/checkpoints')
CHECKPOINT_DIR.mkdir(parents=True, exist_ok=True)Stop the Meter
Compute units drain while a GPU runtime is connected, working or not. When done: Runtime → Disconnect and delete runtime. Check your balance under the runtime resources panel.