You know that feeling when you take an old, battle-worn laptop and make it do something it was never meant to handle? That’s exactly what we did when we decided to install ALBERT (A Lite BERT) on an aging Acer laptop. If you’ve ever tried deep learning on old hardware, you’ll understand why this was part engineering challenge, part act of stubborn defiance.
The Challenge: ALBERT on a Senior Citizen of a Laptop
The laptop in question? A dusty old Acer machine (N3450 2.2 GHz, 4gb ram), still running strong (well, kind of) but never meant to handle modern AI workloads. The mission? Get PyTorch, Transformers, and ALBERT running on it—without CUDA, because, let’s be real, this laptop’s GPU is more suited for Minesweeper than machine learning.
Step 1: Clearing Space (Because 92% Disk Usage Ain’t It)
First order of business: making room. A quick df -h confirmed what we feared—only a few gigabytes of storage left. Old logs, forgotten downloads, and unnecessary packages were sent to digital oblivion. We even had to allocate extra space to /tmp just to prevent massive .whl files from failing mid-download.
Step 2: Installing PyTorch and Transformers (Not Without a Fight)
Installing PyTorch should have been easy, but nope. The first attempt ended with a familiar [Errno 28] No space left on device error. After a bit of cursing and some clever pip --no-cache-dir installs, we finally got PyTorch 2.6.0+cu124 up and running—minus CUDA, of course.
Next up: Transformers. This should have been smooth sailing, but Python had other plans. Running our import transformers test script threw a ModuleNotFoundError. Turns out, sentencepiece (a required dependency) didn’t install correctly. The culprit? Failed to build installable wheels for some pyproject.toml based projects (sentencepiece).
We switched gears, manually installed sentencepiece, and—drumroll—it finally worked! At this point, the laptop had already earned a medal for resilience.
Step 3: Running ALBERT on CPU (The Moment of Truth)
With everything installed, it was time for the grand test:
from transformers import AlbertTokenizer, AlbertModel
import torch
tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
model = AlbertModel.from_pretrained("albert-base-v2")
text = "This old Acer laptop is a legend."
inputs = tokenizer(text, return_tensors="pt")
output = model(**inputs)
print(output.last_hidden_state)
Watching the model download and process our test sentence felt like a scene from an underdog sports movie. Would it crash? Would it catch fire? Would it just refuse to work? None of the above! ALBERT, against all odds, successfully generated embeddings for our text.
Final Thoughts: A Victory for Old Hardware
The takeaway? You don’t need cutting-edge hardware to experiment with AI. Sure, this setup won’t be training billion-parameter models anytime soon, but for learning, testing, and small-scale experimentation, it’s proof that old machines still have some life left in them.
So, if you have an aging laptop lying around, give it a second chance. It might just surprise you. And if it doesn’t, well… at least you tried. 😉