How to Practice ML With No GPU, No Dataset and No Team

Name: TensorTonic
Author: TensorTonic

Practice machine learning with no GPU, dataset or team: what runs on a CPU, free GPU options, built-in datasets, a worked example and how to get feedback.

By TensorTonic's founderLast updated November 3, 20267 min read

How can you practice machine learning without a GPU?

You can practice most of machine learning on an ordinary laptop. Classical ML and small neural networks train on a CPU, free cloud notebooks give you a GPU when you need one, built-in and public datasets replace collecting your own, and automated tests and communities stand in for a team. Only large models need serious hardware.

The problem library runs in the browser, so it needs no local setup, and the table below covers what to do about each missing piece.

What you can still do without a GPU, dataset or team
If you have no	You can still	Free options
GPU	Train classical ML and small networks on a CPU, and move big runs to a free notebook	scikit-learn and PyTorch on CPU, Google Colab, Kaggle Notebooks
NVIDIA GPU for CUDA	Run your kernels on remote GPUs	Browser platforms such as TensorTonic and LeetGPU, or hourly cloud rental
Dataset	Use built-in, synthetic or public data, or practise on problems that need no data	scikit-learn datasets, UCI ML Repository, Hugging Face Datasets, Kaggle Datasets
Team	Get feedback from tests, reference implementations, code review and communities	Automated tests, open-source pull requests, the GPU MODE Discord, Kaggle discussions

What can you train on a CPU?

A laptop CPU handles more than most people expect.

All of classical ML runs on a CPU, including linear and logistic regression, trees, random forests, gradient boosting, clustering and PCA on the dataset sizes used for learning.
Small neural networks such as MLPs and small CNNs on datasets the size of MNIST train more slowly than on a GPU, but they train.
Tiny language models train too, and Andrej Karpathy's nanoGPT includes settings for a small character-level model on a laptop CPU.
Anything you implement from scratch, such as optimizers, losses, attention and backpropagation, is written and tested on small arrays where speed does not matter.

Here is a complete experiment that needs no GPU and no downloaded data. scikit-learn generates a synthetic classification dataset, and a logistic regression model trains on it.

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

X, y = make_classification(n_samples=5000, n_features=20,
                           n_informative=5, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=0)

model = LogisticRegression(max_iter=1000).fit(X_train, y_train)
print(f"test accuracy: {model.score(X_test, y_test):.3f}")

It prints a test accuracy of 0.922 and took a fraction of a second on an ordinary CPU when we ran it. Synthetic data has a further advantage, because you control the answer. Change n_informative or add noise and watch how the model responds, which teaches more than another run on a famous dataset.

Where can you get a GPU for free?

Two free notebook services cover most learners' needs.

Google Colab is a hosted Jupyter notebook whose free tier offers limited, variable access to an NVIDIA GPU, which suits short experiments.
Kaggle Notebooks are hosted notebooks with a weekly GPU quota shown in your account and direct access to Kaggle's datasets, which suits longer runs.

Limits on both change over time, so check the current terms before planning around them. The habit that saves the most quota is to develop and debug on a CPU with a small slice of the data, then switch to a GPU only for the full training run.

Can you learn CUDA without an NVIDIA GPU?

Yes. CUDA code has to run on an NVIDIA GPU, but that GPU does not have to be yours. TensorTonic runs CUDA and Triton kernels in the browser on several NVIDIA GPUs, from a T4 to an H200, and compares your timings with a reference. LeetGPU is another browser option for GPU programming, and cloud providers rent GPUs by the hour. The CUDA Kernels study plan is a good place to start, and our guide on how to learn CUDA covers the learning path itself.

Where do you get data when you have no dataset?

You rarely need to collect your own data to learn.

scikit-learn ships small classic datasets and generators for synthetic data, and PyTorch's torchvision can download standard image datasets.
Public repositories such as the UCI Machine Learning Repository, Hugging Face Datasets and Kaggle Datasets host large collections of documented datasets.
Many of the most useful exercises need no dataset at all, only small arrays and tests. Implementing an optimizer, a loss function or an attention layer teaches the internals without any data pipeline.

The one thing public datasets cannot teach is messy real-world data collection and labelling. If you want that experience, build a small dataset yourself from something you care about, such as your own messages, photos or a public API.

How do you get feedback without a team?

On a team, other people catch your mistakes. Alone, you have to set that up yourself.

Problems with automated test suites tell you immediately whether your implementation is right, including edge cases you would not think to check.
Comparing your from-scratch model with the scikit-learn or PyTorch version on the same data catches bugs fast. If they disagree, one of them has a bug, and it is usually yours.
A pull request to an ML library gets reviewed by the people who maintain it.
Communities such as the GPU MODE Discord, Kaggle discussions and ML subreddits answer specific, well-asked questions.
Timed mock assessments give you an interview-style score without an interviewer.

When your training goes wrong with no one to ask, our guide to why training loss is not decreasing walks through the usual causes. The guided projects add structure when you want a bigger build to work towards.

What can you not practice without serious hardware?

Without your own powerful GPUs, you will not train large models from scratch, run distributed training across machines or fine-tune large language models beyond small sizes. These are mostly skills learned on the job or in research labs. Everything that comes before them, which covers much of what interviews test, you can practice on a laptop.

Practice this

K-Means Assignment Step assigns points to the nearest centroid with no dataset needed.
Stratified Train-Test Split splits data while keeping class proportions.
Logistic Regression Training Loop trains a classifier from scratch on a CPU.
Implement Leaky ReLU as a CUDA Kernel runs a CUDA kernel in the browser on a real GPU.

For more GPU work, see the CUDA practice problems guide.

Can you learn deep learning on a laptop?

Yes. You can learn backpropagation, CNNs, RNNs and transformers on a laptop by training small versions and implementing the components yourself. Use a free notebook GPU when a model is too slow on your CPU.

Is Google Colab enough to learn machine learning?

For learning, usually yes. Its free tier handles most course exercises and small projects. Its limits show up in long training runs, which is where Kaggle Notebooks or paid compute come in.

Can you use a Mac for machine learning?

Yes, for most learning. PyTorch can use Apple silicon GPUs through its MPS backend, which speeds up small models. CUDA does not run on Macs, so for CUDA you need a remote NVIDIA GPU.