AutoResearch Tutorial: How to Run It Step by Step
This tutorial is designed for readers who want a practical path into AutoResearch without pretending the project is a one-click beginner app. The goal is to help you choose a realistic environment, start with manageable experiments, and avoid wasting time on the wrong setup.
Step 1: Read the Official Repository First
Start with the official
karpathy/autoresearch repository.
Before you run anything, you should understand the default assumptions, the overall experiment loop, and the
three most important files: prepare.py, train.py, and program.md.
If you want a quicker overview before diving into the source, the GitHub guide gives you the shortest reading path.
Step 2: Choose the Right Starting Environment
The original project assumes a one-GPU NVIDIA workflow. If you already have suitable hardware, that may be the simplest place to begin. If you do not, or if you want to validate the repo before investing in local hardware, compare a short cloud run against a smaller local adaptation.
Use the hardware guide if you are on a Mac or smaller GPU. Use the GPU cloud guide if your main question is whether it is worth renting compute for a first serious attempt.
Step 3: Start Smaller Than You Think
One of the easiest mistakes with AutoResearch is trying to reproduce the most ambitious version of the project before you have a stable environment. For a first run, reduce variables where possible. Smaller experiments are more useful than a broken full-scale setup because they let you confirm that the loop is functioning at all.
- keep the first session short
- avoid expensive long reservations until the environment works
- prefer a smaller or adapted path if your hardware is limited
- document the exact environment that succeeds
Step 4: Use Forks When the Default Path Does Not Fit
If you are outside the default NVIDIA setup, forks are not a sign that you are “doing it wrong.” They are often the most efficient way to learn from the project on your own hardware. The main repository and community links are usually the best place to discover those adaptations first.
This is especially important if your real goal is understanding the research loop rather than matching every default assumption in the main repo.
Step 5: Decide Whether to Keep Going Locally or Move to Cloud
Once you have proven that you understand the repo and can execute a small run, the next decision is strategic: continue locally, or move to cloud for more predictable access to stronger GPUs. This is where many users save time by being honest about their goal. If you are validating interest, cloud can be a cleaner next step. If you are building a repeatable personal workflow, local hardware may still make more sense.
Common Mistakes to Avoid
- treating the repo like a polished beginner app instead of a fast-moving experimental project
- spending heavily on compute before the environment is stable
- ignoring forks when your platform differs from the default assumptions
- changing too many variables at once and losing track of what actually worked
FAQ
Is this a copy-paste install tutorial?
Not exactly. AutoResearch is a fast-moving repository, so the most useful tutorial is one that helps you choose the right setup path and avoid common mistakes rather than pretending every machine can use the same commands.
Should I start locally or in GPU cloud?
If you already have suitable NVIDIA hardware, local can be fine. If you are testing the repo for the first time or need stronger GPUs, GPU cloud can be the easier way to validate the setup.
What is the safest way to begin?
Read the official repository first, begin with small experiments, use a fork if your hardware differs from the default path, and avoid committing to expensive long runs before your environment is stable.