{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "trentorch-trentorch--build-ml-framework-from-numpy",
 "repo": "TrenTorch/TrenTorch",
 "repo_url": "https://github.com/TrenTorch/TrenTorch",
 "repo_slug": "trentorch-trentorch",
 "title": "Build a working ML framework from raw NumPy modules",
 "page_title": "How to build a working ML framework from raw NumPy modules with Tren Torch",
 "summary": "Work through 20 CLI modules that rebuild tensors, autograd, optimizers, and transformers.",
 "who": "A developer who uses PyTorch daily but wants to understand what loss.backward() actually does, and has a terminal and an afternoon.",
 "problem": "Importing a framework hides how tensors, gradients, and training loops work, so debugging and design choices stay opaque.",
 "outcome": "The learner has a local trentorch package built from their own solutions and has run the first module.",
 "steps": [
  "`git clone https://github.com/TrenTorch/TrenTorch.git`",
  "`cd TrenTorch/TrenTorch_CLI`",
  "`python3 -m venv .venv && source .venv/bin/activate`",
  "`pip install -r requirements.txt && pip install -e .`",
  "`tren setup`",
  "`tren module start 01`"
 ],
 "agent_prompt": "Clone https://github.com/TrenTorch/TrenTorch, enter TrenTorch_CLI, create a virtualenv, install requirements and the package, run `tren setup`, then `tren module start 01`. Report the path of the generated notebook under data/modules and confirm that the stub file exists without any import of torch.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "45 minutes",
 "basis": "readme",
 "caveat": "The README says the CLI track uses Python and a local virtualenv, and that the notebooks contain stubs you must solve yourself, so the first module will not run until you write your own code.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "Python 3 with venv for the CLI track",
  "Node.js and npm for the web app track",
  "A GitHub, Google, or email account for the browser version (optional)"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/trentorch-trentorch/build-ml-framework-from-numpy/",
 "md": "https://dedupedai.com/use-cases/trentorch-trentorch/build-ml-framework-from-numpy/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/trentorch-trentorch--build-ml-framework-from-numpy.json",
 "api_version": "v1"
}