TinyTorch: Building PyTorch from Scratch for Deeper Learning
An open-source curriculum teaches ML framework internals through hands-on implementation.
1Teaching version necessity
Just as MINIX simplified Unix for teaching purposes, TinyTorch provides a minimal implementation of PyTorch's core concepts. The project follows historical precedents in systems education where complex production tools (like LLVM/GCC) spawned simplified teaching versions (like Tiger compiler). This approach lets learners understand framework internals without GPU requirements or cloud dependencies, running on laptops with just 4GB RAM.
2Pedagogical structure
The curriculum comprises 20 modules across four tiers, delivered through Jupyter notebooks with fill-in-the-blank sections. It progresses from foundational tensor operations through autograd to transformers, with each tier building on the previous one. Historical ML milestones serve as validation checkpoints - students must recreate working implementations of Perceptron (1958), backpropagation (1986), CNNs (1998), and transformers (2017) using their own code.
3API mirroring strategy
TinyTorch deliberately replicates PyTorch's API surface exactly, creating direct knowledge transfer between learning and production environments. This means implementations of core operations like loss.backward() or optimizer steps maintain identical signatures to their PyTorch counterparts. The resemblance stops at implementation details - no CUDA, JIT, or distributed components are included to maintain the 4GB RAM requirement.
4Memory awareness
From Module 01, students implement memory_footprint() before matrix multiplication, calculating costs like 19MB per ImageNet batch. Later modules reveal Adam optimizer's 3× memory overhead compared to SGD through direct buffer allocation and measurement. This hands-on approach to memory management creates durable understanding that transfers to debugging real PyTorch applications.
5Autograd implementation
Module 06 introduces gradient computation through runtime monkey-patching of the Tensor class, mirroring PyTorch 0.4's Variable-Tensor merge. Students implement requires_grad, .grad, and .backward() on their existing Tensor implementation, maintaining working code while adding functionality. This progressive disclosure approach helps learners internalize the computational graph concept before encountering PyTorch's production autograd.
6Performance tradeoffs
The pure Python implementation runs 100-10,000× slower than PyTorch, which became an unexpected teaching advantage. When a student's Conv2d takes 97 seconds versus PyTorch's 10ms, the value of vectorization becomes experientially obvious rather than theoretically abstract. This intentional slowness makes optimization motivations concrete.
7Community adoption
From 2,000 GitHub stars in August 2025 to 27,000+ by 2026, TinyTorch saw rapid organic growth after social media exposure. The project now serves 682 members across 92 institutions, with strongest adoption in resource-constrained environments. The community maintains additional resources like Marimo labs for trade-off exploration and MLSys·im for performance modeling.
8Industry applications
Beyond academia, companies use TinyTorch for onboarding - as 2-3 week intensives, quarterly training programs, or targeted debugging workshops. Teams focus on specific subsystems (like autograd or attention) that cause production issues. This industry adoption validates the curriculum's practical value for professional engineers, not just students.
9Instructor infrastructure
The project includes NBGrader autograding, validation scripts, and detailed instructor documentation covering setup, rubrics, and common errors. Three integration models support different academic contexts: 20-module full courses, 8-module half-semester units, or standalone optimization seminars. This institutional support structure was crucial for widespread adoption.
10Sustainability planning
With maintenance commitment through 2027, the project addresses academic open-source's common failure mode - PI distraction. A two-week PR review target and planned governance transition will establish an educator advisory board. This explicit succession planning aims to prevent knowledge siloing and ensure long-term viability beyond the original creators.
11Current limitations
TinyTorch intentionally omits GPU, distributed training, and gradient synchronization to maintain accessibility. The CPU-only implementation excels at teaching memory and compute concepts but leaves parallel data loading and GPU memory management as competency gaps. These omissions reflect deliberate tradeoffs to preserve the 4GB RAM floor rather than implementation oversights.
12Future directions
The project seeks PyTorch contributors to verify module semantics against production internals, ensuring taught mental models are accurate. Other opportunities include controlled learning outcome studies and potential expansion into omitted competency areas if hardware constraints can be maintained. The Fall 2026 classroom readiness target focuses on refining existing content rather than adding scope.
Original article: PyTorch Blog — TinyTorch: Building PyTorch from Scratch for Deeper Learning