github-starred/cs249r_book
2
0
Fork 0
mirror of https://github.com/harvard-edge/cs249r_book.git synced 2026-05-10 15:49:25 -05:00
Code Issues 81 Packages Projects Releases 24 Wiki Activity

24 Releases 31 Tags

RSS Feed
  • mlsysim-v0.1.1 2732a26740
    Compare

    Stable

    GiteaMirror released this 2026-04-24 13:59:31 -05:00 | 1545 commits to dev since this release

    📅 Originally published on GitHub: Fri, 24 Apr 2026 19:02:57 GMT
    🏷️ Git tag created: Fri, 24 Apr 2026 18:59:31 GMT

    MLSys·im 0.1.1 — Paper Title Correction

    First-principles infrastructure modeling for the Machine Learning Systems textbook.

    Metadata-only patch release. No code or API changes; safe drop-in
    replacement for 0.1.0. Corrects the paper title cited in three places
    to match the actual title of the companion paper.

    📚 Documentation

    • Paper title corrected across CITATION.cff, the BibTeX snippet in
      README.md, and the reference docstring in mlsysim/core/walls.py.
      Was: "A Composable Analytical Framework for Machine Learning Systems."
      Now: "MLSys·im: First-Principles Infrastructure Modeling for Machine
      Learning Systems."

    🏗️ Packaging & Dependencies

    • Version bumped to 0.1.1 across pyproject.toml, mlsysim/__init__.py,
      and CITATION.cff; date-released updated to 2026-04-24.

    Contributors

    • @profvjreddi

    Install

    pip install --upgrade mlsysim==0.1.1

    Links

    About MLSys·im

    MLSys·im is the first-principles infrastructure modeling engine that
    produces every quantitative result in the Machine Learning Systems
    textbook — memory bandwidth calculations, roofline analyses, TCO projections,
    sustainability estimates, and every other number a reader encounters. Each
    figure and equation in the textbook is computed, not hand-typed; mlsysim
    is the computation.

    The framework codifies 22 systems walls — the physical and logical
    constraints that bound ML system performance — into composable solvers,
    with SI units enforced at runtime. Designed for three audiences: students
    building quantitative intuition, instructors running live classroom
    demonstrations, and researchers doing rapid what-if analysis.

    Downloads
  • mlsysim-v0.1.0 caa57a46d7
    Compare

    Stable

    GiteaMirror released this 2026-04-24 12:56:51 -05:00 | 1600 commits to dev since this release

    📅 Originally published on GitHub: Fri, 24 Apr 2026 17:57:22 GMT
    🏷️ Git tag created: Fri, 24 Apr 2026 17:56:51 GMT

    MLSys·im 0.1.0 — Initial Release

    Release date: 2026-04-01

    MLSys·im is a first-principles analytical engine for predicting performance, cost, and carbon footprint of ML systems. It is the computational companion to the Machine Learning Systems textbook.

    This is the first public release. The API surface, the 22-wall taxonomy, and the solver portfolio are all considered stable for the 0.1.x line.

    Install

    pip install mlsysim

    Verify the install:

    python -c "import mlsysim; print(mlsysim.__version__)"
mlsysim eval Llama3_8B H100 --batch-size 32

    Requires Python 3.10+. No GPU required — the engine computes from closed-form equations.

    Five-line quickstart

    import mlsysim
from mlsysim import Engine

profile = Engine.solve(
    model      = mlsysim.Models.ResNet50,
    hardware   = mlsysim.Hardware.Cloud.A100,
    batch_size = 1,
    precision  = "fp16",
)

print(f"Bottleneck: {profile.bottleneck}")              # → Memory
print(f"Latency:    {profile.latency.to('ms'):~.2f}")   # → 0.54 ms
print(f"Throughput: {profile.throughput:.0f}")          # → 1843 / second

    Highlights

    Core framework

    • 22-wall taxonomy organizing every constraint that bounds ML system performance, across six domains (Node, Data, Algorithm, Fleet, Ops, Analysis).
    • 26 analytical solvers (Model, Solver, Optimizer classes) covering all 22 walls.
    • Pint unit system with dimensional analysis enforced at runtime.
    • TraceableConstant pattern — every default carries a citation.
    • Pipeline composer for chaining solvers with explain() and run().
    • 3-tier evaluation scorecard: Feasibility → Performance → Macro/Economics.
    • Design Space Exploration (DSE) engine with declarative search and constraint evaluation.

    Hardware Registry (15+ accelerators)

    V100, A100, H100, H200, B200, GB200 NVL72, MI300X, TPUv5p, T4, Cerebras CS-3, Jetson Orin NX, ESP32-S3, nRF52840, Himax WE-I Plus, DGX Spark, MacBook M3 Max, iPhone 15 Pro, Pixel 8.

    Full precision support: FP32, TF32, BF16, FP16, FP8, INT8, INT4. Multi-level memory hierarchy with HBM, SRAM, and Flash (TinyML). All specifications verified against manufacturer datasheets.

    Model Registry

    GPT-2/3/4, Llama-2/3 (7B/8B/70B), BERT Base/Large, ResNet-50, MobileNetV2, AlexNet, Mamba, Stable Diffusion v1.5, DS-CNN, WakeVision. HuggingFace importer included for arbitrary Transformer workloads.

    Analytical models

    SingleNodeModel, NetworkRooflineModel, EfficiencyModel, ForwardModel, ServingModel, ContinuousBatchingModel, WeightStreamingModel, TailLatencyModel, DataModel, TransformationModel, TopologyModel, ScalingModel, InferenceScalingModel, CompressionModel, DistributedModel, ReliabilityModel, OrchestrationModel, EconomicsModel, SustainabilityModel, CheckpointModel, ResponsibleEngineeringModel, SensitivitySolver, SynthesisSolver, ParallelismOptimizer, BatchingOptimizer, PlacementOptimizer.

    CLI

    mlsysim eval        Evaluate the analytical physics of an ML system (YAML or CLI flags)
mlsysim serve       Evaluate LLM serving (prefill + decode)
mlsysim optimize    Search the design space for optimal configurations
mlsysim zoo         Explore the built-in registries
mlsysim audit       Profile your local hardware against the Iron Law
mlsysim schema      Export the JSON Schema for the mlsys.yaml configuration file

    Testing

    367 tests, 100% pass rate. Coverage includes formula unit tests with known answers, full solver suite, physics-bound validation across all registry hardware, wall-taxonomy completeness, pipeline composition, and three optimization backends (exhaustive, OR-tools, scipy).

    Documentation

    Known limitations & gotchas

    • First-order analytical model. Predictions are typically within 15–30% of measured throughput on well-optimized workloads. Use MLSys·im to compare options and identify bottlenecks; validate with empirical benchmarks before committing to a production SLA. See accuracy.qmd for full validation against MLPerf v4.0.
    • Slide PDFs. Many tutorials cross-link to lecture slides at github.com/harvard-edge/cs249r_book/releases/download/slides-latest/*.pdf. The slides-latest release tag is not yet published; these links will resolve once the slides ship.
    • Hosted notebook launchers. Google Colab and Binder buttons are planned but not wired up for 0.1.0. Tutorials run locally on any Python 3.10+ environment.

    Project links

    Downloads
  • tinytorch-v0.1.10 d9afa1b970
    Compare

    Stable

    GiteaMirror released this 2026-04-23 20:13:04 -05:00 | 1574 commits to dev since this release

    📅 Originally published on GitHub: Fri, 24 Apr 2026 14:11:23 GMT
    🏷️ Git tag created: Fri, 24 Apr 2026 01:13:04 GMT

    TinyTorch v0.1.10

    Largest Lab Guide upgrade since the series began, plus substantive framework work: new Tensor API surface (view, masked_fill, ndim, numel, contiguous), no_grad() context manager, Python 3.10+ baseline, 28 security alerts resolved, seven batches of module-audit fixes, reproducibility via seeded default_rng(7), and 847/847 tests green.

    ✨ New Features

    Lab Guide PDF (the flagship of this release)

    • Glossary back matter: 90 alphabetical entries covering tensor/memory, autograd, training systems, architecture, optimization, and ML basics, with module cross-references (see glossary.qmd).
    • Module opener hooks: every module starts with a 2–3 sentence systems-first framing paragraph leading with memory, bandwidth, arithmetic intensity, HBM, or roofline implications before the ML story.
    • Code listing captions + List of Listings: ~60 substantive code blocks carry Listing N.M — Description captions; populates a new List of Listings in the front matter.
    • List of Figures + List of Tables in front matter (151 newly-captioned tables).
    • Running headers with chapter + section: verso Chapter N · Title, recto N.M · Section, wordmark centered. H&P / CLRS convention.
    • Personal instructor note signed callout at the end of the conclusion.
    • Single-source build: make install-deps && make pdf works identically locally and in CI; deps live in pdf/apt-requirements.txt and pdf/tex-requirements.txt.

    TinyTorch Framework

    • Tensor API expansion: added view(), masked_fill(), Tensor stacking, ndim, numel(), and contiguous() (closes #1298; PR #1392 by @Shashank-Tripathi-07). PyTorch-compat test coverage added for all new methods.
    • no_grad() context manager: autograd now supports with no_grad(): inference blocks plus graph cleanup between passes.
    • Tito CLI: module path --about renamed to module path --guide and repointed at the Quarto chapter (consistent with the Lab Guide becoming the reference).

    🐛 Framework Bug Fixes

    Autograd and training

    • Tanh wired into enable_autograd() — was silently producing zero gradients.
    • Trainer.evaluate accuracy for regression models corrected (was misreporting).
    • GELU gradient mismatch + float32 test precision fixed.
    • Trainer init: guard requires_grad loop against non-Tensor params; ensure model params have requires_grad=True (2 commits).
    • M06 _reduce_broadcast_grad aligned with module conventions.

    Module-level

    • Quantization (M15): constant tensor quantized to all-zeros, losing the original value (#1444).
    • MaxPool2d: API mismatch in milestone 04 CIFAR script fixed (#1278).
    • Export paths: corrected for modules 09 and 13.
    • Token constants: refactor cleanup from PR #1279 (#1256).
    • M19 benchmarking: MLPerf trademark attribution added, educational-purposes disclaimer, table alignment fix, addresses feedback from #1196.
    • M02 activations: improved activation graph visualization.

    Module audit fixes

    • Seven batches of audit fixes landed: batches 1–7 covering critical fixes, medium/low documentation and accuracy, and test-infrastructure cleanup. Final state: 847/847 tests passing.

    🔬 Tests

    • Finite-difference gradient correctness tests added for Module 06.
    • Module 08 training infrastructure coverage tests added.
    • Gradient correctness suite restored with per-op tolerances (#1342).
    • Module 10 tokenization tests now use real Tensor params instead of raw numpy arrays.
    • Module 08 scheduler lr assertion corrected (epoch 0, not 1).

    🔧 Engineering

    • Reproducibility: migrated from legacy np.random to default_rng(7) — seeded, per-call RNG across all modules.
    • Python baseline: minimum version bumped to 3.10. Milestone 05 docs updated.
    • src/*/ABOUT.md cleanup: 20 stale duplicates deleted (−20,876 lines); the single-source ABOUT.md now lives in the correct companion-doc location.
    • Security: all 28 GitHub code-scanning alerts resolved.
    • Tito: register --tinytorch pytest flag in conftest; fix UnicodeDecodeError on Windows in tito module complete (#1184); null-synced_modules guard in submission progress response.

    📖 Content Improvements

    • Systems-first narrative: every module hook leads with the systems angle (memory, bandwidth, compute, hardware utilization) before pivoting to ML theory.
    • Check Your Understanding callouts: converted from prose sections to callout-tip format with 3–5 technical-specific checkboxes per module.
    • Key Takeaways: 3–4 bullet recap plus next-module hook at the end of every module chapter.
    • Systems Implication callouts unified to callout-note across all 21 instances; answers converted to callout-tip collapse="true" across 101 Q&A pairs.
    • Cross-reference audit: 216 orphan table/figure/listing labels got natural prose references (87% coverage).
    • Further Reading hyperlinks: 20 external URLs verified and linked (Jay Alammar, arXiv papers, Karpathy's blog, Jurafsky & Martin SLP3).
    • Broadcasting pitfall now taught alongside the broadcasting feature (M01 tensor).
    • log_softmax implementation cleaned up with clearer variable names and reuse.
    • Type hints: added to M03 layers, M04 losses, M05 dataloader (#1167).
    • Big-picture diagram: redesigned as a 4-layer stack (Capstone → Optimization → Architecture → Foundation) in neutral palette with MIT-red Capstone accent.

    🎨 Design and Typography

    • Book-style typography: linestretch: 1.1, first-line indent (parindent: 1.2em), tight parskip. Stripe-Press / Swift-Book density.
    • Thin single orange header rule (previously double rule).
    • 23 module-diagram SVGs aligned to the book palette via a Gemini multimodal audit pass.
    • Arraystretch 1.2 + enumitem for table and list breathing room.
    • Text-only callout titles (no stripped emojis; class semantics drive visual distinction).

    🐛 Lab Guide Bug Fixes

    • Tokenization module: restored missing ```{python} fence that caused Pandoc to render Python variable-definition comments as chapter headings.
    • Single-PDF guarantee: Makefile self-heals when Quarto's post-render cleanup strands the artifact at pdf/ instead of pdf/_build/. Build-end banner prints the canonical path.
    • 3 broken URLs fixed: GPT-2 cloudfront → OpenAI CDN, PyTorch .md → .html, mlu-explain /relu/ → /neural-networks/.
    • Duplicate trailing ## Get Started removed from 4 modules (copy-paste artifact).
    • Orphan big-picture-module-flow.svg removed from images/diagrams/ (canonical lives at images/svg/).

    🔧 CI / Infrastructure

    • Single-source deps: make install-deps reads pdf/apt-requirements.txt and pdf/tex-requirements.txt — same command works locally and in CI.
    • tinytorch-build-pdfs.yml and tinytorch-update-pdfs.yml simplified to make install-deps && make pdf (no inline tlmgr package list).
    • make clean extended to remove stale *_files/ directories at the Quarto project root.

    📚 Documentation

    • Early Explorer callout removed from getting-started.qmd — no longer appropriate now that the Lab Guide is shipping.
    • Callout convention documented in the preamble: six semantic callout types keyed off Quarto's five shipped classes plus title conventions.
    • README tables converted from markdown to HTML format for consistent rendering across GitHub and the Lab Guide.

    👥 Contributors

    Thanks to everyone who contributed to this release:

    • @profvjreddi — editorial direction and polish across all 20 modules
    • @hzeljko — sustained code, diagram, and infrastructure contributions
    • @Shashank-Tripathi-07 — Tensor PyTorch-compat API (ndim, numel, view, contiguous, masked_fill; PR #1392; first-time contributor!)
    • @farhan523 — ongoing documentation and module improvements
    • @adityamulik — null synced_modules fix in tito submission progress
    • @harishb00 — type hints across M03/M04/M05

    🆕 New Contributors

    • @Shashank-Tripathi-07 made their first code contribution (PR #1392)

    Full Changelog: https://github.com/harvard-edge/cs249r_book/compare/tinytorch-v0.1.9...tinytorch-v0.1.10

    Website: https://mlsysbook.ai/tinytorch/

    PDF: https://mlsysbook.ai/tinytorch/assets/downloads/TinyTorch-Guide.pdf

    Glossary (new): https://mlsysbook.ai/tinytorch/glossary.html

    Downloads
  • slides-latest f9ad401c08
    Compare

    Stable

    GiteaMirror released this 2026-04-21 16:28:33 -05:00 | 1912 commits to dev since this release

    📅 Originally published on GitHub: Tue, 21 Apr 2026 21:37:27 GMT
    🏷️ Git tag created: Tue, 21 Apr 2026 21:28:33 GMT

    ML Systems Lecture Slides

    Complete slide decks for the ML Systems curriculum.

    Volume I (17 decks): Introduction through Conclusion — single-machine ML systems
    Volume II (18 decks): Introduction through Conclusion — distributed ML at scale
    TinyML (5 chapters): HarvardX Professional Certificate — edge & embedded ML

    Vol I/II decks include speaker notes, active learning exercises, and original SVG diagrams.

    Downloads — PDF

    • MLSysBook-Slides-All-PDF.zip — All 35 Vol I/II decks
    • MLSysBook-Slides-Vol1-PDF.zip — Volume I only
    • MLSysBook-Slides-Vol2-PDF.zip — Volume II only

    Downloads — PowerPoint

    Image-based PPTX (not editable text) — use for presenter mode and annotations.

    • MLSysBook-Slides-All-PPTX.zip — All 35 Vol I/II decks
    • MLSysBook-Slides-Vol1-PPTX.zip — Volume I only
    • MLSysBook-Slides-Vol2-PPTX.zip — Volume II only

    Downloads — TinyML (HarvardX edX)

    • MLSysBook-TinyML-All.zip — All slides, readings, and supplementary materials
    • MLSysBook-TinyML-Slides.zip — 178 slide decks only
    • MLSysBook-TinyML-Readings.zip — 127 readings only

    Individual Vol I/II chapter files (PDF + PPTX) are also attached below.

    Source

    Downloads
  • tinytorch-v0.1.9 6a28376b0a
    Compare

    Stable

    GiteaMirror released this 2026-02-17 18:11:17 -06:00 | 5073 commits to dev since this release

    📅 Originally published on GitHub: Wed, 18 Feb 2026 00:19:13 GMT
    🏷️ Git tag created: Wed, 18 Feb 2026 00:11:17 GMT

    TinyTorch v0.1.9

    Computed values across all ABOUT.md docs, VS Code extension thin client, progressive disclosure improvements, and community contributions.

    ✨ New Features

    • Computed Values in ABOUT.md: Converted all 20 module ABOUT.md files to MyST Markdown Notebooks with inline Python-computed values via {glue:text} — eliminates hardcoded arithmetic errors and ensures all numerical claims are always correct
    • VS Code Extension: New thin client architecture over Tito CLI with notebook editor support, build tree, and module explorer
    • Version Badge: Auto-updating version badge in site navbar, refreshed on every release via CI

    📖 Content Improvements

    • Progressive Disclosure: Enforced scaffolding across 9 modules — solution blocks decomposed for pedagogical consistency
    • Function Decomposition: Standardized naming conventions and formatting across all 20 modules
    • Module 15 (Quantization): Corrected INT8 zero-point values in quantization docs
    • Module 16 (Compression): Fixed sparsity percentage bugs
    • Module 19 (Benchmarking): Aligned MLPerf box-drawing characters and tree indentation
    • EmbeddingBackward: Moved from Module 06 to Module 11 where it belongs conceptually

    🐛 Bug Fixes

    • Windows Install: Fixed install issues on Windows/Git Bash by @adil-mubashir-ch in #1169
    • SocratiQ Typo: Fixed typo in SocratiQ introduction by @BunningsWarehouseOfficial in #1170
    • Google Auth iframe: Fixed Google auth and slow index.html loading by @kai4avaya in #1172
    • Notebook Filenames: Aligned notebook filenames with Tito convention across all docs (fixes #1176 — thanks @sotoblanco)
    • Missing Exports: Added missing #| export directives across 10 modules
    • PDF Build: Capped Mermaid figure sizes and fixed nested code fences for LaTeX output
    • VS Code Extension: Fixed notebooks opening in raw JSON instead of interactive editor

    📚 Documentation

    • Updated TITO reference docs to match actual CLI commands
    • Fixed broken paths in CONTRIBUTING.md and INSTRUCTOR.md
    • Added intra-module scaffolding subsection to progressive disclosure paper

    🔧 CI/Infrastructure

    • Slide decks download from release during deployment
    • VS Code extension artifacts properly gitignored

    👥 Contributors

    Thanks to all contributors who made this release possible:

    • @adil-mubashir-ch
    • @BunningsWarehouseOfficial
    • @kai4avaya
    • @sotoblanco
    • @harishb00a
    • @profvjreddi

    🆕 New Contributors

    • @adil-mubashir-ch made their first contribution in #1169
    • @sotoblanco reported #1176 (notebook filename mismatch)
    • @harishb00a contributed documentation improvements

    Full Changelog: https://github.com/harvard-edge/cs249r_book/compare/tinytorch-v0.1.8...tinytorch-v0.1.9

    Website: https://mlsysbook.ai/tinytorch/

    Downloads
  • tinytorch-v0.1.8 2d9a5b46e5
    Compare

    Stable

    GiteaMirror released this 2026-02-07 15:02:14 -06:00 | 5125 commits to dev since this release

    📅 Originally published on GitHub: Sun, 08 Feb 2026 04:06:00 GMT
    🏷️ Git tag created: Sat, 07 Feb 2026 21:02:14 GMT

    TinyTorch v0.1.8

    Content updates, website improvements, and community contributions.

    ✨ New Features

    • Team Page: Auto-generated team page from .all-contributorsrc with reorganized Community section
    • Slide Viewer: Embedded PDF slide viewer on all module pages for in-browser viewing
    • Milestone Visualization: Step-by-step visualization for milestones by @AndreaMattiaGaravagno in #1151
    • Site-Only Deploy: New workflow option to deploy website without version bump

    🐛 Bug Fixes

    • Attention Module: Corrected O(n²) complexity explanation and memory table bug — reported in #1150
    • Activations Module: Fixed misleading GELU hint about 1.702 constant — reported in #1154
    • Activations Module: Expanded GELU explanation with both approximation forms
    • Layers Module: Corrected Xavier/Glorot initialization terminology
    • Tito CLI: Resolved Jupyter kernel mismatch causing ModuleNotFoundError (#1147)
    • Paper Build: Escaped special LaTeX characters breaking PDF build
    • Milestones: Fixed bold cyan frame alignment by @AndreaMattiaGaravagno in #1152
    • Content: Fixed small typo by @minhdang26403 in #1163

    📚 Documentation

    • Specify GenAI usage in slides by @AndreaMattiaGaravagno in #1149
    • Added @oscarf189 and @Takosaga as contributors

    🔧 CI/Infrastructure

    • Download slide decks from release during deployment
    • Fixed auto-label permissions for fork PRs (#1153)
    • Handle branch names with slashes in fresh install test (#1158)

    👥 Contributors

    Thanks to all contributors who made this release possible:

    • @AndreaMattiaGaravagno
    • @minhdang26403
    • @oscarf189
    • @Takosaga
    • @profvjreddi

    🆕 New Contributors

    • @AndreaMattiaGaravagno made their first contribution in #1149
    • @minhdang26403 made their first contribution in #1163
    • @oscarf189
    • @Takosaga

    Full Changelog: https://github.com/harvard-edge/cs249r_book/compare/tinytorch-v0.1.7...tinytorch-v0.1.8

    Website: https://mlsysbook.ai/tinytorch/

    Downloads
  • tinytorch-v0.1.7 01fc7df6a0
    Compare

    Stable

    GiteaMirror released this 2026-01-29 15:57:48 -06:00 | 5180 commits to dev since this release

    📅 Originally published on GitHub: Thu, 29 Jan 2026 22:48:20 GMT
    🏷️ Git tag created: Thu, 29 Jan 2026 21:57:48 GMT

    TinyTorch v0.1.7

    Critical fix for module exports that were silently failing in CI and some user environments.

    🐛 Bug Fixes

    • Export System: Uses nbdev Python API instead of CLI for reliable cross-platform exports
    • Export System: Fixed directory detection when running from tinytorch/ directory
    • Export System: Failures now show full error details for debugging - reported by @lalalostcode in #1146
    • Milestones: Fixed Tensor class passing in MLPerf step functions

    ✨ Improvements

    • Paper Link: Now links to arXiv with external link icon (↗) instead of download
    • CLI: Invalid commands show helpful error messages

    🔧 CI/Infrastructure

    • Renamed Publish (Dev) → Preview (Dev) for clearer workflow naming
    • All tests run on all platforms by default
    • Test types aligned with CLI naming (--user-journey)

    👥 Contributors

    Thanks to all contributors who made this release possible:

    • @lalalostcode
    • @profvjreddi

    Full Changelog: https://github.com/harvard-edge/cs249r_book/compare/tinytorch-v0.1.6...tinytorch-v0.1.7

    Website: https://mlsysbook.ai/tinytorch/

    Downloads
  • tinytorch-v0.1.6 cb1062355e
    Compare

    Stable

    GiteaMirror released this 2026-01-27 12:00:22 -06:00 | 5228 commits to dev since this release

    📅 Originally published on GitHub: Tue, 27 Jan 2026 18:00:32 GMT
    🏷️ Git tag created: Tue, 27 Jan 2026 18:00:22 GMT

    Windows/Git Bash Support 🪟

    The installer script now works on Windows via Git Bash!

    Changes

    • Platform detection for OS-specific guidance during installation
    • More reliable pip invocation using $PYTHON_CMD -m pip
    • Cross-platform line endings via .gitattributes
    • Virtual environment activation works correctly on Windows

    Contributors

    Thanks to the community for Windows support:

    • @Kobra299 - reported the Windows issue (#1078)
    • @rnjema - developed Windows installation improvements (PR #1105)
    • @joeswagson - developed PowerShell installer concept (PR #1083)

    Installation

    Windows (Git Bash):

    curl -sSL mlsysbook.ai/tinytorch/install.sh | bash
cd tinytorch
source .venv/Scripts/activate
tito setup

    macOS/Linux:

    curl -sSL mlsysbook.ai/tinytorch/install.sh | bash
cd tinytorch
source .venv/bin/activate
tito setup

    Full Changelog: https://github.com/harvard-edge/cs249r_book/blob/main/tinytorch/CHANGELOG.md

    Downloads
  • tinytorch-v0.1.5 da97497546
    Compare

    Stable

    GiteaMirror released this 2026-01-27 07:55:07 -06:00 | 5251 commits to dev since this release

    📅 Originally published on GitHub: Tue, 27 Jan 2026 14:00:21 GMT
    🏷️ Git tag created: Tue, 27 Jan 2026 13:55:07 GMT

    TinyTorch v0.1.5

    This release includes Windows support, bug fixes, and documentation improvements.

    ✨ New Features

    • Windows Support: Full Windows compatibility with Git Bash
      • Added PYTHONUTF8 and PYTHONIOENCODING for proper Unicode/emoji handling
      • Windows OS matrix support in CI for progressive testing

    🐛 Bug Fixes

    • Activations Module: Fixed Softmax forward pass implementation by @minhdang26403 in #1141
    • Activations Module: Removed unnecessary Sigmoid clipping by @minhdang26403 in #1140
    • Activations Module: Fixed typo and answer render error by @minhdang26403 in #1139
    • Convolutions Module: Fixed computation example (Position 1,1: 8→7) - reported by @ngbolin in #1144
    • Convolutions Module: Fixed pooling example element lists and averages
    • Tensor Module: Fixed matrix multiplication docstring examples
    • Profiling Module: Fixed convolution FLOPs calculation
    • Optimizer: Fixed gradient bug and CI improvements by @profvjreddi in #1136

    📝 Documentation

    • Fixed broken chapter links in README by @BunningsWarehouseOfficial in #1132
    • Fixed typos by @didier-durand in #1133
    • Star button now links to GitHub stars explanation section

    🔧 CI/Infrastructure

    • Windows CI improvements (using windows-2022 for stability)
    • Validate workflow now only runs on dev push, not main
    • Updated workflow references to tinytorch-validate-dev

    👥 Contributors

    Thanks to all contributors who made this release possible:

    • @minhdang26403
    • @BunningsWarehouseOfficial
    • @didier-durand
    • @ngbolin
    • @profvjreddi

    🆕 New Contributors

    • @BunningsWarehouseOfficial made their first contribution in #1132

    Full Changelog: https://github.com/harvard-edge/cs249r_book/compare/tinytorch-v0.1.4...tinytorch-v0.1.5

    Website: https://mlsysbook.ai/tinytorch/

    Downloads
  • tinytorch-slides-v0.1.0 cdec74a422
    Compare

    Stable

    GiteaMirror released this 2026-01-25 11:10:01 -06:00 | 5362 commits to dev since this release

    📅 Originally published on GitHub: Sun, 25 Jan 2026 17:51:59 GMT
    🏷️ Git tag created: Sun, 25 Jan 2026 17:10:01 GMT

    Initial release of TinyTorch lecture slides (PDF format) for all 18 modules.

    Downloads