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about: Sam Foreman about/more: 🪪 More ideas: 💡 Ideas more: ➕ More now: Now posts: 📬 Posts posts/2023/12/05: 🔳 l2hmc-qcd Example: 4D SU(3) posts/2025: 📆 2025 posts/2025/04/28: 🔥 Building PyTorch 2.6 from Source on Aurora posts/2025/05/03: 🚧 Frameworks Issue with numpy \› 2 posts/2025/06: 06 posts/2025/06/01: 📰 Nice Headings posts/2025/06/02: 🧜‍♀️ Mermaid posts/2025/06/14: 🏗️ Building PyTorch 2.8 from Source on Aurora posts/2025/09/12: 🍹 BlendCorpus + TorchTitan @ ALCF posts/2025/09/17: 📊 pbs-tui: TUI for PBS Job Scheduler Monitoring posts/2025/10/06: 🎨 Mixing Between Distributions While Training posts/2025/11/12: 🧊 Cooling Down Checkpoints: Best Practices for Model Evaluation posts/2026/01/07: 🎉 Happy New Year! posts/2026/01/10: 🍋 ezpz: distributed PyTorch across any hardware posts/2026/02/28: ⏱️ Comparing Launchers on Aurora posts/2026/02/28: ## torchrun posts/2026/02/28: ## ezpz posts/2026/04/27: Pre-Training AuroraGPT with TorchTitan posts/2026/04/27: ## Two-Week Summary (Apr 12–27, 2026) posts/2026/04/27: ## Detailed Breakdown posts/2026/04/27: ### Week 1: Apr 12–18 — Benchmarking, LR Finder, XPU Fixes posts/2026/04/27: #### Benchmarking (Apr 12–15) posts/2026/04/27: #### LR Finder (Apr 12–14) posts/2026/04/27: #### Scaling Study (Apr 12) posts/2026/04/27: #### Upstream Syncs (Apr 12–18, syncs 6–14) posts/2026/04/27: #### XPU Bug Fixes (Apr 18) posts/2026/04/27: #### RL Experiment (Apr 18) posts/2026/04/27: ### Week 1.5: Apr 18–25 — Production Readiness posts/2026/04/27: #### Torch 2.12 Benchmarks (Apr 18) posts/2026/04/27: #### LR Finder Extensions (Apr 20–21) posts/2026/04/27: #### XPU Fixes (Apr 23) posts/2026/04/27: #### Torch 2.13 Environment (Apr 25) posts/2026/04/27: #### 2B Scaling Study on Torch 2.13 (Apr 25) posts/2026/04/27: #### Production Training (Apr 25) posts/2026/04/27: ### Week 2: Apr 26–27 — Optimizer Competition posts/2026/04/27: #### RL Multi-Task Refactor (Apr 26) posts/2026/04/27: #### Docs Reorganization (Apr 26) posts/2026/04/27: #### Generic HF Dataset Streaming (Apr 26) posts/2026/04/27: #### New Optimizers (Apr 26) posts/2026/04/27: #### Architecture Tweaks (Apr 26–27) posts/2026/04/27: ## Competition Results posts/2026/04/27: ### Round 1–3: Speedrun — 2N, GBS=48, 1000 steps posts/2026/04/27: ### 10B Full Training — 8N, GBS=384, ~3,178 steps posts/2026/04/27: ### Round 4: Reproducible Speedrun — 2N, GAS=8, GBS=384, 1000 steps posts/2026/04/27: ## Key Discoveries posts/2026/04/27: ## Infrastructure Built posts/2026/04/27: ## High-Level posts/2026/04/27: ## Detailed Breakdown posts/2026/04/27: ### Week 1: Apr 12–18 — Benchmarking, LR Finder, XPU Fixes posts/2026/04/27: #### Benchmarking (Apr 12–15) posts/2026/04/27: #### LR Finder (Apr 12–14) posts/2026/04/27: #### Scaling Study (Apr 12) posts/2026/04/27: #### Upstream Syncs (Apr 12–18, syncs 6–14) posts/2026/04/27: #### XPU Bug Fixes (Apr 18) posts/2026/04/27: #### RL Experiment (Apr 18) posts/2026/04/27: ### Week 1.5: Apr 18–25 — Production Readiness posts/2026/04/27: #### Torch 2.12 Benchmarks (Apr 18) posts/2026/04/27: #### LR Finder Extensions (Apr 20–21) posts/2026/04/27: #### XPU Fixes (Apr 23) posts/2026/04/27: #### Torch 2.13 Environment (Apr 25) posts/2026/04/27: #### 2B Scaling Study on Torch 2.13 (Apr 25) posts/2026/04/27: #### Production Training (Apr 25) posts/2026/04/27: ### Week 2: Apr 26–27 — Optimizer Competition posts/2026/04/27: #### RL Multi-Task Refactor (Apr 26) posts/2026/04/27: #### Docs Reorganization (Apr 26) posts/2026/04/27: #### Generic HF Dataset Streaming (Apr 26) posts/2026/04/27: #### New Optimizers (Apr 26) posts/2026/04/27: #### Architecture Tweaks (Apr 26–27) posts/2026/04/27: ## Competition Results posts/2026/04/27: ### Round 1–3: 1000-step speedruns, 2 nodes, GBS=48 (17 configs) posts/2026/04/27: ### Round 4 (10B full training, 8 nodes, GBS=384, 5 configs) posts/2026/04/27: ### Round 5 (2 nodes, GAS=8, GBS=384, local dataset, 8 configs — in progress) posts/2026/04/27: ## Key Discoveries posts/2026/04/27: ## Infrastructure Built posts/2026/05/01: Running 50k Python Processes on Aurora with ezpz yeet posts/2026/06/27: Local AI Apps on ALCF: Argo, Inference Endpoints, and One Gateway posts/2026/06/28: Migrating from Quarto to Astro: samforeman.me → samf.sh posts/2026/08/08: Pre-Training LLMs on a Supercomputer posts/2026/09/22: Working From Anywhere: Persistent Access to Compute and Context posts/2026/09/25: A Small Service Mesh for My Macs and Supercomputers posts/ai-for-physics: ⚛️ AI for Physics posts/ai-for-physics/diffusion: 🎲 MCMC + Diffusion Sampling posts/ai-for-physics/l2hmc-qcd: 🎢 L2HMC for LQCD posts/ai-for-physics/l2hmc-qcd/2du1: 🎢 l2hmc-qcd Example: 2D U(1) posts/auroragpt: 🤖 AuroraGPT posts/auroragpt/aurora-gpt: 🏎️ Megatron-DeepSpeed on Intel XPU posts/auroragpt/checkpoints: 💾 Converting Checkpoints posts/auroragpt/determinstic-flash-attn/deterministic-flash-attn: 🎰 Deterministic flash-attn posts/auroragpt/flash-attn-sunspot: 📸 flash-attn on Sunspot posts/auroragpt/long-sequences: 🚂 Loooooooong Sequence Lengths posts/auroragpt/mpi4py-reproducer: 🐛 mpi4py bug on Sunspot posts/auroragpt/spike-skipper: 🏔️ Spike Skipper posts/auroragpt/startup-times: 🐢 Starting Up Distributed Training on Aurora posts/auroragpt/startup-times: ## Response posts/auroragpt/startup-times: ### Measuring / Calculating Startup Time posts/auroragpt/startup-times: ## Minimal Working Example posts/croon: croon: Synced Lyrics in the Terminal, for Whatever Is Playing posts/dope-slides: 💅 How to Make Dope Slides posts/drafts/2025/09/22: 📝 2025 Annual Report posts/ezpz-at-alcf: 🍋 ezpz @ ALCF posts/ezpz-v1: 📝 ezpz-v1 posts/globusfs: globusfs: an fsspec Filesystem for Globus Collections posts/jupyter: 📗 Jupyter posts/jupyter/test: 🏁 l2hmc Example: 2D $U(1)$ posts/resume: 🧑🏻‍💻 Sam Foreman’s Résumé posts/svgbob: 🫥 svgbob posts/torchtune-aurora: 🪛 Torchtune on Aurora posts/torchtune-patch-aurora: 🚑 Torchtune Patch on Aurora posts/wandb-tui: wandb-tui: Comparing W&B Runs Without Leaving the Terminal projects: 📚 Projects talks: 🎙️ Talks talks/2025/09/24: Training Foundation Models on Supercomputers talks/2025/10/08: AERIS: Argonne's Earth Systems Model talks/2025/10/15: Training Foundation Models on Supercomputers talks/2025/10/24: Training Foundation Models on Supercomputers talks/2025/12/16: AuroraGPT: Training Foundation Models on Supercomputers talks/2026/06/03: Production Pre-Training at Scale: The Good, the Bad, and the Restarts talks/2026/07/14: Pre-Training AuroraGPT at Scale on Aurora talks/2026/08/03: Pre-Training LLMs on a Supercomputer talks/ai-for-science-2024: Parallel Training Methods talks/alcf-hpc-workshop-2024/alcf-hpc-workshop-2024: Deep Learning and Foundation Models at Scale talks/aurora-gpt-fm-for-electric-grid/auroragpt-fm-for-electric-grid: AuroraGPT: Foundation Models for Science talks/auroragpt-siam25: AuroraGPT talks/auroragpt/alcf-hpc-workshop-2024/auroragpt-alcf-hands-on-hpc-workshop-2024: AuroraGPT: ANL's General Purpose Scientific LLM talks/demo-slides: AuroraGPT: Training Foundation Models on Supercomputers talks/hpc-user-forum/auroragpt: AuroraGPT talks/incite-hackathon-2025: ALCF Incite Hackathon 2025 talks/incite-hackathon-2025/auroragpt: LLMs on Aurora: Overview talks/incite-hackathon-2025/ezpz: LLMs on Aurora: Hands-On talks/llms-at-scale: Training LLMs at Scale talks/llms-on-polaris: Training LLMs on Polaris talks/openskai25: Open SkAI2025 talks/openskai25/ai4science: Scientific AI at Scale: AuroraGPT talks/openskai25/training: Scientific AI at Scale: Distributed Training webtui: Style webtui/components/accordion: Accordion webtui/components/badge: Badge webtui/components/button: Button webtui/components/checkbox: Checkbox webtui/components/dialog: Dialog webtui/components/input: Input webtui/components/popover: Popover webtui/components/pre: Pre webtui/components/progress: Progress webtui/components/radio: Radio webtui/components/range: Range webtui/components/separator: Separator webtui/components/spinner: Spinner webtui/components/switch: Switch webtui/components/table: Table webtui/components/textarea: Textarea webtui/components/tooltip: Popover webtui/components/typography: Typography webtui/components/view: View webtui/contributing/contributing: Contributing webtui/contributing/contributing: ## 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Sam Foreman

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🔥 What I Work on

As a member of the AI / ML Group at ALCF, I work on:

📍 How I got here

My current research focuses on using deep generative modeling to help build better sampling algorithms in lattice gauge theory. In particular, I’m interested in building gauge equivariant neural network architectures and using inductive priors to incorporate physical symmetries into machine learning models.


I received my PhD in Physics from the University of Iowa in 2019 and my thesis was on Learning Better Physics: A Machine Learning Approach to Lattice Gauge Theory.


Prior to this, I completed two bachelors degrees (Engineering Physics and Applied Mathematics, 2015) at The University of Illinois at Urbana-Champaign. My undergraduate dissertation was titled Energy Storage in Quantum Resonators and was supervised by Professor Alfred Hübler within the Center for Complex Systems Research at UIUC.

This work ultimately resulted in a patent !!

Time Tracking

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