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torchrun posts/2026/02/28: ## ezpz posts/2025/10/06: 🎨 Mixing Between Distributions While Training posts/2026/01/07: 🎉 Happy New Year! posts/2026/05/01: Running 50k Python Processes on Aurora with ezpz yeet 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/ai-for-physics/l2hmc-qcd/2du1: 🎢 l2hmc-qcd Example: 2D U(1) posts/2026/01/10: 🍋 ezpz: distributed PyTorch across any hardware posts/jupyter/l2hmc/4dsu3: 🔳 l2hmc-qcd Example: 4D SU(3) talks/2025/10/08: AERIS: Argonne's Earth Systems Model talks/2025/09/24: Training Foundation Models on Supercomputers talks/2025/12/16: AuroraGPT: Training Foundation Models on Supercomputers talks/2025/10/15: Training Foundation Models on Supercomputers talks/2026/06/03: Production Pre-Training at Scale: The Good, the Bad, and the Restarts talks/2025/10/24: Training Foundation Models on Supercomputers posts/ai-for-physics/l2hmc-qcd/4dsu3nb/index-broken: 🕸️ l2hmc-qcd Example: 4D SU(3) posts/2025/09/12: 🍹 BlendCorpus + TorchTitan @ ALCF posts/drafts/2025/09/22: 📝 2025 Annual Report
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v5/21 Tighter Bio Text

 Sam Foreman

Computational Scientist · Argonne National Laboratory

I'm a Computational Scientist in the AI / ML Group at the Argonne Leadership Computing Facility . My work focuses on large-scale distributed training of foundation models for scientific applications. I co-lead the Models & Pre-Training team for the AuroraGPT project.