Preparatory scripts for BIDS tabular phenotypic data in large neuroimaging datasets.
Custom AWS Transform agent that migrates PyTorch/Triton kernels to AWS Trainium NKI (@nki.jit) and compiles, numerically verifies, and profiles every candidate on a real Trainium device before opening a PR.
A tool for converting multi-sample from one format to another.
FFT and complex-valued tensor operations for AWS Trainium via NKI (cuFFT-equivalent) — Cooley-Tukey, Bluestein, STFT, ComplexTensor and complex NN layers.
Random number generation for AWS Trainium via NKI (cuRAND-equivalent) — Philox counter-based RNG, standard distributions, Sobol / Halton / Latin-hypercube quasi-random sequences for Monte Carlo and QMC.
Production LLM pipeline on AWS Trainium and Inferentia: LoRA fine-tune Llama 3.1 8B on a trn1.2xlarge, ship the adapter through S3, serve it with vLLM on an inf2.xlarge, and measure everything (TTFT/TPOT percentiles, tokens/s, MFU, goodput at SLO) with compile costs included and failures recorded as receipts.
Scientific computing library suite for AWS Trainium via NKI — the cuFFT/cuBLAS/cuRAND/cuSOLVER/cuSPARSE/cuTENSOR equivalents for Neuron. Python-first, PyTorch fallback everywhere, Apache-2.0.
Sparse matrix operations for AWS Trainium via NKI (cuSPARSE-equivalent) — CSR/COO formats, SpMV and SpMM via gather-matmul-scatter, Schwarz integral screening for quantum chemistry.
BLAS Levels 1–3 for AWS Trainium via NKI (cuBLAS-equivalent) — GEMM with stationary-tile reuse, batched GEMM, TRSM, validated DF-MP2 for quantum chemistry.
Linear solvers and eigendecomposition for AWS Trainium via NKI (cuSOLVER-equivalent) — Jacobi eigh, Cholesky/LU/QR factorizations, CG/GMRES iterative solvers, Newton-Schulz inverse square root.
Clean-room port of NVIDIA Aerial 5G L1 to AWS Trainium via NKI. Agentic, hardware-in-the-loop. Views are the author's own.
Tensor contractions for AWS Trainium via NKI (cuTENSOR-equivalent) — einsum with contraction planning, CP/PARAFAC and Tucker decompositions, density-fitted post-Hartree-Fock patterns.
ml researcher competition
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