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Beyond seeds: which determinism flags actually mattered for reproducible fine-tunes on a T4?
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Beyond seeds: which determinism flags actually mattered for reproducible fine-tunes on a T4?

DEV Community: pytorch·futurexindiafx-gif·about 4 hours ago
#gwlPKlxk
#devtopytorch#devto#dev#torch#fine

I set torch, random, and numpy seeds, and my small fine-tune still moves run to run on a T4. Same...

15s
The last mile of a long road: faster NumPy in the browser
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The last mile of a long road: faster NumPy in the browser

Hacker News·about 1 month ago
#JKKurMMf
#notebook#numpy#webassembly#openblas#fortran

NumPy on Emscripten-forge now links OpenBLAS in WebAssembly, with large speedups for matmul and linear algebra in the browser.

15s
From NumPy to JAX: My First "Aha!" Moments with Accelerated AI
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From NumPy to JAX: My First "Aha!" Moments with Accelerated AI

DEV Community: tutorial·Devansh Bajaj·5 months ago
#unzUISlS
#dev#python#array#numpy#standard

Building open-source solutions for my 100 Days of AI Agents challenge meant I needed to start looking...

15s
4 NumPy Labs: Master Array Creation, Math Games, and KNN Regression
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4 NumPy Labs: Master Array Creation, Math Games, and KNN Regression

DEV Community·Labby·6 months ago
#32fFadKV
#numpy#python#datascience#tutorial#arrays

Level up your data skills with 4 hands-on NumPy labs. Learn to shape arrays, perform complex math, and build a K-Nearest Neighbors regression algorithm from scratch.

15s
Master NumPy: 4 Essential Labs for Statistics, Sorting, and Linear Algebra
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Master NumPy: 4 Essential Labs for Statistics, Sorting, and Linear Algebra

DEV Community·Labby·6 months ago
#6f7B7lAv
#numpy#python#datascience#tutorial#challenge

Boost your data science skills with 4 hands-on NumPy labs. Master list statistics, sorting, searching, linear algebra, and dot product calculations today.

15s
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Tracing a 13x PyTorch Slowdown to a Hidden NumPy Synchronization

DEV Community: pytorch·Ingero Team·6 months ago
#z0ZFA4vY
#dev#class#code#numpy#ingero

TL;DR: A .cpu().numpy() call buried inside a forward pass was forcing a full CPU-GPU synchronization on every batch, every loop iteration. The GPU would finish its work in milliseconds, then sit idle for ~2 seconds waiting for Python and NumPy to catch…

15s