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Santosh-Gupta
SpeedTorch
Python2019–2020
Santosh-Gupta / SpeedTorch

SpeedTorch

Library for faster pinned CPU <-> GPU transfer in Pytorch

2Distinct people
2Mentions
0Explicit recommenders
681GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Library for faster pinned CPU <-> GPU transfer in Pytorch

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month2 dated mentions

Observed mentions: 2019-09-18 02:09:18 UTC to 2020-04-27 18:13:01 UTC.

012019-09-01 00:00:00 UTC to 2019-10-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsSep 20192019-10-01 00:00:00 UTC to 2019-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-11-01 00:00:00 UTC to 2019-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-12-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsDec 20192020-01-01 00:00:00 UTC to 2020-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-01 00:00:00 UTC to 2020-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-03-01 00:00:00 UTC to 2020-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-04-01 00:00:00 UTC to 2020-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsApr 2020

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Sep 20192019-09-01 00:00:00 UTC2019-10-01 00:00:00 UTC110
Oct 20192019-10-01 00:00:00 UTC2019-11-01 00:00:00 UTC000
Nov 20192019-11-01 00:00:00 UTC2019-12-01 00:00:00 UTC000
Dec 20192019-12-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
Jan 20202020-01-01 00:00:00 UTC2020-02-01 00:00:00 UTC000
Feb 20202020-02-01 00:00:00 UTC2020-03-01 00:00:00 UTC000
Mar 20202020-03-01 00:00:00 UTC2020-04-01 00:00:00 UTC000
Apr 20202020-04-01 00:00:00 UTC2020-05-01 00:00:00 UTC101

What Hacker News says

2 sources
Very useful! Thanks! > Although PyTorch has offered a series of tutorials on distributed training, I found it insufficient or overwhelming to help the beginners to do state-of-the-art PyTorch distributed training Can confirm. Am relatively junior, spent way too long trying to understand the documentation. Tangentially related that bothers me: distributed training is is poorly documented, distributed memory management is barely documented and not fully supported. If your models are too large to fit in the memory of a single GPU (like really big word embeddings, which are not exotic by any stretch) things get wacky. Distributing model parts across different GPUs is supported but poorly documented and a bit hacky. If your models are too large for all of your GPUs you're stuck with an outside package like SpeedTorch (https://github.com/Santos
Read the rest
h-Gupta/SpeedTorch) to pin everything in regular memory.
In PyTorch Distributed Training

MAKE YOURSELF AT HOME

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