Easy Parallel Library (EPL) is a general and efficient deep learning framework for distributed model training.
active 2023-08-31 → 2025-11-27 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 820 days from 2023-08-31 to 2025-11-27. Pushes: 0 total, peak 0 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 3 total, peak 1 in a day. Comments: 8 total, peak 2 in a day. Stars: 47 total, peak 2 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
Recent activity
Latest issues, pull requests and releases
- Issue comment#31a13427722024-09-18 08:56Has there been an improvement in single-GPU training speed?
- Issue#31a13427722024-09-18 08:47Has there been an improvement in single-GPU training speed?
- Issue comment#30adoda2024-04-24 07:56epl单机单卡和单机多卡训练step如何理解
- Issue comment#30adoda2024-04-24 07:56epl单机单卡和单机多卡训练step如何理解
- Issue comment#28gyr-kdgc2023-10-13 09:242台服务器分布式跑example中的resnet_split.py遇到无限等待的情况
- Issue comment#28gyr-kdgc2023-10-12 07:182台服务器分布式跑example中的resnet_split.py遇到无限等待的情况
- Issue comment#28gyr-kdgc2023-10-12 02:572台服务器分布式跑example中的resnet_split.py遇到无限等待的情况
- Issue#30SueeH2023-09-20 09:50epl单卡显存降低和
- Issue comment#28adoda2023-09-04 06:482台服务器分布式跑example中的resnet_split.py遇到无限等待的情况
- Issue comment#29adoda2023-09-04 06:442机2卡实验NCCL报错
- Issue#29wind8182023-08-31 07:152机2卡实验NCCL报错
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 47 stars here means stars gained during the window, not the repo's star count.