sehoffmann/dmlcloud
Painless distributed training with torch
This library helps machine learning engineers and researchers scale up their deep learning model training on high-performance computing (HPC) clusters. It takes standard PyTorch training scripts and makes it easy to distribute the workload across multiple GPUs and nodes. The output is a faster training process for complex models, enabling quicker experimentation and deployment.
Available on PyPI.
Use this if you are a machine learning engineer or researcher using PyTorch and need to train large models efficiently across multiple GPUs or machines in an HPC environment like a Slurm cluster.
Not ideal if you are looking for a high-level deep learning framework that abstracts away most of the PyTorch code, or if you only train models on a single GPU.
Stars
12
Forks
1
Language
Python
License
BSD-3-Clause
Category
Last pushed
Mar 19, 2026
Commits (30d)
0
Dependencies
7
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