ignitevision
Archived This repository is archived: read-only and no longer maintained.
Overview
In this example, we show how to use Ignite to train a neural network:
Alternatively, install the all requirements using pip install -r requirements.txt.
Run the example on a single GPU:
For details on accepted arguments:
If user would like to provide already downloaded dataset, the path can be setup in parameters as
Let's start training on a single node with 2 gpus:
Please, make sure to have Horovod installed before running.
Let's start training on a single node with 2 gpus:
Let's start training on two nodes with 2 gpus each. We assuming that master node can be connected as master, e.g. ping master.
Initial training with a stop on 1000 iteration (~11 epochs)
Resume from the latest checkpoint
Initial training on a single node with 2 gpus with a stop on 1000 iteration (~11 epochs):
Resume from the latest checkpoint
Similar commands can be adapted for other cases.
From the project’s README on GitHub.
At a glance
| Repository | Kentucky-Open-Science/ignitevision |
|---|---|
| Research area | Computer vision |
| Primary language | Python |
| Languages | Python 99.2%, Shell 0.8% |
| License | None specified |
| Stars / forks | 0 / 0 |
| Open issues and pull requests | 0 |
| Created | 2021-09-17 |
| Last push | 2025-10-06 |
| Default branch | master |
| Topics | computer-vision |
What the README covers
- Requirements:
- Usage:
- Distributed training
- Check resume training
- Distributed training
- ClearML fileserver
Top contributors
- @codybum (24 commits)
- @armstrongsam25 (1 commit)
- @snyk-bot (1 commit)
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Get the code
git clone https://github.com/Kentucky-Open-Science/ignitevision.git