Run OneTrainer in a docker container locally or in the cloud.
This image is an extension of Ai-Dock/Linux-Desktop with OneTrainer preinstalled for user convenience.
These container images are tested extensively at Vast.ai & Runpod.io but compatibility with other GPU cloud services is expected.
Note
These images do not bundle models or third-party configurations. You should use a provisioning script to automatically configure your container. You can find examples in config/provisioning.
All AI-Dock containers share a common base which is designed to make running on cloud services such as vast.ai and runpod.io as straightforward and user friendly as possible.
Common features and options are documented in the base wiki but any additional features unique to this image will be detailed below.
The :latest tag points to :latest-cuda
Tags follow these patterns:
-
:pytorch-[pytorch-version]-py[python-version]-cuda-[x.x.x]-base-[ubuntu-version] -
:latest-cuda→:pytorch-2.1.2-py3.10-cuda-11.8.0-base-22.04 -
:latest-cuda-jupyter→:jupyter-pytorch-2.1.2-py3.10-cuda-11.8.0-base-22.04
Browse here for an image suitable for your target environment.
Supported Python versions: 3.10
Supported Pytorch versions: 2.1.2, 2.2.0
Supported Platforms: NVIDIA CUDA
| Variable | Description |
|---|---|
AUTO_UPDATE |
Update OneTrainer on startup (default true) |
ONETRAINER_BRANCH |
OneTrainer branch/commit hash. (default master) |
ONETRAINER_FLAGS |
Startup flags. eg. --generic-option1 --generic-option2 |
See the base environment variables here for more configuration options.
| Environment | Packages |
|---|---|
onetrainer |
OneTrainer and dependencies |
This micromamba environment will be activated on shell login.
See the base micromamba environments here.
Vast.ai
Runpod.io
The author (@robballantyne) may be compensated if you sign up to services linked in this document. Testing multiple variants of GPU images in many different environments is both costly and time-consuming; This helps to offset costs