finetuning-scheduler
PyTorch Lightning extension for fine-tuning schedules
GraphCanon updated 3w · GitHub synced 3w
Decision brief
finetuning-scheduler accelerates and enhances PyTorch Lightning model fine-tuning with flexible schedules.
Good fit when
- For projects using PyTorch Lightning that require dynamic, flexible scheduling for model fine-tuning.
- When experimenting with rapid iterations of transfer learning on foundation models needing specific fine-tuning control.
Avoid when
- If your project uses a different framework than PyTorch or requires no schedule flexibility in training stages.
- For teams that prefer manual scheduling and do not need the speed boost offered by finetuning-scheduler's automation.
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (3d since push)
- As of 3w
- Provenance
- Not a fork · Personal account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install finetuning-schedulerSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A tool that accelerates and enhances model experimentation through flexible fine-tuning schedules using PyTorch Lightning.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 3, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 3, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 3, 2026)
uv pip install finetuning-schedulerSource link
Tags
README
Step 0: Install from PyPI
Starting with version 2.10, uv is the preferred installation approach for Fine-Tuning Scheduler.
---
# Install uv if you haven't already (one-time setup)
curl -LsSf https://astral.sh/uv/install.sh | sh
---
# Install Fine-Tuning Scheduler
uv pip install finetuning-scheduler
Additional installation options
Install Optional Packages
To install additional packages required for examples:
uv pip install finetuning-scheduler['examples']
or to include packages for examples, development and testing:
uv pip install finetuning-scheduler['all']
Source Installation Examples
Using the build script (recommended):
The build_fts_env.sh script automatically handles Lightning commit pinning and optional PyTorch nightly installation:
git clone https://github.com/speediedan/finetuning-scheduler.git
cd finetuning-scheduler
---
# Standard development build (handles Lightning pin automatically and installs an optional PyTorch prerelease if configured in `requirements/ci/torch-pre.txt`)
./scripts/build_fts_env.sh --repo-home=${PWD} --target-env-name=fts_latest --venv-dir=/path/to/.venvs
---
# To configure PyTorch prerelease used by the build scripts, edit `requirements/ci/torch-pre.txt`:
---
## Manual installation with a PyTorch prerelease (nightly/test)
When using a PyTorch prerelease (nightly or test), use a two-step installation approach:
```bash
git clone https://github.com/speediedan/finetuning-scheduler.git
cd finetuning-scheduler
---
# Step 1: Install a PyTorch prerelease (adjust version and CUDA target as needed; see configuration in requirements/ci/torch-pre.txt)
---
# Step 2: Install FTS with Lightning commit pin (torch already installed, will be skipped)
export UV_OVERRIDE=${PWD}/requirements/ci/overrides.txt
uv pip install -e ".[all]"
The prerelease version is configured via requirements/ci/torch-pre.txt. The lock_ci_requirements.sh and build_fts_env.sh scripts will read this file to determine whether to pre-install a prerelease PyTorch during builds.
Install a specific FTS version from source using the standalone pytorch-lighting package:
export FTS_VERSION=2.14.0
export PACKAGE_NAME=pytorch
git clone -b v${FTS_VERSION} https://github.com/speediedan/finetuning-scheduler
cd finetuning-scheduler
export UV_OVERRIDE=${PWD}/requirements/ci/overrides.txt
uv pip install -e ".[all]"
Latest Docker Image
Note, publishing of new finetuning-scheduler version-specific docker images was paused after the 2.0.2 patch release. If new version-specific images are required, please raise an issue.
Installation Using the Standalone pytorch-lightning Package
applicable to versions >= 2.0.0
Now that the core Lightning package is lightning rather than pytorch-lightning, Fine-Tuning Scheduler (FTS) by default depends upon the lightning package rather than the standalone pytorch-lightning. If you would like to continue to use FTS with the standalone pytorch-lightning package instead, you can still do so as follows:
Install a given FTS release (for example v2.0.0) using standalone pytorch-lightning:
export FTS_VERSION=2.0.0
export PACKAGE_NAME=pytorch
wget https://github.com/speediedan/finetuning-scheduler/releases/download/v${FTS_VERSION}/finetuning-scheduler-${FTS_VERSION}.tar.gz
uv pip install finetuning-scheduler-${FTS_VERSION}.tar.gz
For agents
This page has a .md twin and JSON over the API.