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finetuning-scheduler

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A PyTorch Lightning extension that accelerates and enhances foundation model experimentation with flexible fine-tuning schedules.

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Python Apache-2.0Created Feb 4, 2022

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Maintenance: Slowing (166d since push); As of today · Source: github_public_v1
Provenance: Not a fork · Personal account; As of today · Source: github_public_v1
Security (OSV): No lockfile; As of today · Source: none

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Overview

A PyTorch Lightning extension that accelerates and enhances foundation model experimentation with flexible fine-tuning schedules.

Capability facts

CLI: CLI entrypoint
Source: pyproject.toml:[project.scripts] · Jul 11, 2026
Languages: python
Source: github.language+pyproject.toml · Jul 11, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Jul 11, 2026)

uv pip install finetuning-scheduler

Source link

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=2.11.0
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