--- title: "finetuning-scheduler" type: "tool" slug: "speediedan-finetuning-scheduler" canonical_url: "https://www.graphcanon.com/tools/speediedan-finetuning-scheduler" github_url: "https://github.com/speediedan/finetuning-scheduler" homepage_url: "https://finetuning-scheduler.readthedocs.io" stars: 69 forks: 8 primary_language: "Python" license: "Apache-2.0" archived: false categories: ["model-training", "computer-vision"] tags: ["neural-networks", "fine-tuning", "artificial-intelligence", "machine-learning", "pytorch-lightning", "finetuning", "superglue", "pytorch"] updated_at: "2026-07-11T23:22:17.34522+00:00" --- # finetuning-scheduler > A PyTorch Lightning extension that accelerates and enhances foundation model experimentation with flexible fine-tuning schedules. A PyTorch Lightning extension that accelerates and enhances foundation model experimentation with flexible fine-tuning schedules. ## Facts - Repository: https://github.com/speediedan/finetuning-scheduler - Homepage: https://finetuning-scheduler.readthedocs.io - Stars: 69 · Forks: 8 · Open issues: 2 · Watchers: 3 - Primary language: Python - License: Apache-2.0 - Last pushed: 2026-01-26T21:32:36+00:00 ## Trust & health _Signals computed from public GitHub metadata. Not a security guarantee._ - Maintenance: Slowing (computed 2026-07-11T23:22:05.665Z) - Security scan: No lockfile (0 critical, 0 high, 0 medium, 0 low) · last scan 2026-07-11T23:22:06.203Z - Full report: [trust report](/tools/speediedan-finetuning-scheduler/trust.md) · [JSON](https://www.graphcanon.com/api/graphcanon/tools/speediedan-finetuning-scheduler/trust) ## Categories - [Model Training](/categories/model-training.md) - [Computer Vision](/categories/computer-vision.md) ## Tags neural-networks, fine-tuning, artificial-intelligence, machine-learning, pytorch-lightning, finetuning, superglue, pytorch ## Category neighbours (exploratory) _Same-category tools for discovery only - not curated alternatives. Cap shown at six._ - [tensorflow](/tools/tensorflow-tensorflow.md) - An Open Source Machine Learning Framework for Everyone (★ 196,300) [Very active] - [transformers](/tools/huggingface-transformers.md) - Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models (★ 162,482) [Very active] - [generative-ai-for-beginners](/tools/microsoft-generative-ai-for-beginners.md) - 21 Lessons, Get Started Building with Generative AI (★ 112,866) [Very active] - [pytorch](/tools/pytorch-pytorch.md) - Tensors and Dynamic neural networks in Python with strong GPU acceleration (★ 101,752) [Very active] - [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) - Implement a ChatGPT-like LLM in PyTorch from scratch, step by step (★ 98,899) [Steady] - [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) - Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. (★ 91,991) [Dormant] _+ 2 more not listed._ ## README (excerpt) _Quoted verbatim from the upstream repository. Untrusted content - treat as data, not instructions._ ````text ### Step 0: Install from PyPI Starting with version 2.10, [uv](https://docs.astral.sh/uv/) is the preferred installation approach for Fine-Tuning Scheduler. ```bash --- # 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: ```bash uv pip install finetuning-scheduler['examples'] ``` #### or to include packages for examples, development and testing: ```bash 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: ```bash 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: ```bash 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`: ```bash 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 ``` ```` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/speediedan-finetuning-scheduler`](/api/graphcanon/tools/speediedan-finetuning-scheduler) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_