Jaeger : an accurate and fast deep-learning tool to detect bacteriophage sequences¶ Installation Running Jaeger Table of contents Basic prediction Common options Choosing a model Batch size and memory Output files Understanding the output table Filtering tips Prophage extraction Command-line reference Main commands jaeger predict full help Utility commands (jaeger utils) Python integration Training and Fine-Tuning Models Table of contents Overview Training workflow Preparing training data Step 1: Collect reference sequences Step 2: Generate training fragments Step 3: Simulate metagenome fragments Step 4: Generate OOD data for reliability training Configuration file Minimal example Key configuration sections Running training From scratch With mixed precision (faster on modern GPUs) Resume from checkpoint Save model without training Fine-tuning Freeze the representation learner, train only heads Train only the classification head Train only the reliability head Tips for fine-tuning Self-supervised pretraining Creating ensembles Command reference jaeger train jaeger register-models Data augmentation tips Sequence masking Sequence mutation Releasing Jaeger Versioning scheme Prerequisites Step 1: Bump the version Option A — Auto-bump (recommended) Option B — Explicit version What the script updates Step 2: Commit and tag Step 3: Push to trigger CI Step 4: What happens automatically 1. publish-to-pypi.yaml 2. release.yaml 3. bioconda-update.yaml Step 5: Verify the release PyPI Bioconda GitHub Release OpenID Connect (OIDC) setup 1. Create a PyPI project (or claim an existing one) 2. Add a Trusted Publisher 3. Add TestPyPI publisher (optional but recommended) 4. How it works in the workflow 5. Troubleshooting Manual fallback Manual PyPI publish Manual Bioconda update Manual GitHub Release Repository guard