Training models

Most users should start with the released models. Train a model when you have new measurements, need a controlled experiment, or are preparing a release. Always evaluate a trained ensemble on held-out data before using it for scientific conclusions.

Choose a workflow

Data and goal	Command
One or a few well-covered alleles	mhcflurry class1-train-allele-specific-models
Measurements spanning many alleles	mhcflurry class1-train-pan-allele-models
Full retrain, selection, calibration, and evaluation	mhcflurry train pan-allele-release

The low-level commands fit candidate models. Production ensembles also require model selection and percentile calibration; use the release workflow when you need the complete pipeline.

Training data

Affinity training tables use one row per measurement. Allele-specific training requires allele, peptide, measurement_value, and measurement_type. Use measurement_type to identify quantitative or qualitative measurements. The optional measurement_inequality column records =, <, or >; if the column is omitted, all measurements are treated as equalities.

Alleles must be sequence-resolved MHC class I names. Allele-specific training does not require a pseudosequence table unless the hyperparameters enable cross-allele pretraining with pretrain_min_points.

The curated data used for released models is available as a download:

mhcflurry downloads fetch data_curated
mhcflurry downloads path data_curated

Small allele-specific example

The training command accepts a YAML list of architectures. This single, four-member architecture is suitable for learning the workflow; it is not a replacement for the released ensemble search:

- activation: tanh
  dropout_probability: 0.0
  early_stopping: true
  layer_sizes: [8]
  locally_connected_layers: []
  loss: custom:mse_with_inequalities
  max_epochs: 500
  minibatch_size: 16384
  n_models: 4
  output_activation: sigmoid
  patience: 20
  peptide_amino_acid_encoding: BLOSUM62
  random_negative_affinity_max: 50000.0
  random_negative_affinity_min: 20000.0
  random_negative_constant: 25
  random_negative_rate: 0.0
  validation_split: 0.1

Save it as hyperparameters.yaml, then run:

mhcflurry class1-train-allele-specific-models \
    --data "$(mhcflurry downloads path data_curated)/curated_training_data.csv.bz2" \
    --hyperparameters hyperparameters.yaml \
    --allele 'HLA-A*02:01' \
    --min-measurements-per-allele 75 \
    --out-models-dir models

The output directory is a complete predictor. Keep its manifest, metadata, and weights together; pass the directory to prediction commands with --models.

Pan-allele and release training

Pan-allele training additionally needs an allele pseudosequence table and a model-selection step. Each training allele must resolve to a key in that table; rows for alleles without a matching pseudosequence are excluded. Start with the command help and the maintained training recipes rather than copying individual flags from an old run:

mhcflurry class1-train-pan-allele-models --help
mhcflurry train pan-allele-release --help

The release workflow can run locally or on a configured remote backend. It records source and workflow provenance, resumes completed phases, compares the candidate with public models, and copies review artifacts back to the control machine. Deployment is opt-in.

Leave worker counts, GPU packing, DataLoader workers, and prediction batches on auto initially. See Configuration and performance before pinning resource values and the maintained training scripts for the current release recipe.

Evaluate before use

Run mhcflurry eval compare-models against a public baseline and inspect the summary metrics and plots. Prediction-affecting changes need held-out evidence, not only a successful unit-test run. See Evaluating trained models for the evaluation workflow.