---

name: fairseq-training description: "Graphormer fairseq-train templates for graph prediction and Graphormer3D IS2RE." disable-model-invocation: true metadata: disco-role: operating license: MIT

fairseq-training

Use this sub-skill to build or sanity-check Graphormer fairseq-train commands for:

• ZINC
• PCQM4M v1
• PCQM4Mv2
• MolHIV FLAG training portions
• OC20 / IS2RE with Graphormer3D

Start here

• Training workflows
• CLI parameters
• Troubleshooting
• Command builder

Command shape

• Graphormer training commands use fairseq-train --user-dir <graphormer-user-dir> ....
• Property prediction workflows use graph_prediction or graph_prediction_with_flag.
• OC20 / IS2RE uses is2re with graphormer3d_base.
• Keep the task, criterion, dataset, and architecture aligned with the chosen workflow.

Route elsewhere

• Custom dataset module contracts: datasets-and-customization.
• Pretrained checkpoint loading and evaluation: pretrained-and-evaluation.
• Model, task, and criterion internals: model-extension.
• Distributional Graphormer / DiG: distributional-graphormer.

Helper

• python scripts/build_graphormer_train_command.py --help
• The helper only prints a shell command; it never launches training.