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name: hydra-pipelines description: "Construct, debug, and explain classic SPLADE Hydra workflows for train/index/retrieve/evaluate/flops/export without opening the source repository." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

SPLADE Hydra Pipelines

Use this sub-skill when a user needs the original SPLADE Hydra entry points: python -m splade.all, python -m splade.train, python -m splade.index, python -m splade.retrieve, python -m splade.evaluate, python -m splade.flops, or python -m splade.create_anserini.

Start Here

1. Choose exactly one config source:
   • SPLADE_CONFIG_NAME=<name-or-yaml> for a config bundled with the installed SPLADE package's conf/ tree.
   • SPLADE_CONFIG_FULLPATH=/path/to/config.yaml for a saved experiment config, usually from a previous config.checkpoint_dir.
2. Decide whether the command needs a trained checkpoint config:
   • splade.train creates config.checkpoint_dir/config.yaml.
   • splade.index, splade.retrieve, splade.flops, and splade.create_anserini normally load config.checkpoint_dir/config.yaml.
   • For Hugging Face model-only evaluation, add config.pretrained_no_yamlconfig=true and set init_dict.model_type_or_dir=<hf-model-or-local-model-dir>.
3. Fill required output directories before running any workflow based on config_default: config.checkpoint_dir=..., config.index_dir=..., and config.out_dir=....
4. Use Hydra package overrides for config groups, such as train/data=msmarco, index=msmarco, or retrieve_evaluate=toy; use package-position paths for parameters, such as init_dict.model_type_or_dir=... or config.top_k=1000.

Common Tasks

• Generate safe command templates with scripts/splade_hydra_command_builder.py; it validates required arguments and prints commands without running SPLADE.
• Follow references/hydra-workflows.md for toy, training, indexing, retrieval, evaluation, FLOPS, pretrained-model, and create-Anserini command shapes.
• Follow references/configuration.md for config names, saved config paths, group overrides, output directories, and post-training config flow.
• Follow references/troubleshooting.md for unresolved ???, config-source conflicts, Hydra override mistakes, version_base dependency drift, pytrec_eval, CPU/GPU, and model-download failures.

Boundaries

• For Hugging Face Trainer training, multiple negatives, reranking commands, or splade.hf_train, use ../hf-training-reranking/SKILL.md.
• For SPLADE model classes, datasets, data schemas, and inverted-index APIs, use ../model-data-api/SKILL.md.
• For Anserini/PISA/BEIR/static pruning depth, use ../pruning-export-evaluation/SKILL.md; this sub-skill only covers splade.create_anserini as the handoff command.