Yifan Yang
4eb4c64b2a
The shipped env_template.py and loader_template.py described the same fictional async execute / evaluate / build_prompt API documented in docs/reference/api.md. As a result TemplateBenchmarkEnv(cfg) raised 'TypeError: Can't instantiate abstract class' for every copy-and-paste user who followed the in-tree scaffold. Rewrite the template so it's a working starting point: - env_template.py: TemplateBenchmarkEnv(EnvAdapter) now implements all five real abstract methods (build_train_env, build_eval_env, rollout, reflect, get_task_types) with no-op defaults documented as TODO. Instantiable today; pytest 60/60 still passes. - loader_template.py: TemplateBenchmarkLoader(SplitDataLoader) implements load_split_items for .json / .jsonl input and explains the optional load_raw_items override for split_mode="ratio". - README.md: usage steps now point at scripts/train.py's _ENV_REGISTRY (the real registry) instead of a non-existent BENCHMARK_REGISTRY in skillopt/envs/__init__.py, and link to the rewritten new-benchmark guide. - config_template.yaml: _base_ is a string path (not a list, which the loader rejects); skill_init is commented out with a note so the template config doesn't reference a file the user hasn't created. Verified locally: 'from skillopt.envs._template.env_template import TemplateBenchmarkEnv; TemplateBenchmarkEnv()' succeeds. Refs microsoft/SkillOpt#30. Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
44 lines
1.7 KiB
Markdown
44 lines
1.7 KiB
Markdown
# Benchmark Template
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This directory provides scaffold files for adding a new benchmark to SkillOpt.
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## Files
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- `env_template.py` — Environment adapter template (subclasses
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`EnvAdapter`; implements the 5 abstract methods so the file is
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instantiable out of the box).
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- `loader_template.py` — Data loader template (subclasses
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`SplitDataLoader`; implements `load_split_items` for `.json`/`.jsonl`).
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- `config_template.yaml` — Config file template.
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## Usage
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1. **Copy the directory:**
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```bash
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cp -r skillopt/envs/_template skillopt/envs/your_benchmark
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```
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2. **Rename the files** (drop the `_template` suffix):
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```bash
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cd skillopt/envs/your_benchmark
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mv env_template.py adapter.py
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mv loader_template.py loader.py
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```
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…and inside each file rename the classes
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(`TemplateBenchmarkEnv → YourBenchmarkAdapter`,
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`TemplateBenchmarkLoader → YourBenchmarkLoader`)
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and fix the cross-import in `adapter.py`.
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3. **Implement the TODO blocks** inside `adapter.py:rollout` and the
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`_normalize_item` helper in `loader.py`. If you want real reflection,
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uncomment the `run_minibatch_reflect` block in `adapter.py:reflect`.
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4. **Register** the adapter — add a `try / except ImportError` block in
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`scripts/train.py`'s `_register_builtins()` mapping the registry key
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to your `YourBenchmarkAdapter` class. There is no
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`BENCHMARK_REGISTRY` dict in `skillopt/envs/__init__.py`; the live
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registry is `_ENV_REGISTRY` in `scripts/train.py`.
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5. **Create the config** at `configs/your_benchmark/default.yaml`
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(start from `config_template.yaml`). `_base_` is a **string path**,
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not a list.
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See the [Add a New Benchmark guide](../../../docs/guide/new-benchmark.md)
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for the full step-by-step with a worked `docfaithful` example.
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