docs/run-batch.md

Batch Evaluation Reference

tools/run_batch.py is the convenience wrapper for running a materialized task selection with Pier. It does not build task images. It reads the immutable environment and verifier references from each task.toml, pulls those images for linux/amd64, writes a redacted run record, and invokes Pier with --no-force-build --no-delete --yes.

Prerequisites

From a downloaded release directory:

uv tool install "datacurve-pier==0.3.0"
docker login
python3 tools/materialize.py \
  --task-id 002,005-007 \
  --output tasks-selected-small --force

The --path passed to run_batch.py must be a materialized directory containing task_NNN/task.toml directories. The runner never selects tasks implicitly and never reads task definitions from GitHub at runtime.

Provider Profiles

Use an env file outside the checkout. The parser accepts KEY=value, optional export KEY=value, comments, and quoted values. It never prints credential values or writes them to batch-run.json.

Codex and OpenAI-compatible gateways

run_batch.py translates the following fields into Pier's Codex provider configuration when --agent codex is used:

Variable	Required	Meaning
MODEL	No	Exact model route sent to the gateway; default gpt-5
OPENAI_API_KEY	Yes for a real run	Gateway credential
CODEX_BASE_URL	No	OpenAI-compatible gateway URL; defaults to https://api.openai.com/v1
CODEX_WIRE_API	No	responses or chat; defaults to responses
CODEX_VERSION	No	Codex runtime version passed to Pier
CODEX_REASONING_EFFORT	No	Reasoning effort passed to the Codex adapter

Example:

MODEL=gpt-5
OPENAI_API_KEY=replace-with-your-key
CODEX_BASE_URL=https://gateway.example.edu/v1
CODEX_WIRE_API=responses
CODEX_VERSION=latest
CODEX_REASONING_EFFORT=high

CODEX_BASE_URL selects the model gateway. It is different from a network proxy. A network proxy is configured with standard HTTP_PROXY, HTTPS_PROXY, and NO_PROXY variables. For Docker Desktop, a proxy running on the host is usually reached from a container as host.docker.internal, not 127.0.0.1.

Claude Code and mini-swe-agent

These harnesses receive their provider variables through Pier's --env-file:

# Claude Code
ANTHROPIC_AUTH_TOKEN=replace-with-your-gateway-key
ANTHROPIC_BASE_URL=https://api.anthropic.com
ANTHROPIC_CUSTOM_HEADERS=

# mini-swe-agent with an OpenAI-compatible provider
OPENAI_API_KEY=replace-with-your-gateway-key
OPENAI_BASE_URL=https://gateway.example.edu/v1

The model route is selected with the repeatable --model option. Provider variables not listed here can be added to the env file and are passed through to the selected harness by Pier.

Basic Commands

Run a no-model infrastructure smoke:

python3 tools/run_batch.py \
  --path tasks-selected-small \
  --agent nop \
  --n-concurrent 1 \
  --n-attempts 1 \
  --jobs-dir jobs \
  --job-name smoke

Run Codex through a gateway:

python3 tools/run_batch.py \
  --path tasks-selected-small \
  --agent codex \
  --env-file ~/.config/swe-bench-science/codex.env \
  --n-concurrent 2 \
  --n-attempts 1 \
  --max-retries 1 \
  --jobs-dir jobs \
  --job-name codex-small

Run Claude Code or mini-swe-agent:

python3 tools/run_batch.py \
  --path tasks-selected-small \
  --agent claude-code \
  --env-file ~/.config/swe-bench-science/claude.env \
  --model anthropic/claude-opus-4-7 \
  --n-concurrent 1 \
  --jobs-dir jobs \
  --job-name claude-small

For an approximately 120-second agent-stage smoke, add --agent-timeout-multiplier 0.0223. This does not shorten the verifier timeout or any native build timeout.

Patch and Verifier Boundary

Each materialized task contains a Pier pre_artifacts.sh hook. Pier runs this hook after the agent exits and before it collects artifacts. The hook computes artifacts/model.patch against the task image's original baseline root commit, so an agent-created commit is still included in the patch. A clean or timed-out agent produces an explicit empty patch rather than a missing artifact.

For tasks with a separate verifier image, the verifier entrypoint applies that patch to its clean task workspace before running public and private tests. The verifier result therefore evaluates the agent workspace, not the untouched baseline. A missing pre_artifacts.sh is rejected by run_batch.py; rerun materialize.py with the current tools to regenerate the task selection.

Private-test collection is directory-based. The verifier runs pytest on /tests/private_tests, so task authors may use names such as test_res_export.py or test_scientific_invariants.py; no test_task_NNN.py filename is required. The task's Compose override mounts the bundle's dynamic grader into an existing prebuilt verifier image, so correcting test discovery does not require rebuilding the image.

Option Reference

Option	Default	Description
--path	required	Materialized task directory
--agent	nop	Pier harness, such as codex, claude-code, mini-swe-agent, or nop
--env	docker	Pier environment backend
--env-file	unset	Provider/harness env file
--model	unset	Model route; repeat for multiple Pier model arguments
--agent-env KEY=VALUE	repeatable	Extra environment value passed to the harness
--agent-kwarg KEY=VALUE	repeatable	Extra Pier agent keyword; useful for adapter-specific settings
--n-concurrent	1	Number of simultaneous tasks
--n-attempts	1	Attempts per task
--max-retries	0	Pier retries after an attempt-level failure
--agent-timeout-multiplier	Pier default	Multiplier for the agent stage timeout
--verifier-timeout-multiplier	Pier default	Multiplier for verifier/build timeout
--jobs-dir	jobs	Directory for Pier jobs and summaries
--job-name	unset	Stable job name used in result paths
--platform	linux/amd64	Docker pull and derived Pier image platform
--pier-bin	pier	Pier executable or absolute path
--skip-pull	off	Skip Docker pulls when immutable refs are already local
--no-auto-provider	off	Do not translate CODEX_* profile values into Codex kwargs
--no-auto-agent-adapter	off	Use Pier's built-in Codex agent instead of the Science Bench adapter
--agent-import-path	unset	Explicit Pier agent import path
--dry-run	off	Pull/validate images and write metadata, but do not invoke Pier

The wrapper always records the selected task IDs, selection hash, image refs, platform, Pier version, agent/model settings, and a redacted Pier command in <path>/batch-run.json.

Results

Pier writes its job output under the selected jobs directory. The wrapper then generates:

jobs/<job-name>/result.json
jobs/<job-name>/summary.json
jobs/<job-name>/summary.csv
jobs/<job-name>/<task>__<trial>/verifier/reward.json
jobs/<job-name>/<task>__<trial>/verifier/ctrf.json
jobs/<job-name>/<task>__<trial>/verifier/test-stdout.txt

The summary CSV is the convenient per-task result table. Use pier view jobs for trajectories and inspect result.json, reward.json, and test-stdout.txt together when diagnosing a failure.

Common Variants

Pull nothing and inspect the fully rendered command:

python3 tools/run_batch.py \
  --path tasks-selected-small \
  --agent codex \
  --env-file ~/.config/swe-bench-science/codex.env \
  --skip-pull \
  --dry-run

Run the 91-task science-knowledge ablation selection after materialization:

python3 tools/materialize.py \
  --task-id 002-082,084,086,090,097-101,111,114 \
  --allow-restricted-licenses \
  --output tasks-science-knowledge-ablation --force

python3 tools/run_batch.py \
  --path tasks-science-knowledge-ablation \
  --agent codex \
  --env-file ~/.config/swe-bench-science/codex.env \
  --n-concurrent 4 \
  --jobs-dir jobs \
  --job-name codex-science-ablation