Remove prepare_harbor_tasks.py; use harbor-private adapters#982
Remove prepare_harbor_tasks.py; use harbor-private adapters#982
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…dapters Task preparation for Harbor is handled on the agent server side by harbor-private adapters (e.g. adapters/swebench/run_adapter.py), which use the swebench Python package to produce correct Docker image names and Dockerfiles. The generic prepare_harbor_tasks.py fallback is no longer needed.
The adapters work in the public harbor repo, not just our private fork.
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Code Review
This pull request removes the prepare_harbor_tasks.py script and updates the documentation to use harbor-private adapters for task preparation. The review feedback suggests simplifying the directory navigation commands in the README, removing a stale command-line flag in the troubleshooting section, and updating a docstring in download_and_process_data.py that still references the deleted script.
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| ```bash | ||
| # On the agent server (CPU machine), inside the harbor repo: | ||
| cd $CWD/harbor/adapters/swebench && uv sync |
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The path $CWD/harbor-private/adapters/swebench is redundant and potentially confusing if the user is already inside the harbor-private repository as stated in the preceding comment. Additionally, $CWD is not a standard environment variable in most shells (typically $PWD is used). Using a relative path from the repository root is clearer.
| cd $CWD/harbor/adapters/swebench && uv sync | |
| cd adapters/swebench && uv sync |
| ### `TaskNotFound` error | ||
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| The task directory for the given `instance_id` doesn't exist under `HARBOR_TASKS_DIR`. Run the appropriate Harbor adapter or `prepare_harbor_tasks.py` first. | ||
| The task directory for the given `instance_id` doesn't exist under `HARBOR_TASKS_DIR`. Run the appropriate harbor adapter first (e.g. `adapters/swebench/run_adapter.py` for SWE-bench tasks). |
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| | `swe_agent_function.py` | Custom agent function — dispatches to Harbor server, returns env metadata | | ||
| | `generate.py` | Reward function, agent metrics aggregation, `RolloutFn` | | ||
| | `download_and_process_data.py` | Download from HuggingFace or local JSONL, convert to Miles format | | ||
| | `prepare_harbor_tasks.py` | Convert Miles JSONL to Harbor task directories (generic fallback) | |
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LGTM |
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On high-latency cross-region clusters (e.g. Abu Dhabi MBZUAI ↔ wandb-cloud US-West) with concurrent actor bursts, a single round-trip can exceed the wandb SDK's 90s default `init_timeout` — tearing down the whole run with a silent handshake abort. Observed on RL360 job 1564420, which forced `WANDB_MODE=offline` as a global default ever since (see https://github.com/LLM360/RL360/issues/87). The issue's original diagnosis assumed a local primary↔secondary socket handshake race. That's not how shared mode works — per wandb's own feature PR (wandb/wandb#6882), each writer spawns an independent wandb-core that talks to the cloud directly; aggregation is server-side by run_id. No local socket exists. The failure mode is pure network/latency, not a local readiness race. Changes ------- - Bump `init_timeout` to 300s for primary and secondary Settings. 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Followups this change enables ----------------------------- - `WANDB_MODE=offline` can be removed from scale.yaml's extra_env default once a pilot run confirms online mode boots cleanly. - The tmux-based `~/bin/wandb-sync-rl360.sh` workaround on David's M2 account becomes obsolete (no more offline-only default). - Near-realtime wandb dashboards replace the ~2-minute-lag offline sync; per-rank system metrics via x_label filtering. --------- Co-authored-by: JD <jaedon.guo@gmail.com> Co-authored-by: Ethan (Yusheng) Su <yushengsu.thu@gmail.com> Co-authored-by: fzyzcjy <5236035+fzyzcjy@users.noreply.github.com> Co-authored-by: Ziang Li <ziangli@umich.edu> Co-authored-by: Zhichen Zeng <zczeng@uw.edu> Co-authored-by: JensenFire <xinji1@microsoft.com> Co-authored-by: Yueming Yuan <yym022502@gmail.com> Co-authored-by: maocheng23 <35615230+maocheng23@users.noreply.github.com> Co-authored-by: Douglas Yang <douglasyang88@gmail.com> Co-authored-by: Yueming Yuan <yueming@Mac.attlocal.net> Co-authored-by: Huapeng Zhou <73010314+PopSoda2002@users.noreply.github.com> Co-authored-by: GuanxingLu <gxlu02@gmail.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-authored-by: Shi-Dong <Shi-Dong@users.noreply.github.com> Co-authored-by: Shi Dong <shi.dong@radixark.ai> Co-authored-by: Jiajun Li <48857426+guapisolo@users.noreply.github.com> Co-authored-by: guapisolo <guapisolo@gmail.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: Yuzhen Zhou <82826991+zyzshishui@users.noreply.github.com> Co-authored-by: Yanbin Jiang <jybsuper@gmail.com> Co-authored-by: Ying Sheng <sqy1415@gmail.com> Co-authored-by: Yisheng Gong <yishenggong9437@gmail.com>
Summary
examples/experimental/swe-agent-v2/prepare_harbor_tasks.py— task preparation is now handled on the agent server side by harbor-private adapters (e.g.adapters/swebench/run_adapter.py), which use theswebenchPython package to produce correct Docker image names and Dockerfiles.prepare_harbor_tasks.pyin the Files table and Troubleshooting section.Context
The generic
prepare_harbor_tasks.pywas a fallback for converting Miles JSONL to Harbor task directories. However, harbor-private now has dedicated adapters for each dataset type (SWE-bench, SWE-Gym, etc.) that handle dataset-specific concerns like Docker image naming conventions and Dockerfile templates correctly. The generic script is no longer needed and was a source of confusion (e.g. Miles PR #951 tried to patch it for SWE-bench, but harbor-private already handles this).Test plan
prepare_harbor_tasks.py