计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
xparl-distributed【效率与协作】Guide safe PARL xparl cluster lifecycle, remote_class actors, parl.connect file distribution, CPU/GPU workers, monitoring, logs, and distributed troubleshooting.
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Use this sub-skill when a task involves PARL distributed execution, xparl,
@parl.remote_class, parl.connect, worker resources, distributed files,
cluster monitors, remote logs, or CPU/GPU actor placement.
Before starting or connecting any cluster, read
references/security-and-operations.md.
xparl intentionally ships executable Python objects and file payloads to
workers; treat every worker and every submitted script as trusted-code execution.
Do not expose xparl master, monitor, or log-server ports to untrusted networks,
and do not accept untrusted workers or code.
references/cli-reference.md and use
scripts/check_xparl_cli.py.@parl.remote_class arguments, wait=False futures,
n_gpu, parl.connect(master_address, distributed_files=[]), serialization,
and file-distribution behavior, read
references/remote-api.md.references/security-and-operations.md.references/troubleshooting.md.Model / Algorithm / Agent
usage around distributed actors, load the sibling
core-framework sub-skill.algorithm-recipes sub-skill after this one.This sub-skill covers xparl mechanics and operational safety. It does not choose RL algorithms, tune training loops, or claim that an optional backend or GPU is available. Route those decisions to the relevant sibling sub-skill and verify the actual runtime before launching long jobs.