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name: xparl-distributed description: "Guide safe PARL xparl cluster lifecycle, remote_class actors, parl.connect file distribution, CPU/GPU workers, monitoring, logs, and distributed troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

xparl Distributed

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.

Safety gate

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.

Route map

• For CLI lifecycle commands, port planning, local CPU/GPU worker commands, and help-only diagnostics, read references/cli-reference.md and use scripts/check_xparl_cli.py.
• For Python APIs, @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.
• For monitor/log-server operations, shutdown boundaries, version consistency, and stale-process handling, read references/security-and-operations.md.
• For symptoms, likely causes, and recovery steps, read references/troubleshooting.md.
• For PARL imports, backend selection, and core Model / Algorithm / Agent usage around distributed actors, load the sibling core-framework sub-skill.
• For complete distributed RL training examples and algorithm-specific actor / learner recipes, load the sibling algorithm-recipes sub-skill after this one.

Non-goals

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.