executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
installation-and-launchInstall ColossalAI, verify PyTorch/CUDA and optional extensions, and launch distributed jobs through ColossalAI CLI or launch APIs.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when the task is about getting ColossalAI importable, interpreting colossalai check -i, using colossalai run, constructing hostfile or torchrun commands, or fixing distributed startup errors.
colossalai from PyPI or source and decide whether BUILD_EXT=1 is appropriate.CUDA_HOME, and optional CUDA extension status.colossalai check -i, colossalai --help, colossalai run --help, or CLI launcher flags.colossalai run commands for single-node, hostfile, include/exclude, extra launch args, or -m module mode.colossalai.launch, launch_from_torch, launch_from_slurm, or launch_from_openmpi inside scripts.Booster plugins or writing the training loop: use ../booster-training/SKILL.md.../parallelism-and-sharding/SKILL.md.../inference-and-serving/SKILL.md.../application-recipes/SKILL.md.python ../../scripts/check_colossalai_environment.py --check-cli
colossalai check -i
colossalai run --nproc_per_node 1 train.py --arg value
Inside a script launched with torchrun or colossalai run, initialize ColossalAI with:
import colossalai
colossalai.launch_from_torch(seed=42)
For manual single-process initialization, pass explicit rank, world size, host, and port:
colossalai.launch(rank=0, world_size=1, host="127.0.0.1", port=29500)
references/installation-and-backends.md explains install variants, PyTorch/CUDA checks, and optional extension signals.references/cli-and-launch.md lists colossalai run flags, launch APIs, and safe command patterns.references/troubleshooting.md maps install, CLI, hostfile, environment-variable, port, and NCCL failures to recovery steps.scripts/colossalai_launch_builder.py safely builds launch commands without running the training script.--master_port for concurrent jobs or repeated test runs on shared hosts.