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installation-and-launch

installation-and-launch

Install ColossalAI, verify PyTorch/CUDA and optional extensions, and launch distributed jobs through ColossalAI CLI or launch APIs.

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name: installation-and-launch description: "Install ColossalAI, verify PyTorch/CUDA and optional extensions, and launch distributed jobs through ColossalAI CLI or launch APIs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Installation and Launch

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.

Route Here

  • Install colossalai from PyPI or source and decide whether BUILD_EXT=1 is appropriate.
  • Check Python, PyTorch, CUDA, GPU visibility, CUDA_HOME, and optional CUDA extension status.
  • Use colossalai check -i, colossalai --help, colossalai run --help, or CLI launcher flags.
  • Generate safe colossalai run commands for single-node, hostfile, include/exclude, extra launch args, or -m module mode.
  • Use colossalai.launch, launch_from_torch, launch_from_slurm, or launch_from_openmpi inside scripts.

Reroute

  • Choosing Booster plugins or writing the training loop: use ../booster-training/SKILL.md.
  • Deciding tensor/pipeline/sequence parallel topology: use ../parallelism-and-sharding/SKILL.md.
  • LLM or diffusion generation/inference commands: use ../inference-and-serving/SKILL.md.
  • ColossalChat, ColossalQA, ColossalEval, Colossal-LLaMA, or ColossalMoE environment isolation: use ../application-recipes/SKILL.md.

Fast Start

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 and Helpers

  • 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.

Operating Rules

  • Do not treat CPU import as proof of CUDA training or inference readiness.
  • Do not install broad app/test requirements just to fix a core import problem.
  • Do not run user training scripts from this sub-skill; generate or inspect commands, then route to the workflow owner.
  • Use a unique --master_port for concurrent jobs or repeated test runs on shared hosts.
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