Validate conda, Python, and CUDA setup for LMDeploy environments.
npx skills add https://github.com/InternLM/lmdeploy --skill check-env-internlm
Please help me install this Agent Skill. Skill: check-env Source: https://github.com/InternLM/lmdeploy/tree/main/.claude/skills/check-env Command: npx skills add https://github.com/InternLM/lmdeploy --skill check-env-internlm
Ensures LMDeploy development environments are correctly set up, preventing runtime failures caused by missing dependencies, incorrect paths, or misconfigured toolchains.
Activate your conda environment, install the package in editable mode if needed, and verify that importing lmdeploy works.
High-intent search queries and answers about installing and using this skill.
To validate your LMDeploy development environment, the Skill checks conda activation, verifies Python importability for the lmdeploy package, and ensures CUDA compatibility to prevent runtime failures.
Importing lmdeploy often fails due to incorrect conda environment activation, missing dependencies, or misconfigured toolchains; this Skill identifies these exact setup issues and provides actionable remediation steps.
You can check CUDA compatibility for LMDeploy testing by running this Skill, which validates CUDA availability and enforces documented dependencies to ensure reliable setup across local machines and CI.
Yes, you can use this environment readiness check for CI workflows because it enforces dependencies documented in SKILL.md, ensuring reproducible setup and consistent dev environments across teams and automated systems.
The best way to troubleshoot local Python setup issues for LMDeploy is to verify environment readiness by checking conda activation, Python importability, and CUDA compatibility to receive actionable resolution guidance.
To fix LMDeploy runtime failures caused by missing dependencies, validate your local development environment to identify incorrect paths or missing toolchains and follow the provided actionable steps to resolve setup issues.
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