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name: environment-and-setup description: "Select and validate the legacy runtime, dependencies, and setup boundaries for NVlabs/MUNIT without running training or inference." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Environment and Setup

Use this sub-skill when a Researcher needs to choose, prepare, or triage a MUNIT runtime before touching data, training, or inference. MUNIT is a legacy CUDA-era PyTorch project: treat environment selection as part of the experiment design, not as a routine modern-package install.

Responsibilities

• Pick a legacy-compatible runtime: Python 2.7 or 3.6, PyTorch 0.4.1, TorchVision 0.2.x, CUDA 9.x, cuDNN 7.x, PyYAML, Pillow, tensorboard, and tensorboardX.
• Explain the historical Docker and conda setup choices and their compatibility limits.
• Define checkpoint, VGG-weight, and dataset acquisition boundaries without performing downloads.
• Run safe environment smoke checks that import dependencies and statically inspect the checkout; do not launch train.py, test.py, test_batch.py, dataset download scripts, CUDA kernels, or full model construction from this sub-skill.
• Diagnose installation and compatibility failures such as missing tensorboardX/PyYAML/Pillow/TorchVision, modern PyTorch load_lua removal, PyYAML Loader changes, and modern-GPU mismatch.

Start Here

1. Read references/legacy-runtime.md to decide whether the task needs exact legacy reproduction, a Docker-style legacy container, or a porting-oriented modern environment.

2. If a Python environment already exists, run the bundled helper from any directory:

   python scripts/check_munit_environment.py --repo-root /path/to/user/munit-checkout
   

   Add --expect-cuda only when the user explicitly wants a CUDA availability probe. The helper does not allocate CUDA tensors by default.

3. If the helper reports missing packages or legacy incompatibilities, read references/troubleshooting.md before changing the environment.

4. Route non-setup work to sibling sub-skills:

   • dataset layout, list files, YAML schema, and config editing: data-and-configuration
   • actual train/resume commands and output interpretation: training
   • single-image, batch, checkpoint, or metric inference commands: inference-and-evaluation
   • network/trainer class internals or code porting details: model-internals

Safety Gates

• Do not run repository demo shell scripts as setup checks: they remove/create dataset folders, download archives, invoke ImageMagick conversion, and start training.
• Do not auto-download pretrained checkpoints, datasets, VGG .t7 files, or Inception weights. Require the user to provide local paths or explicit approval.
• Do not treat a CPU-only import as proof that training or inference can run: the original scripts call .cuda() unconditionally.
• Do not use modern PyTorch success as compatibility proof. Importing MUNIT utilities depends on torch.utils.serialization.load_lua, which is absent from modern PyTorch.