Imported from majiayu000/claude-skill-registry (skills/data/verify-inference-example/SKILL.md). Install upstream with npx skills add majiayu000/claude-skill-registry --skill verify-inference-example. Copyright stays with the author.

Verify Inference Example

Systematically verify that a CVlization inference example is complete, properly structured, and functional.

When to Use

• Validating a new or modified inference example
• Debugging inference pipeline issues
• Ensuring example completeness before commits
• Verifying example works after CVlization updates

Important Context

Shared GPU Environment: This machine may be used by multiple users simultaneously. Before running GPU-intensive inference:

1. Check GPU memory availability with nvidia-smi
2. Wait for sufficient VRAM and low GPU utilization if needed
3. Consider stopping other processes if you have permission
4. If CUDA OOM errors occur, wait and retry when GPU is less busy

Verification Checklist

1. Structure Verification

Check that the example directory contains all required files:

# Navigate to example directory
cd examples/<capability>/<task>/<framework>/

# Expected structure:
# .
# ├── example.yaml        # Required: CVL metadata
# ├── Dockerfile          # Required: Container definition
# ├── build.sh            # Required: Build script
# ├── predict.sh          # Required: Inference script
# ├── predict.py          # Required: Inference code
# ├── examples/           # Required: Sample inputs
# ├── outputs/            # Created at runtime
# └── README.md           # Recommended: Documentation

Key files to check:

• example.yaml - Must have: name, capability, stability, presets (build, predict/inference)
• Dockerfile - Should copy necessary files and install dependencies
• build.sh - Must set SCRIPT_DIR and call docker build
• predict.sh - Must mount volumes correctly and call predict.py
• predict.py - Main inference script
• examples/ - Directory with sample input files

2. Build Verification

# Option 1: Build using script directly
./build.sh

# Option 2: Build using CVL CLI (recommended)
cvl run <example-name> build

# Verify image was created
docker images | grep <example-name>

# Expected: Image appears with recent timestamp

What to check:

• Build completes without errors (both methods)
• All dependencies install successfully
• Image size is reasonable
• cvl info <example-name> shows correct metadata

3. Inference Verification

Run inference with sample inputs:

# Option 1: Run inference using script directly
./predict.sh

# Option 2: Run inference using CVL CLI (recommended)
cvl run <example-name> predict

# With custom inputs (if supported)
./predict.sh path/to/custom/input.jpg

Immediate checks:

• Container starts without errors
• Model loads successfully (check GPU memory with nvidia-smi if using GPU)
• Inference completes (outputs generated)
• Output files created in outputs/ or similar directory
• Results look reasonable (open output files to inspect)

4. Output Verification

Check that inference produces valid outputs:

# Check outputs directory
ls -la outputs/

# Expected: Output files with recent timestamps

# Inspect output content
cat outputs/output.md  # For text outputs
# or
python -m json.tool outputs/output.json  # For JSON outputs

What to verify:

• Output files are created
• Output format is correct (markdown, JSON, etc.)
• Output contains expected content structure
• Output is non-empty and valid

4a. Host Filesystem Persistence (CRITICAL)

IMPORTANT: When running via cvl run, outputs MUST be saved to the HOST filesystem (user's current working directory), not just inside the container. This is critical for usability.

How it works:

• User's cwd is mounted at /mnt/cvl/workspace inside the container
• CVL_INPUTS and CVL_OUTPUTS environment variables point to /mnt/cvl/workspace
• predict.py must use resolve_output_path() to resolve output paths
• predict.py must use resolve_input_path() to resolve input paths

Verify host persistence:

# Run from a test directory (NOT the example directory)
cd /tmp
mkdir -p cvl_test && cd cvl_test

# Run inference
cvl run <example-name> predict

# Check that outputs appear in current directory (NOT in example's outputs/ folder)
ls -la
# Expected: Output files appear here (e.g., output.mp4, result.json)

# Verify outputs are NOT ONLY in the container
ls -la /path/to/example/outputs/
# The container's outputs/ may also have files, but host cwd MUST have them

What to verify:

• Output files appear in user's current working directory when using cvl run
• predict.py imports and uses resolve_output_path from cvlization.paths
• predict.py imports and uses resolve_input_path for input file arguments
• Default output paths are resolved correctly (not hardcoded absolute container paths)

Common issues:

• Outputs only in container: Missing resolve_output_path() - outputs go to /workspace/ or container's outputs/
• Input files not found: Missing resolve_input_path() - can't find files from user's cwd
• Hardcoded paths: Using /workspace/output.mp4 instead of resolve_output_path("output.mp4")

Required code patterns in predict.py:

from cvlization.paths import resolve_input_path, resolve_output_path

# For input files
input_path = resolve_input_path(args.input)  # Resolves against CVL_INPUTS

# For output files
output_path = resolve_output_path(args.output)  # Resolves against CVL_OUTPUTS

# For output directories
args.output_dir = resolve_output_path(args.output_dir.rstrip('/') + '/').rstrip('/')

5. Model Caching Verification

Verify that pretrained models are cached properly:

# Check HuggingFace cache
ls -la ~/.cache/huggingface/hub/
# Expected: Model files downloaded once and reused

# Run inference twice and verify no re-download
./predict.sh 2>&1 | tee first_run.log

# Second run should reuse cached models
./predict.sh 2>&1 | tee second_run.log

# Verify no download messages in second run
grep -i "downloading" second_run.log
# Expected: No new downloads (models already cached)

What to verify:

• Models download to ~/.cache/huggingface/ (or framework-specific cache)
• Second run reuses cached models without re-downloading
• Check predict.py doesn't set custom cache directories that break caching

6. Runtime Checks

GPU VRAM Usage Monitoring (REQUIRED for GPU models):

Monitor GPU VRAM usage before, during, and after inference:

# In another terminal, watch GPU memory in real-time
watch -n 1 nvidia-smi

# Or get detailed memory breakdown
nvidia-smi --query-gpu=index,name,memory.used,memory.total,memory.free,utilization.gpu --format=csv,noheader,nounits

# Record peak VRAM usage during inference
nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | awk '{print $1 " MB"}'

Expected metrics:

• Model loading: VRAM usage increases as model loads into memory
• Inference peak: VRAM spikes during forward pass
• Cleanup: Memory released after inference completes (for short-running containers)
• Temperature: Stable (<85°C)

What to record for verification metadata:

• Peak VRAM usage in GB (e.g., "8.2GB VRAM" or "12.5GB VRAM")
• Percentage of total VRAM (e.g., "52%" for 12.5GB on 24GB GPU)
• Whether 4-bit/8-bit quantization was used (affects VRAM requirements)

Troubleshooting:

• CUDA OOM: Use smaller model variant, enable quantization (4-bit/8-bit), or run on CPU
• High VRAM idle usage: Check if other processes are using GPU
• Memory not released: Container may still be running (docker ps)

Docker Container Health:

# Check container runs and exits cleanly
docker ps -a | head

# Verify mounts (for running container)
docker inspect <container-id> | grep -A 10 Mounts
# Should see: workspace, cvlization_repo, huggingface cache

7. Quick Validation Test

For fast verification during development:

# Run with smallest sample input
./predict.sh examples/small_sample.jpg

# Expected runtime: seconds to few minutes
# Verify: Completes without errors, output generated

8. Update Verification Metadata

After successful verification, update the example.yaml with verification metadata:

First, check GPU info:

# Get GPU model and VRAM
nvidia-smi --query-gpu=name,memory.total --format=csv,noheader

Format:

verification:
  last_verified: 2025-10-25
  last_verification_note: "Verified build, inference, model caching, and outputs on [GPU_MODEL] ([VRAM]GB VRAM)"

What to include in the note:

• What was verified: build, inference, outputs
• Key aspects: model caching, GPU/CPU inference
• GPU info: Dynamically determine GPU model and VRAM using nvidia-smi (e.g., "A10 GPU (24GB VRAM)", "RTX 4090 (24GB)")
  • If no GPU: Use "CPU-only"
• VRAM usage: Peak VRAM used during inference (e.g., "Uses 8.2GB VRAM (34%) with 4-bit quantization")
  • Get with: nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits
  • Convert to GB and calculate percentage of total VRAM
  • Note if quantization (4-bit/8-bit) was used
• Any limitations: e.g., "Requires 8GB VRAM", "GPU memory constraints"
• Quick notes: e.g., "First run downloads 470MB models"

Example complete entry:

name: pose-estimation-dwpose
docker: dwpose
capability: perception/pose_estimation
# ... other fields ...

verification:
  last_verified: 2025-10-25
  last_verification_note: "Verified build, inference with video/image inputs, model caching (470MB models), and JSON outputs on [detected GPU]."

When to update:

• After completing full verification checklist (steps 1-7)
• Only if ALL success criteria pass
• When re-verifying after CVlization updates or fixes

Common Issues and Fixes

Build Failures

# Issue: Dockerfile can't find files
# Fix: Check COPY paths are relative to Dockerfile location

# Issue: flash-attn build from source
# Fix: Never compile flash-attn in verification; use a prebuilt wheel that matches the image's
#      torch and CUDA versions (e.g., install via a direct wheel URL).

# Issue: Dependency conflicts
# Fix: Check requirements.txt versions, update base image

# Issue: Large build context
# Fix: Add .dockerignore file

Inference Failures

# Issue: CUDA out of memory
# Fix: Use smaller model variant or CPU inference

# Issue: Model not found
# Fix: Check model name/path in predict.py, ensure internet connection

# Issue: Input file not found
# Fix: Check file paths, ensure examples/ directory exists

# Issue: Permission denied on outputs
# Fix: Ensure output directories exist and are writable

Output Issues

# Issue: Empty outputs
# Fix: Check model loaded correctly, verify input format

# Issue: Malformed JSON output
# Fix: Check output parsing logic in predict.py

# Issue: Outputs not saved
# Fix: Verify output directory path, check file write permissions

Example Commands

Document AI - Granite Docling

cd examples/perception/doc_ai/granite_docling
./build.sh
./predict.sh
# Check: outputs/output.md contains extracted document structure

Vision-Language - Moondream

cd examples/perception/vision_language/moondream2
./build.sh
./predict.sh examples/demo.jpg
# Check: outputs/ contains image description

CVL Integration

Inference examples integrate with CVL command system:

# List all available examples
cvl list

# Get example info
cvl info granite-docling

# Run example directly (uses example.yaml presets)
cvl run granite-docling build
cvl run granite-docling predict

Success Criteria

An inference example passes verification when:

1. ✅ Structure: All required files present, example.yaml valid
2. ✅ Build: Docker image builds without errors (both ./build.sh and cvl run <name> build)
3. ✅ Inference: Runs successfully on sample inputs (both ./predict.sh and cvl run <name> predict)
4. ✅ Outputs: Valid output files generated in expected format
5. ✅ Host Persistence: Outputs saved to HOST filesystem (user's cwd) when using cvl run, not just container
6. ✅ Input Resolution: Input files from user's cwd are correctly found via resolve_input_path()
7. ✅ Model Caching: Models cached to ~/.cache/ (typically ~/.cache/huggingface/), avoiding repeated downloads
8. ✅ CVL CLI: cvl info <name> shows correct metadata, build and predict presets work
9. ✅ Documentation: README explains how to use the example
10. ✅ Verification Metadata: example.yaml updated with verification field containing last_verified date and last_verification_note

Related Files

Check these files for debugging:

• predict.py - Core inference logic
• predict.sh - Docker run script
• Dockerfile - Environment setup
• example.yaml - CVL metadata and presets
• examples/ - Sample input files
• README.md - Usage instructions

Tips

• Use small sample inputs for fast validation
• Monitor GPU memory with nvidia-smi if using GPU
• Check docker logs <container> if inference hangs
• For HuggingFace models, set HF_TOKEN environment variable if needed
• Most examples support custom input paths as arguments to predict.sh
• Check example.yaml for supported parameters and environment variables
• For diffusion/flow matching models: Reduce sampling steps for faster validation (e.g., --num_steps 5 or -i num_steps=5 for Cog). Most models support step parameters:
  • Common parameter names: num_steps, num_inference_steps, steps
  • Typical defaults: 20-50 steps
  • Fast validation: 5-10 steps (lower quality but completes quickly)
  • Production: Full step count for best quality
  • Examples: Stable Diffusion, SVD, FLUX, AnimateDiff, Flow Matching models