Our review
Builds Docker containers for ML server components.
Strengths
- Automates building multiple ML containers.
- Supports options like --no-cache, --push, --cpu.
- Allows building specific model containers.
Limitations
- Requires Docker and registry access.
- Works with a specific project structure.
- Does not handle deployment or advanced orchestration.
When you need to build and optionally push Docker images for multiple ML models.
For simple single-container builds without advanced options.
Security analysis
CautionThe skill orchestrates Docker builds and pushes, which requires trust in the compose files and registry credentials. While the skill itself doesn't contain destructive commands, the power of Docker warrants caution.
- •Uses Docker commands, which can access the host's Docker daemon and could potentially be exploited if the compose files are malicious.
- •Involves pushing to a container registry, which may expose credentials if not handled securely.
- •No declared allowed-tools, but the skill executes docker compose commands.
Examples
/build-ml --no-cache/build-ml --push finrl/build-ml --cpuname: build-ml description: Build ML server Docker containers argument-hint: "[--push|--no-cache|model]"
ML Server Builder
Build Docker containers for ML server components.
Usage
/build-ml- Build all ML containers/build-ml --no-cache- Build without cache/build-ml --push- Build and push to registry/build-ml finrl- Build specific model container/build-ml --cpu- Build CPU-only versions
ML Server Structure
services/ml-server/
├── docker-compose.yml # ML services orchestration
├── docker-compose.dev.yml # Development config
├── Dockerfile # Base ML image
├── finrl/ # FinRL deep learning
├── stockmixer/ # Stock mixing models
├── master/ # Master orchestrator
├── samba/ # Samba models
├── macrohft/ # Macro HFT models
└── src/ # Shared ML code
Available Models
finrl- Deep reinforcement learning (FinRL)stockmixer- Multi-asset mixingmaster- Model orchestrationsamba- Samba-based modelsmacrohft- Macro HFT strategies
Instructions
When this skill is invoked:
-
Parse arguments:
--no-cache: Add--no-cacheto docker build--push: Push to container registry after build--cpu: Use CPU-only base images- Model name: Build only that model's container
-
Navigate to ML server directory:
cd services/ml-server -
Build containers:
# All containers docker compose build $NO_CACHE # Specific model docker compose build $MODEL $NO_CACHE -
If
--pushspecified:- Verify registry credentials
- Tag images appropriately
- Push to registry:
docker compose push $MODEL
-
Report build results:
- Build time per container
- Final image sizes
- Any build warnings
- Layer cache efficiency
-
Common build issues:
- CUDA not found: Use
--cpuflag or install NVIDIA container toolkit - Out of memory: Increase Docker memory limit
- Pip install fails: Check requirements.txt for version conflicts
- Model weights missing: Download from model registry first
- CUDA not found: Use
Image Tags
latest- Most recent buildv{version}- Release versions{git-sha}- Specific commit buildscpu- CPU-only builds
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