Our review
Builds Docker containers for ML server components using docker compose.
Strengths
- Automates building multiple ML models together or individually.
- Supports advanced options like push to registry, no-cache builds, and CPU-only builds.
- Clear structure for different models (FinRL, StockMixer, etc.).
Limitations
- Depends on having the appropriate Dockerfiles and docker compose setup in the project.
- May fail if model weights need to be downloaded separately or if CUDA dependencies are missing.
- Does not handle deployment or registry management beyond pushing.
Use this skill to quickly build a Docker image for an ML server with multiple model components.
Do not use it if you need to deploy models to Kubernetes or manage complex CI/CD pipelines.
Security analysis
CautionThe skill uses Docker commands (build, push) which are powerful and could be misused if the user runs malicious content, but the instructions themselves are legitimate and do not include destructive, exfiltrating, or obfuscated actions. The skill is safe for its intended purpose of building ML containers.
No concerns found
Examples
/build-ml --push/build-ml --no-cache 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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