ML Server Docker Builder

VerifiedCaution

Build Docker containers for ML server components with support for multiple deep learning models and trading strategies.

Sby Skills Guide Bot
DevelopmentIntermediate
306/2/2026
Claude Code
#docker#ml#container#build

Recommended for

Our review

Builds Docker containers for ML server components, with support for specific models and build options.

Strengths

  • Automates multi-container ML builds
  • Supports --no-cache and --push options
  • Selective model building
  • CPU/GPU architecture handling

Limitations

  • Requires pre-configured Docker files and environment
  • Does not automatically download model weights
  • Depends on Docker Compose and container registry
When to use it

When you frequently rebuild ML containers for different models or configurations.

When not to use it

For simple non-containerized deployments or if Docker environment is not set up.

Security analysis

Caution
Quality score88/100

The skill uses docker compose build and push, which are powerful and could execute malicious code if the Dockerfiles or source contain it. No destructive commands are directly instructed, but the inherent risk of container builds warrants caution.

Findings
  • Docker build executes arbitrary instructions from Dockerfiles; ensure source repository is trusted.
  • Docker push may upload images containing secrets if not properly sanitized; verify registry credentials and image contents.

Examples

Build all ML containers
/build-ml
Build and push FinRL container
/build-ml --push finrl
Force rebuild without cache
/build-ml --no-cache

name: 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 mixing
  • master - Model orchestration
  • samba - Samba-based models
  • macrohft - Macro HFT strategies

Instructions

When this skill is invoked:

  1. Parse arguments:

    • --no-cache: Add --no-cache to docker build
    • --push: Push to container registry after build
    • --cpu: Use CPU-only base images
    • Model name: Build only that model's container
  2. Navigate to ML server directory:

    cd services/ml-server
    
  3. Build containers:

    # All containers
    docker compose build $NO_CACHE
    
    # Specific model
    docker compose build $MODEL $NO_CACHE
    
  4. If --push specified:

    • Verify registry credentials
    • Tag images appropriately
    • Push to registry:
      docker compose push $MODEL
      
  5. Report build results:

    • Build time per container
    • Final image sizes
    • Any build warnings
    • Layer cache efficiency
  6. Common build issues:

    • CUDA not found: Use --cpu flag 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

Image Tags

  • latest - Most recent build
  • v{version} - Release versions
  • {git-sha} - Specific commit builds
  • cpu - CPU-only builds
Related skills