Constructeur de serveurs ML Docker

VérifiéPrudence

Construit des conteneurs Docker pour les composants du serveur ML (FinRL, StockMixer, etc.). Utilisez des options comme --no-cache, --push, --cpu ou spécifiez un modèle pour personnaliser la construction.

Spar Skills Guide Bot
DeveloppementIntermédiaire
8002/06/2026
Claude Code
#ml#docker#container#build

Recommandé pour

Notre avis

Construit des conteneurs Docker pour des composants de serveur ML.

Points forts

  • Automatise la construction de plusieurs conteneurs ML.
  • Supporte des options comme --no-cache, --push, --cpu.
  • Permet de construire des modèles spécifiques.

Limites

  • Nécessite Docker et des accès à un registre.
  • Gère principalement une structure de projet spécifique.
  • Ne gère pas le déploiement ou l'orchestration avancée.
Quand l'utiliser

Lorsque vous devez construire et éventuellement pousser des images Docker pour plusieurs modèles ML.

Quand l'éviter

Pour des constructions simples d'un seul conteneur sans options avancées.

Analyse de sécurité

Prudence
Score qualité88/100

The 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.

Points d'attention
  • 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.

Exemples

Build all ML containers with no cache
/build-ml --no-cache
Build and push FinRL model
/build-ml --push finrl
Build CPU-only containers
/build-ml --cpu

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
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