Notre avis
Construit des conteneurs Docker pour les composants d'un serveur ML, avec prise en charge de modèles spécifiques et d'options de compilation.
Points forts
- Automatise la construction multi-conteneurs pour ML
- Support des options --no-cache et --push
- Construction sélective par modèle
- Gestion des architectures CPU/GPU
Limites
- Nécessite des fichiers Docker et un environnement configurés
- Ne gère pas le téléchargement automatique des poids de modèles
- Dépend de Docker Compose et du registre de conteneurs
Lorsque vous devez reconstruire fréquemment des conteneurs ML pour différents modèles ou configurations.
Pour des déploiements simples sans conteneurisation ou si l'environnement Docker n'est pas installé.
Analyse de sécurité
PrudenceThe 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.
- •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.
Exemples
/build-ml/build-ml --push finrl/build-ml --no-cachename: 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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