Skills Guide

ML Server Docker Builder

VerifiedCaution

Builds Docker containers for ML server components (FinRL, StockMixer, etc.). Supports options like --no-cache, --push, --cpu, or building a specific model.

Download and use
Sby Skills Guide Bot
DevelopmentIntermediate
6/2/2026
Claude Code
#ml#docker#container#build

Recommended for

Backend developerClaude CodeCode reviewFrontend developerFullstack developerDevOps engineer

Automated summary

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 to use it

When you need to build and optionally push Docker images for multiple ML models.

When not to use it

For simple single-container builds without advanced options.

Security analysis

Caution
Quality score88/100

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.

Findings
  • •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.
How this analysis is made

Examples

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

From the file to a first result

The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.

View the source and supporting files

This download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.

1. Download the file

Download SKILL.md

From the root of a test project, save the file as .skill-downloads/ml-server-docker-builder-mmh8qr68/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.

Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.

mkdir -p '.skill-downloads/ml-server-docker-builder-mmh8qr68' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/ml-server-docker-builder-mmh8qr68/download' --output '.skill-downloads/ml-server-docker-builder-mmh8qr68/SKILL.md.part' && mv '.skill-downloads/ml-server-docker-builder-mmh8qr68/SKILL.md.part' '.skill-downloads/ml-server-docker-builder-mmh8qr68/SKILL.md'

2. Check prerequisites

Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.

No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.

The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.

3. Load the skill

After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.

Claude Code
test -f '.skill-downloads/ml-server-docker-builder-mmh8qr68/SKILL.md' && test -s '.skill-downloads/ml-server-docker-builder-mmh8qr68/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/build-ml' && cp -R '.skill-downloads/ml-server-docker-builder-mmh8qr68' '.claude/skills/build-ml'

OpenSkills

OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.

Official OpenSkills installation
openskills install './.skill-downloads/ml-server-docker-builder-mmh8qr68'

openskills read 'ml-server-docker-builder-mmh8qr68'

Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.

For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.

openskills sync

Manual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.

4. Try a first task

Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:

Read .skill-downloads/ml-server-docker-builder-mmh8qr68/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.

Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.

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