mlship 0.2.0

Turn ML models into APIs with one command

mlship

Turn any ML model into a REST API with one command.

mlship serve model.pkl

Deploy your machine learning models locally in seconds - no Docker, no YAML, no configuration files.

Why mlship? Read WHY_MLSHIP.md to see how mlship compares to transformers-serve, vLLM, Ollama, and BentoML.

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Features

• ✅ One-command deployment - No configuration needed
• ✅ Multi-framework - sklearn, PyTorch, TensorFlow, HuggingFace (local + Hub)
• ✅ HuggingFace Hub - Serve models directly from Hub without downloading
• ✅ Built-in benchmarking - Measure latency and throughput with mlship benchmark
• ✅ Auto-generated API - REST API with interactive docs
• ✅ Works offline - Zero internet dependency after installation
• ✅ Fast - Deploy in seconds, predictions in milliseconds

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Quick Start

# Install
pip install mlship

# Serve any model
mlship serve model.pkl

Try HuggingFace Hub Models (No Files Needed!)

# Sentiment analysis
mlship serve distilbert-base-uncased-finetuned-sst-2-english --source huggingface

# Test it
curl -X POST http://localhost:8000/predict \
  -H "Content-Type: application/json" \
  -d '{"features": "This product is amazing!"}'

📖 See QUICKSTART.md for complete hands-on examples with:

• HuggingFace models (sentiment analysis, text generation)
• Local models (sklearn, PyTorch, TensorFlow)
• Training code, curl commands, and expected responses

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Supported Frameworks

Framework	File Format	Example
scikit-learn	.pkl, .joblib	mlship serve model.pkl
PyTorch	.pt, .pth	mlship serve model.pt
TensorFlow	.h5, .keras, SavedModel	mlship serve model.h5
HuggingFace (local)	Model directory	mlship serve ./sentiment-model/
HuggingFace (Hub)	Model ID	mlship serve bert-base-uncased --source huggingface

Note: For custom PyTorch models, use TorchScript format (torch.jit.save()) for best compatibility.

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HuggingFace Hub Support

Serve models directly from HuggingFace Hub:

mlship serve gpt2 --source huggingface
mlship serve distilbert-base-uncased-finetuned-sst-2-english --source huggingface

Models are downloaded on first use and cached locally. See QUICKSTART.md for more examples.

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API Endpoints

Every model automatically gets:

• POST /predict - Make predictions
• GET /health - Health check
• GET /info - Model metadata
• GET /docs - Interactive Swagger UI documentation

Examples in QUICKSTART.md.

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Advanced Usage

# Custom port
mlship serve model.pkl --port 5000

# Development mode (auto-reload on code changes)
mlship serve model.pkl --reload

# Custom model name
mlship serve model.pkl --name "fraud-detector"

# Custom preprocessing/postprocessing
mlship serve model.pkl --pipeline my_module.MyPipeline

# Benchmark performance
mlship benchmark model.pkl --requests 1000

# Benchmark with custom warmup and port
mlship benchmark model.pkl --requests 500 --warmup 10 --port 9000

# Benchmark with custom payload
mlship benchmark model.pkl --payload '{"features": [5.1, 3.5, 1.4, 0.2]}'

# Benchmark HuggingFace model
mlship benchmark distilbert-base-uncased-finetuned-sst-2-english --source huggingface --requests 50

# Benchmark with JSON output (for CI/CD)
mlship benchmark model.pkl --output json > results.json

Benchmark options:

Option	Default	Description
--requests N	100	Number of benchmark requests
--warmup N	5	Number of warmup requests
--port PORT	8000	Server port
--payload JSON	auto	Custom test payload as JSON string
--source	local	Model source (local or huggingface)
--output	text	Output format (text or json)

See CONTRIBUTING.md for custom pipeline documentation.

See QUICKSTART.md for detailed benchmarking guide.

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Use Cases

For Students & Learners

• Learn model serving without framework-specific tools
• One tool works for entire ML curriculum (sklearn → PyTorch → transformers)

For Data Scientists

• Prototype models locally before production
• Test models with realistic API interactions
• Share models with teammates without cloud setup

For Educators

• Teach framework-agnostic model serving concepts
• Create reproducible examples that work across frameworks

Read WHY_MLSHIP.md for detailed positioning.

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Documentation

• QUICKSTART.md - 🚀 Start here! Complete hands-on guide with:

  • HuggingFace Hub examples (sentiment analysis, text generation)
  • Local model examples (sklearn, PyTorch, TensorFlow)
  • Training code, curl commands, and expected responses
  • Perfect for first-time users and students

• WHY_MLSHIP.md - 🎯 Understanding mlship's unique value

  • Comparison with transformers-serve, vLLM, Ollama, BentoML
  • Multi-framework advantage explained
  • Target audience and use cases
  • When to use mlship vs alternatives

• CONTRIBUTING.md - 🛠️ For contributors

  • Development setup and workflow
  • Running tests and code style
  • Custom pipeline development
  • How to submit pull requests

• ARCHITECTURE.md - 🏗️ Technical deep dive

  • Design decisions and philosophy
  • Performance optimizations
  • Framework detection system
  • Internal architecture

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Installation

pip install mlship

With specific frameworks:

pip install mlship[sklearn]       # scikit-learn
pip install mlship[pytorch]       # PyTorch
pip install mlship[tensorflow]    # TensorFlow
pip install mlship[huggingface]   # HuggingFace
pip install mlship[all]           # All frameworks

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Contributing

We welcome contributions! See CONTRIBUTING.md for:

• Development setup
• Running tests
• Code style guidelines
• Custom pipeline development

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Support

• Issues: GitHub Issues
• Documentation: See docs linked above
• Examples: Check the examples/ directory

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License

MIT License - see LICENSE file for details.

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What Makes mlship Different?

mlship is the only zero-code tool that supports sklearn, PyTorch, TensorFlow, AND HuggingFace models with a single command. Read WHY_MLSHIP.md for detailed comparison with transformers-serve, vLLM, Ollama, and BentoML.

Quick comparison:

• ✅ Multi-framework (not just one)
• ✅ Zero code required (no Python files)
• ✅ Local-first (no cloud dependency)
• ✅ HuggingFace Hub integration
• ✅ Perfect for learning and prototyping

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Roadmap

✅ Implemented:

• ✅ Multi-framework support - sklearn, PyTorch, TensorFlow, HuggingFace
• ✅ HuggingFace Hub integration - Serve models directly from Hub with --source huggingface
• ✅ PyTorch TorchScript support - Full support for custom PyTorch models via TorchScript
• ✅ Built-in benchmarking - Measure latency (p50/p95/p99) and throughput with mlship benchmark
• ✅ Zero-code deployment - One command to serve any model
• ✅ Auto-generated REST API - With interactive Swagger docs
• ✅ Custom pipelines - Preprocessing/postprocessing support
• ✅ Local-first - Works completely offline (after installation)

🔄 Planned:

• 🔄 More HuggingFace tasks - Question answering, translation, summarization, NER (requires flexible input schemas)
• 🔄 PyTorch Hub integration - Serve models directly from PyTorch Hub with --source pytorch-hub
• 🔄 TensorFlow Hub integration - Serve models from TensorFlow Hub with --source tensorflow-hub
• 🔄 XGBoost & LightGBM support - First-class support for gradient boosting frameworks
• 🔄 Model versioning - Support specific model versions (e.g., --revision main)
• 🔄 GPU support - Automatic GPU detection and utilization
• 🔄 Batch inference - Efficient batch prediction endpoints
• 🔄 Authentication - Optional API key authentication for deployments

Want to contribute? See CONTRIBUTING.md or open an issue with your ideas!

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Happy serving! 🚀