mlflow-modal-deploy 0.5.1

MLflow deployment plugin for Modal serverless GPU infrastructure (actively maintained)

mlflow-modal-deploy

Deploy MLflow models to Modal's serverless GPU infrastructure with a single command.

Installation

pip install mlflow-modal-deploy

Features

• One-command deployment: Deploy any MLflow model to Modal's serverless infrastructure
• GPU support: T4, L4, L40S, A10, A100, A100-40GB, A100-80GB, H100, H200, B200
• Auto-scaling: Configure min/max containers, scale-down windows
• Dynamic batching: Built-in request batching for high-throughput workloads
• Automatic dependency detection: Extracts requirements from model artifacts
• Wheel file support: Handles private dependencies packaged as wheel files
• MLflow CLI integration: Use familiar mlflow deployments commands

Quick Start

Python API

from mlflow.deployments import get_deploy_client

# Get the Modal deployment client
client = get_deploy_client("modal")

# Deploy a model
deployment = client.create_deployment(
    name="my-classifier",
    model_uri="runs:/abc123/model",
    config={
        "gpu": "T4",
        "memory": 2048,
        "min_containers": 1,
    }
)

print(f"Deployed to: {deployment['endpoint_url']}")

# Make predictions
predictions = client.predict(
    deployment_name="my-classifier",
    inputs={"feature1": [1, 2, 3], "feature2": [4, 5, 6]}
)

CLI

# Deploy a model
mlflow deployments create -t modal -m runs:/abc123/model --name my-model

# Deploy with GPU
mlflow deployments create -t modal -m runs:/abc123/model --name gpu-model \
    -C gpu=T4 -C memory=4096

# List deployments
mlflow deployments list -t modal

# Get deployment info
mlflow deployments get -t modal --name my-model

# Delete deployment
mlflow deployments delete -t modal --name my-model

Configuration Options

Option	Type	Default	Description
gpu	str/list	None	GPU type (T4, L4, L40S, A10, A100, A100-40GB, A100-80GB, H100, H200, B200), multi-GPU (H100:8), dedicated (H100!), or fallback list (["H100", "A100"])
memory	int	512	Memory allocation in MB
cpu	float	1.0	CPU cores
timeout	int	300	Request timeout in seconds
startup_timeout	int	None	Container startup timeout (overrides timeout during model loading)
scaledown_window	int	60	Seconds before idle container scales down
concurrent_inputs	int	1	Max concurrent requests per container
target_inputs	int	None	Target concurrency for autoscaler (enables smarter scaling)
min_containers	int	0	Minimum warm containers
max_containers	int	None	Maximum containers
buffer_containers	int	None	Extra idle containers to maintain under load
enable_batching	bool	False	Enable dynamic batching
max_batch_size	int	8	Max batch size when batching enabled
batch_wait_ms	int	100	Batch wait time in milliseconds
python_version	str	auto	Python version (auto-detected from model)
extra_pip_packages	list	[]	Additional pip packages to install at deployment time
pip_index_url	str	None	Custom PyPI index URL for private packages
pip_extra_index_url	str	None	Additional PyPI index URL (fallback)
modal_secret	str	None	Modal secret name containing pip credentials

Authentication

Configure Modal authentication before deploying:

# Interactive setup
modal setup

# Or use environment variables
export MODAL_TOKEN_ID=your-token-id
export MODAL_TOKEN_SECRET=your-token-secret

Advanced Usage

Deploy to Specific Workspace

# Use workspace-specific URI
client = get_deploy_client("modal:/production")

Or via CLI:

mlflow deployments create -t modal:/production -m runs:/abc123/model --name my-model

High-Throughput Deployment with Batching

client.create_deployment(
    name="batch-classifier",
    model_uri="runs:/abc123/model",
    config={
        "gpu": "A100",
        "enable_batching": True,
        "max_batch_size": 32,
        "batch_wait_ms": 50,
        "min_containers": 2,
        "max_containers": 20,
    }
)

Adding Extra Packages at Deployment Time

Use extra_pip_packages when the model's auto-detected requirements are incomplete or you need production-specific packages:

client.create_deployment(
    name="my-model",
    model_uri="runs:/abc123/model",
    config={
        "gpu": "A100",
        "extra_pip_packages": [
            "accelerate>=0.24",      # GPU inference optimization
            "prometheus_client",     # Monitoring
            "structlog",             # Production logging
        ],
    }
)

Common use cases:

• Missing transitive dependencies: Packages MLflow didn't auto-detect
• Inference optimizations: accelerate, bitsandbytes, onnxruntime-gpu
• Production monitoring: prometheus_client, opentelemetry-api
• Version overrides: Pin specific versions for compatibility

Deploying with Private Packages

For private PyPI servers or authenticated package repositories:

Step 1: Create a Modal secret with your credentials:

# Create a secret with your private PyPI credentials
modal secret create pypi-auth \
    PIP_INDEX_URL="https://user:[e-mail]/simple/" \
    PIP_EXTRA_INDEX_URL="https://pypi.org/simple/"

Step 2: Reference the secret in your deployment:

client.create_deployment(
    name="my-model",
    model_uri="runs:/abc123/model",
    config={
        # Option 1: Use Modal secret for authenticated access
        "modal_secret": "pypi-auth",
        "extra_pip_packages": ["my-private-package>=1.0"],

        # Option 2: Direct URL (for unauthenticated private repos)
        # "pip_index_url": "https://pypi.my-company.com/simple/",
        # "pip_extra_index_url": "https://pypi.org/simple/",
    }
)

Supported private package sources:

• Private PyPI servers: Artifactory, CodeArtifact, DevPI, Nexus
• Authenticated indexes: Any pip-compatible index with auth tokens
• Wheel files: Already supported via the code/ directory in model artifacts

Models with Private Dependencies

If your model includes wheel files in the code/ directory, they are automatically detected and installed:

model/
├── MLmodel
├── requirements.txt
├── code/
│   └── my_private_package-1.0.0-py3-none-any.whl  # Auto-detected
└── ...

Local Development

Test your deployment locally before deploying to Modal:

from mlflow_modal import run_local

run_local(
    target_uri="modal",
    name="test-model",
    model_uri="runs:/abc123/model",
    config={"gpu": "T4"}
)

Requirements

• Python 3.10+
• MLflow 2.10.0+
• Modal 1.0.0+

Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

Development Setup

# Clone the repository
git clone https://github.com/debu-sinha/mlflow-modal-deploy.git
cd mlflow-modal-deploy

# Install with dev dependencies
uv sync --extra dev

# Install pre-commit hooks
uv run pre-commit install

# Run tests
uv run pytest tests/ -v

License

Apache License 2.0

Acknowledgments

• MLflow - Open source platform for the ML lifecycle
• Modal - Serverless cloud for AI/ML

Support

• GitHub Issues - Bug reports and feature requests
• MLflow Slack - Community discussion