4 Repos
Awesome GitHub RepositoriesModel Containerization Tools
Utilities for packaging machine learning models and their dependencies into standardized container images.
Distinguishing note: Specifically targets the packaging of ML models for portability, distinct from general-purpose container build tools.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Model Containerization Tools. Refine with filters or upvote what's useful.
Awesome Model Containerization Tools GitHub Repositories
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- mlflow/mlflow
mlflow/mlflow
26,554Auf GitHub ansehenPackage machine learning models into standardized containers with their dependencies and metadata to ensure consistent execution across various deployment environments.
Pythonagentopsagentsai - replicate/cog
replicate/cog
9,424Auf GitHub ansehenCog is a machine learning packaging tool and containerized model wrapper that bundles models and their dependencies into standardized Docker containers. It functions as an environment manager and inference server, ensuring consistent model execution across different hardware systems by resolving GPU drivers, system libraries, and Python dependencies. The project distinguishes itself by automatically generating RESTful HTTP servers and OpenAPI schemas based on defined model input and output types. It manages large model weights as external fixtures to optimize image size and utilizes a slot-ba
Bundles machine learning models and their dependencies into standardized Docker containers.
Go - graviraja/mlops-basics
graviraja/MLOps-Basics
8,585Auf GitHub ansehenMLOps-Basics is a collection of implementation guides and blueprints for automating the machine learning lifecycle. It provides practical workflows for managing the transition of models from training to production deployment, focusing on the integration of operational tools into the machine learning pipeline. The project features specific architectural patterns for deploying containerized models using serverless infrastructure and cloud registries. It includes frameworks for tracking large datasets and model artifacts via remote storage, as well as guides for converting models into standardiz
Provides utilities for packaging machine learning models and their dependencies into standardized Docker images for portability.
Jupyter Notebook - jina-ai/discoart
jina-ai/discoart
3,829Auf GitHub ansehenDiscoart is a diffusion model orchestration framework and distributed GPU generation engine designed to automate and scale image generation workflows across hardware clusters. It functions as a generative AI model API, providing HTTP and gRPC endpoints to trigger and retrieve images from diffusion models as a network service. The system distinguishes itself through a comprehensive task management layer that includes timeline-based prompt and parameter scheduling. It manages the generative art lifecycle by supporting state-based session serialization for recovery, YAML-based configuration mana
Packages diffusion models and dependencies into standardized container images for portable execution across hardware.
Pythonclip-guided-diffusioncreative-aicreative-art
