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2 repositorios

Awesome GitHub RepositoriesPipeline Resource Allocators

Systems for dynamically assigning CPU, memory, or GPU resources to specific pipeline steps.

Distinct from GPU Resource Allocators: Distinct from GPU Resource Allocators: focuses on general pipeline step resource allocation rather than just GPU-specific buffers.

Explore 2 awesome GitHub repositories matching devops & infrastructure · Pipeline Resource Allocators. Refine with filters or upvote what's useful.

Awesome Pipeline Resource Allocators GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • maiot-io/zenmlAvatar de maiot-io

    maiot-io/zenml

    5,452Ver en GitHub↗

    ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself

    Allocates specific CPU, memory, or GPU resources to pipeline execution to meet performance demands.

    Python
    Ver en GitHub↗5,452
  • zenml-io/zenmlAvatar de zenml-io

    zenml-io/zenml

    5,451Ver en GitHub↗

    ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented

    Assigns specific CPU, GPU, and memory requirements to individual pipeline steps to ensure sufficient compute capacity.

    Pythonagentopsagentsai
    Ver en GitHub↗5,451
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