3 مستودعات
Provisioned cloud environments featuring GPU acceleration and interactive notebooks for ML development.
Distinct from Cloud Development Environments: Combines cloud provisioning specifically with GPU-accelerated notebook environments for ML
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ModelScope is a comprehensive machine learning platform that functions as a model hub, training framework, inference engine, and cloud development environment. It provides a centralized repository for discovering, downloading, and managing pre-trained models and datasets across multiple modalities, including natural language, vision, and speech. The platform features a unified interface for multimodal model inference and a standardized framework for fine-tuning and evaluating large-scale models. It supports distributed training to scale workloads across multiple processors and provides contai
Launches GPU-accelerated notebook environments in the cloud for immediate model development and testing.
This project is an educational resource and software architecture framework focused on the technical foundations of large language model engineering. It provides a collection of guides and design patterns for building and maintaining professional, scalable systems using large language models. The resource outlines practical implementation patterns for orchestrating workflows that combine prompt engineering, model calls, and vector databases. It focuses on transforming prompt development into a structured engineering process to ensure reliable model outputs in production environments. The cov
Notebooks are designed to run on remote GPU instances, with instructions for attaching to cloud compute resources.
Azure Machine Learning Notebooks is a cloud-based environment for developing and executing interactive Jupyter notebooks within a managed machine learning workspace. It provides managed machine learning compute through cloud-based workstations and containerized environments pre-configured with GPU drivers and kernels for high-performance model training. The project functions as a distributed GPU training platform and an ML experiment tracking system to monitor training metrics and version data assets. It also serves as an MLOps pipeline orchestrator for automating modular workflows and a mode
Provides managed cloud workstations with pre-configured GPU drivers and kernels for interactive notebook development.