Deploying ClearML as an Azure ML Alternative

说明与用途

ClearML 可替代 Azure ML，提供自托管 MLOps 平台，支持实验跟踪、流水线、超参数优化和 Triton 模型服务，适用于任何基础设施。

问题

Azure Machine Learning ties experiment tracking, pipelines, and model serving to Azure-specific APIs and managed‑compute pricing, limiting flexibility and cost control.

结果

ClearML provides a fully self‑hosted alternative to Azure ML, enabling experiment tracking, pipeline orchestration, hyper‑parameter tuning, and model serving with Triton on any Linux infrastructure, while eliminating Azure‑specific costs and dependencies.

工作流

• Set up Linux server with Docker, Docker Compose, and DNS records for subdomains
• Configure system limits and create directories for ClearML data
• Download and edit docker‑compose.yml to use Traefik routing and bridge networks
• Create .env with ClearML host URLs
• Start ClearML services with docker compose
• Set up Traefik with TLS via Let’s Encrypt and dynamic routing
• Create admin account and generate credentials in ClearML UI
• Deploy an agent on the same or a separate machine, installing clearml‑agent and initializing with credentials
• Start the agent daemon, optionally specifying GPU queues
• Install ClearML SDK in a Python virtual environment
• Use the SDK to track experiments, build pipelines, run hyper‑parameter optimization, and serve models with Triton

证据

ClearML Server with Docker Compose and Traefik, configures agents for remote execution, tracks an experiment, builds a pipeline, runs hyperparameter optimization, and serves a model with Triton.

来源