Workload Management & Orchestration Series: ClearML

If you've worked in high-performance computing (HPC), machine learning operations (MLOps), data science, data management or any scenario that involves vending shared compute resources to a user community, you've probably used various workload management and/or orchestration tools. This series explores some of these at a high level to help you understand each tool's characteristics beyond the marketing hype.

ClearML: Streamlining the ML lifecycle

ClearML is a powerful MLOps platform built to help teams streamline and automate every part of the machine learning lifecycle, from experimentation to production. While originally known as an open-source experiment tracking tool, ClearML has grown into a full-stack MLOps solution that supports orchestration, data and model versioning, pipeline automation, and efficient collaboration across teams.

Built for ML teams at scale

ClearML offers a modular architecture that can be adopted incrementally, starting with experiment tracking and scaling up to full workload orchestration and production deployment. This flexibility makes ClearML well-suited for teams of all sizes, whether you're running local Jupyter notebooks or managing distributed GPU clusters.

Core capabilities

Experiment tracking

Features include:

• Automatically logs code versions, parameters, metrics, logs, plots and artifacts.
• Solves: Manual experiment tracking, inconsistent documentation, reproducibility issues, and lack of collaboration across teams.

Task orchestration and scheduling

Features include:

• Remote execution of experiments across compute resources using ClearML Agent.
• Dynamic queuing and scheduling based on priorities and availability.
• Solves: Ad-hoc resource usage, underutilized infrastructure, manual job management and inefficient scaling.

Pipeline management

Features include:

• Define and orchestrate ML workflows using Python decorators or YAML.
• Auto-manages dependencies and artifact passing between steps.
• Solves: Fragile hand-offs between pipeline stages, hard-to-reproduce workflows, and lack of visibility into pipeline progress.

Data and model management

Features include:

• Dataset versioning, lineage tracking and centralized model registry.
• Solves: Data drift, unclear data/model provenance, and challenges in reusing or sharing assets.

Dashboard and UI

Features include:

• Web-based interface for viewing experiments, managing pipelines, tracking models and datasets.
• Solves: Lack of transparency and usability in ML operations, difficulty comparing experiments or understanding pipeline status.

Dynamic resource management

ClearML supports integration with Kubernetes for containerized workloads, and can automatically scale compute resources in cloud environments. Queued jobs are scheduled based on available compute, GPU requirements, or even spot instance usage, helping teams minimize idle hardware and maximize throughput.

Multi-tenancy and role-based access

ClearML Enterprise supports granular access controls, allowing different teams or departments to share infrastructure while keeping projects and data isolated. Projects can be scoped to individual users, groups or departments, and access can be controlled using ClearML's RBAC system.

Deployment model: What infra and platform teams should know

While ClearML is often praised for its developer-friendly interface and ease of use, it's also designed with infrastructure and platform engineering teams in mind. Whether you're managing a shared GPU cluster, designing a hybrid cloud strategy or enforcing strict security policies, ClearML's deployment model is built to meet you where you are.

At the heart of ClearML's architecture is a client-server model where the ClearML Server acts as the central coordination point for logging, tracking and task scheduling. The server itself is modular and stateless, typically deployed using Docker Compose or Helm, and is made up of three main components:

1. A Web UI for visualizing experiments and pipelines.
2. An API server that brokers communication between agents and clients.
3. A backend composed of PostgreSQL and MongoDB for storing metadata, artifacts and task state.

For queueing and performance optimization, you can optionally include Redis, which improves responsiveness and supports higher task throughput.

Compute workloads are handled by ClearML Agents — lightweight Python-based processes that run on any machine or node you want to turn into a worker. These agents poll the ClearML Server for queued tasks, set up the appropriate environment (using virtualenv, Conda, or Docker), and execute the task in an isolated runtime.

From an infrastructure perspective, this offers a few major benefits:

• You don't need to run the agent on every node — only where you want to allow jobs to be scheduled.
• Agents are decoupled from the core server and can be spun up dynamically, which makes scaling with Kubernetes or cloud auto-scaling groups straightforward.

In cloud environments, you can use ClearML's autoscaler to launch agents on-demand using spot instances or custom machine types, keeping costs in check without compromising performance.

The ClearML Server itself can run entirely on-prem, air-gapped or behind a VPN, making it compatible with regulated or secure environments.

For Kubernetes-native teams, ClearML integrates cleanly with existing clusters — agents can be deployed as pods that pick up jobs from designated queues, and teams can define priority queues, isolate GPU pools, or enforce custom affinity rules. Because agents and queues are loosely coupled, high availability is easily achieved by simply running redundant agents across your compute nodes.

For platform teams building internal ML platforms or AI factories, this architecture offers a clean separation of concerns: Application teams can focus on experimentation and model development, while infrastructure teams retain control over scheduling, compute access and scaling policies — all without building a DevOps stack from scratch.

Ultimately, whether you're deploying on a few workstations or scaling across dozens of GPU nodes in a hybrid setup, ClearML's architecture gives you the flexibility to start small and scale intelligently without locking you into a rigid control plane or requiring agents everywhere.

Unique advantages

ClearML's advantages include:

• Single-line setup: Get started with experiment tracking using one Python line.
• Unified platform: No need for separate tools for tracking, orchestration and pipelines.
• Full reproducibility: Track every aspect of a task — code, data, model, environment, etc.
• Built-in DevOps: Use ClearML Agent to manage, scale and automate ML workloads without building a custom DevOps layer.

Integrations

ClearML is framework-agnostic and integrates easily with popular tools in the ML ecosystem, such as:

• Development: Jupyter, PyCharm, VS Code
• Frameworks: PyTorch, TensorFlow, XGBoost, Scikit-learn
• Model serving: Triton Inference Server, MLflow
• Storage: S3, GCS, Azure Blob, local file systems
• Version control: GitHub, GitLab
• CI/CD: Jenkins, GitHub Actions, GitLab CI

Summary

ClearML is an end-to-end MLOps platform that offers the best of both worlds: the simplicity and flexibility of open-source tooling combined with the scalability and security features enterprises require. From a single experiment to thousands of production-grade pipelines, ClearML helps teams accelerate AI development without reinventing the wheel.