ClearML Setup for MLOps and Experiment Tracking

Lost experiment context? A proper ClearML MLOps setup solves this at the protocol level: every run captures everything — from git commit to GPU consumption. In a typical ML project, a data scientist spends up to 30% of their time retraining; ClearML cuts that time by 70%. Our team has 10+ years of MLOps experience, and we guarantee a reproducible setup.

In practice: a team of five specialists can spend hours searching for the right configuration. ClearML provides automatic experiment tracking, data management, and orchestration. We have deployed the platform in 50+ projects, from startups to enterprise. In one case — a fraud detection startup — reproduction time dropped from 3 days to 4 hours. Contact us for a free consultation and project assessment.

Why ClearML is Better than MLflow for Teams

Both tools solve tracking, but ClearML wins with its built-in task queue and automatic reproduction. The ClearML Agent can execute any experiment on another machine with a single command. MLflow requires external schedulers (Airflow, Kubeflow) for reruns. In our projects, ClearML speeds up reproduction 10x, saving significant GPU idle costs.

How to Set Up ClearML on Your Own Server

Installing the Self-Hosted Server

# Docker Compose for self-hosted git clone https://github.com/allegroai/clearml-server cd clearml-server docker compose -f docker-compose.yml up -d # Web UI: http://localhost:8080 

For production, add PostgreSQL and MinIO/S3 via environment variables. We have prepared a docker-compose.prod.yml template that includes SSL and backups. Setup on GPU nodes requires images with CUDA and NVIDIA drivers. The Agent automatically detects GPUs — not a single extra line of code.

Basic Usage

from clearml import Task, Logger # Initialization — automatically captures git status, pip packages, config task = Task.init( project_name="Fraud Detection", task_name="LGBM Baseline", task_type=Task.TaskTypes.training, ) # Parameters task.connect({ "learning_rate": 0.05, "n_estimators": 500, "dataset_version": "v2.3" }) # Metric logging logger = task.get_logger() for epoch in range(100): logger.report_scalar("Loss", "train", iteration=epoch, value=train_loss) logger.report_scalar("Loss", "val", iteration=epoch, value=val_loss) logger.report_scalar("F1", "val", iteration=epoch, value=val_f1) # Tables and images logger.report_table("Test Predictions", "Confusion Matrix", iteration=0, table_plot=cm_df) logger.report_matplotlib_figure("ROC Curve", "ROC", iteration=0, figure=fig) 

All metrics are available in the web UI immediately after launch. No additional setup required. You can log not only scalars but also images, tables, and audio.

ClearML Agent for Reproduction

Unique feature: automatic reproduction of any experiment:

# Start the agent (on another machine, including GPU) clearml-agent daemon --queue default --detached # Clone and re-run an experiment clearml-agent execute --id <task_id> 

The Agent pulls the environment from cache. Our clients save up to 30% of time on retraining.

Hyperparameter Optimization

from clearml.automation import HyperParameterOptimizer, RandomSearch optimizer = HyperParameterOptimizer( base_task_id=task.id, hyper_parameters=[ UniformParameterRange("learning_rate", min_value=0.001, max_value=0.1), DiscreteParameterRange("n_estimators", values=[100, 200, 500]), ], objective_metric_title="F1", objective_metric_series="val", objective_metric_sign="max", max_number_of_concurrent_tasks=4, optimizer_class=RandomSearch, total_max_jobs=50, ) optimizer.start() 

Hyperparameter optimization runs on the queue without blocking — agents execute tasks in parallel. Results are immediately available in the comparison table.

Common Mistakes When Setting Up ClearML

Additionally: many forget to configure artifact retention policies — by default ClearML stores everything indefinitely, filling up storage. We recommend setting a TTL via server configuration.

What's Included in Our Work

We provide a complete package:

• ClearML deployment on your infrastructure (Docker, Kubernetes, bare metal)
• Integration with your tools (GitLab, Jupyter, S3, GPU)
• Agent and queue configuration
• Usage documentation
• Team training (2–4 hour workshop)
• Support during launch phase (2 weeks)

Our team has over 10 years of MLOps experience and we guarantee a reproducible setup. We are a certified ClearML implementation partner.

MLOps — Wikipedia — terminology and methodologies.

Our Process

1. Analysis: audit of current MLOps stack, scalability requirements
2. Design: server, queue, and storage architecture
3. Implementation: deployment, integration setup, task template creation
4. Testing: reproducibility checks, load testing
5. Deployment: handover to operations, team training

Our Results

Over several years, we have implemented ClearML in 50+ projects, from startups to enterprise. Average reduction in experiment reproduction time: 70%. Clients save up to 50% of their MLOps infrastructure budget when migrating from MLflow Enterprise. We have a 95% success rate in reducing reproduction time.

Order ClearML setup for your stack. We will assess your project for free — contact us. Get a consultation today.