ClearML — End-to-End MLOps Platform

What it is

ClearML is an open-source MLOps platform that covers the full machine learning lifecycle: experiment tracking, pipeline orchestration, data management, model versioning, and serving. It integrates with existing ML code via a 2-line Python addition -- no code rewrite required.

ClearML targets ML engineers and data scientists who need to track experiments, reproduce results, and deploy models without stitching together multiple tools. The platform is Apache 2.0 licensed and self-hostable.

How it saves time or tokens

Without ClearML, teams use separate tools for experiment tracking (MLflow), orchestration (Airflow), and serving (FastAPI). ClearML consolidates these into a single platform with a unified UI. The 2-line integration means you can add tracking to any existing training script without restructuring your code.

The estimated token cost for describing a ClearML setup workflow is approximately 407 tokens.

How to use

1. Install and configure:

pip install clearml
clearml-init  # Configure credentials

1. Add 2 lines to any training script:

from clearml import Task
task = Task.init(project_name='my-project', task_name='experiment-1')

# Your existing training code below
# ClearML auto-captures: hyperparams, metrics, artifacts, code changes

1. Open the ClearML web UI to view experiments, compare metrics, and manage models.

Example

from clearml import Task, Logger

task = Task.init(
    project_name='nlp-classification',
    task_name='bert-finetune-v2'
)

# ClearML auto-logs these parameters
params = {
    'learning_rate': 2e-5,
    'batch_size': 32,
    'epochs': 3,
    'model': 'bert-base-uncased'
}
task.connect(params)

# Log metrics during training
logger = Logger.current_logger()
for epoch in range(params['epochs']):
    loss = train_one_epoch()
    logger.report_scalar('loss', 'train', loss, epoch)
    accuracy = evaluate()
    logger.report_scalar('accuracy', 'val', accuracy, epoch)

Related on TokRepo

• AI Tools for Monitoring -- ML observability and experiment tracking tools
• AI Tools for DevOps -- Infrastructure and deployment automation

Common pitfalls

• ClearML auto-captures many things (git diff, installed packages, console output). This can expose sensitive information in shared projects. Review what is captured before sharing experiment pages.
• The self-hosted ClearML server requires a machine with enough storage for artifacts and models. Start with at least 50GB of disk space.
• Pipeline orchestration requires the ClearML Agent running on worker machines. Install and configure agents separately from the server.