Troubleshooting ClearML: Common Pitfalls in Enterprise-Scale ML Workflows

Details: Category: Machine Learning and AI Tools; By Mindful Chase; 06.Aug; Hits: 24

ClearML is a powerful open-source MLOps suite for experiment tracking, orchestration, and model deployment. While it simplifies lifecycle management for machine learning teams, users working in enterprise-scale setups frequently encounter subtle operational issues—especially when integrating remote workers, managing storage backends, or deploying agents at scale. These challenges often lead to silent experiment failures, orphaned tasks, or inconsistent artifact synchronization across distributed nodes. Addressing these issues requires deep understanding of ClearML's architecture and robust DevOps practices.

Mindful Chase

Writing Code, Writing Stories

tbd

Experience

tbd

More to Explore

Understanding ClearML Architectural Overview

Core Components

ClearML's system consists of:

ClearML Server: REST API, Web UI, and data backend
ClearML Agent: Runs jobs and manages environments
ClearML SDK: Python interface for experiment tracking

Common Sources of Systemic Failures

Failures in enterprise-scale ClearML installations often stem from:

Improperly configured queues or agents
Storage backend mismatches (e.g., S3, NFS, GCS)
Silent timeouts due to firewall or DNS misconfigurations

Diagnosing Orphaned or Failed Tasks

Use Task Status Codes

Check for stuck tasks with statuses like 'Queued' or 'Running' long after expected completion. ClearML UI and CLI can help.

clearml-task --list --status queued

Analyze ClearML Agent Logs

Agent logs often reveal errors in environment setup, missing packages, or Docker failures.

tail -f ~/.clearml/agent.log

Investigate Communication Between Agent and Server

Firewall or proxy rules may prevent agents from registering or pulling jobs, leading to visible but inactive workers.

Storage and Artifact Sync Issues

Symptoms of Storage Misconfiguration

Missing model checkpoints, failed dataset uploads, or broken links in the UI point to misaligned storage configurations.

Check Storage Credentials

Verify that your clearml.conf or environment variables correctly configure credentials and bucket paths:

[aws]
aws_access_key_id=AKIA...
aws_secret_access_key=xxxx
bucket_name=clearml-artifacts

Validate Upload Flow

Test manual uploads using the ClearML SDK to isolate backend write issues:

from clearml import StorageManager
StorageManager.upload_file('model.pkl')

Worker Environment Problems

Docker Image Failures

If using Docker mode, missing base images or invalid build specs will cause silent task failure.

# Agent log output
Could not start task: failed to pull image "clearml/base:cuda-11.2"

Virtualenv Issues

Virtual environments sometimes fail if dependencies conflict or system packages are missing. Pin versions carefully in requirements.txt or use conda where supported.

Fixing Common Issues Step-by-Step

Step 1: Confirm Agent Connectivity

Ensure agent shows up as 'Connected' in the Web UI and can pull tasks from queues.

Step 2: Verify Queue and Task Assignment

Each worker must be explicitly assigned to a queue. Use the UI or CLI:

clearml-agent daemon --queue default --gpus 0

Step 3: Inspect Environment Build Logs

Set verbosity to debug to capture detailed errors in pip install, Docker init, or dataset fetches.

clearml-agent daemon --log-level DEBUG

Step 4: Validate Storage Access

Run artifact upload and download tests using the SDK. Use signed URLs or presigned tokens when applicable.

Step 5: Restart Agent With Clean Cache

Corrupted cache folders can cause retries or task skips. Clear the agent cache:

rm -rf ~/.clearml/cache/*

Best Practices for ClearML Stability at Scale

Use centralized logging (e.g., ELK, Datadog) for agent and task monitoring
Pin package versions to prevent environment drift
Use named queues for specialized jobs (GPU, CPU, large memory)
Deploy HA storage (e.g., S3 with lifecycle rules)
Keep agent, server, and SDK versions in sync across teams

Conclusion

ClearML offers exceptional flexibility in orchestrating machine learning pipelines, but that flexibility introduces operational risk without disciplined configuration and monitoring. Orphaned tasks, failed artifact sync, or silent environment issues can halt production workflows without triggering alerts. Teams must treat ClearML deployment as part of their core DevOps stack—leveraging version control, observability, and testing rigorously. When done right, ClearML becomes a scalable foundation for repeatable, transparent ML development.

FAQs

1. Why do ClearML tasks remain in 'queued' status indefinitely?

Usually, no available agent is subscribed to the queue. Verify agent registration, queue name, and connectivity to the server.

2. How do I resolve missing artifacts in the UI?

Check if storage credentials and paths are correctly configured in clearml.conf. Broken links often point to permission or bucket errors.

3. Can I use ClearML without Docker?

Yes. Use virtualenv mode by specifying '--docker-mode disabled'. Ensure all required packages are available in the base environment.

4. How do I prevent environment rebuilds on every task?

Use cached environments or pre-built Docker images with pinned dependencies. Alternatively, enable reuse of venvs in agent settings.

5. What's the best way to scale ClearML for a large team?

Deploy agents on Kubernetes, use object storage like S3, and segment workloads by queue type. Monitor with centralized logging for early detection of issues.

Contact Us