Why does `clearml-init` keep failing with permission errors?

Usually a credentials issue. Make sure you're copying the full credentials from the web UI, including the secret key. If you're using Docker, the credentials need to be inside the container - either mount them or set environment variables.

My experiments aren't showing up in the UI. What's broken?

First check: Is your Task.init() call actually running? Add a print statement after it. Second: Check the console output for connection errors. Third: Verify you're looking at the right project in the UI. The project name is case-sensitive and creates new projects if it doesn't match exactly.

How do I get ClearML working with Jupyter notebooks without it tracking every cell?

Use `Task.init()` in the first cell, then `Task.current_task().close()` when you're done. Or set `auto_connect_frameworks={'matplotlib': False}` to stop it from capturing every plot you make during exploration.

My ClearML agent keeps crashing with "No space left on device" errors.

The agent downloads Docker images and creates conda environments that eat disk space. Set up log rotation and periodically clean Docker with `docker system prune -a`. Also check if your /tmp directory is full - the agent uses it for temporary files.

Why does my remote job fail with package import errors when it works locally?

Environment mismatch. The agent recreates your environment from requirements.txt or detects packages automatically. Check what ClearML captured in the "Installed Packages" section of your experiment. Often missing: system-level dependencies, conda packages not in pip, or development installs.

Agent shows "Running" but nothing happens for hours. What's the bullshit?

Check agent logs with `clearml-agent daemon --debug`. Common causes: Docker image pull taking forever, conda environment creation stuck, or network timeouts. Kill the agent and restart it - it's more reliable than trying to debug why it's stuck.

My large datasets keep failing to upload. How do I fix this?

ClearML times out on large files. Either: 1. Use `Task.upload_artifact()` with chunking for files >1GB 2. Pre-upload to S3/GCS and reference the URLs 3. Use ClearML Data for datasets - it handles chunking automatically 4. Increase network timeout in clearml.conf (though this often just delays the problem)

ClearML Data keeps complaining about "dataset already exists" when I try to version it.

Each dataset version needs a unique name. Use `clearml-data create --name dataset_v2` instead of reusing names. Or finalize the existing version first with `clearml-data close` before creating a new one.

PyTorch distributed training isn't logging properly. Only rank 0 shows up.

Known issue. Only the master process (rank 0) should call Task.init(). Use something like: ```python if torch.distributed.get_rank() == 0: task = Task.init(...) ``` Otherwise you get duplicate experiments or connection conflicts.

My custom metrics aren't appearing in the web UI.

ClearML auto-logs common metrics but misses custom ones. Use explicit logging: ```python Task.current_task().logger.report_scalar("custom_metric", "accuracy", value=0.95, iteration=epoch) ``` Make sure you call it inside the training loop, not just once at the end.

The web UI is incredibly slow with thousands of experiments. Any fixes?

Archive old experiments you don't need. Use the API for bulk operations instead of the web UI. Consider splitting projects - the UI scales poorly beyond ~1000 experiments per project. You can also upgrade your server hardware, but that's just throwing money at the problem.

My MongoDB is running out of disk space. How do I clean it up?

ClearML stores everything in MongoDB, including plots and artifacts metadata. Clean up with: 1. Delete old experiments from the UI 2. Run MongoDB compact operations 3. Consider moving large artifacts to external storage (S3, etc.) instead of storing in MongoDB

Experiments randomly fail with "Connection lost" errors.

Network instability between agent and server. Add retry logic or use a more stable network. Check if your load balancer has aggressive timeouts. Sometimes the MongoDB connection drops and ClearML doesn't handle it gracefully.

My model checkpoint upload failed and now I can't reproduce the experiment.

Always save checkpoints locally as backup. ClearML's auto-upload isn't 100% reliable. Use `task.upload_artifact()` explicitly for critical files, and check the upload succeeded before deleting local copies.

Where do I find actual solutions when ClearML breaks?

1. GitHub issues - search closed issues, not just open ones 2. ClearML Slack - the community is helpful and the devs are active 3. Stack Overflow clearml tag - but fewer answers than GitHub issues 4. The docs are decent but sometimes outdated - check GitHub examples instead

How do I report bugs effectively?

Include: ClearML version, Python version, OS, exact error message, and minimal code to reproduce. The devs are responsive but need details. "It doesn't work" gets ignored.

When should I pay for hosted vs self-host?

Self-host if you have DevOps resources and want control. Pay for hosted ($15/user/month for Pro) if you just want it to work and don't mind vendor lock-in. The hosted free tier (100GB) is generous - you'll know when you need to pay.

Is ClearML worth the complexity compared to just using MLflow?

If you only need experiment tracking, MLflow is simpler. If you need remote execution, data versioning, and pipeline orchestration in one tool, ClearML saves you from stitching together multiple tools. But expect to spend time debugging ClearML's quirks.

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ClearML MLOps Platform - AI-Optimized Technical Reference

Executive Summary

What: Open-source MLOps platform for automatic ML experiment tracking, remote execution, and model management
Key Value: Eliminates "which model version was that?" scenarios through automatic tracking with minimal code changes
Primary Use Case: Teams struggling with experiment reproducibility, resource tracking, and model lineage

Core Architecture

Components

ClearML Server: Data storage and coordination hub (MongoDB backend)
ClearML SDK: Python integration for automatic tracking via monkey-patching
ClearML Agent: Remote execution engine for compute resources
ClearML Data: Git-like dataset versioning system

Integration Method

from clearml import Task
task = Task.init(project_name="my_project", task_name="experiment_1")

Result: Automatic capture of code state, environment, parameters, metrics, and resources

Critical Implementation Intelligence

Automatic Tracking Capabilities

Code State: Git commit hash, branch, uncommitted changes (as diff)
Environment: Python version, package versions, CUDA version
Parameters: All hyperparameters, configuration files, command line arguments
Metrics: Loss curves, accuracy, custom metrics via framework hooks
Resources: Real-time CPU/GPU/memory/disk usage monitoring
Artifacts: Model checkpoints, datasets, plots with auto-upload

Framework Integration (Monkey-Patching)

PyTorch: Intercepts tensor.backward() calls, logs gradients
TensorFlow: Hooks into session runs and summary writes
Matplotlib: Auto-uploads plots to web UI
Tensorboard: Syncs logs automatically

Production Failure Modes and Solutions

Automatic Tracking Failures (10% of cases)

Custom logging frameworks: Manual logging required via Task.current_task().logger.report_scalar()
Distributed training: Only rank 0 should call Task.init() to prevent duplicates
Conda environments: ClearML must run inside same environment or captures wrong Python path

ClearML Agent Issues

Critical Failure: Agent crashes with Docker images >10GB

Solution: Use Docker mode instead of pip/conda despite slower setup
Workaround: Pre-build smaller, optimized images

Environment Recreation Problems:

Conda environment creation: 20+ minute delays
Network timeouts during long uploads kill jobs
Solution: Pin exact versions, use requirements.txt over automatic detection

Multi-node distributed training: Complex setup, often breaks

Recommendation: Use single-node multi-GPU until absolutely necessary

Storage and Upload Failures

Large artifact uploads (>5GB) timeout:

Solution: Use Task.upload_artifact() with chunking
Alternative: Pre-upload to S3/GCS, reference URLs

MongoDB storage scaling: Becomes bottleneck at ~10,000 experiments

Performance degradation: 30+ second queries
Solution: Database sharding required for serious scale

Memory and Resource Issues

Long-running experiments: Memory leaks, especially with large PyTorch models
Mitigation: Restart agents periodically
GPU billing shock: Resource monitoring caught $3000/month waste from misconfigured data loaders

Resource Requirements and Costs

Time Investments

Initial setup: 1-2 hours for basic tracking
Agent configuration: 4-8 hours for reliable remote execution
Pipeline setup: 1-2 days for complex workflows
Debugging time: 2-4 hours monthly for production issues

Compute Requirements

Server: Minimum 8GB RAM, 100GB storage for small teams
MongoDB scaling: Plan for database growth at 10MB per experiment
Network: Stable connection essential - unstable networks cause random failures

Human Expertise Requirements

Basic use: Any ML engineer familiar with Python
Production deployment: DevOps knowledge for server setup, Docker, networking
Advanced features: Understanding of distributed systems for multi-node training

Decision Support Matrix

When ClearML Adds Value

Team size >3: Collaboration benefits outweigh setup costs
Experiments >50: Manual tracking becomes unmaintainable
Reproducibility critical: Regulatory or business requirements
Resource costs >$1000/month: Optimization tracking pays for itself
Multiple compute environments: Consistent tracking across resources

When Alternatives Are Better

Simple experiment tracking only: MLflow is simpler
Individual researchers: Overhead not justified
Kubernetes-native workflows: Kubeflow better integrated
Budget <$500/month: Hosted solutions may be cost-prohibitive

Competitive Analysis - Operational Trade-offs

Platform	Setup Difficulty	Tracking Quality	Remote Execution	Cost at Scale	Breaking Points
ClearML	Moderate	Excellent auto-tracking	Built-in agents	Free self-hosted	MongoDB scaling at 10K experiments
MLflow	Easy	Manual but flexible	None - DIY	Free core	No built-in orchestration
W&B	Very easy	Excellent UI	None	Expensive >$200/month	Vendor lock-in, API limits
Neptune.ai	Easy	Good organization	None	Moderate pricing	Limited pipeline features
Kubeflow	Expert required	DIY nightmare	Kubernetes native	Infrastructure costs	Kubernetes complexity

Critical Configuration Warnings

Production Settings That Will Fail

Default timeout values: Too aggressive for large uploads
Automatic package detection: Misses system dependencies
MongoDB default configuration: Cannot handle production load

Security Considerations

Credential management: Agent credentials must be in container/environment
Network access: Agents need outbound HTTPS to server
Data exposure: All experiment data stored in MongoDB - plan access controls

Common Debugging Scenarios

"Experiments not appearing in UI"

Check Task.init() actually executes (add print statement)
Verify console for connection errors
Confirm exact project name match (case-sensitive)

"Agent shows Running but does nothing"

Check agent logs with --debug flag
Kill and restart agent (more reliable than debugging)
Common causes: Docker pulls, conda stuck, network timeouts

"Environment mismatch errors"

Check "Installed Packages" section in experiment
Missing: system dependencies, conda packages, dev installs
Solution: Explicit requirements.txt or Docker images

"Large dataset upload failures"

Use ClearML Data for datasets >1GB
Pre-upload to cloud storage, reference URLs
Increase network timeouts (delays problem, doesn't solve it)

Production-Ready Deployment Checklist

Infrastructure Requirements

MongoDB configured for production load
Storage backend configured (S3/GCS recommended)
Network connectivity stable between agents and server
Log rotation configured on agent machines
Backup strategy for experiment data

Operational Requirements

Agent monitoring and automatic restart
Version pinning for ClearML components
Credential management strategy
Archive strategy for old experiments
Performance monitoring for MongoDB queries

Team Onboarding

Documentation for common workflows
Troubleshooting runbook for agent issues
Data governance policies for experiment artifacts
Resource budget and monitoring alerts

Implementation Recommendation

Phase 1 (Week 1): Start with hosted free tier, add Task.init() to single training script
Phase 2 (Month 1): Set up remote execution with agents on existing compute
Phase 3 (Month 2-3): Implement data versioning and pipeline orchestration
Phase 4 (Month 3+): Self-host if free tier limits reached, implement production monitoring

Success Metric: 90% of experiments automatically tracked without manual intervention
Failure Signal: Team spending >2 hours/week on ClearML debugging - consider alternatives

Resources for Implementation

Essential Starting Points

Quick Start PyTorch Example: 15-line working example
ClearML Hosted Free Tier: 100GB free, start here
Installation Guide: Follow exactly, don't skip steps

Troubleshooting Resources

GitHub Closed Issues: Search error messages here first
ClearML Slack Community: Active developer support
Agent Troubleshooting Guide: Environment and network issues

Production Deployment

Docker Compose Setup: Simplest self-hosting
Kubernetes Helm Charts: For scale requirements
Configuration Guide: Timeout and storage settings

Useful Links for Further Investigation

ClearML Resources That Actually Help

Link	Description
GitHub Issues - Closed Tab	Search here first when things break. The closed issues have actual solutions, not just complaints. Use keywords from your error message.
ClearML Slack Community	The community is actually helpful. Post your error logs and you'll usually get a response within hours. The ClearML devs are active here.
Examples Directory	Real code that works. Skip the docs and copy-paste from here when you're trying to integrate with a new framework.
Installation Guide	Follow this exactly. Don't skip steps or you'll spend hours debugging credential issues.
Agent Setup	Essential for remote execution. The Docker mode is more reliable than pip/conda mode, despite being slower to set up.
API Reference	Useful when the automatic tracking misses something and you need to log manually. The search function actually works, unlike most documentation sites.
ClearML Data Tutorial	Dataset versioning that doesn't suck. Follow this if you're tired of "which data did we use for that model?" conversations.
Hyperparameter Optimization	Working examples of hyperparameter sweeps. Copy the patterns here instead of trying to build from scratch.
Stack Overflow clearml tag	Fewer answers than GitHub, but sometimes the solutions are cleaner. Good for specific integration questions.
ClearML Hosted Service (Free Tier)	100GB free tier. Sign up here instead of self-hosting until you know ClearML works for your use case. You can always migrate later.
Quick Start Example	15-line PyTorch example that shows how ClearML integration actually works. Run this first to make sure your setup works.
Docker Compose Setup	The simplest self-hosting option. Use this unless you need Kubernetes complexity. The docker-compose.yml file just works.
Kubernetes Helm Charts	For when Docker Compose isn't enough. The default values work for most cases - don't over-customize initially.
Agent Troubleshooting	When your remote jobs fail mysteriously. Most issues are environment-related or network timeouts.
Configuration Guide	Edit clearml.conf when default settings don't work. Common fixes: timeout values, storage paths, server URLs.
PyTorch Examples	Covers basic training, distributed training, and custom logging. The distributed example shows how to handle multi-GPU setups.
Jupyter Integration	How to track notebook experiments without capturing every cell during exploration. Essential for data science workflows.
MLflow Migration Guide	How to move from MLflow to ClearML. Understanding the architecture helps plan migration.
Weights & Biases Comparison	Honest comparison from ClearML perspective. Helps you decide if ClearML fits your needs.
Pipeline Orchestration Examples	When you outgrow simple experiment tracking. Start with the basic pipeline example before trying complex workflows.
Custom Logging	For metrics that automatic tracking misses. Essential when you're tracking custom visualizations or business metrics.
free hosted service	Start with the free hosted service and add Task.init() to a single training script. You'll know within 10 minutes if ClearML fits your workflow.

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ClearML MLOps Platform - AI-Optimized Technical Reference

Executive Summary

Core Architecture

Components

Integration Method

Critical Implementation Intelligence

Automatic Tracking Capabilities

Framework Integration (Monkey-Patching)

Production Failure Modes and Solutions

Automatic Tracking Failures (10% of cases)

ClearML Agent Issues

Storage and Upload Failures

Memory and Resource Issues

Resource Requirements and Costs

Time Investments

Compute Requirements

Human Expertise Requirements

Decision Support Matrix

When ClearML Adds Value

When Alternatives Are Better

Competitive Analysis - Operational Trade-offs

Critical Configuration Warnings

Production Settings That Will Fail

Security Considerations

Common Debugging Scenarios

"Experiments not appearing in UI"

"Agent shows Running but does nothing"

"Environment mismatch errors"

"Large dataset upload failures"

Production-Ready Deployment Checklist

Infrastructure Requirements

Operational Requirements

Team Onboarding

Implementation Recommendation

Resources for Implementation

Essential Starting Points

Troubleshooting Resources

Production Deployment

Useful Links for Further Investigation

ClearML Resources That Actually Help

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