Troubleshooting ClearML: Common Issues and Solutions

Details: Category: Machine Learning and AI Tools; By Mindful Chase; 28.Feb; Hits: 3

ClearML is an open-source machine learning (ML) platform designed for experiment tracking, orchestration, and dataset management. It helps streamline ML workflows, but users often encounter issues such as failed experiment tracking, misconfigured agents, performance bottlenecks, API integration errors, and storage-related challenges. This article explores common troubleshooting scenarios in ClearML, their root causes, and effective solutions.

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1. ClearML Experiment Tracking Not Working

Understanding the Issue

Experiments fail to appear in the ClearML dashboard, or logs are missing.

Root Causes

Incorrect ClearML configuration file.
Connectivity issues between the client and the ClearML server.
Missing or expired ClearML credentials.

Fix

Verify the ClearML configuration file ~/clearml.conf:

clearml-init

Ensure the ClearML server is accessible:

ping app.clear.ml

Regenerate API credentials if expired:

ClearML Web UI → Profile → API Credentials

2. ClearML Agent Not Executing Jobs

Understanding the Issue

ClearML agent does not pick up or execute queued tasks.

Root Causes

Agent not registered or misconfigured.
Required dependencies missing on the execution machine.
Insufficient permissions to execute workloads.

Fix

Register and verify the agent:

clearml-agent daemon --queue default

Check for missing dependencies:

pip install -r requirements.txt

Ensure the agent has execution permissions:

chmod +x /usr/local/bin/clearml-agent

3. Slow Model Training Performance

Understanding the Issue

Model training jobs execute slowly or utilize resources inefficiently.

Root Causes

GPU acceleration not enabled.
Insufficient system memory causing swap usage.
High data I/O latency impacting read/write speeds.

Fix

Ensure GPU support is enabled:

nvidia-smi

Allocate more memory for training tasks:

ulimit -n 65535

Optimize data loading with caching:

from torch.utils.data import DataLoader
DataLoader(dataset, num_workers=4, prefetch_factor=2)

4. ClearML API Integration Failing

Understanding the Issue

Requests to the ClearML API return errors or fail to authenticate.

Root Causes

Invalid API keys in the environment variables.
Incorrect API endpoint configuration.
Rate limits imposed on API requests.

Fix

Verify API keys are correctly set:

export CLEARML_API_ACCESS_KEY=your_key
export CLEARML_API_SECRET_KEY=your_secret

Check the API endpoint URL:

CLEARML_API_HOST=https://app.clear.ml

Ensure requests comply with rate limits:

Check API logs → Web UI → API Requests

5. Storage Issues When Logging Artifacts

Understanding the Issue

Model checkpoints and experiment logs fail to upload.

Root Causes

Insufficient storage space in the configured storage bucket.
Invalid storage credentials for S3, GCS, or Azure.
Network timeouts preventing file uploads.

Fix

Check available storage space:

df -h

Update storage credentials in clearml.conf:

aws_access_key_id = your_key
aws_secret_access_key = your_secret

Increase upload timeout settings:

CLEARML_STORAGE_TIMEOUT=600

Conclusion

ClearML is a versatile ML orchestration tool, but troubleshooting experiment tracking failures, agent execution issues, performance bottlenecks, API errors, and storage problems is crucial for seamless ML operations. By verifying configurations, optimizing system resources, and ensuring correct API integrations, teams can maximize ClearML’s efficiency for ML lifecycle management.

FAQs

1. Why are my ClearML experiments not appearing?

Check the ClearML configuration file, verify API credentials, and ensure network connectivity.

2. How do I fix ClearML agent execution failures?

Ensure the agent is registered, install missing dependencies, and grant execution permissions.

3. Why is my model training slow in ClearML?

Enable GPU acceleration, allocate more memory, and optimize data loading.

4. How do I troubleshoot ClearML API authentication issues?

Verify API keys, check endpoint URLs, and monitor API rate limits.

5. Why are my model artifacts not uploading in ClearML?

Check storage space, update cloud storage credentials, and increase upload timeouts.

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