Understanding Domino Data Lab Architecture

Core Components

  • Workspaces: Interactive sessions using Jupyter, RStudio, or VSCode for model development.
  • Executors: Compute infrastructure that runs jobs, workspaces, and scheduled tasks.
  • Projects: Version-controlled spaces containing code, notebooks, results, and environments.
  • Environments: Docker-based reproducible environments configured per project or user.
  • Model API: Deployment system for publishing models as REST endpoints with autoscaling and monitoring.

Deployment Modes

Domino supports hybrid, on-premises, and multi-cloud deployments with Kubernetes orchestration (EKS, GKE, AKS). It integrates with Git, S3, AD/LDAP, monitoring systems (Prometheus, ELK), and model registries like MLflow.

Common Troubleshooting Scenarios in Domino

1. Workspace Launch Failures

Workspaces may fail to launch due to environment misconfigurations, resource quota issues, or image pull errors.

Symptoms: Errors like “Executor unavailable,” “ImagePullBackOff,” or long pending status in the UI.

Solutions:

  • Ensure Docker images are built successfully and accessible to the executor nodes.
  • Verify Kubernetes resource limits and node pool autoscaling settings.
  • Check if the user-defined environment exceeds the available GPU/CPU/memory constraints.

2. Stuck Jobs and Execution Failures

Scheduled or manual jobs can get stuck due to queue saturation, stale pods, or missing dependencies in the execution environment.

Solutions:

  • Check executor logs from the admin panel for signs of resource exhaustion or eviction.
  • Restart Domino Executor Agents if they are not updating job status properly.
  • Use base images aligned with project dependencies to prevent runtime errors.

3. Model API Deployment Errors

Models deployed through the Model API may fail due to container build errors, port conflicts, or inference script bugs.

Symptoms: Model endpoint returns HTTP 500 or times out.

Solutions:

  • Check the logs for container startup and model load issues.
  • Ensure app.py or inference file adheres to Domino’s API contract.
  • Allocate appropriate resources for models requiring GPU or high memory usage.

4. Git Integration Problems

Projects may fail to sync with Git repositories due to authentication failures or SSH key misconfiguration.

Solutions:

  • Ensure the Git access token or SSH key is properly set in the user’s account settings.
  • Confirm that the repository URL is correctly formatted and accessible from Domino’s network.
  • Check the Git server logs or Domino logs for permission errors or rate limits.

5. Environment Build Failures

Environment creation may fail due to Docker build errors, version conflicts, or network access issues.

Solutions:

  • Use official Domino base images and build upon them for compatibility.
  • Pin package versions in requirements.txt or Dockerfile to avoid unexpected updates.
  • Validate external repositories and proxies are reachable from build nodes.

Advanced Diagnostics and Monitoring

Monitor with Admin Center and Prometheus

Domino provides native integration with Prometheus and dashboards to track CPU, memory, executor pool utilization, and API latency.

Review Execution Logs

Each workspace and job logs stdout/stderr output, available via the UI or CLI. Review container startup, environment load, and code execution stages.

Kubernetes Log Access

Access K8s logs using kubectl logs for pods or kubectl describe pod for executor issues.

kubectl get pods -n domino-computekubectl logs mypod -n domino-compute

Audit and User Activity Logs

Domino logs user activity and audit events for compliance and traceability. Review these for unauthorized access or configuration changes.

Network and DNS Troubleshooting

Failures to pull packages or hit APIs may be due to restricted outbound traffic.

  • Use curl or ping inside a workspace to validate connectivity.
  • Ensure VPC peering, DNS resolution, and security group rules allow required traffic.

Organizational Pitfalls in Domino Usage

  • Overuse of custom environments: Leads to maintenance overhead and reproducibility issues.
  • Lack of model versioning: Makes rollback and audits difficult.
  • Insufficient resource governance: Results in cluster saturation and job failures.
  • Poor environment naming conventions: Causes confusion across teams.
  • No documentation for executor tuning: Leads to repeated troubleshooting efforts.

Step-by-Step Fixes for Frequent Problems

Fix: Workspace Fails to Launch

  1. Open workspace logs and check image pull or startup failures.
  2. Validate that the environment image exists and was built successfully.
  3. Ensure that the project doesn’t exceed the configured resource quota.

Fix: Model Endpoint Crashes

  1. Check model.log for container and inference script errors.
  2. Test the model code locally with Docker or via workspace simulation.
  3. Ensure the container exposes the correct port and uses supported frameworks.

Fix: Git Authentication Error

  1. Re-add SSH keys or personal access token (PAT) in user profile.
  2. Test SSH or HTTPS clone manually in workspace terminal.
  3. Verify that Domino compute pods can reach the Git endpoint.

Fix: Long Job Queue Times

  1. Check executor pool scaling settings and utilization metrics.
  2. Enable auto-scaling if supported by your Kubernetes cluster.
  3. Prioritize critical workloads using tags and resource class separation.

Fix: Environment Build Error

  1. Review build.log for Docker syntax or package installation failures.
  2. Temporarily remove custom packages to isolate the issue.
  3. Use a known good base image and reintroduce changes incrementally.

Best Practices for Scalable Domino Operations

  • Use standard environments: Base new environments on Domino-maintained images for stability and support.
  • Enable resource quotas: Prevent one user from exhausting compute capacity.
  • Monitor executor usage: Right-size node pools and executor templates.
  • Automate environment validation: Run test builds before making them available org-wide.
  • Encourage reproducibility: Use Git, environment snapshots, and parameterized jobs for consistent results.

Conclusion

Domino Data Lab is a comprehensive platform for managing the full lifecycle of data science and machine learning workflows, but its richness in features comes with architectural and operational complexity. Issues around environment builds, model deployment, workspace reliability, and resource constraints can quickly impact productivity and delivery timelines. By implementing structured diagnostics, monitoring, and governance strategies—and following best practices for reproducibility and automation—organizations can fully leverage Domino's capabilities for scalable, secure, and collaborative AI development.

FAQs

1. Why does my Domino workspace keep restarting or failing to start?

This could be due to environment build failures, image pull errors, or insufficient compute resources. Check logs and verify executor node availability.

2. How do I monitor model performance after deployment?

Domino provides request logging and metrics for deployed models. You can also integrate with Prometheus and Grafana for latency, error rate, and throughput monitoring.

3. Can I use custom Docker images in Domino?

Yes. You can upload your own Dockerfile or build from a base image. Ensure all dependencies and ports are correctly configured.

4. How do I troubleshoot Git issues in my project?

Check whether your Git credentials are set up correctly in Domino. Ensure the remote repository is reachable and SSH/HTTPS access is allowed from the Domino cluster.

5. What should I do if a job stays in the pending queue too long?

Check executor pool utilization and scaling settings. Ensure there are enough nodes available and that resource requests are not blocking smaller jobs.