XManager on Cloud (XMC) serves as a central command platform for both human researchers and autonomous agents building AI. Built on the experiment model of Google DeepMind's battle-tested XManager, XMC is designed to operate at scale to boost research velocity across AI labs and enterprise organizations.

flowchart LR
    Client["Humans & agents<br/>(xmanager CLI, Python, APIs)"] -->|"gRPC / HTTPS"| CP["XMC control plane"]
    CP -->|"Submits JobSets"| K8s["Kubernetes + Kueue<br/>(GPU / TPU / CPU)"]
    K8s -->|"Status"| CP
    CP --> UI["XMC web UI"]
    K8s -.->|"Metrics (optional)"| MP["XMC metrics pipeline"]
    MP -.-> UI

XMC organizes work around three concepts:

The Architecture Overview and Execution Model cover the services, the execution lifecycle, and how XMC maps work units to Kueue and JobSet.

You can deploy XMC to GKE or evaluate it locally on KinD in three steps:

1. Stand up an XMC environment
   • For GKE deployments, provision a stack with Cluster Toolkit v2 or Terraform and Helm using the Installation & Deployment Guide.
   • For local evaluation, boot a KinD control plane with python3 tools/dev_env/dev_env.py up (requires a GCP project for Cloud Spanner, gcloud, and bazel; see Local & Hybrid Development).
2. Install the XManager launcher and sign in The launcher and xmanager CLI come from google-deepmind/xmanager, published on PyPI:

   pip install 'xmanager>=0.8.5.post1'
   export XMC_DOMAIN=<your-xmc-domain>
   xmanager login

   When working from a clone of this repository with local protobuf changes or xdash, run python3 deploy/cli/install_xmanager.py instead (see CLI Setup).
3. Run an example workload Launch a multi-WorkUnit job from one of the ready-to-run templates in examples/:
   • examples/simple_job: multi-WorkUnit CPU/GPU sweep on a Kueue LocalQueue.
   • examples/multipod_job and examples/pathways_job: distributed multi-node and Pathways workloads.
   • examples/metrics_dashboard_job and examples/xdash_dashboard: live metric streaming and programmatic dashboards.
   • examples/managed_xprof_artifact_job: Managed XProf hardware profiling attached to a work unit.
   • examples/xmc_lite_job: standalone xm_local.Kubernetes launch without the XMC server.

Metrics streaming and Managed XProf are optional and off by default. Enable metrics by deploying the Redpanda and ClickHouse add-ons in deploy/addons/metrics and enabling redpanda, clickhouse, and ingestion-gateway in the stable-2026a Component profile (xmc-profile); enable Managed XProf with managed_xprof_gcs_bucket (managedXprofGcsBucket in Helm; see the Cluster Toolkit v2 README).

• XManager launcher (google-deepmind/xmanager): Python launch API and xmanager CLI that define and submit experiments to XMC.
• Installation & Deployment Guide: Cluster Toolkit v2 blueprints, Terraform and Helm setup, DNS and TLS configuration, and upgrades.
• Authentication & Experiment Access: interactive xmanager login, CI service accounts, and experiment ACL sharing.
• Python Metrics SDK (xmc) and Dashboard SDK (xdash): in-job metric logging and dashboard definitions.

• Architecture Overview: control, execution, telemetry, and component planes, followed by Component Reference, Execution Model, and Use Cases.
• Main Flows: sequence walkthroughs for experiment lifecycle, work unit actuation, reconciliation, and metrics & observability.
• Deployment Topologies and Gateway Routing Design: Cluster Toolkit Blueprint v1 vs v2 layouts, Kubernetes Gateway API routing, OAuth2 session handling, and identity mappings.

• Contributing Guide: Google CLA requirements, issue templates, and pull request workflow.
• Local & Hybrid Development: KinD inner loop (dev_env up, dev_env reload) and hybrid --remote-k8s execution.
• Developer Guide, E2E & UI Testing, and Agent Workflows.
• Automation scripts under ./tools and ./deploy require Python 3.13.14 or newer:

  python3 ./tools/common/setup_venv.py --venv-dir ./tools/venv --requirements ./tools/common/requirements.txt

• For questions about XMC, deployment guidance, or Google Cloud Managed Training Clusters, email [email protected] or reach out to your Google Cloud account team.
• File bug reports and feature requests in GitHub Issues. Self-hosted deployments receive best-effort support without contractual SLAs; for a managed offering with contractual SLAs, see Google Cloud Managed Training Clusters.
• Report security vulnerabilities privately through g.co/vulnz rather than in public issues.
• Released under the Apache 2.0 License.