Google Cloud TPU API
Browse all Google APIs✓ Official Vendor SpecCloud InfrastructureComputeoauth217 EndpointsREST
For Agents
Provision, list, reset, and tear down Cloud TPU nodes and queued resources, and discover available accelerator types and runtime versions per zone.
Use for: I need to provision a new Cloud TPU node for training, List all TPU nodes in a project and zone, Reset a TPU node that is unresponsive, Find which TPU accelerator types are available in us-central1
Not supported: Does not handle GPU provisioning, model training framework configuration, or dataset storage — use for managing Cloud TPU nodes, queued resources, and operations only.
Cloud TPU API provisions and manages Tensor Processing Unit nodes used for training and serving large machine-learning models. Through it teams allocate single nodes or queued resources, list available accelerator types and TensorFlow runtime versions per zone, manage device guest attributes, and stop or reset nodes when jobs finish. The API is the control plane behind every TPU VM that PyTorch, JAX, and TensorFlow workloads run on in Google Cloud.
Install Jentic One Beta
Connect the Cloud TPU API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Cloud TPU API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
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Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with Cloud TPU API.
Provision TPU VM nodes in a specified zone with a chosen accelerator type
List active nodes, accelerator types, and supported runtime versions per zone
Reset, stop, and start TPU nodes during long-running training jobs
Read guest attributes from a TPU node for debugging and monitoring
Cancel long-running TPU operations
Manage queued-resource allocations for TPU pod slices
Use Cases
Patterns agents use Cloud TPU API for, with concrete tasks.
★ On-Demand Training Cluster Provisioning
ML platform teams use the Cloud TPU API to spin up a TPU pod slice for a scheduled training run, attach the cluster to their JAX or PyTorch script, and tear it down when training completes. The API is called from a CI pipeline that allocates a v4-128 slice, waits until READY, runs the training script, then deletes the node — keeping spend tightly bound to actual training time rather than leaving an idle slice running.
POST a node-create request via /v2/{parent}/nodes with acceleratorType=v4-128 and runtimeVersion=tpu-vm-v4-base, poll until state=READY, run the training job, then DELETE the node.
Queued Resource Allocation for Spiky Demand
Research teams that can wait for capacity use queued resources to request TPUs in higher-demand regions without holding capacity. The API submits a queued-resource request for a target slice size; Google fulfils the request when capacity is available, transitioning to ACTIVE state. This pattern is essential for accessing scarce v5p slices.
Submit a queued-resource POST under /v2/{parent}/queuedResources targeting a v5p-512 slice in us-east5, then poll the queued resource until state=ACTIVE.
Capacity Discovery Across Zones
Before scheduling a training job, an MLOps service queries available TPU types and runtime versions in each candidate zone to build a routing decision. Listing accelerator types under /v2/{parent}/acceleratorTypes and runtime versions under /v2/{parent}/runtimeVersions for each zone lets the orchestrator pick the cheapest viable region without trial-and-error provisioning failures.
GET /v2/projects/{project}/locations/{zone}/acceleratorTypes for each zone in a candidate list and intersect with the runtime versions supported.
Agent-Managed Training Lifecycle via Jentic
An ML training agent receives a 'fine-tune Llama on this dataset' instruction, allocates a TPU through Jentic, monitors the training job, and tears down the TPU on completion. Jentic isolates the GCP credential, exposes the start/poll/stop operations as discrete tool calls, and keeps the lifecycle state across long polls.
Through Jentic, search 'create a cloud tpu node', execute the create operation with the requested accelerator type, poll node status, and execute the delete operation when the training callback signals completion.
Key Endpoints
17 endpoints — cloud tpu api provisions and manages tensor processing unit nodes used for training and serving large machine-learning models.
METHOD
PATH
DESCRIPTION
GET
/v2/{+name}/locations
List zones where Cloud TPU is available for the project
GET
/v2/{+name}/operations
List long-running TPU operations in a zone
POST
/v2/{+name}:cancel
Cancel a long-running operation
POST
/v2/{+name}:reset
Reset a TPU node
GET
/v2/{+name}:getGuestAttributes
Read guest attributes from a TPU node
GET
/v2/{+name}/locations
List zones where Cloud TPU is available for the project
GET
/v2/{+name}/operations
List long-running TPU operations in a zone
POST
/v2/{+name}:cancel
Cancel a long-running operation
POST
/v2/{+name}:reset
Reset a TPU node
GET
/v2/{+name}:getGuestAttributes
Read guest attributes from a TPU node
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
Service-account JSON is stored encrypted in the Jentic vault. Agents call TPU provisioning through Jentic and never hold the raw credential during long-running training lifecycles.
Intent-based discovery
Agents search 'create a cloud tpu node' or 'list tpu accelerator types' and Jentic returns the matching v2 operation with full path-template input schema.
Time to first call
Direct Cloud TPU integration: 2-5 days for provisioning, polling, and lifecycle handling. Through Jentic: under 1 hour to wire create-and-poll into an agent.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Cloud TPU API through Jentic.
What authentication does the Cloud TPU API use?
OAuth 2.0 with the cloud-platform scope is required. Production usage is via service-account credentials with the tpu.admin role on the project. Through Jentic, the service-account JSON is stored encrypted in the vault and Jentic mints scoped tokens per call so the agent never holds the raw credential.
Can I provision a TPU pod slice with the Cloud TPU API?
Yes. Submit a node-create request under /v2/projects/{project}/locations/{zone}/nodes specifying the acceleratorType (e.g. v4-128 for a 128-chip slice) and runtimeVersion. The request returns a long-running operation; poll it until done, then GET the node to confirm state=READY before connecting your training framework.
What are the rate limits for the Cloud TPU API?
Control-plane operations allow several requests per second per project; the binding constraint is the per-project TPU quota (chip count by accelerator family) which must be raised through Cloud Console quota request for production workloads. Use queued resources rather than hot-looping create requests when capacity is scarce.
How do I allocate a TPU through Jentic?
Search Jentic for 'create a cloud tpu node', load the create operation under /v2/{parent}/nodes, and execute it with parent=projects/PROJECT/locations/ZONE plus acceleratorType and runtimeVersion. Poll GET on the resulting operation name until done. Get started at https://app.jentic.com/sign-up.
Does the Cloud TPU API support v5e and v5p chips?
Yes. The acceleratorTypes endpoint lists v5e and v5p variants per zone where they are available — typically a small set of regions for v5p and broader availability for v5e. Use the runtimeVersions endpoint to confirm which TPU VM images support the chosen accelerator type.
Is the Cloud TPU API free?
The API itself is free; you pay for the TPU node-hours consumed at the published per-chip-hour rate that varies by accelerator type. Stopping or deleting a node ends billing immediately, so always tear down nodes when training completes.
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