cwic sandbox runner policy edit [RUNNER-ID] -f policy.json, or send it as the policy field of a runner update.
For the field-by-field walkthrough, see Configure a sandbox policy.
CoreWeave sandboxes are in public preview. For access, contact your CoreWeave account team, CoreWeave Support, or email support@coreweave.com.
GPU data science cluster
A cluster for long-lived per-user work: generous compute, GPU nodes, and internet access for pulling datasets and packages, with the instance metadata endpoint carved out.policy.json
allowedRuntimeClasses fails closed. A sandbox can only pin a class you name, and the two defaults decide what it gets when it pins nothing.
Shared agent cluster with allowlisted egress
A multi-tenant cluster for agent workloads. Agents reach a fixed set of upstreams and each other within the organization, and nothing else.policy.json
Ephemeral CI cluster
Short-lived, small, and narrow. Sandboxes exist for the length of a test run.policy.json
Untrusted code execution
The tightest posture on this page. VM isolation is mandatory, no egress is permitted at all, and the sandbox cannot request its way out of either.policy.json
allowedImages pins one image rather than a registry, so a sandbox cannot select an arbitrary image from a trusted registry.
Internal worker cluster
Long-running background workers that other sandboxes in the organization reach over a service, with outbound access for third-party API calls.policy.json
Inbound access is not policy-controlled yet. Ingress constraints are not enforced, and a sandbox that declares ingress rules is rejected, so this example sets egress only.
Vary the posture across clusters
Because a policy belongs to a cluster, different postures are different clusters rather than different documents on one cluster. A team that needs both an open data science environment and a locked-down execution environment runs two clusters and gives each the matching policy. To vary one dimension by region, apply the same policy to each cluster with the region-specific values changed. There is no shared or inherited policy document, so keep the source of truth in version control and apply it per cluster.See also
- Configure a sandbox policy: every constraint group, with the fields used in these examples.
- Policies overview: how a sandbox resolves against a policy.
- Agentic RL training: reward functions and multi-turn agent rollouts, with working Python.
- Deploy and manage a runner: the rest of the runner configuration.