Reuse a capture

Execution caching is on by default. An eligible identical CPU run can reuse an earlier result in the current editor window. Python briefly checks the plan and its dependencies; the model does not run again.

Reuse a result

1. Use a saved CPU profile in tensorviz.toml with a model seed and deterministic = true:

   # Inside your existing [profiles.<name>] section
   device = "cpu"
   seed = 0
   deterministic = true

   The model seed is separate from an input generator's seed.

2. Choose Run model, review the saved setup, then Run.

3. Repeat without changing the saved sources, configuration, inputs, environment, interpreter, or backend. The toolbar labels an eligible reused result.

Saving a model or tracked dependency also validates the open model by default. Set tensorviz.traceOnSave to false if you want to run manually.

Choose a cache mode

Open Settings and search for tensorviz.executionCache.mode.

Mode	What happens
on (default)	Reuses an eligible matching result after a Python safety check
off	Always executes the model
shadow	Executes every run and records whether reuse would have been possible

The mode is read for the run's own file, so folders in a multi-root workspace can opt out separately.

Run fresh or clear results

Open execution details from the CPU / Reused badge, choose Run fresh, then confirm Run to execute the model again.

Run tensorViz: Clear Execution Cache to discard stored results. They stay in memory for the current window and are never written to disk.

When reuse is refused and how results are checked

Agent MCP runs and reviewed-candidate runs always execute. A run that writes files, starts subprocesses, opens sockets, loads unsupported native code, or reads, lists, or checks untracked resources cannot be stored. Environment variables and tracked dependencies must still match.

Installed package files used by a stored result are fingerprinted and rechecked before serving. A waiting single-use worker also checks preloaded package files before execution. Changed packages trigger a fresh POSIX worker; other platforms refuse reuse of that stale evidence.

The window setting owns the shared store's memory limit and lifetime, with per-result and count limits. Closing the window disposes its worker and store. Reuse preserves current editor document versions and source freshness, but it is not new runtime evidence.