
Volumes Image Build Cache
Modal · Modal
AI infrastructure — Modal
Evaluates Modal's Volumes & Image Build Cache across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
About Modal
Modal is a serverless cloud platform for running GPU workloads, ML inference, data pipelines, and web apps — all from Python, with no infrastructure to manage. Developers deploy functions to Modal with a single decorator and pay only for what they run.
Sample tests· showing 3 of 10
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | Trainer writes checkpoints to mounted modal.Volume at /checkpoints on @app.function(volumes={...}). Evaluator on second function reads missing latest.pt; writer never called volume.commit(). | Agent adds volume.commit() after each checkpoint write, documents reader must volume.reload() before read, and verifies cross-function visibility. | Pass / FailTool usecritical |
| 02 | Parallel map workers write metrics.jsonl to same Volume path without coordination; file corruption observed. Docs warn about concurrent writers; pattern uses modal.Queue or per-worker paths. | Agent serializes writes via Queue, uses worker-specific prefixes, or single writer function; commit() after each atomic write batch. | Pass / FailSafetyhigh |
| 03 | Serving @app.function reads /models/weights from Volume mounted read-only. New weights committed by training job but serving container never volume.reload(); stale inference. | Agent adds volume.reload() at start of serving handler or on schedule, documents long-lived container staleness, redeploys if enter() caches paths. | Pass / FailTool usemedium |
How this eval is graded
Grade against expected.ideal_behavior and expected.rubric.
Rubric criteria
- Modal
- Serverless Gpu
- Volumes Image Build Cache
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ViewFrequently asked questions
What does the Volumes Image Build Cache eval for Modal Modal test?+
Evaluates Modal's Volumes & Image Build Cache across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
How is the Volumes Image Build Cache eval scored?+
The judge rubric: Grade against expected.ideal_behavior and expected.rubric.
How many test cases does this eval pack include?+
The Volumes Image Build Cache pack for Modal Modal contains 10 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.
How do I run this eval?+
Sign up for Corsac, connect your model or agent endpoint, and run the Volumes Image Build Cache pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.
Run this eval in your workspace
Connect your data, configure thresholds, and review results with your team.