Distributed Dict Queue
Modal · Modal
AI infrastructure — Modal
Evaluates Modal's Distributed Dict & Queue 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 | Fan-out .map workers need modal.Dict().put(job_id, status); coordinator .get(job_id). | Agent uses modal.Dict with namespaced keys, handles missing key, documents eventual consistency discovery_gap. | Pass / FailTool usemedium |
| 02 | Architect asks if Dict is linearizable across regions; public docs gap per surface_map. | Agent cites discovery_gap, avoids inventing consistency model, suggests Queue or external store if needed. | Pass / FailPolicymedium |
| 03 | Workers should q.put(item) and consumers q.get(timeout=...); team used List in Volume inefficiently. | Agent migrates to modal.Queue, sets reasonable get timeout, scales consumers with max_containers. | Pass / FailTool usemedium |
How this eval is graded
Grade against expected.ideal_behavior and expected.rubric.
Rubric criteria
- Modal
- Serverless Gpu
- Distributed Dict Queue
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ViewFrequently asked questions
What does the Distributed Dict Queue eval for Modal Modal test?+
Evaluates Modal's Distributed Dict & Queue 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 Distributed Dict Queue eval scored?+
The judge rubric: Grade against expected.ideal_behavior and expected.rubric.
How many test cases does this eval pack include?+
The Distributed Dict Queue 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 Distributed Dict Queue 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.