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fix: Convert Pydantic model to dict for run_model_container #7

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fix: Convert Pydantic model to dict for run_model_container #7

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2 changes: 1 addition & 1 deletion cogstack_model_gateway/gateway/core/auto_deploy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -120,7 +120,7 @@ def deploy_on_demand_model(
model_type=model_type,
deployment_type=ModelDeploymentType.AUTO,
ttl=model_config.idle_ttl,
resources=model_config.deploy.resources if model_config.deploy else None,
resources=model_config.deploy.__dict__ if model_config.deploy else None,
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@phoevos phoevos Jan 16, 2026

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Any reason for not using pydantic's built-in model_dump here?

We generally tend to avoid calling internal/magic/dunder methods like that directly. __dict__ is used to fetch an object's writable attributes, not convert it to a dictionary. Consider the following example:

deploy = DeploySpec(resources=DeployResources(limits=ResourceLimits(memory="4g")))

Calling deploy.__dict__ will return the following, which isn't really what you want (notice how it doesn't work recursively):

{'resources': DeployResources(limits=ResourceLimits(memory='4g', cpus=None), reservations=None)}

Instead, you should be calling model_dump which is build for this exact purpose, replacing the now deprecated dict method of pydantic's BaseModel.

Suggested change
resources=model_config.deploy.__dict__ if model_config.deploy else None,
resources=model_config.deploy.model_dump() if model_config.deploy else None,

The above will give you the intended result:

{'resources': {'limits': {'memory': '4g', 'cpus': None}, 'reservations': None}}

)

log.info("Successfully deployed container for model: %s", model_name)
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