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Use ml.m5.4xlarge as default deploy instance type for Mitra v1 models #298

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Merged
shchur merged 1 commit into from
Oct 12, 2026
Merged

Use ml.m5.4xlarge as default deploy instance type for Mitra v1 models #298

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2 changes: 1 addition & 1 deletion docs/tutorials/foundation-model-tabular.md
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Expand Up @@ -135,7 +135,7 @@ predictions = future.result() # blocks until the job finishes, returns a Series

## Real-time inference

Deploy the model to a SageMaker endpoint with {py:meth}`~autogluon.cloud.TabularFoundationModel.deploy`, then send requests through the returned {py:class}`~autogluon.cloud.TabularEndpoint`. Pick an `instance_type` based on cost and latency requirements (defaults to `ml.g5.xlarge`):
Deploy the model to a SageMaker endpoint with {py:meth}`~autogluon.cloud.TabularFoundationModel.deploy`, then send requests through the returned {py:class}`~autogluon.cloud.TabularEndpoint`. Pick an `instance_type` based on cost and latency requirements (defaults to `ml.m5.4xlarge` for Mitra v1 and `ml.g5.xlarge` for the other models):

```python
endpoint = model.deploy(instance_type="ml.m5.4xlarge") # takes a few minutes
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2 changes: 2 additions & 0 deletions src/autogluon/cloud/model/registry.py
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Expand Up @@ -102,6 +102,7 @@ class FoundationModelConfig:
model_source_hyperparameter="hf_cls_model",
inference_hyperparameters={"fine_tune": False},
predict_instance_type="ml.m5.4xlarge",
deploy_instance_type="ml.m5.4xlarge",
),
"mitra-regressor": FoundationModelConfig(
problem_type="regression",
Expand All @@ -110,6 +111,7 @@ class FoundationModelConfig:
model_source_hyperparameter="hf_reg_model",
inference_hyperparameters={"fine_tune": False},
predict_instance_type="ml.m5.4xlarge",
deploy_instance_type="ml.m5.4xlarge",
),
"mitra-v2-classifier": FoundationModelConfig(
problem_type="multiclass",
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