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fix: Update neo multiversion support to include edge devices #3875

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Merged
merged 1 commit into from
Jun 2, 2023
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fix: Update neo multiversion support to include edge devices #3875

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14 changes: 14 additions & 0 deletions src/sagemaker/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,17 @@

NEO_IOC_TARGET_DEVICES = ["ml_c4", "ml_c5", "ml_m4", "ml_m5", "ml_p2", "ml_p3", "ml_g4dn"]

NEO_MULTIVERSION_UNSUPPORTED = [
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Curious which edge targets do we want to support?

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https://docs.aws.amazon.com/sagemaker/latest/dg/neo-supported-devices-edge-devices.html

"imx8mplus",
"jacinto_tda4vm",
"coreml",
"sitara_am57x",
"amba_cv2",
"amba_cv22",
"amba_cv25",
"lambda",
]


class ModelBase(abc.ABC):
"""An object that encapsulates a trained model.
Expand Down Expand Up @@ -836,11 +847,14 @@ def multi_version_compilation_supported(
multi_version_frameworks_support_mapping = {
"inferentia": ["pytorch", "tensorflow", "mxnet"],
"neo_ioc_targets": ["pytorch", "tensorflow"],
"neo_edge_targets": ["pytorch", "tensorflow"],
}
if target_instance_type in NEO_IOC_TARGET_DEVICES:
return framework in multi_version_frameworks_support_mapping["neo_ioc_targets"]
if target_instance_type == "ml_inf1":
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@HappyAmazonian HappyAmazonian Jun 1, 2023 •
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Will trainium and inferentia2 support multi version too? It probably doesn't matter now. We may want to change it after they get released

return framework in multi_version_frameworks_support_mapping["inferentia"]
if target_instance_type not in NEO_MULTIVERSION_UNSUPPORTED:
return framework in multi_version_frameworks_support_mapping["neo_edge_targets"]
return False

if multi_version_compilation_supported(target_instance_type, framework, framework_version):
Expand Down
36 changes: 36 additions & 0 deletions tests/unit/sagemaker/model/test_neo.py
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Expand Up @@ -382,3 +382,39 @@ def test_compile_validates_framework_version(sagemaker_session):
)

assert model.image_uri is None

sagemaker_session.wait_for_compilation_job = Mock(
return_value={
"CompilationJobStatus": "Completed",
"ModelArtifacts": {"S3ModelArtifacts": "s3://output-path/model.tar.gz"},
"InferenceImage": None,
}
)

config = model._compilation_job_config(
"rasp3b",
{"data": [1, 3, 1024, 1024]},
"s3://output",
"role",
900,
"compile-model",
"pytorch",
None,
framework_version="1.6.1",
)

assert config["input_model_config"]["FrameworkVersion"] == "1.6"

config = model._compilation_job_config(
"amba_cv2",
{"data": [1, 3, 1024, 1024]},
"s3://output",
"role",
900,
"compile-model",
"pytorch",
None,
framework_version="1.6.1",
)

assert config["input_model_config"].get("FrameworkVersion", None) is None