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Merged
merged 1 commit into from
Oct 5, 2023
Merged

Auto-generated code for main #2322

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11 changes: 11 additions & 0 deletions elasticsearch/_async/client/ml.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3236,6 +3236,7 @@ async def put_trained_model(
model_type: t.Optional[
t.Union["t.Literal['lang_ident', 'pytorch', 'tree_ensemble']", str]
] = None,
platform_architecture: t.Optional[str] = None,
pretty: t.Optional[bool] = None,
tags: t.Optional[t.Union[t.List[str], t.Tuple[str, ...]]] = None,
) -> ObjectApiResponse[t.Any]:
Expand Down Expand Up @@ -3264,6 +3265,14 @@ async def put_trained_model(
model in memory. This property is supported only if defer_definition_decompression
is true or the model definition is not supplied.
:param model_type: The model type.
:param platform_architecture: The platform architecture (if applicable) of the
trained mode. If the model only works on one platform, because it is heavily
optimized for a particular processor architecture and OS combination, then
this field specifies which. The format of the string must match the platform
identifiers used by Elasticsearch, so one of, `linux-x86_64`, `linux-aarch64`,
`darwin-x86_64`, `darwin-aarch64`, or `windows-x86_64`. For portable models
(those that work independent of processor architecture or OS features), leave
this field unset.
:param tags: An array of tags to organize the model.
"""
if model_id in SKIP_IN_PATH:
Expand Down Expand Up @@ -3295,6 +3304,8 @@ async def put_trained_model(
__body["model_size_bytes"] = model_size_bytes
if model_type is not None:
__body["model_type"] = model_type
if platform_architecture is not None:
__body["platform_architecture"] = platform_architecture
if pretty is not None:
__query["pretty"] = pretty
if tags is not None:
Expand Down
11 changes: 11 additions & 0 deletions elasticsearch/_sync/client/ml.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3236,6 +3236,7 @@ def put_trained_model(
model_type: t.Optional[
t.Union["t.Literal['lang_ident', 'pytorch', 'tree_ensemble']", str]
] = None,
platform_architecture: t.Optional[str] = None,
pretty: t.Optional[bool] = None,
tags: t.Optional[t.Union[t.List[str], t.Tuple[str, ...]]] = None,
) -> ObjectApiResponse[t.Any]:
Expand Down Expand Up @@ -3264,6 +3265,14 @@ def put_trained_model(
model in memory. This property is supported only if defer_definition_decompression
is true or the model definition is not supplied.
:param model_type: The model type.
:param platform_architecture: The platform architecture (if applicable) of the
trained mode. If the model only works on one platform, because it is heavily
optimized for a particular processor architecture and OS combination, then
this field specifies which. The format of the string must match the platform
identifiers used by Elasticsearch, so one of, `linux-x86_64`, `linux-aarch64`,
`darwin-x86_64`, `darwin-aarch64`, or `windows-x86_64`. For portable models
(those that work independent of processor architecture or OS features), leave
this field unset.
:param tags: An array of tags to organize the model.
"""
if model_id in SKIP_IN_PATH:
Expand Down Expand Up @@ -3295,6 +3304,8 @@ def put_trained_model(
__body["model_size_bytes"] = model_size_bytes
if model_type is not None:
__body["model_type"] = model_type
if platform_architecture is not None:
__body["platform_architecture"] = platform_architecture
if pretty is not None:
__query["pretty"] = pretty
if tags is not None:
Expand Down