Add supported inference and incremental training configs#4637

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zhaoqizqwang merged 10 commits into from
May 2, 2024
Merged

Add supported inference and incremental training configs #4637

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supported inference configs
haotiaa May 1, 2024
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add tests
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format
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21 changes: 16 additions & 5 deletions src/sagemaker/jumpstart/estimator.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -504,7 +504,7 @@ def __init__(
enable_remote_debug (bool or PipelineVariable): Optional.
Specifies whether RemoteDebug is enabled for the training job
config_name (Optional[str]):
Name of the JumpStart Model config to apply. (Default: None).
Name of the training configuration to apply to the Estimator. (Default: None).

Raises:
ValueError: If the model ID is not recognized by JumpStart.
Expand DownExpand Up@@ -686,6 +686,7 @@ def attach(
model_version: Optional[str] = None,
sagemaker_session: session.Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
model_channel_name: str = "model",
config_name: Optional[str] = None,
) -> "JumpStartEstimator":
"""Attach to an existing training job.

Expand DownExpand Up@@ -721,6 +722,8 @@ def attach(
model data will be downloaded (default: 'model'). If no channel
with the same name exists in the training job, this option will
be ignored.
config_name (str): Optional. Name of the training configuration to use
when attaching to the training job. (Default: None).

Returns:
Instance of the calling ``JumpStartEstimator`` Class with the attached
Expand All@@ -732,7 +735,6 @@ def attach(
"""
config_name = None
if model_id is None:

model_id, model_version, _, config_name = get_model_info_from_training_job(
training_job_name=training_job_name, sagemaker_session=sagemaker_session
)
Expand All@@ -746,6 +748,9 @@ def attach(
"tolerate_deprecated_model": True, # model is already trained
}

if config_name:
additional_kwargs.update({"config_name": config_name})

model_specs = verify_model_region_and_return_specs(
model_id=model_id,
version=model_version,
Expand DownExpand Up@@ -804,6 +809,7 @@ def deploy(
dependencies: Optional[List[str]] = None,
git_config: Optional[Dict[str, str]] = None,
use_compiled_model: bool = False,
inference_config_name: Optional[str] = None,
) -> PredictorBase:
"""Creates endpoint from training job.

Expand DownExpand Up@@ -1039,6 +1045,8 @@ def deploy(
(Default: None).
use_compiled_model (bool): Flag to select whether to use compiled
(optimized) model. (Default: False).
inference_config_name (Optional[str]): Name of the inference configuration to
be used in the model. (Default: None).
"""
self.orig_predictor_cls = predictor_cls

Expand DownExpand Up@@ -1091,7 +1099,8 @@ def deploy(
git_config=git_config,
use_compiled_model=use_compiled_model,
training_instance_type=self.instance_type,
config_name=self.config_name,
training_config_name=self.config_name,
inference_config_name=inference_config_name,
)

predictor = super(JumpStartEstimator, self).deploy(
Expand All@@ -1108,7 +1117,7 @@ def deploy(
tolerate_deprecated_model=self.tolerate_deprecated_model,
tolerate_vulnerable_model=self.tolerate_vulnerable_model,
sagemaker_session=self.sagemaker_session,
config_name=self.config_name,
config_name=estimator_deploy_kwargs.config_name,
)

# If a predictor class was passed, do not mutate predictor
Expand DownExpand Up@@ -1140,7 +1149,9 @@ def set_training_config(self, config_name: str) -> None:
config_name (str): The name of the config.
"""
self.__init__(
model_id=self.model_id, model_version=self.model_version, config_name=config_name
model_id=self.model_id,
model_version=self.model_version,
config_name=config_name,
)

def __str__(self) -> str:
Expand Down
35 changes: 31 additions & 4 deletions src/sagemaker/jumpstart/factory/estimator.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -207,6 +207,7 @@ def get_init_kwargs(
estimator_init_kwargs = _add_role_to_kwargs(estimator_init_kwargs)
estimator_init_kwargs = _add_env_to_kwargs(estimator_init_kwargs)
estimator_init_kwargs = _add_tags_to_kwargs(estimator_init_kwargs)
estimator_init_kwargs = _add_config_name_to_kwargs(estimator_init_kwargs)

return estimator_init_kwargs

Expand DownExpand Up@@ -291,7 +292,8 @@ def get_deploy_kwargs(
use_compiled_model: Optional[bool] = None,
model_name: Optional[str] = None,
training_instance_type: Optional[str] = None,
config_name: Optional[str] = None,
training_config_name: Optional[str] = None,
inference_config_name: Optional[str] = None,
) -> JumpStartEstimatorDeployKwargs:
"""Returns kwargs required to call `deploy` on `sagemaker.estimator.Estimator` object."""

Expand DownExpand Up@@ -319,7 +321,8 @@ def get_deploy_kwargs(
tolerate_vulnerable_model=tolerate_vulnerable_model,
tolerate_deprecated_model=tolerate_deprecated_model,
sagemaker_session=sagemaker_session,
config_name=config_name,
training_config_name=training_config_name,
config_name=inference_config_name,
)

model_init_kwargs: JumpStartModelInitKwargs = model.get_init_kwargs(
Expand DownExpand Up@@ -348,7 +351,7 @@ def get_deploy_kwargs(
tolerate_deprecated_model=tolerate_deprecated_model,
training_instance_type=training_instance_type,
disable_instance_type_logging=True,
config_name=config_name,
config_name=model_deploy_kwargs.config_name,
)

estimator_deploy_kwargs: JumpStartEstimatorDeployKwargs = JumpStartEstimatorDeployKwargs(
Expand DownExpand Up@@ -393,7 +396,7 @@ def get_deploy_kwargs(
tolerate_vulnerable_model=model_init_kwargs.tolerate_vulnerable_model,
tolerate_deprecated_model=model_init_kwargs.tolerate_deprecated_model,
use_compiled_model=use_compiled_model,
config_name=config_name,
config_name=model_deploy_kwargs.config_name,
)

return estimator_deploy_kwargs
Expand DownExpand Up@@ -793,3 +796,27 @@ def _add_fit_extra_kwargs(kwargs: JumpStartEstimatorFitKwargs) -> JumpStartEstim
setattr(kwargs, key, value)

return kwargs


def _add_config_name_to_kwargs(
kwargs: JumpStartEstimatorInitKwargs,
) -> JumpStartEstimatorInitKwargs:
"""Sets tags in kwargs based on default or override, returns full kwargs."""

specs = verify_model_region_and_return_specs(
model_id=kwargs.model_id,
version=kwargs.model_version,
scope=JumpStartScriptScope.TRAINING,
region=kwargs.region,
tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
sagemaker_session=kwargs.sagemaker_session,
config_name=kwargs.config_name,
)

if specs.training_configs and specs.training_configs.get_top_config_from_ranking():
kwargs.config_name = (
kwargs.config_name or specs.training_configs.get_top_config_from_ranking().config_name
)

return kwargs
77 changes: 68 additions & 9 deletions src/sagemaker/jumpstart/factory/model.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -42,6 +42,7 @@
JumpStartModelDeployKwargs,
JumpStartModelInitKwargs,
JumpStartModelRegisterKwargs,
JumpStartModelSpecs,
)
from sagemaker.jumpstart.utils import (
add_jumpstart_model_info_tags,
Expand DownExpand Up@@ -548,7 +549,27 @@ def _add_resources_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModel
return kwargs


def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
def _select_inference_config_from_training_config(
specs: JumpStartModelSpecs, training_config_name: str
) -> Optional[str]:
"""Selects the inference config from the training config.

Args:
specs (JumpStartModelSpecs): The specs for the model.
training_config_name (str): The name of the training config.

Returns:
str: The name of the inference config.
"""
if specs.training_configs:
resolved_training_config = specs.training_configs.configs.get(training_config_name)
if resolved_training_config:
return resolved_training_config.default_inference_config

return None


def _add_config_name_to_init_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
"""Sets default config name to the kwargs. Returns full kwargs.

Raises:
Expand All@@ -566,13 +587,9 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
model_type=kwargs.model_type,
config_name=kwargs.config_name,
)
if (
specs.inference_configs
and specs.inference_configs.get_top_config_from_ranking().config_name
):
kwargs.config_name = (
kwargs.config_name or specs.inference_configs.get_top_config_from_ranking().config_name
)
if specs.inference_configs:
default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
kwargs.config_name = kwargs.config_name or default_config_name

if not kwargs.config_name:
return kwargs
Expand All@@ -593,6 +610,42 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
return kwargs


def _add_config_name_to_deploy_kwargs(
kwargs: JumpStartModelDeployKwargs, training_config_name: Optional[str] = None
) -> JumpStartModelInitKwargs:
"""Sets default config name to the kwargs. Returns full kwargs.

If a training_config_name is passed, then choose the inference config
based on the supported inference configs in that training config.

Raises:
ValueError: If the instance_type is not supported with the current config.
"""

specs = verify_model_region_and_return_specs(
model_id=kwargs.model_id,
version=kwargs.model_version,
scope=JumpStartScriptScope.INFERENCE,
region=kwargs.region,
tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
sagemaker_session=kwargs.sagemaker_session,
model_type=kwargs.model_type,
config_name=kwargs.config_name,
)

if training_config_name:
kwargs.config_name = _select_inference_config_from_training_config(
specs=specs, training_config_name=training_config_name
)

if specs.inference_configs:
default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
kwargs.config_name = kwargs.config_name or default_config_name

return kwargs


def get_deploy_kwargs(
model_id: str,
model_version: Optional[str] = None,
Expand DownExpand Up@@ -623,6 +676,7 @@ def get_deploy_kwargs(
resources: Optional[ResourceRequirements] = None,
managed_instance_scaling: Optional[str] = None,
endpoint_type: Optional[EndpointType] = None,
training_config_name: Optional[str] = None,
config_name: Optional[str] = None,
) -> JumpStartModelDeployKwargs:
"""Returns kwargs required to call `deploy` on `sagemaker.estimator.Model` object."""
Expand DownExpand Up@@ -664,6 +718,10 @@ def get_deploy_kwargs(

deploy_kwargs = _add_endpoint_name_to_kwargs(kwargs=deploy_kwargs)

deploy_kwargs = _add_config_name_to_deploy_kwargs(
kwargs=deploy_kwargs, training_config_name=training_config_name
)

deploy_kwargs = _add_instance_type_to_kwargs(kwargs=deploy_kwargs)

deploy_kwargs.initial_instance_count = initial_instance_count or 1
Expand DownExpand Up@@ -858,6 +916,7 @@ def get_init_kwargs(
model_init_kwargs = _add_model_package_arn_to_kwargs(kwargs=model_init_kwargs)

model_init_kwargs = _add_resources_to_kwargs(kwargs=model_init_kwargs)
model_init_kwargs = _add_config_name_to_kwargs(kwargs=model_init_kwargs)

model_init_kwargs = _add_config_name_to_init_kwargs(kwargs=model_init_kwargs)

return model_init_kwargs
48 changes: 28 additions & 20 deletions src/sagemaker/jumpstart/types.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -1077,30 +1077,52 @@ class JumpStartMetadataConfig(JumpStartDataHolderType):
"config_components",
"resolved_metadata_config",
"config_name",
"default_inference_config",
"default_incremental_trainig_config",
"supported_inference_configs",
"supported_incremental_training_configs",
]

def __init__(
self,
config_name: str,
config: Dict[str, Any],
base_fields: Dict[str, Any],
config_components: Dict[str, JumpStartConfigComponent],
benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]],
):
"""Initializes a JumpStartMetadataConfig object from its json representation.

Args:
config_name (str): Name of the config,
config (Dict[str, Any]):
Dictionary representation of the config.
base_fields (Dict[str, Any]):
The default base fields that are used to construct the final resolved config.
config_components (Dict[str, JumpStartConfigComponent]):
The list of components that are used to construct the resolved config.
benchmark_metrics (Dict[str, List[JumpStartBenchmarkStat]]):
The dictionary of benchmark metrics with name being the key.
"""
self.base_fields = base_fields
self.config_components: Dict[str, JumpStartConfigComponent] = config_components
self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = benchmark_metrics
self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = (
{
stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
for stat_name, stats in config.get("benchmark_metrics").items()
}
if config and config.get("benchmark_metrics")
else None
)
self.resolved_metadata_config: Optional[Dict[str, Any]] = None
self.config_name: Optional[str] = config_name
self.default_inference_config: Optional[str] = config.get("default_inference_config")
self.default_incremental_trainig_config: Optional[str] = config.get(
"default_incremental_training_config"
)
self.supported_inference_configs: Optional[List[str]] = config.get(
"supported_inference_configs"
)
self.supported_incremental_training_configs: Optional[List[str]] = config.get(
"supported_incremental_training_configs"
)

def to_json(self) -> Dict[str, Any]:
"""Returns json representation of JumpStartMetadataConfig object."""
Expand DownExpand Up@@ -1255,6 +1277,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
{
alias: JumpStartMetadataConfig(
alias,
config,
json_obj,
(
{
Expand All@@ -1264,14 +1287,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
if config and config.get("component_names")
else None
),
(
{
stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
for stat_name, stats in config.get("benchmark_metrics").items()
}
if config and config.get("benchmark_metrics")
else None
),
)
for alias, config in json_obj["inference_configs"].items()
}
Expand DownExpand Up@@ -1308,6 +1323,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
{
alias: JumpStartMetadataConfig(
alias,
config,
json_obj,
(
{
Expand All@@ -1317,14 +1333,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
if config and config.get("component_names")
else None
),
(
{
stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
for stat_name, stats in config.get("benchmark_metrics").items()
}
if config and config.get("benchmark_metrics")
else None
),
)
for alias, config in json_obj["training_configs"].items()
}
Expand Down
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Add supported inference and incremental training configs#4637

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 Merged

zhaoqizqwang
merged 10 commits into

from

May 2, 2024
 Merged


Add supported inference and incremental training configs
#4637
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a0fd751

supported inference configs


haotiaa May 1, 2024
7f08766

add tests


haotiaa May 1, 2024
1a1a6bb

format


haotiaa May 1, 2024
edf61af

tests


haotiaa May 1, 2024
4ec00fd

tests


haotiaa May 1, 2024
6340736

address comments


haotiaa May 1, 2024
dc33402

format and address comments


haotiaa May 1, 2024
096de7b

updates


haotiaa May 1, 2024
fd720d8

formt


haotiaa May 1, 2024
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format


haotiaa May 2, 2024

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21 changes: 16 additions & 5 deletions
src/sagemaker/jumpstart/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -504,7 +504,7 @@ def __init__(
 enable_remote_debug (bool or PipelineVariable): Optional.
 Specifies whether RemoteDebug is enabled for the training job
 config_name (Optional[str]):
 Name of the JumpStart Model config to apply. (Default: None).
 Name of the training configuration to apply to the Estimator. (Default: None).

 Raises:
 ValueError: If the model ID is not recognized by JumpStart.
Expand DownExpand Up@@ -686,6 +686,7 @@ def attach(
 model_version: Optional[str] = None,
 sagemaker_session: session.Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
 model_channel_name: str = "model",
 config_name: Optional[str] = None,
 ) -> "JumpStartEstimator":
 """Attach to an existing training job.

Expand DownExpand Up@@ -721,6 +722,8 @@ def attach(
 model data will be downloaded (default: 'model'). If no channel
 with the same name exists in the training job, this option will
 be ignored.
 config_name (str): Optional. Name of the training configuration to use
 when attaching to the training job. (Default: None).

 Returns:
 Instance of the calling ``JumpStartEstimator`` Class with the attached
Expand All@@ -732,7 +735,6 @@ def attach(
 """
 config_name = None
 if model_id is None:

 model_id, model_version, _, config_name = get_model_info_from_training_job(
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
Expand All@@ -746,6 +748,9 @@ def attach(
 "tolerate_deprecated_model": True, # model is already trained
 }

 if config_name:
 additional_kwargs.update({"config_name": config_name})

 model_specs = verify_model_region_and_return_specs(
 model_id=model_id,
 version=model_version,
Expand DownExpand Up@@ -804,6 +809,7 @@ def deploy(
 dependencies: Optional[List[str]] = None,
 git_config: Optional[Dict[str, str]] = None,
 use_compiled_model: bool = False,
 inference_config_name: Optional[str] = None,
 ) -> PredictorBase:
 """Creates endpoint from training job.

Expand DownExpand Up@@ -1039,6 +1045,8 @@ def deploy(
 (Default: None).
 use_compiled_model (bool): Flag to select whether to use compiled
 (optimized) model. (Default: False).
 inference_config_name (Optional[str]): Name of the inference configuration to
 be used in the model. (Default: None).
 """
 self.orig_predictor_cls = predictor_cls

Expand DownExpand Up@@ -1091,7 +1099,8 @@ def deploy(
 git_config=git_config,
 use_compiled_model=use_compiled_model,
 training_instance_type=self.instance_type,
 config_name=self.config_name,
 training_config_name=self.config_name,
 inference_config_name=inference_config_name,
 )

 predictor = super(JumpStartEstimator, self).deploy(
Expand All@@ -1108,7 +1117,7 @@ def deploy(
 tolerate_deprecated_model=self.tolerate_deprecated_model,
 tolerate_vulnerable_model=self.tolerate_vulnerable_model,
 sagemaker_session=self.sagemaker_session,
 config_name=self.config_name,
 config_name=estimator_deploy_kwargs.config_name,
 )

 # If a predictor class was passed, do not mutate predictor
Expand DownExpand Up@@ -1140,7 +1149,9 @@ def set_training_config(self, config_name: str) -> None:
 config_name (str): The name of the config.
 """
 self.__init__(
 model_id=self.model_id, model_version=self.model_version, config_name=config_name
 model_id=self.model_id,
 model_version=self.model_version,
 config_name=config_name,
 )

 def __str__(self) -> str:
Expand Down

35 changes: 31 additions & 4 deletions
src/sagemaker/jumpstart/factory/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -207,6 +207,7 @@ def get_init_kwargs(
 estimator_init_kwargs = _add_role_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_env_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_tags_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_config_name_to_kwargs(estimator_init_kwargs)

 return estimator_init_kwargs

Expand DownExpand Up@@ -291,7 +292,8 @@ def get_deploy_kwargs(
 use_compiled_model: Optional[bool] = None,
 model_name: Optional[str] = None,
 training_instance_type: Optional[str] = None,
 config_name: Optional[str] = None,
 training_config_name: Optional[str] = None,
 inference_config_name: Optional[str] = None,
) -> JumpStartEstimatorDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Estimator` object."""

Expand DownExpand Up@@ -319,7 +321,8 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=tolerate_vulnerable_model,
 tolerate_deprecated_model=tolerate_deprecated_model,
 sagemaker_session=sagemaker_session,
 config_name=config_name,
 training_config_name=training_config_name,
 config_name=inference_config_name,
 )

 model_init_kwargs: JumpStartModelInitKwargs = model.get_init_kwargs(
Expand DownExpand Up@@ -348,7 +351,7 @@ def get_deploy_kwargs(
 tolerate_deprecated_model=tolerate_deprecated_model,
 training_instance_type=training_instance_type,
 disable_instance_type_logging=True,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 estimator_deploy_kwargs: JumpStartEstimatorDeployKwargs = JumpStartEstimatorDeployKwargs(
Expand DownExpand Up@@ -393,7 +396,7 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=model_init_kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=model_init_kwargs.tolerate_deprecated_model,
 use_compiled_model=use_compiled_model,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 return estimator_deploy_kwargs
Expand DownExpand Up@@ -793,3 +796,27 @@ def _add_fit_extra_kwargs(kwargs: JumpStartEstimatorFitKwargs) -> JumpStartEstim
 setattr(kwargs, key, value)

 return kwargs


def _add_config_name_to_kwargs(
 kwargs: JumpStartEstimatorInitKwargs,
) -> JumpStartEstimatorInitKwargs:
 """Sets tags in kwargs based on default or override, returns full kwargs."""

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.TRAINING,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 config_name=kwargs.config_name,
 )

 if specs.training_configs and specs.training_configs.get_top_config_from_ranking():
 kwargs.config_name = (
 kwargs.config_name or specs.training_configs.get_top_config_from_ranking().config_name
 )

 return kwargs

77 changes: 68 additions & 9 deletions
src/sagemaker/jumpstart/factory/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -42,6 +42,7 @@
 JumpStartModelDeployKwargs,
 JumpStartModelInitKwargs,
 JumpStartModelRegisterKwargs,
 JumpStartModelSpecs,
)
from sagemaker.jumpstart.utils import (
 add_jumpstart_model_info_tags,
Expand DownExpand Up@@ -548,7 +549,27 @@ def _add_resources_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModel
 return kwargs


def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
def _select_inference_config_from_training_config(
 specs: JumpStartModelSpecs, training_config_name: str
) -> Optional[str]:
 """Selects the inference config from the training config.

 Args:
 specs (JumpStartModelSpecs): The specs for the model.
 training_config_name (str): The name of the training config.

 Returns:
 str: The name of the inference config.
 """
 if specs.training_configs:
 resolved_training_config = specs.training_configs.configs.get(training_config_name)
 if resolved_training_config:
 return resolved_training_config.default_inference_config

 return None


def _add_config_name_to_init_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 Raises:
Expand All@@ -566,13 +587,9 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )
 if (
 specs.inference_configs
 and specs.inference_configs.get_top_config_from_ranking().config_name
 ):
 kwargs.config_name = (
 kwargs.config_name or specs.inference_configs.get_top_config_from_ranking().config_name
 )
 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 if not kwargs.config_name:
 return kwargs
Expand All@@ -593,6 +610,42 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 return kwargs


def _add_config_name_to_deploy_kwargs(
 kwargs: JumpStartModelDeployKwargs, training_config_name: Optional[str] = None
) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 If a training_config_name is passed, then choose the inference config
 based on the supported inference configs in that training config.

 Raises:
 ValueError: If the instance_type is not supported with the current config.
 """

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.INFERENCE,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )

 if training_config_name:
 kwargs.config_name = _select_inference_config_from_training_config(
 specs=specs, training_config_name=training_config_name
 )

 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 return kwargs


def get_deploy_kwargs(
 model_id: str,
 model_version: Optional[str] = None,
Expand DownExpand Up@@ -623,6 +676,7 @@ def get_deploy_kwargs(
 resources: Optional[ResourceRequirements] = None,
 managed_instance_scaling: Optional[str] = None,
 endpoint_type: Optional[EndpointType] = None,
 training_config_name: Optional[str] = None,
 config_name: Optional[str] = None,
) -> JumpStartModelDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Model` object."""
Expand DownExpand Up@@ -664,6 +718,10 @@ def get_deploy_kwargs(

 deploy_kwargs = _add_endpoint_name_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs = _add_config_name_to_deploy_kwargs(
 kwargs=deploy_kwargs, training_config_name=training_config_name
 )

 deploy_kwargs = _add_instance_type_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs.initial_instance_count = initial_instance_count or 1
Expand DownExpand Up@@ -858,6 +916,7 @@ def get_init_kwargs(
 model_init_kwargs = _add_model_package_arn_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_resources_to_kwargs(kwargs=model_init_kwargs)
 model_init_kwargs = _add_config_name_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_config_name_to_init_kwargs(kwargs=model_init_kwargs)

 return model_init_kwargs

48 changes: 28 additions & 20 deletions
src/sagemaker/jumpstart/types.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1077,30 +1077,52 @@ class JumpStartMetadataConfig(JumpStartDataHolderType):
 "config_components",
 "resolved_metadata_config",
 "config_name",
 "default_inference_config",
 "default_incremental_trainig_config",
 "supported_inference_configs",
 "supported_incremental_training_configs",
 ]

 def __init__(
 self,
 config_name: str,
 config: Dict[str, Any],
 base_fields: Dict[str, Any],
 config_components: Dict[str, JumpStartConfigComponent],
 benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]],
 ):
 """Initializes a JumpStartMetadataConfig object from its json representation.

 Args:
 config_name (str): Name of the config,
 config (Dict[str, Any]):
 Dictionary representation of the config.
 base_fields (Dict[str, Any]):
 The default base fields that are used to construct the final resolved config.
 config_components (Dict[str, JumpStartConfigComponent]):
 The list of components that are used to construct the resolved config.
 benchmark_metrics (Dict[str, List[JumpStartBenchmarkStat]]):
 The dictionary of benchmark metrics with name being the key.
 """
 self.base_fields = base_fields
 self.config_components: Dict[str, JumpStartConfigComponent] = config_components
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = benchmark_metrics
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 )
 self.resolved_metadata_config: Optional[Dict[str, Any]] = None
 self.config_name: Optional[str] = config_name
 self.default_inference_config: Optional[str] = config.get("default_inference_config")
 self.default_incremental_trainig_config: Optional[str] = config.get(
 "default_incremental_training_config"
 )
 self.supported_inference_configs: Optional[List[str]] = config.get(
 "supported_inference_configs"
 )
 self.supported_incremental_training_configs: Optional[List[str]] = config.get(
 "supported_incremental_training_configs"
 )

 def to_json(self) -> Dict[str, Any]:
 """Returns json representation of JumpStartMetadataConfig object."""
Expand DownExpand Up@@ -1255,6 +1277,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1264,14 +1287,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["inference_configs"].items()
 }
Expand DownExpand Up@@ -1308,6 +1323,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1317,14 +1333,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["training_configs"].items()
 }
Expand Down
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May 2, 2024
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Add supported inference and incremental training configs
#4637
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supported inference configs


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21 changes: 16 additions & 5 deletions
src/sagemaker/jumpstart/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -504,7 +504,7 @@ def __init__(
 enable_remote_debug (bool or PipelineVariable): Optional.
 Specifies whether RemoteDebug is enabled for the training job
 config_name (Optional[str]):
 Name of the JumpStart Model config to apply. (Default: None).
 Name of the training configuration to apply to the Estimator. (Default: None).

 Raises:
 ValueError: If the model ID is not recognized by JumpStart.
Expand DownExpand Up@@ -686,6 +686,7 @@ def attach(
 model_version: Optional[str] = None,
 sagemaker_session: session.Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
 model_channel_name: str = "model",
 config_name: Optional[str] = None,
 ) -> "JumpStartEstimator":
 """Attach to an existing training job.

Expand DownExpand Up@@ -721,6 +722,8 @@ def attach(
 model data will be downloaded (default: 'model'). If no channel
 with the same name exists in the training job, this option will
 be ignored.
 config_name (str): Optional. Name of the training configuration to use
 when attaching to the training job. (Default: None).

 Returns:
 Instance of the calling ``JumpStartEstimator`` Class with the attached
Expand All@@ -732,7 +735,6 @@ def attach(
 """
 config_name = None
 if model_id is None:

 model_id, model_version, _, config_name = get_model_info_from_training_job(
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
Expand All@@ -746,6 +748,9 @@ def attach(
 "tolerate_deprecated_model": True, # model is already trained
 }

 if config_name:
 additional_kwargs.update({"config_name": config_name})

 model_specs = verify_model_region_and_return_specs(
 model_id=model_id,
 version=model_version,
Expand DownExpand Up@@ -804,6 +809,7 @@ def deploy(
 dependencies: Optional[List[str]] = None,
 git_config: Optional[Dict[str, str]] = None,
 use_compiled_model: bool = False,
 inference_config_name: Optional[str] = None,
 ) -> PredictorBase:
 """Creates endpoint from training job.

Expand DownExpand Up@@ -1039,6 +1045,8 @@ def deploy(
 (Default: None).
 use_compiled_model (bool): Flag to select whether to use compiled
 (optimized) model. (Default: False).
 inference_config_name (Optional[str]): Name of the inference configuration to
 be used in the model. (Default: None).
 """
 self.orig_predictor_cls = predictor_cls

Expand DownExpand Up@@ -1091,7 +1099,8 @@ def deploy(
 git_config=git_config,
 use_compiled_model=use_compiled_model,
 training_instance_type=self.instance_type,
 config_name=self.config_name,
 training_config_name=self.config_name,
 inference_config_name=inference_config_name,
 )

 predictor = super(JumpStartEstimator, self).deploy(
Expand All@@ -1108,7 +1117,7 @@ def deploy(
 tolerate_deprecated_model=self.tolerate_deprecated_model,
 tolerate_vulnerable_model=self.tolerate_vulnerable_model,
 sagemaker_session=self.sagemaker_session,
 config_name=self.config_name,
 config_name=estimator_deploy_kwargs.config_name,
 )

 # If a predictor class was passed, do not mutate predictor
Expand DownExpand Up@@ -1140,7 +1149,9 @@ def set_training_config(self, config_name: str) -> None:
 config_name (str): The name of the config.
 """
 self.__init__(
 model_id=self.model_id, model_version=self.model_version, config_name=config_name
 model_id=self.model_id,
 model_version=self.model_version,
 config_name=config_name,
 )

 def __str__(self) -> str:
Expand Down

35 changes: 31 additions & 4 deletions
src/sagemaker/jumpstart/factory/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -207,6 +207,7 @@ def get_init_kwargs(
 estimator_init_kwargs = _add_role_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_env_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_tags_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_config_name_to_kwargs(estimator_init_kwargs)

 return estimator_init_kwargs

Expand DownExpand Up@@ -291,7 +292,8 @@ def get_deploy_kwargs(
 use_compiled_model: Optional[bool] = None,
 model_name: Optional[str] = None,
 training_instance_type: Optional[str] = None,
 config_name: Optional[str] = None,
 training_config_name: Optional[str] = None,
 inference_config_name: Optional[str] = None,
) -> JumpStartEstimatorDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Estimator` object."""

Expand DownExpand Up@@ -319,7 +321,8 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=tolerate_vulnerable_model,
 tolerate_deprecated_model=tolerate_deprecated_model,
 sagemaker_session=sagemaker_session,
 config_name=config_name,
 training_config_name=training_config_name,
 config_name=inference_config_name,
 )

 model_init_kwargs: JumpStartModelInitKwargs = model.get_init_kwargs(
Expand DownExpand Up@@ -348,7 +351,7 @@ def get_deploy_kwargs(
 tolerate_deprecated_model=tolerate_deprecated_model,
 training_instance_type=training_instance_type,
 disable_instance_type_logging=True,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 estimator_deploy_kwargs: JumpStartEstimatorDeployKwargs = JumpStartEstimatorDeployKwargs(
Expand DownExpand Up@@ -393,7 +396,7 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=model_init_kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=model_init_kwargs.tolerate_deprecated_model,
 use_compiled_model=use_compiled_model,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 return estimator_deploy_kwargs
Expand DownExpand Up@@ -793,3 +796,27 @@ def _add_fit_extra_kwargs(kwargs: JumpStartEstimatorFitKwargs) -> JumpStartEstim
 setattr(kwargs, key, value)

 return kwargs


def _add_config_name_to_kwargs(
 kwargs: JumpStartEstimatorInitKwargs,
) -> JumpStartEstimatorInitKwargs:
 """Sets tags in kwargs based on default or override, returns full kwargs."""

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.TRAINING,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 config_name=kwargs.config_name,
 )

 if specs.training_configs and specs.training_configs.get_top_config_from_ranking():
 kwargs.config_name = (
 kwargs.config_name or specs.training_configs.get_top_config_from_ranking().config_name
 )

 return kwargs

77 changes: 68 additions & 9 deletions
src/sagemaker/jumpstart/factory/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -42,6 +42,7 @@
 JumpStartModelDeployKwargs,
 JumpStartModelInitKwargs,
 JumpStartModelRegisterKwargs,
 JumpStartModelSpecs,
)
from sagemaker.jumpstart.utils import (
 add_jumpstart_model_info_tags,
Expand DownExpand Up@@ -548,7 +549,27 @@ def _add_resources_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModel
 return kwargs


def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
def _select_inference_config_from_training_config(
 specs: JumpStartModelSpecs, training_config_name: str
) -> Optional[str]:
 """Selects the inference config from the training config.

 Args:
 specs (JumpStartModelSpecs): The specs for the model.
 training_config_name (str): The name of the training config.

 Returns:
 str: The name of the inference config.
 """
 if specs.training_configs:
 resolved_training_config = specs.training_configs.configs.get(training_config_name)
 if resolved_training_config:
 return resolved_training_config.default_inference_config

 return None


def _add_config_name_to_init_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 Raises:
Expand All@@ -566,13 +587,9 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )
 if (
 specs.inference_configs
 and specs.inference_configs.get_top_config_from_ranking().config_name
 ):
 kwargs.config_name = (
 kwargs.config_name or specs.inference_configs.get_top_config_from_ranking().config_name
 )
 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 if not kwargs.config_name:
 return kwargs
Expand All@@ -593,6 +610,42 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 return kwargs


def _add_config_name_to_deploy_kwargs(
 kwargs: JumpStartModelDeployKwargs, training_config_name: Optional[str] = None
) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 If a training_config_name is passed, then choose the inference config
 based on the supported inference configs in that training config.

 Raises:
 ValueError: If the instance_type is not supported with the current config.
 """

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.INFERENCE,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )

 if training_config_name:
 kwargs.config_name = _select_inference_config_from_training_config(
 specs=specs, training_config_name=training_config_name
 )

 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 return kwargs


def get_deploy_kwargs(
 model_id: str,
 model_version: Optional[str] = None,
Expand DownExpand Up@@ -623,6 +676,7 @@ def get_deploy_kwargs(
 resources: Optional[ResourceRequirements] = None,
 managed_instance_scaling: Optional[str] = None,
 endpoint_type: Optional[EndpointType] = None,
 training_config_name: Optional[str] = None,
 config_name: Optional[str] = None,
) -> JumpStartModelDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Model` object."""
Expand DownExpand Up@@ -664,6 +718,10 @@ def get_deploy_kwargs(

 deploy_kwargs = _add_endpoint_name_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs = _add_config_name_to_deploy_kwargs(
 kwargs=deploy_kwargs, training_config_name=training_config_name
 )

 deploy_kwargs = _add_instance_type_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs.initial_instance_count = initial_instance_count or 1
Expand DownExpand Up@@ -858,6 +916,7 @@ def get_init_kwargs(
 model_init_kwargs = _add_model_package_arn_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_resources_to_kwargs(kwargs=model_init_kwargs)
 model_init_kwargs = _add_config_name_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_config_name_to_init_kwargs(kwargs=model_init_kwargs)

 return model_init_kwargs

48 changes: 28 additions & 20 deletions
src/sagemaker/jumpstart/types.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1077,30 +1077,52 @@ class JumpStartMetadataConfig(JumpStartDataHolderType):
 "config_components",
 "resolved_metadata_config",
 "config_name",
 "default_inference_config",
 "default_incremental_trainig_config",
 "supported_inference_configs",
 "supported_incremental_training_configs",
 ]

 def __init__(
 self,
 config_name: str,
 config: Dict[str, Any],
 base_fields: Dict[str, Any],
 config_components: Dict[str, JumpStartConfigComponent],
 benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]],
 ):
 """Initializes a JumpStartMetadataConfig object from its json representation.

 Args:
 config_name (str): Name of the config,
 config (Dict[str, Any]):
 Dictionary representation of the config.
 base_fields (Dict[str, Any]):
 The default base fields that are used to construct the final resolved config.
 config_components (Dict[str, JumpStartConfigComponent]):
 The list of components that are used to construct the resolved config.
 benchmark_metrics (Dict[str, List[JumpStartBenchmarkStat]]):
 The dictionary of benchmark metrics with name being the key.
 """
 self.base_fields = base_fields
 self.config_components: Dict[str, JumpStartConfigComponent] = config_components
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = benchmark_metrics
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 )
 self.resolved_metadata_config: Optional[Dict[str, Any]] = None
 self.config_name: Optional[str] = config_name
 self.default_inference_config: Optional[str] = config.get("default_inference_config")
 self.default_incremental_trainig_config: Optional[str] = config.get(
 "default_incremental_training_config"
 )
 self.supported_inference_configs: Optional[List[str]] = config.get(
 "supported_inference_configs"
 )
 self.supported_incremental_training_configs: Optional[List[str]] = config.get(
 "supported_incremental_training_configs"
 )

 def to_json(self) -> Dict[str, Any]:
 """Returns json representation of JumpStartMetadataConfig object."""
Expand DownExpand Up@@ -1255,6 +1277,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1264,14 +1287,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["inference_configs"].items()
 }
Expand DownExpand Up@@ -1308,6 +1323,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1317,14 +1333,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["training_configs"].items()
 }
Expand Down
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injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Add supported inference and incremental training configs
#4637
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21 changes: 16 additions & 5 deletions
src/sagemaker/jumpstart/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -504,7 +504,7 @@ def __init__(
 enable_remote_debug (bool or PipelineVariable): Optional.
 Specifies whether RemoteDebug is enabled for the training job
 config_name (Optional[str]):
 Name of the JumpStart Model config to apply. (Default: None).
 Name of the training configuration to apply to the Estimator. (Default: None).

 Raises:
 ValueError: If the model ID is not recognized by JumpStart.
Expand DownExpand Up@@ -686,6 +686,7 @@ def attach(
 model_version: Optional[str] = None,
 sagemaker_session: session.Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
 model_channel_name: str = "model",
 config_name: Optional[str] = None,
 ) -> "JumpStartEstimator":
 """Attach to an existing training job.

Expand DownExpand Up@@ -721,6 +722,8 @@ def attach(
 model data will be downloaded (default: 'model'). If no channel
 with the same name exists in the training job, this option will
 be ignored.
 config_name (str): Optional. Name of the training configuration to use
 when attaching to the training job. (Default: None).

 Returns:
 Instance of the calling ``JumpStartEstimator`` Class with the attached
Expand All@@ -732,7 +735,6 @@ def attach(
 """
 config_name = None
 if model_id is None:

 model_id, model_version, _, config_name = get_model_info_from_training_job(
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
Expand All@@ -746,6 +748,9 @@ def attach(
 "tolerate_deprecated_model": True, # model is already trained
 }

 if config_name:
 additional_kwargs.update({"config_name": config_name})

 model_specs = verify_model_region_and_return_specs(
 model_id=model_id,
 version=model_version,
Expand DownExpand Up@@ -804,6 +809,7 @@ def deploy(
 dependencies: Optional[List[str]] = None,
 git_config: Optional[Dict[str, str]] = None,
 use_compiled_model: bool = False,
 inference_config_name: Optional[str] = None,
 ) -> PredictorBase:
 """Creates endpoint from training job.

Expand DownExpand Up@@ -1039,6 +1045,8 @@ def deploy(
 (Default: None).
 use_compiled_model (bool): Flag to select whether to use compiled
 (optimized) model. (Default: False).
 inference_config_name (Optional[str]): Name of the inference configuration to
 be used in the model. (Default: None).
 """
 self.orig_predictor_cls = predictor_cls

Expand DownExpand Up@@ -1091,7 +1099,8 @@ def deploy(
 git_config=git_config,
 use_compiled_model=use_compiled_model,
 training_instance_type=self.instance_type,
 config_name=self.config_name,
 training_config_name=self.config_name,
 inference_config_name=inference_config_name,
 )

 predictor = super(JumpStartEstimator, self).deploy(
Expand All@@ -1108,7 +1117,7 @@ def deploy(
 tolerate_deprecated_model=self.tolerate_deprecated_model,
 tolerate_vulnerable_model=self.tolerate_vulnerable_model,
 sagemaker_session=self.sagemaker_session,
 config_name=self.config_name,
 config_name=estimator_deploy_kwargs.config_name,
 )

 # If a predictor class was passed, do not mutate predictor
Expand DownExpand Up@@ -1140,7 +1149,9 @@ def set_training_config(self, config_name: str) -> None:
 config_name (str): The name of the config.
 """
 self.__init__(
 model_id=self.model_id, model_version=self.model_version, config_name=config_name
 model_id=self.model_id,
 model_version=self.model_version,
 config_name=config_name,
 )

 def __str__(self) -> str:
Expand Down

35 changes: 31 additions & 4 deletions
src/sagemaker/jumpstart/factory/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -207,6 +207,7 @@ def get_init_kwargs(
 estimator_init_kwargs = _add_role_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_env_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_tags_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_config_name_to_kwargs(estimator_init_kwargs)

 return estimator_init_kwargs

Expand DownExpand Up@@ -291,7 +292,8 @@ def get_deploy_kwargs(
 use_compiled_model: Optional[bool] = None,
 model_name: Optional[str] = None,
 training_instance_type: Optional[str] = None,
 config_name: Optional[str] = None,
 training_config_name: Optional[str] = None,
 inference_config_name: Optional[str] = None,
) -> JumpStartEstimatorDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Estimator` object."""

Expand DownExpand Up@@ -319,7 +321,8 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=tolerate_vulnerable_model,
 tolerate_deprecated_model=tolerate_deprecated_model,
 sagemaker_session=sagemaker_session,
 config_name=config_name,
 training_config_name=training_config_name,
 config_name=inference_config_name,
 )

 model_init_kwargs: JumpStartModelInitKwargs = model.get_init_kwargs(
Expand DownExpand Up@@ -348,7 +351,7 @@ def get_deploy_kwargs(
 tolerate_deprecated_model=tolerate_deprecated_model,
 training_instance_type=training_instance_type,
 disable_instance_type_logging=True,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 estimator_deploy_kwargs: JumpStartEstimatorDeployKwargs = JumpStartEstimatorDeployKwargs(
Expand DownExpand Up@@ -393,7 +396,7 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=model_init_kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=model_init_kwargs.tolerate_deprecated_model,
 use_compiled_model=use_compiled_model,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 return estimator_deploy_kwargs
Expand DownExpand Up@@ -793,3 +796,27 @@ def _add_fit_extra_kwargs(kwargs: JumpStartEstimatorFitKwargs) -> JumpStartEstim
 setattr(kwargs, key, value)

 return kwargs


def _add_config_name_to_kwargs(
 kwargs: JumpStartEstimatorInitKwargs,
) -> JumpStartEstimatorInitKwargs:
 """Sets tags in kwargs based on default or override, returns full kwargs."""

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.TRAINING,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 config_name=kwargs.config_name,
 )

 if specs.training_configs and specs.training_configs.get_top_config_from_ranking():
 kwargs.config_name = (
 kwargs.config_name or specs.training_configs.get_top_config_from_ranking().config_name
 )

 return kwargs

77 changes: 68 additions & 9 deletions
src/sagemaker/jumpstart/factory/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -42,6 +42,7 @@
 JumpStartModelDeployKwargs,
 JumpStartModelInitKwargs,
 JumpStartModelRegisterKwargs,
 JumpStartModelSpecs,
)
from sagemaker.jumpstart.utils import (
 add_jumpstart_model_info_tags,
Expand DownExpand Up@@ -548,7 +549,27 @@ def _add_resources_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModel
 return kwargs


def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
def _select_inference_config_from_training_config(
 specs: JumpStartModelSpecs, training_config_name: str
) -> Optional[str]:
 """Selects the inference config from the training config.

 Args:
 specs (JumpStartModelSpecs): The specs for the model.
 training_config_name (str): The name of the training config.

 Returns:
 str: The name of the inference config.
 """
 if specs.training_configs:
 resolved_training_config = specs.training_configs.configs.get(training_config_name)
 if resolved_training_config:
 return resolved_training_config.default_inference_config

 return None


def _add_config_name_to_init_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 Raises:
Expand All@@ -566,13 +587,9 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )
 if (
 specs.inference_configs
 and specs.inference_configs.get_top_config_from_ranking().config_name
 ):
 kwargs.config_name = (
 kwargs.config_name or specs.inference_configs.get_top_config_from_ranking().config_name
 )
 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 if not kwargs.config_name:
 return kwargs
Expand All@@ -593,6 +610,42 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 return kwargs


def _add_config_name_to_deploy_kwargs(
 kwargs: JumpStartModelDeployKwargs, training_config_name: Optional[str] = None
) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 If a training_config_name is passed, then choose the inference config
 based on the supported inference configs in that training config.

 Raises:
 ValueError: If the instance_type is not supported with the current config.
 """

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.INFERENCE,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )

 if training_config_name:
 kwargs.config_name = _select_inference_config_from_training_config(
 specs=specs, training_config_name=training_config_name
 )

 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 return kwargs


def get_deploy_kwargs(
 model_id: str,
 model_version: Optional[str] = None,
Expand DownExpand Up@@ -623,6 +676,7 @@ def get_deploy_kwargs(
 resources: Optional[ResourceRequirements] = None,
 managed_instance_scaling: Optional[str] = None,
 endpoint_type: Optional[EndpointType] = None,
 training_config_name: Optional[str] = None,
 config_name: Optional[str] = None,
) -> JumpStartModelDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Model` object."""
Expand DownExpand Up@@ -664,6 +718,10 @@ def get_deploy_kwargs(

 deploy_kwargs = _add_endpoint_name_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs = _add_config_name_to_deploy_kwargs(
 kwargs=deploy_kwargs, training_config_name=training_config_name
 )

 deploy_kwargs = _add_instance_type_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs.initial_instance_count = initial_instance_count or 1
Expand DownExpand Up@@ -858,6 +916,7 @@ def get_init_kwargs(
 model_init_kwargs = _add_model_package_arn_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_resources_to_kwargs(kwargs=model_init_kwargs)
 model_init_kwargs = _add_config_name_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_config_name_to_init_kwargs(kwargs=model_init_kwargs)

 return model_init_kwargs

48 changes: 28 additions & 20 deletions
src/sagemaker/jumpstart/types.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1077,30 +1077,52 @@ class JumpStartMetadataConfig(JumpStartDataHolderType):
 "config_components",
 "resolved_metadata_config",
 "config_name",
 "default_inference_config",
 "default_incremental_trainig_config",
 "supported_inference_configs",
 "supported_incremental_training_configs",
 ]

 def __init__(
 self,
 config_name: str,
 config: Dict[str, Any],
 base_fields: Dict[str, Any],
 config_components: Dict[str, JumpStartConfigComponent],
 benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]],
 ):
 """Initializes a JumpStartMetadataConfig object from its json representation.

 Args:
 config_name (str): Name of the config,
 config (Dict[str, Any]):
 Dictionary representation of the config.
 base_fields (Dict[str, Any]):
 The default base fields that are used to construct the final resolved config.
 config_components (Dict[str, JumpStartConfigComponent]):
 The list of components that are used to construct the resolved config.
 benchmark_metrics (Dict[str, List[JumpStartBenchmarkStat]]):
 The dictionary of benchmark metrics with name being the key.
 """
 self.base_fields = base_fields
 self.config_components: Dict[str, JumpStartConfigComponent] = config_components
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = benchmark_metrics
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 )
 self.resolved_metadata_config: Optional[Dict[str, Any]] = None
 self.config_name: Optional[str] = config_name
 self.default_inference_config: Optional[str] = config.get("default_inference_config")
 self.default_incremental_trainig_config: Optional[str] = config.get(
 "default_incremental_training_config"
 )
 self.supported_inference_configs: Optional[List[str]] = config.get(
 "supported_inference_configs"
 )
 self.supported_incremental_training_configs: Optional[List[str]] = config.get(
 "supported_incremental_training_configs"
 )

 def to_json(self) -> Dict[str, Any]:
 """Returns json representation of JumpStartMetadataConfig object."""
Expand DownExpand Up@@ -1255,6 +1277,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1264,14 +1287,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["inference_configs"].items()
 }
Expand DownExpand Up@@ -1308,6 +1323,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1317,14 +1333,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["training_configs"].items()
 }
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
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var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Add supported inference and incremental training configs#4637

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 Merged

zhaoqizqwang
merged 10 commits into

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May 2, 2024
 Merged


Add supported inference and incremental training configs
#4637
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a0fd751

supported inference configs


haotiaa May 1, 2024
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add tests


haotiaa May 1, 2024
1a1a6bb

format


haotiaa May 1, 2024
edf61af

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address comments


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21 changes: 16 additions & 5 deletions
src/sagemaker/jumpstart/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -504,7 +504,7 @@ def __init__(
 enable_remote_debug (bool or PipelineVariable): Optional.
 Specifies whether RemoteDebug is enabled for the training job
 config_name (Optional[str]):
 Name of the JumpStart Model config to apply. (Default: None).
 Name of the training configuration to apply to the Estimator. (Default: None).

 Raises:
 ValueError: If the model ID is not recognized by JumpStart.
Expand DownExpand Up@@ -686,6 +686,7 @@ def attach(
 model_version: Optional[str] = None,
 sagemaker_session: session.Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
 model_channel_name: str = "model",
 config_name: Optional[str] = None,
 ) -> "JumpStartEstimator":
 """Attach to an existing training job.

Expand DownExpand Up@@ -721,6 +722,8 @@ def attach(
 model data will be downloaded (default: 'model'). If no channel
 with the same name exists in the training job, this option will
 be ignored.
 config_name (str): Optional. Name of the training configuration to use
 when attaching to the training job. (Default: None).

 Returns:
 Instance of the calling ``JumpStartEstimator`` Class with the attached
Expand All@@ -732,7 +735,6 @@ def attach(
 """
 config_name = None
 if model_id is None:

 model_id, model_version, _, config_name = get_model_info_from_training_job(
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
Expand All@@ -746,6 +748,9 @@ def attach(
 "tolerate_deprecated_model": True, # model is already trained
 }

 if config_name:
 additional_kwargs.update({"config_name": config_name})

 model_specs = verify_model_region_and_return_specs(
 model_id=model_id,
 version=model_version,
Expand DownExpand Up@@ -804,6 +809,7 @@ def deploy(
 dependencies: Optional[List[str]] = None,
 git_config: Optional[Dict[str, str]] = None,
 use_compiled_model: bool = False,
 inference_config_name: Optional[str] = None,
 ) -> PredictorBase:
 """Creates endpoint from training job.

Expand DownExpand Up@@ -1039,6 +1045,8 @@ def deploy(
 (Default: None).
 use_compiled_model (bool): Flag to select whether to use compiled
 (optimized) model. (Default: False).
 inference_config_name (Optional[str]): Name of the inference configuration to
 be used in the model. (Default: None).
 """
 self.orig_predictor_cls = predictor_cls

Expand DownExpand Up@@ -1091,7 +1099,8 @@ def deploy(
 git_config=git_config,
 use_compiled_model=use_compiled_model,
 training_instance_type=self.instance_type,
 config_name=self.config_name,
 training_config_name=self.config_name,
 inference_config_name=inference_config_name,
 )

 predictor = super(JumpStartEstimator, self).deploy(
Expand All@@ -1108,7 +1117,7 @@ def deploy(
 tolerate_deprecated_model=self.tolerate_deprecated_model,
 tolerate_vulnerable_model=self.tolerate_vulnerable_model,
 sagemaker_session=self.sagemaker_session,
 config_name=self.config_name,
 config_name=estimator_deploy_kwargs.config_name,
 )

 # If a predictor class was passed, do not mutate predictor
Expand DownExpand Up@@ -1140,7 +1149,9 @@ def set_training_config(self, config_name: str) -> None:
 config_name (str): The name of the config.
 """
 self.__init__(
 model_id=self.model_id, model_version=self.model_version, config_name=config_name
 model_id=self.model_id,
 model_version=self.model_version,
 config_name=config_name,
 )

 def __str__(self) -> str:
Expand Down

35 changes: 31 additions & 4 deletions
src/sagemaker/jumpstart/factory/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -207,6 +207,7 @@ def get_init_kwargs(
 estimator_init_kwargs = _add_role_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_env_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_tags_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_config_name_to_kwargs(estimator_init_kwargs)

 return estimator_init_kwargs

Expand DownExpand Up@@ -291,7 +292,8 @@ def get_deploy_kwargs(
 use_compiled_model: Optional[bool] = None,
 model_name: Optional[str] = None,
 training_instance_type: Optional[str] = None,
 config_name: Optional[str] = None,
 training_config_name: Optional[str] = None,
 inference_config_name: Optional[str] = None,
) -> JumpStartEstimatorDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Estimator` object."""

Expand DownExpand Up@@ -319,7 +321,8 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=tolerate_vulnerable_model,
 tolerate_deprecated_model=tolerate_deprecated_model,
 sagemaker_session=sagemaker_session,
 config_name=config_name,
 training_config_name=training_config_name,
 config_name=inference_config_name,
 )

 model_init_kwargs: JumpStartModelInitKwargs = model.get_init_kwargs(
Expand DownExpand Up@@ -348,7 +351,7 @@ def get_deploy_kwargs(
 tolerate_deprecated_model=tolerate_deprecated_model,
 training_instance_type=training_instance_type,
 disable_instance_type_logging=True,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 estimator_deploy_kwargs: JumpStartEstimatorDeployKwargs = JumpStartEstimatorDeployKwargs(
Expand DownExpand Up@@ -393,7 +396,7 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=model_init_kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=model_init_kwargs.tolerate_deprecated_model,
 use_compiled_model=use_compiled_model,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 return estimator_deploy_kwargs
Expand DownExpand Up@@ -793,3 +796,27 @@ def _add_fit_extra_kwargs(kwargs: JumpStartEstimatorFitKwargs) -> JumpStartEstim
 setattr(kwargs, key, value)

 return kwargs


def _add_config_name_to_kwargs(
 kwargs: JumpStartEstimatorInitKwargs,
) -> JumpStartEstimatorInitKwargs:
 """Sets tags in kwargs based on default or override, returns full kwargs."""

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.TRAINING,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 config_name=kwargs.config_name,
 )

 if specs.training_configs and specs.training_configs.get_top_config_from_ranking():
 kwargs.config_name = (
 kwargs.config_name or specs.training_configs.get_top_config_from_ranking().config_name
 )

 return kwargs

77 changes: 68 additions & 9 deletions
src/sagemaker/jumpstart/factory/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -42,6 +42,7 @@
 JumpStartModelDeployKwargs,
 JumpStartModelInitKwargs,
 JumpStartModelRegisterKwargs,
 JumpStartModelSpecs,
)
from sagemaker.jumpstart.utils import (
 add_jumpstart_model_info_tags,
Expand DownExpand Up@@ -548,7 +549,27 @@ def _add_resources_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModel
 return kwargs


def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
def _select_inference_config_from_training_config(
 specs: JumpStartModelSpecs, training_config_name: str
) -> Optional[str]:
 """Selects the inference config from the training config.

 Args:
 specs (JumpStartModelSpecs): The specs for the model.
 training_config_name (str): The name of the training config.

 Returns:
 str: The name of the inference config.
 """
 if specs.training_configs:
 resolved_training_config = specs.training_configs.configs.get(training_config_name)
 if resolved_training_config:
 return resolved_training_config.default_inference_config

 return None


def _add_config_name_to_init_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 Raises:
Expand All@@ -566,13 +587,9 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )
 if (
 specs.inference_configs
 and specs.inference_configs.get_top_config_from_ranking().config_name
 ):
 kwargs.config_name = (
 kwargs.config_name or specs.inference_configs.get_top_config_from_ranking().config_name
 )
 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 if not kwargs.config_name:
 return kwargs
Expand All@@ -593,6 +610,42 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 return kwargs


def _add_config_name_to_deploy_kwargs(
 kwargs: JumpStartModelDeployKwargs, training_config_name: Optional[str] = None
) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 If a training_config_name is passed, then choose the inference config
 based on the supported inference configs in that training config.

 Raises:
 ValueError: If the instance_type is not supported with the current config.
 """

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.INFERENCE,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )

 if training_config_name:
 kwargs.config_name = _select_inference_config_from_training_config(
 specs=specs, training_config_name=training_config_name
 )

 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 return kwargs


def get_deploy_kwargs(
 model_id: str,
 model_version: Optional[str] = None,
Expand DownExpand Up@@ -623,6 +676,7 @@ def get_deploy_kwargs(
 resources: Optional[ResourceRequirements] = None,
 managed_instance_scaling: Optional[str] = None,
 endpoint_type: Optional[EndpointType] = None,
 training_config_name: Optional[str] = None,
 config_name: Optional[str] = None,
) -> JumpStartModelDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Model` object."""
Expand DownExpand Up@@ -664,6 +718,10 @@ def get_deploy_kwargs(

 deploy_kwargs = _add_endpoint_name_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs = _add_config_name_to_deploy_kwargs(
 kwargs=deploy_kwargs, training_config_name=training_config_name
 )

 deploy_kwargs = _add_instance_type_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs.initial_instance_count = initial_instance_count or 1
Expand DownExpand Up@@ -858,6 +916,7 @@ def get_init_kwargs(
 model_init_kwargs = _add_model_package_arn_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_resources_to_kwargs(kwargs=model_init_kwargs)
 model_init_kwargs = _add_config_name_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_config_name_to_init_kwargs(kwargs=model_init_kwargs)

 return model_init_kwargs

48 changes: 28 additions & 20 deletions
src/sagemaker/jumpstart/types.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1077,30 +1077,52 @@ class JumpStartMetadataConfig(JumpStartDataHolderType):
 "config_components",
 "resolved_metadata_config",
 "config_name",
 "default_inference_config",
 "default_incremental_trainig_config",
 "supported_inference_configs",
 "supported_incremental_training_configs",
 ]

 def __init__(
 self,
 config_name: str,
 config: Dict[str, Any],
 base_fields: Dict[str, Any],
 config_components: Dict[str, JumpStartConfigComponent],
 benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]],
 ):
 """Initializes a JumpStartMetadataConfig object from its json representation.

 Args:
 config_name (str): Name of the config,
 config (Dict[str, Any]):
 Dictionary representation of the config.
 base_fields (Dict[str, Any]):
 The default base fields that are used to construct the final resolved config.
 config_components (Dict[str, JumpStartConfigComponent]):
 The list of components that are used to construct the resolved config.
 benchmark_metrics (Dict[str, List[JumpStartBenchmarkStat]]):
 The dictionary of benchmark metrics with name being the key.
 """
 self.base_fields = base_fields
 self.config_components: Dict[str, JumpStartConfigComponent] = config_components
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = benchmark_metrics
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 )
 self.resolved_metadata_config: Optional[Dict[str, Any]] = None
 self.config_name: Optional[str] = config_name
 self.default_inference_config: Optional[str] = config.get("default_inference_config")
 self.default_incremental_trainig_config: Optional[str] = config.get(
 "default_incremental_training_config"
 )
 self.supported_inference_configs: Optional[List[str]] = config.get(
 "supported_inference_configs"
 )
 self.supported_incremental_training_configs: Optional[List[str]] = config.get(
 "supported_incremental_training_configs"
 )

 def to_json(self) -> Dict[str, Any]:
 """Returns json representation of JumpStartMetadataConfig object."""
Expand DownExpand Up@@ -1255,6 +1277,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1264,14 +1287,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["inference_configs"].items()
 }
Expand DownExpand Up@@ -1308,6 +1323,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1317,14 +1333,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["training_configs"].items()
 }
Expand Down
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May 2, 2024
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Add supported inference and incremental training configs
#4637
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supported inference configs


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21 changes: 16 additions & 5 deletions
src/sagemaker/jumpstart/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -504,7 +504,7 @@ def __init__(
 enable_remote_debug (bool or PipelineVariable): Optional.
 Specifies whether RemoteDebug is enabled for the training job
 config_name (Optional[str]):
 Name of the JumpStart Model config to apply. (Default: None).
 Name of the training configuration to apply to the Estimator. (Default: None).

 Raises:
 ValueError: If the model ID is not recognized by JumpStart.
Expand DownExpand Up@@ -686,6 +686,7 @@ def attach(
 model_version: Optional[str] = None,
 sagemaker_session: session.Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
 model_channel_name: str = "model",
 config_name: Optional[str] = None,
 ) -> "JumpStartEstimator":
 """Attach to an existing training job.

Expand DownExpand Up@@ -721,6 +722,8 @@ def attach(
 model data will be downloaded (default: 'model'). If no channel
 with the same name exists in the training job, this option will
 be ignored.
 config_name (str): Optional. Name of the training configuration to use
 when attaching to the training job. (Default: None).

 Returns:
 Instance of the calling ``JumpStartEstimator`` Class with the attached
Expand All@@ -732,7 +735,6 @@ def attach(
 """
 config_name = None
 if model_id is None:

 model_id, model_version, _, config_name = get_model_info_from_training_job(
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
Expand All@@ -746,6 +748,9 @@ def attach(
 "tolerate_deprecated_model": True, # model is already trained
 }

 if config_name:
 additional_kwargs.update({"config_name": config_name})

 model_specs = verify_model_region_and_return_specs(
 model_id=model_id,
 version=model_version,
Expand DownExpand Up@@ -804,6 +809,7 @@ def deploy(
 dependencies: Optional[List[str]] = None,
 git_config: Optional[Dict[str, str]] = None,
 use_compiled_model: bool = False,
 inference_config_name: Optional[str] = None,
 ) -> PredictorBase:
 """Creates endpoint from training job.

Expand DownExpand Up@@ -1039,6 +1045,8 @@ def deploy(
 (Default: None).
 use_compiled_model (bool): Flag to select whether to use compiled
 (optimized) model. (Default: False).
 inference_config_name (Optional[str]): Name of the inference configuration to
 be used in the model. (Default: None).
 """
 self.orig_predictor_cls = predictor_cls

Expand DownExpand Up@@ -1091,7 +1099,8 @@ def deploy(
 git_config=git_config,
 use_compiled_model=use_compiled_model,
 training_instance_type=self.instance_type,
 config_name=self.config_name,
 training_config_name=self.config_name,
 inference_config_name=inference_config_name,
 )

 predictor = super(JumpStartEstimator, self).deploy(
Expand All@@ -1108,7 +1117,7 @@ def deploy(
 tolerate_deprecated_model=self.tolerate_deprecated_model,
 tolerate_vulnerable_model=self.tolerate_vulnerable_model,
 sagemaker_session=self.sagemaker_session,
 config_name=self.config_name,
 config_name=estimator_deploy_kwargs.config_name,
 )

 # If a predictor class was passed, do not mutate predictor
Expand DownExpand Up@@ -1140,7 +1149,9 @@ def set_training_config(self, config_name: str) -> None:
 config_name (str): The name of the config.
 """
 self.__init__(
 model_id=self.model_id, model_version=self.model_version, config_name=config_name
 model_id=self.model_id,
 model_version=self.model_version,
 config_name=config_name,
 )

 def __str__(self) -> str:
Expand Down

35 changes: 31 additions & 4 deletions
src/sagemaker/jumpstart/factory/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -207,6 +207,7 @@ def get_init_kwargs(
 estimator_init_kwargs = _add_role_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_env_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_tags_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_config_name_to_kwargs(estimator_init_kwargs)

 return estimator_init_kwargs

Expand DownExpand Up@@ -291,7 +292,8 @@ def get_deploy_kwargs(
 use_compiled_model: Optional[bool] = None,
 model_name: Optional[str] = None,
 training_instance_type: Optional[str] = None,
 config_name: Optional[str] = None,
 training_config_name: Optional[str] = None,
 inference_config_name: Optional[str] = None,
) -> JumpStartEstimatorDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Estimator` object."""

Expand DownExpand Up@@ -319,7 +321,8 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=tolerate_vulnerable_model,
 tolerate_deprecated_model=tolerate_deprecated_model,
 sagemaker_session=sagemaker_session,
 config_name=config_name,
 training_config_name=training_config_name,
 config_name=inference_config_name,
 )

 model_init_kwargs: JumpStartModelInitKwargs = model.get_init_kwargs(
Expand DownExpand Up@@ -348,7 +351,7 @@ def get_deploy_kwargs(
 tolerate_deprecated_model=tolerate_deprecated_model,
 training_instance_type=training_instance_type,
 disable_instance_type_logging=True,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 estimator_deploy_kwargs: JumpStartEstimatorDeployKwargs = JumpStartEstimatorDeployKwargs(
Expand DownExpand Up@@ -393,7 +396,7 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=model_init_kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=model_init_kwargs.tolerate_deprecated_model,
 use_compiled_model=use_compiled_model,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 return estimator_deploy_kwargs
Expand DownExpand Up@@ -793,3 +796,27 @@ def _add_fit_extra_kwargs(kwargs: JumpStartEstimatorFitKwargs) -> JumpStartEstim
 setattr(kwargs, key, value)

 return kwargs


def _add_config_name_to_kwargs(
 kwargs: JumpStartEstimatorInitKwargs,
) -> JumpStartEstimatorInitKwargs:
 """Sets tags in kwargs based on default or override, returns full kwargs."""

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.TRAINING,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 config_name=kwargs.config_name,
 )

 if specs.training_configs and specs.training_configs.get_top_config_from_ranking():
 kwargs.config_name = (
 kwargs.config_name or specs.training_configs.get_top_config_from_ranking().config_name
 )

 return kwargs

77 changes: 68 additions & 9 deletions
src/sagemaker/jumpstart/factory/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -42,6 +42,7 @@
 JumpStartModelDeployKwargs,
 JumpStartModelInitKwargs,
 JumpStartModelRegisterKwargs,
 JumpStartModelSpecs,
)
from sagemaker.jumpstart.utils import (
 add_jumpstart_model_info_tags,
Expand DownExpand Up@@ -548,7 +549,27 @@ def _add_resources_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModel
 return kwargs


def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
def _select_inference_config_from_training_config(
 specs: JumpStartModelSpecs, training_config_name: str
) -> Optional[str]:
 """Selects the inference config from the training config.

 Args:
 specs (JumpStartModelSpecs): The specs for the model.
 training_config_name (str): The name of the training config.

 Returns:
 str: The name of the inference config.
 """
 if specs.training_configs:
 resolved_training_config = specs.training_configs.configs.get(training_config_name)
 if resolved_training_config:
 return resolved_training_config.default_inference_config

 return None


def _add_config_name_to_init_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 Raises:
Expand All@@ -566,13 +587,9 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )
 if (
 specs.inference_configs
 and specs.inference_configs.get_top_config_from_ranking().config_name
 ):
 kwargs.config_name = (
 kwargs.config_name or specs.inference_configs.get_top_config_from_ranking().config_name
 )
 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 if not kwargs.config_name:
 return kwargs
Expand All@@ -593,6 +610,42 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 return kwargs


def _add_config_name_to_deploy_kwargs(
 kwargs: JumpStartModelDeployKwargs, training_config_name: Optional[str] = None
) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 If a training_config_name is passed, then choose the inference config
 based on the supported inference configs in that training config.

 Raises:
 ValueError: If the instance_type is not supported with the current config.
 """

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.INFERENCE,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )

 if training_config_name:
 kwargs.config_name = _select_inference_config_from_training_config(
 specs=specs, training_config_name=training_config_name
 )

 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 return kwargs


def get_deploy_kwargs(
 model_id: str,
 model_version: Optional[str] = None,
Expand DownExpand Up@@ -623,6 +676,7 @@ def get_deploy_kwargs(
 resources: Optional[ResourceRequirements] = None,
 managed_instance_scaling: Optional[str] = None,
 endpoint_type: Optional[EndpointType] = None,
 training_config_name: Optional[str] = None,
 config_name: Optional[str] = None,
) -> JumpStartModelDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Model` object."""
Expand DownExpand Up@@ -664,6 +718,10 @@ def get_deploy_kwargs(

 deploy_kwargs = _add_endpoint_name_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs = _add_config_name_to_deploy_kwargs(
 kwargs=deploy_kwargs, training_config_name=training_config_name
 )

 deploy_kwargs = _add_instance_type_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs.initial_instance_count = initial_instance_count or 1
Expand DownExpand Up@@ -858,6 +916,7 @@ def get_init_kwargs(
 model_init_kwargs = _add_model_package_arn_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_resources_to_kwargs(kwargs=model_init_kwargs)
 model_init_kwargs = _add_config_name_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_config_name_to_init_kwargs(kwargs=model_init_kwargs)

 return model_init_kwargs

48 changes: 28 additions & 20 deletions
src/sagemaker/jumpstart/types.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1077,30 +1077,52 @@ class JumpStartMetadataConfig(JumpStartDataHolderType):
 "config_components",
 "resolved_metadata_config",
 "config_name",
 "default_inference_config",
 "default_incremental_trainig_config",
 "supported_inference_configs",
 "supported_incremental_training_configs",
 ]

 def __init__(
 self,
 config_name: str,
 config: Dict[str, Any],
 base_fields: Dict[str, Any],
 config_components: Dict[str, JumpStartConfigComponent],
 benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]],
 ):
 """Initializes a JumpStartMetadataConfig object from its json representation.

 Args:
 config_name (str): Name of the config,
 config (Dict[str, Any]):
 Dictionary representation of the config.
 base_fields (Dict[str, Any]):
 The default base fields that are used to construct the final resolved config.
 config_components (Dict[str, JumpStartConfigComponent]):
 The list of components that are used to construct the resolved config.
 benchmark_metrics (Dict[str, List[JumpStartBenchmarkStat]]):
 The dictionary of benchmark metrics with name being the key.
 """
 self.base_fields = base_fields
 self.config_components: Dict[str, JumpStartConfigComponent] = config_components
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = benchmark_metrics
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 )
 self.resolved_metadata_config: Optional[Dict[str, Any]] = None
 self.config_name: Optional[str] = config_name
 self.default_inference_config: Optional[str] = config.get("default_inference_config")
 self.default_incremental_trainig_config: Optional[str] = config.get(
 "default_incremental_training_config"
 )
 self.supported_inference_configs: Optional[List[str]] = config.get(
 "supported_inference_configs"
 )
 self.supported_incremental_training_configs: Optional[List[str]] = config.get(
 "supported_incremental_training_configs"
 )

 def to_json(self) -> Dict[str, Any]:
 """Returns json representation of JumpStartMetadataConfig object."""
Expand DownExpand Up@@ -1255,6 +1277,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1264,14 +1287,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["inference_configs"].items()
 }
Expand DownExpand Up@@ -1308,6 +1323,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1317,14 +1333,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["training_configs"].items()
 }
Expand Down
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May 2, 2024
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Add supported inference and incremental training configs
#4637
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supported inference configs


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21 changes: 16 additions & 5 deletions
src/sagemaker/jumpstart/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -504,7 +504,7 @@ def __init__(
 enable_remote_debug (bool or PipelineVariable): Optional.
 Specifies whether RemoteDebug is enabled for the training job
 config_name (Optional[str]):
 Name of the JumpStart Model config to apply. (Default: None).
 Name of the training configuration to apply to the Estimator. (Default: None).

 Raises:
 ValueError: If the model ID is not recognized by JumpStart.
Expand DownExpand Up@@ -686,6 +686,7 @@ def attach(
 model_version: Optional[str] = None,
 sagemaker_session: session.Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
 model_channel_name: str = "model",
 config_name: Optional[str] = None,
 ) -> "JumpStartEstimator":
 """Attach to an existing training job.

Expand DownExpand Up@@ -721,6 +722,8 @@ def attach(
 model data will be downloaded (default: 'model'). If no channel
 with the same name exists in the training job, this option will
 be ignored.
 config_name (str): Optional. Name of the training configuration to use
 when attaching to the training job. (Default: None).

 Returns:
 Instance of the calling ``JumpStartEstimator`` Class with the attached
Expand All@@ -732,7 +735,6 @@ def attach(
 """
 config_name = None
 if model_id is None:

 model_id, model_version, _, config_name = get_model_info_from_training_job(
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
Expand All@@ -746,6 +748,9 @@ def attach(
 "tolerate_deprecated_model": True, # model is already trained
 }

 if config_name:
 additional_kwargs.update({"config_name": config_name})

 model_specs = verify_model_region_and_return_specs(
 model_id=model_id,
 version=model_version,
Expand DownExpand Up@@ -804,6 +809,7 @@ def deploy(
 dependencies: Optional[List[str]] = None,
 git_config: Optional[Dict[str, str]] = None,
 use_compiled_model: bool = False,
 inference_config_name: Optional[str] = None,
 ) -> PredictorBase:
 """Creates endpoint from training job.

Expand DownExpand Up@@ -1039,6 +1045,8 @@ def deploy(
 (Default: None).
 use_compiled_model (bool): Flag to select whether to use compiled
 (optimized) model. (Default: False).
 inference_config_name (Optional[str]): Name of the inference configuration to
 be used in the model. (Default: None).
 """
 self.orig_predictor_cls = predictor_cls

Expand DownExpand Up@@ -1091,7 +1099,8 @@ def deploy(
 git_config=git_config,
 use_compiled_model=use_compiled_model,
 training_instance_type=self.instance_type,
 config_name=self.config_name,
 training_config_name=self.config_name,
 inference_config_name=inference_config_name,
 )

 predictor = super(JumpStartEstimator, self).deploy(
Expand All@@ -1108,7 +1117,7 @@ def deploy(
 tolerate_deprecated_model=self.tolerate_deprecated_model,
 tolerate_vulnerable_model=self.tolerate_vulnerable_model,
 sagemaker_session=self.sagemaker_session,
 config_name=self.config_name,
 config_name=estimator_deploy_kwargs.config_name,
 )

 # If a predictor class was passed, do not mutate predictor
Expand DownExpand Up@@ -1140,7 +1149,9 @@ def set_training_config(self, config_name: str) -> None:
 config_name (str): The name of the config.
 """
 self.__init__(
 model_id=self.model_id, model_version=self.model_version, config_name=config_name
 model_id=self.model_id,
 model_version=self.model_version,
 config_name=config_name,
 )

 def __str__(self) -> str:
Expand Down

35 changes: 31 additions & 4 deletions
src/sagemaker/jumpstart/factory/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -207,6 +207,7 @@ def get_init_kwargs(
 estimator_init_kwargs = _add_role_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_env_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_tags_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_config_name_to_kwargs(estimator_init_kwargs)

 return estimator_init_kwargs

Expand DownExpand Up@@ -291,7 +292,8 @@ def get_deploy_kwargs(
 use_compiled_model: Optional[bool] = None,
 model_name: Optional[str] = None,
 training_instance_type: Optional[str] = None,
 config_name: Optional[str] = None,
 training_config_name: Optional[str] = None,
 inference_config_name: Optional[str] = None,
) -> JumpStartEstimatorDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Estimator` object."""

Expand DownExpand Up@@ -319,7 +321,8 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=tolerate_vulnerable_model,
 tolerate_deprecated_model=tolerate_deprecated_model,
 sagemaker_session=sagemaker_session,
 config_name=config_name,
 training_config_name=training_config_name,
 config_name=inference_config_name,
 )

 model_init_kwargs: JumpStartModelInitKwargs = model.get_init_kwargs(
Expand DownExpand Up@@ -348,7 +351,7 @@ def get_deploy_kwargs(
 tolerate_deprecated_model=tolerate_deprecated_model,
 training_instance_type=training_instance_type,
 disable_instance_type_logging=True,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 estimator_deploy_kwargs: JumpStartEstimatorDeployKwargs = JumpStartEstimatorDeployKwargs(
Expand DownExpand Up@@ -393,7 +396,7 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=model_init_kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=model_init_kwargs.tolerate_deprecated_model,
 use_compiled_model=use_compiled_model,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 return estimator_deploy_kwargs
Expand DownExpand Up@@ -793,3 +796,27 @@ def _add_fit_extra_kwargs(kwargs: JumpStartEstimatorFitKwargs) -> JumpStartEstim
 setattr(kwargs, key, value)

 return kwargs


def _add_config_name_to_kwargs(
 kwargs: JumpStartEstimatorInitKwargs,
) -> JumpStartEstimatorInitKwargs:
 """Sets tags in kwargs based on default or override, returns full kwargs."""

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.TRAINING,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 config_name=kwargs.config_name,
 )

 if specs.training_configs and specs.training_configs.get_top_config_from_ranking():
 kwargs.config_name = (
 kwargs.config_name or specs.training_configs.get_top_config_from_ranking().config_name
 )

 return kwargs

77 changes: 68 additions & 9 deletions
src/sagemaker/jumpstart/factory/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -42,6 +42,7 @@
 JumpStartModelDeployKwargs,
 JumpStartModelInitKwargs,
 JumpStartModelRegisterKwargs,
 JumpStartModelSpecs,
)
from sagemaker.jumpstart.utils import (
 add_jumpstart_model_info_tags,
Expand DownExpand Up@@ -548,7 +549,27 @@ def _add_resources_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModel
 return kwargs


def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
def _select_inference_config_from_training_config(
 specs: JumpStartModelSpecs, training_config_name: str
) -> Optional[str]:
 """Selects the inference config from the training config.

 Args:
 specs (JumpStartModelSpecs): The specs for the model.
 training_config_name (str): The name of the training config.

 Returns:
 str: The name of the inference config.
 """
 if specs.training_configs:
 resolved_training_config = specs.training_configs.configs.get(training_config_name)
 if resolved_training_config:
 return resolved_training_config.default_inference_config

 return None


def _add_config_name_to_init_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 Raises:
Expand All@@ -566,13 +587,9 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )
 if (
 specs.inference_configs
 and specs.inference_configs.get_top_config_from_ranking().config_name
 ):
 kwargs.config_name = (
 kwargs.config_name or specs.inference_configs.get_top_config_from_ranking().config_name
 )
 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 if not kwargs.config_name:
 return kwargs
Expand All@@ -593,6 +610,42 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 return kwargs


def _add_config_name_to_deploy_kwargs(
 kwargs: JumpStartModelDeployKwargs, training_config_name: Optional[str] = None
) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 If a training_config_name is passed, then choose the inference config
 based on the supported inference configs in that training config.

 Raises:
 ValueError: If the instance_type is not supported with the current config.
 """

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.INFERENCE,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )

 if training_config_name:
 kwargs.config_name = _select_inference_config_from_training_config(
 specs=specs, training_config_name=training_config_name
 )

 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 return kwargs


def get_deploy_kwargs(
 model_id: str,
 model_version: Optional[str] = None,
Expand DownExpand Up@@ -623,6 +676,7 @@ def get_deploy_kwargs(
 resources: Optional[ResourceRequirements] = None,
 managed_instance_scaling: Optional[str] = None,
 endpoint_type: Optional[EndpointType] = None,
 training_config_name: Optional[str] = None,
 config_name: Optional[str] = None,
) -> JumpStartModelDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Model` object."""
Expand DownExpand Up@@ -664,6 +718,10 @@ def get_deploy_kwargs(

 deploy_kwargs = _add_endpoint_name_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs = _add_config_name_to_deploy_kwargs(
 kwargs=deploy_kwargs, training_config_name=training_config_name
 )

 deploy_kwargs = _add_instance_type_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs.initial_instance_count = initial_instance_count or 1
Expand DownExpand Up@@ -858,6 +916,7 @@ def get_init_kwargs(
 model_init_kwargs = _add_model_package_arn_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_resources_to_kwargs(kwargs=model_init_kwargs)
 model_init_kwargs = _add_config_name_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_config_name_to_init_kwargs(kwargs=model_init_kwargs)

 return model_init_kwargs

48 changes: 28 additions & 20 deletions
src/sagemaker/jumpstart/types.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1077,30 +1077,52 @@ class JumpStartMetadataConfig(JumpStartDataHolderType):
 "config_components",
 "resolved_metadata_config",
 "config_name",
 "default_inference_config",
 "default_incremental_trainig_config",
 "supported_inference_configs",
 "supported_incremental_training_configs",
 ]

 def __init__(
 self,
 config_name: str,
 config: Dict[str, Any],
 base_fields: Dict[str, Any],
 config_components: Dict[str, JumpStartConfigComponent],
 benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]],
 ):
 """Initializes a JumpStartMetadataConfig object from its json representation.

 Args:
 config_name (str): Name of the config,
 config (Dict[str, Any]):
 Dictionary representation of the config.
 base_fields (Dict[str, Any]):
 The default base fields that are used to construct the final resolved config.
 config_components (Dict[str, JumpStartConfigComponent]):
 The list of components that are used to construct the resolved config.
 benchmark_metrics (Dict[str, List[JumpStartBenchmarkStat]]):
 The dictionary of benchmark metrics with name being the key.
 """
 self.base_fields = base_fields
 self.config_components: Dict[str, JumpStartConfigComponent] = config_components
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = benchmark_metrics
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 )
 self.resolved_metadata_config: Optional[Dict[str, Any]] = None
 self.config_name: Optional[str] = config_name
 self.default_inference_config: Optional[str] = config.get("default_inference_config")
 self.default_incremental_trainig_config: Optional[str] = config.get(
 "default_incremental_training_config"
 )
 self.supported_inference_configs: Optional[List[str]] = config.get(
 "supported_inference_configs"
 )
 self.supported_incremental_training_configs: Optional[List[str]] = config.get(
 "supported_incremental_training_configs"
 )

 def to_json(self) -> Dict[str, Any]:
 """Returns json representation of JumpStartMetadataConfig object."""
Expand DownExpand Up@@ -1255,6 +1277,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1264,14 +1287,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["inference_configs"].items()
 }
Expand DownExpand Up@@ -1308,6 +1323,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1317,14 +1333,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["training_configs"].items()
 }
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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May 2, 2024
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Add supported inference and incremental training configs
#4637
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supported inference configs


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21 changes: 16 additions & 5 deletions
src/sagemaker/jumpstart/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -504,7 +504,7 @@ def __init__(
 enable_remote_debug (bool or PipelineVariable): Optional.
 Specifies whether RemoteDebug is enabled for the training job
 config_name (Optional[str]):
 Name of the JumpStart Model config to apply. (Default: None).
 Name of the training configuration to apply to the Estimator. (Default: None).

 Raises:
 ValueError: If the model ID is not recognized by JumpStart.
Expand DownExpand Up@@ -686,6 +686,7 @@ def attach(
 model_version: Optional[str] = None,
 sagemaker_session: session.Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
 model_channel_name: str = "model",
 config_name: Optional[str] = None,
 ) -> "JumpStartEstimator":
 """Attach to an existing training job.

Expand DownExpand Up@@ -721,6 +722,8 @@ def attach(
 model data will be downloaded (default: 'model'). If no channel
 with the same name exists in the training job, this option will
 be ignored.
 config_name (str): Optional. Name of the training configuration to use
 when attaching to the training job. (Default: None).

 Returns:
 Instance of the calling ``JumpStartEstimator`` Class with the attached
Expand All@@ -732,7 +735,6 @@ def attach(
 """
 config_name = None
 if model_id is None:

 model_id, model_version, _, config_name = get_model_info_from_training_job(
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
Expand All@@ -746,6 +748,9 @@ def attach(
 "tolerate_deprecated_model": True, # model is already trained
 }

 if config_name:
 additional_kwargs.update({"config_name": config_name})

 model_specs = verify_model_region_and_return_specs(
 model_id=model_id,
 version=model_version,
Expand DownExpand Up@@ -804,6 +809,7 @@ def deploy(
 dependencies: Optional[List[str]] = None,
 git_config: Optional[Dict[str, str]] = None,
 use_compiled_model: bool = False,
 inference_config_name: Optional[str] = None,
 ) -> PredictorBase:
 """Creates endpoint from training job.

Expand DownExpand Up@@ -1039,6 +1045,8 @@ def deploy(
 (Default: None).
 use_compiled_model (bool): Flag to select whether to use compiled
 (optimized) model. (Default: False).
 inference_config_name (Optional[str]): Name of the inference configuration to
 be used in the model. (Default: None).
 """
 self.orig_predictor_cls = predictor_cls

Expand DownExpand Up@@ -1091,7 +1099,8 @@ def deploy(
 git_config=git_config,
 use_compiled_model=use_compiled_model,
 training_instance_type=self.instance_type,
 config_name=self.config_name,
 training_config_name=self.config_name,
 inference_config_name=inference_config_name,
 )

 predictor = super(JumpStartEstimator, self).deploy(
Expand All@@ -1108,7 +1117,7 @@ def deploy(
 tolerate_deprecated_model=self.tolerate_deprecated_model,
 tolerate_vulnerable_model=self.tolerate_vulnerable_model,
 sagemaker_session=self.sagemaker_session,
 config_name=self.config_name,
 config_name=estimator_deploy_kwargs.config_name,
 )

 # If a predictor class was passed, do not mutate predictor
Expand DownExpand Up@@ -1140,7 +1149,9 @@ def set_training_config(self, config_name: str) -> None:
 config_name (str): The name of the config.
 """
 self.__init__(
 model_id=self.model_id, model_version=self.model_version, config_name=config_name
 model_id=self.model_id,
 model_version=self.model_version,
 config_name=config_name,
 )

 def __str__(self) -> str:
Expand Down

35 changes: 31 additions & 4 deletions
src/sagemaker/jumpstart/factory/estimator.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -207,6 +207,7 @@ def get_init_kwargs(
 estimator_init_kwargs = _add_role_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_env_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_tags_to_kwargs(estimator_init_kwargs)
 estimator_init_kwargs = _add_config_name_to_kwargs(estimator_init_kwargs)

 return estimator_init_kwargs

Expand DownExpand Up@@ -291,7 +292,8 @@ def get_deploy_kwargs(
 use_compiled_model: Optional[bool] = None,
 model_name: Optional[str] = None,
 training_instance_type: Optional[str] = None,
 config_name: Optional[str] = None,
 training_config_name: Optional[str] = None,
 inference_config_name: Optional[str] = None,
) -> JumpStartEstimatorDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Estimator` object."""

Expand DownExpand Up@@ -319,7 +321,8 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=tolerate_vulnerable_model,
 tolerate_deprecated_model=tolerate_deprecated_model,
 sagemaker_session=sagemaker_session,
 config_name=config_name,
 training_config_name=training_config_name,
 config_name=inference_config_name,
 )

 model_init_kwargs: JumpStartModelInitKwargs = model.get_init_kwargs(
Expand DownExpand Up@@ -348,7 +351,7 @@ def get_deploy_kwargs(
 tolerate_deprecated_model=tolerate_deprecated_model,
 training_instance_type=training_instance_type,
 disable_instance_type_logging=True,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 estimator_deploy_kwargs: JumpStartEstimatorDeployKwargs = JumpStartEstimatorDeployKwargs(
Expand DownExpand Up@@ -393,7 +396,7 @@ def get_deploy_kwargs(
 tolerate_vulnerable_model=model_init_kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=model_init_kwargs.tolerate_deprecated_model,
 use_compiled_model=use_compiled_model,
 config_name=config_name,
 config_name=model_deploy_kwargs.config_name,
 )

 return estimator_deploy_kwargs
Expand DownExpand Up@@ -793,3 +796,27 @@ def _add_fit_extra_kwargs(kwargs: JumpStartEstimatorFitKwargs) -> JumpStartEstim
 setattr(kwargs, key, value)

 return kwargs


def _add_config_name_to_kwargs(
 kwargs: JumpStartEstimatorInitKwargs,
) -> JumpStartEstimatorInitKwargs:
 """Sets tags in kwargs based on default or override, returns full kwargs."""

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.TRAINING,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 config_name=kwargs.config_name,
 )

 if specs.training_configs and specs.training_configs.get_top_config_from_ranking():
 kwargs.config_name = (
 kwargs.config_name or specs.training_configs.get_top_config_from_ranking().config_name
 )

 return kwargs

77 changes: 68 additions & 9 deletions
src/sagemaker/jumpstart/factory/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -42,6 +42,7 @@
 JumpStartModelDeployKwargs,
 JumpStartModelInitKwargs,
 JumpStartModelRegisterKwargs,
 JumpStartModelSpecs,
)
from sagemaker.jumpstart.utils import (
 add_jumpstart_model_info_tags,
Expand DownExpand Up@@ -548,7 +549,27 @@ def _add_resources_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModel
 return kwargs


def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
def _select_inference_config_from_training_config(
 specs: JumpStartModelSpecs, training_config_name: str
) -> Optional[str]:
 """Selects the inference config from the training config.

 Args:
 specs (JumpStartModelSpecs): The specs for the model.
 training_config_name (str): The name of the training config.

 Returns:
 str: The name of the inference config.
 """
 if specs.training_configs:
 resolved_training_config = specs.training_configs.configs.get(training_config_name)
 if resolved_training_config:
 return resolved_training_config.default_inference_config

 return None


def _add_config_name_to_init_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 Raises:
Expand All@@ -566,13 +587,9 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )
 if (
 specs.inference_configs
 and specs.inference_configs.get_top_config_from_ranking().config_name
 ):
 kwargs.config_name = (
 kwargs.config_name or specs.inference_configs.get_top_config_from_ranking().config_name
 )
 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 if not kwargs.config_name:
 return kwargs
Expand All@@ -593,6 +610,42 @@ def _add_config_name_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartMod
 return kwargs


def _add_config_name_to_deploy_kwargs(
 kwargs: JumpStartModelDeployKwargs, training_config_name: Optional[str] = None
) -> JumpStartModelInitKwargs:
 """Sets default config name to the kwargs. Returns full kwargs.

 If a training_config_name is passed, then choose the inference config
 based on the supported inference configs in that training config.

 Raises:
 ValueError: If the instance_type is not supported with the current config.
 """

 specs = verify_model_region_and_return_specs(
 model_id=kwargs.model_id,
 version=kwargs.model_version,
 scope=JumpStartScriptScope.INFERENCE,
 region=kwargs.region,
 tolerate_vulnerable_model=kwargs.tolerate_vulnerable_model,
 tolerate_deprecated_model=kwargs.tolerate_deprecated_model,
 sagemaker_session=kwargs.sagemaker_session,
 model_type=kwargs.model_type,
 config_name=kwargs.config_name,
 )

 if training_config_name:
 kwargs.config_name = _select_inference_config_from_training_config(
 specs=specs, training_config_name=training_config_name
 )

 if specs.inference_configs:
 default_config_name = specs.inference_configs.get_top_config_from_ranking().config_name
 kwargs.config_name = kwargs.config_name or default_config_name

 return kwargs


def get_deploy_kwargs(
 model_id: str,
 model_version: Optional[str] = None,
Expand DownExpand Up@@ -623,6 +676,7 @@ def get_deploy_kwargs(
 resources: Optional[ResourceRequirements] = None,
 managed_instance_scaling: Optional[str] = None,
 endpoint_type: Optional[EndpointType] = None,
 training_config_name: Optional[str] = None,
 config_name: Optional[str] = None,
) -> JumpStartModelDeployKwargs:
 """Returns kwargs required to call `deploy` on `sagemaker.estimator.Model` object."""
Expand DownExpand Up@@ -664,6 +718,10 @@ def get_deploy_kwargs(

 deploy_kwargs = _add_endpoint_name_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs = _add_config_name_to_deploy_kwargs(
 kwargs=deploy_kwargs, training_config_name=training_config_name
 )

 deploy_kwargs = _add_instance_type_to_kwargs(kwargs=deploy_kwargs)

 deploy_kwargs.initial_instance_count = initial_instance_count or 1
Expand DownExpand Up@@ -858,6 +916,7 @@ def get_init_kwargs(
 model_init_kwargs = _add_model_package_arn_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_resources_to_kwargs(kwargs=model_init_kwargs)
 model_init_kwargs = _add_config_name_to_kwargs(kwargs=model_init_kwargs)

 model_init_kwargs = _add_config_name_to_init_kwargs(kwargs=model_init_kwargs)

 return model_init_kwargs

48 changes: 28 additions & 20 deletions
src/sagemaker/jumpstart/types.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1077,30 +1077,52 @@ class JumpStartMetadataConfig(JumpStartDataHolderType):
 "config_components",
 "resolved_metadata_config",
 "config_name",
 "default_inference_config",
 "default_incremental_trainig_config",
 "supported_inference_configs",
 "supported_incremental_training_configs",
 ]

 def __init__(
 self,
 config_name: str,
 config: Dict[str, Any],
 base_fields: Dict[str, Any],
 config_components: Dict[str, JumpStartConfigComponent],
 benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]],
 ):
 """Initializes a JumpStartMetadataConfig object from its json representation.

 Args:
 config_name (str): Name of the config,
 config (Dict[str, Any]):
 Dictionary representation of the config.
 base_fields (Dict[str, Any]):
 The default base fields that are used to construct the final resolved config.
 config_components (Dict[str, JumpStartConfigComponent]):
 The list of components that are used to construct the resolved config.
 benchmark_metrics (Dict[str, List[JumpStartBenchmarkStat]]):
 The dictionary of benchmark metrics with name being the key.
 """
 self.base_fields = base_fields
 self.config_components: Dict[str, JumpStartConfigComponent] = config_components
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = benchmark_metrics
 self.benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]] = (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 )
 self.resolved_metadata_config: Optional[Dict[str, Any]] = None
 self.config_name: Optional[str] = config_name
 self.default_inference_config: Optional[str] = config.get("default_inference_config")
 self.default_incremental_trainig_config: Optional[str] = config.get(
 "default_incremental_training_config"
 )
 self.supported_inference_configs: Optional[List[str]] = config.get(
 "supported_inference_configs"
 )
 self.supported_incremental_training_configs: Optional[List[str]] = config.get(
 "supported_incremental_training_configs"
 )

 def to_json(self) -> Dict[str, Any]:
 """Returns json representation of JumpStartMetadataConfig object."""
Expand DownExpand Up@@ -1255,6 +1277,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1264,14 +1287,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["inference_configs"].items()
 }
Expand DownExpand Up@@ -1308,6 +1323,7 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 {
 alias: JumpStartMetadataConfig(
 alias,
 config,
 json_obj,
 (
 {
Expand All@@ -1317,14 +1333,6 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
 if config and config.get("component_names")
 else None
 ),
 (
 {
 stat_name: [JumpStartBenchmarkStat(stat) for stat in stats]
 for stat_name, stats in config.get("benchmark_metrics").items()
 }
 if config and config.get("benchmark_metrics")
 else None
 ),
 )
 for alias, config in json_obj["training_configs"].items()
 }
Expand Down
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