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/*
* Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package software.amazon.awssdk.services.sagemaker.model;
import java.io.Serializable;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Objects;
import java.util.Optional;
import java.util.function.BiConsumer;
import java.util.function.Consumer;
import java.util.function.Function;
import software.amazon.awssdk.annotations.Generated;
import software.amazon.awssdk.core.SdkField;
import software.amazon.awssdk.core.SdkPojo;
import software.amazon.awssdk.core.protocol.MarshallLocation;
import software.amazon.awssdk.core.protocol.MarshallingType;
import software.amazon.awssdk.core.traits.LocationTrait;
import software.amazon.awssdk.utils.ToString;
import software.amazon.awssdk.utils.builder.CopyableBuilder;
import software.amazon.awssdk.utils.builder.ToCopyableBuilder;
/**
*
* Specifies the S3 location of ML model data to deploy.
*
*/
@Generated("software.amazon.awssdk:codegen")
public final class S3ModelDataSource implements SdkPojo, Serializable,
ToCopyableBuilder {
private static final SdkField S3_URI_FIELD = SdkField. builder(MarshallingType.STRING).memberName("S3Uri")
.getter(getter(S3ModelDataSource::s3Uri)).setter(setter(Builder::s3Uri))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("S3Uri").build()).build();
private static final SdkField S3_DATA_TYPE_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("S3DataType").getter(getter(S3ModelDataSource::s3DataTypeAsString)).setter(setter(Builder::s3DataType))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("S3DataType").build()).build();
private static final SdkField COMPRESSION_TYPE_FIELD = SdkField. builder(MarshallingType.STRING)
.memberName("CompressionType").getter(getter(S3ModelDataSource::compressionTypeAsString))
.setter(setter(Builder::compressionType))
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("CompressionType").build()).build();
private static final SdkField MODEL_ACCESS_CONFIG_FIELD = SdkField
. builder(MarshallingType.SDK_POJO).memberName("ModelAccessConfig")
.getter(getter(S3ModelDataSource::modelAccessConfig)).setter(setter(Builder::modelAccessConfig))
.constructor(ModelAccessConfig::builder)
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("ModelAccessConfig").build()).build();
private static final SdkField HUB_ACCESS_CONFIG_FIELD = SdkField
. builder(MarshallingType.SDK_POJO).memberName("HubAccessConfig")
.getter(getter(S3ModelDataSource::hubAccessConfig)).setter(setter(Builder::hubAccessConfig))
.constructor(InferenceHubAccessConfig::builder)
.traits(LocationTrait.builder().location(MarshallLocation.PAYLOAD).locationName("HubAccessConfig").build()).build();
private static final List> SDK_FIELDS = Collections.unmodifiableList(Arrays.asList(S3_URI_FIELD,
S3_DATA_TYPE_FIELD, COMPRESSION_TYPE_FIELD, MODEL_ACCESS_CONFIG_FIELD, HUB_ACCESS_CONFIG_FIELD));
private static final long serialVersionUID = 1L;
private final String s3Uri;
private final String s3DataType;
private final String compressionType;
private final ModelAccessConfig modelAccessConfig;
private final InferenceHubAccessConfig hubAccessConfig;
private S3ModelDataSource(BuilderImpl builder) {
this.s3Uri = builder.s3Uri;
this.s3DataType = builder.s3DataType;
this.compressionType = builder.compressionType;
this.modelAccessConfig = builder.modelAccessConfig;
this.hubAccessConfig = builder.hubAccessConfig;
}
/**
*
* Specifies the S3 path of ML model data to deploy.
*
*
* @return Specifies the S3 path of ML model data to deploy.
*/
public final String s3Uri() {
return s3Uri;
}
/**
*
* Specifies the type of ML model data to deploy.
*
*
* If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects
* that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix
* identified by S3Uri always ends with a forward slash (/).
*
*
* If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
*
*
* If the service returns an enum value that is not available in the current SDK version, {@link #s3DataType} will
* return {@link S3ModelDataType#UNKNOWN_TO_SDK_VERSION}. The raw value returned by the service is available from
* {@link #s3DataTypeAsString}.
*
*
* @return Specifies the type of ML model data to deploy.
*
* If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all
* objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name
* prefix identified by S3Uri always ends with a forward slash (/).
*
*
* If you choose S3Object, S3Uri identifies an object that is the ML model data to
* deploy.
* @see S3ModelDataType
*/
public final S3ModelDataType s3DataType() {
return S3ModelDataType.fromValue(s3DataType);
}
/**
*
* Specifies the type of ML model data to deploy.
*
*
* If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects
* that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix
* identified by S3Uri always ends with a forward slash (/).
*
*
* If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
*
*
* If the service returns an enum value that is not available in the current SDK version, {@link #s3DataType} will
* return {@link S3ModelDataType#UNKNOWN_TO_SDK_VERSION}. The raw value returned by the service is available from
* {@link #s3DataTypeAsString}.
*
*
* @return Specifies the type of ML model data to deploy.
*
* If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all
* objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name
* prefix identified by S3Uri always ends with a forward slash (/).
*
*
* If you choose S3Object, S3Uri identifies an object that is the ML model data to
* deploy.
* @see S3ModelDataType
*/
public final String s3DataTypeAsString() {
return s3DataType;
}
/**
*
* Specifies how the ML model data is prepared.
*
*
* If you choose Gzip and choose S3Object as the value of S3DataType,
* S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to
* decompress and untar the object during model deployment.
*
*
* If you choose None and chooose S3Object as the value of S3DataType,
* S3Uri identifies an object that represents an uncompressed ML model to deploy.
*
*
* If you choose None and choose S3Prefix as the value of S3DataType, S3Uri
* identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
*
*
* If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model
* directory for use by your inference code:
*
*
* -
*
* If you choose S3Object as the value of S3DataType, then SageMaker will split the key of
* the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file
* holding the content of the S3 object.
*
*
* -
*
* If you choose S3Prefix as the value of S3DataType, then for each S3 object under the
* key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder
* as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker
* will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename
* of the file holding the content of the S3 object.
*
*
* -
*
* Do not use any of the following as file names or directory names:
*
*
* -
*
* An empty or blank string
*
*
* -
*
* A string which contains null bytes
*
*
* -
*
* A string longer than 255 bytes
*
*
* -
*
* A single dot (.)
*
*
* -
*
* A double dot (..)
*
*
*
*
* -
*
* Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists
* of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and
* you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the
* value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a
* regular file) and /opt/ml/model/weights/ (a directory).
*
*
* -
*
* Do not organize the model artifacts in S3 console using folders.
* When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide.
* They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file
* names, leading to model deployment failure.
*
*
*
*
* If the service returns an enum value that is not available in the current SDK version, {@link #compressionType}
* will return {@link ModelCompressionType#UNKNOWN_TO_SDK_VERSION}. The raw value returned by the service is
* available from {@link #compressionTypeAsString}.
*
*
* @return Specifies how the ML model data is prepared.
*
* If you choose Gzip and choose S3Object as the value of S3DataType,
* S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to
* decompress and untar the object during model deployment.
*
*
* If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.
*
*
* If you choose None and choose S3Prefix as the value of S3DataType,
* S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML
* model to deploy.
*
*
* If you choose None, then SageMaker will follow rules below when creating model data files under
* /opt/ml/model directory for use by your inference code:
*
*
* -
*
* If you choose S3Object as the value of S3DataType, then SageMaker will split
* the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the
* filename of the file holding the content of the S3 object.
*
*
* -
*
* If you choose S3Prefix as the value of S3DataType, then for each S3 object
* under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and
* use the remainder as the path (relative to /opt/ml/model) of the file holding the content of
* the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory
* names and the last part as filename of the file holding the content of the S3 object.
*
*
* -
*
* Do not use any of the following as file names or directory names:
*
*
* -
*
* An empty or blank string
*
*
* -
*
* A string which contains null bytes
*
*
* -
*
* A string longer than 255 bytes
*
*
* -
*
* A single dot (.)
*
*
* -
*
* A double dot (..)
*
*
*
*
* -
*
* Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model
* consists of two S3 objects s3://mybucket/model/weights and
* s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the
* value of S3Uri and S3Prefix as the value of S3DataType, then it
* will result in name clash between /opt/ml/model/weights (a regular file) and
* /opt/ml/model/weights/ (a directory).
*
*
* -
*
* Do not organize the model artifacts in S3 console using
* folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the
* folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker
* restrictions on model artifact file names, leading to model deployment failure.
*
*
* @see ModelCompressionType
*/
public final ModelCompressionType compressionType() {
return ModelCompressionType.fromValue(compressionType);
}
/**
*
* Specifies how the ML model data is prepared.
*
*
* If you choose Gzip and choose S3Object as the value of S3DataType,
* S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to
* decompress and untar the object during model deployment.
*
*
* If you choose None and chooose S3Object as the value of S3DataType,
* S3Uri identifies an object that represents an uncompressed ML model to deploy.
*
*
* If you choose None and choose S3Prefix as the value of S3DataType, S3Uri
* identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
*
*
* If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model
* directory for use by your inference code:
*
*
* -
*
* If you choose S3Object as the value of S3DataType, then SageMaker will split the key of
* the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file
* holding the content of the S3 object.
*
*
* -
*
* If you choose S3Prefix as the value of S3DataType, then for each S3 object under the
* key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder
* as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker
* will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename
* of the file holding the content of the S3 object.
*
*
* -
*
* Do not use any of the following as file names or directory names:
*
*
* -
*
* An empty or blank string
*
*
* -
*
* A string which contains null bytes
*
*
* -
*
* A string longer than 255 bytes
*
*
* -
*
* A single dot (.)
*
*
* -
*
* A double dot (..)
*
*
*
*
* -
*
* Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists
* of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and
* you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the
* value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a
* regular file) and /opt/ml/model/weights/ (a directory).
*
*
* -
*
* Do not organize the model artifacts in S3 console using folders.
* When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide.
* They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file
* names, leading to model deployment failure.
*
*
*
*
* If the service returns an enum value that is not available in the current SDK version, {@link #compressionType}
* will return {@link ModelCompressionType#UNKNOWN_TO_SDK_VERSION}. The raw value returned by the service is
* available from {@link #compressionTypeAsString}.
*
*
* @return Specifies how the ML model data is prepared.
*
* If you choose Gzip and choose S3Object as the value of S3DataType,
* S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to
* decompress and untar the object during model deployment.
*
*
* If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.
*
*
* If you choose None and choose S3Prefix as the value of S3DataType,
* S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML
* model to deploy.
*
*
* If you choose None, then SageMaker will follow rules below when creating model data files under
* /opt/ml/model directory for use by your inference code:
*
*
* -
*
* If you choose S3Object as the value of S3DataType, then SageMaker will split
* the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the
* filename of the file holding the content of the S3 object.
*
*
* -
*
* If you choose S3Prefix as the value of S3DataType, then for each S3 object
* under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and
* use the remainder as the path (relative to /opt/ml/model) of the file holding the content of
* the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory
* names and the last part as filename of the file holding the content of the S3 object.
*
*
* -
*
* Do not use any of the following as file names or directory names:
*
*
* -
*
* An empty or blank string
*
*
* -
*
* A string which contains null bytes
*
*
* -
*
* A string longer than 255 bytes
*
*
* -
*
* A single dot (.)
*
*
* -
*
* A double dot (..)
*
*
*
*
* -
*
* Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model
* consists of two S3 objects s3://mybucket/model/weights and
* s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the
* value of S3Uri and S3Prefix as the value of S3DataType, then it
* will result in name clash between /opt/ml/model/weights (a regular file) and
* /opt/ml/model/weights/ (a directory).
*
*
* -
*
* Do not organize the model artifacts in S3 console using
* folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the
* folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker
* restrictions on model artifact file names, leading to model deployment failure.
*
*
* @see ModelCompressionType
*/
public final String compressionTypeAsString() {
return compressionType;
}
/**
*
* Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license
* agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with
* any applicable license terms and making sure they are acceptable for your use case before downloading or using a
* model.
*
*
* @return Specifies the access configuration file for the ML model. You can explicitly accept the model end-user
* license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and
* complying with any applicable license terms and making sure they are acceptable for your use case before
* downloading or using a model.
*/
public final ModelAccessConfig modelAccessConfig() {
return modelAccessConfig;
}
/**
*
* Configuration information for hub access.
*
*
* @return Configuration information for hub access.
*/
public final InferenceHubAccessConfig hubAccessConfig() {
return hubAccessConfig;
}
@Override
public Builder toBuilder() {
return new BuilderImpl(this);
}
public static Builder builder() {
return new BuilderImpl();
}
public static Class serializableBuilderClass() {
return BuilderImpl.class;
}
@Override
public final int hashCode() {
int hashCode = 1;
hashCode = 31 * hashCode + Objects.hashCode(s3Uri());
hashCode = 31 * hashCode + Objects.hashCode(s3DataTypeAsString());
hashCode = 31 * hashCode + Objects.hashCode(compressionTypeAsString());
hashCode = 31 * hashCode + Objects.hashCode(modelAccessConfig());
hashCode = 31 * hashCode + Objects.hashCode(hubAccessConfig());
return hashCode;
}
@Override
public final boolean equals(Object obj) {
return equalsBySdkFields(obj);
}
@Override
public final boolean equalsBySdkFields(Object obj) {
if (this == obj) {
return true;
}
if (obj == null) {
return false;
}
if (!(obj instanceof S3ModelDataSource)) {
return false;
}
S3ModelDataSource other = (S3ModelDataSource) obj;
return Objects.equals(s3Uri(), other.s3Uri()) && Objects.equals(s3DataTypeAsString(), other.s3DataTypeAsString())
&& Objects.equals(compressionTypeAsString(), other.compressionTypeAsString())
&& Objects.equals(modelAccessConfig(), other.modelAccessConfig())
&& Objects.equals(hubAccessConfig(), other.hubAccessConfig());
}
/**
* Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be
* redacted from this string using a placeholder value.
*/
@Override
public final String toString() {
return ToString.builder("S3ModelDataSource").add("S3Uri", s3Uri()).add("S3DataType", s3DataTypeAsString())
.add("CompressionType", compressionTypeAsString()).add("ModelAccessConfig", modelAccessConfig())
.add("HubAccessConfig", hubAccessConfig()).build();
}
public final Optional getValueForField(String fieldName, Class clazz) {
switch (fieldName) {
case "S3Uri":
return Optional.ofNullable(clazz.cast(s3Uri()));
case "S3DataType":
return Optional.ofNullable(clazz.cast(s3DataTypeAsString()));
case "CompressionType":
return Optional.ofNullable(clazz.cast(compressionTypeAsString()));
case "ModelAccessConfig":
return Optional.ofNullable(clazz.cast(modelAccessConfig()));
case "HubAccessConfig":
return Optional.ofNullable(clazz.cast(hubAccessConfig()));
default:
return Optional.empty();
}
}
@Override
public final List> sdkFields() {
return SDK_FIELDS;
}
private static Function getter(Function g) {
return obj -> g.apply((S3ModelDataSource) obj);
}
private static BiConsumer setter(BiConsumer s) {
return (obj, val) -> s.accept((Builder) obj, val);
}
public interface Builder extends SdkPojo, CopyableBuilder {
/**
*
* Specifies the S3 path of ML model data to deploy.
*
*
* @param s3Uri
* Specifies the S3 path of ML model data to deploy.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder s3Uri(String s3Uri);
/**
*
* Specifies the type of ML model data to deploy.
*
*
* If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all
* objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name
* prefix identified by S3Uri always ends with a forward slash (/).
*
*
* If you choose S3Object, S3Uri identifies an object that is the ML model data to
* deploy.
*
*
* @param s3DataType
* Specifies the type of ML model data to deploy.
*
* If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses
* all objects that match the specified key name prefix as part of the ML model data to deploy. A valid
* key name prefix identified by S3Uri always ends with a forward slash (/).
*
*
* If you choose S3Object, S3Uri identifies an object that is the ML model data
* to deploy.
* @see S3ModelDataType
* @return Returns a reference to this object so that method calls can be chained together.
* @see S3ModelDataType
*/
Builder s3DataType(String s3DataType);
/**
*
* Specifies the type of ML model data to deploy.
*
*
* If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all
* objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name
* prefix identified by S3Uri always ends with a forward slash (/).
*
*
* If you choose S3Object, S3Uri identifies an object that is the ML model data to
* deploy.
*
*
* @param s3DataType
* Specifies the type of ML model data to deploy.
*
* If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses
* all objects that match the specified key name prefix as part of the ML model data to deploy. A valid
* key name prefix identified by S3Uri always ends with a forward slash (/).
*
*
* If you choose S3Object, S3Uri identifies an object that is the ML model data
* to deploy.
* @see S3ModelDataType
* @return Returns a reference to this object so that method calls can be chained together.
* @see S3ModelDataType
*/
Builder s3DataType(S3ModelDataType s3DataType);
/**
*
* Specifies how the ML model data is prepared.
*
*
* If you choose Gzip and choose S3Object as the value of S3DataType,
* S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to
* decompress and untar the object during model deployment.
*
*
* If you choose None and chooose S3Object as the value of S3DataType,
* S3Uri identifies an object that represents an uncompressed ML model to deploy.
*
*
* If you choose None and choose S3Prefix as the value of S3DataType,
* S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model
* to deploy.
*
*
* If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model
* directory for use by your inference code:
*
*
* -
*
* If you choose S3Object as the value of S3DataType, then SageMaker will split the
* key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of
* the file holding the content of the S3 object.
*
*
* -
*
* If you choose S3Prefix as the value of S3DataType, then for each S3 object under
* the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the
* remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3
* object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the
* last part as filename of the file holding the content of the S3 object.
*
*
* -
*
* Do not use any of the following as file names or directory names:
*
*
* -
*
* An empty or blank string
*
*
* -
*
* A string which contains null bytes
*
*
* -
*
* A string longer than 255 bytes
*
*
* -
*
* A single dot (.)
*
*
* -
*
* A double dot (..)
*
*
*
*
* -
*
* Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model
* consists of two S3 objects s3://mybucket/model/weights and
* s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value
* of S3Uri and S3Prefix as the value of S3DataType, then it will result
* in name clash between /opt/ml/model/weights (a regular file) and
* /opt/ml/model/weights/ (a directory).
*
*
* -
*
* Do not organize the model artifacts in S3 console using folders.
* When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you
* provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model
* artifact file names, leading to model deployment failure.
*
*
*
*
* @param compressionType
* Specifies how the ML model data is prepared.
*
* If you choose Gzip and choose S3Object as the value of
* S3DataType, S3Uri identifies an object that is a gzip-compressed TAR
* archive. SageMaker will attempt to decompress and untar the object during model deployment.
*
*
* If you choose None and chooose S3Object as the value of
* S3DataType, S3Uri identifies an object that represents an uncompressed ML
* model to deploy.
*
*
* If you choose None and choose S3Prefix as the value of S3DataType,
* S3Uri identifies a key name prefix, under which all objects represents the uncompressed
* ML model to deploy.
*
*
* If you choose None, then SageMaker will follow rules below when creating model data files under
* /opt/ml/model directory for use by your inference code:
*
*
* -
*
* If you choose S3Object as the value of S3DataType, then SageMaker will split
* the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the
* filename of the file holding the content of the S3 object.
*
*
* -
*
* If you choose S3Prefix as the value of S3DataType, then for each S3 object
* under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix,
* and use the remainder as the path (relative to /opt/ml/model) of the file holding the
* content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as
* directory names and the last part as filename of the file holding the content of the S3 object.
*
*
* -
*
* Do not use any of the following as file names or directory names:
*
*
* -
*
* An empty or blank string
*
*
* -
*
* A string which contains null bytes
*
*
* -
*
* A string longer than 255 bytes
*
*
* -
*
* A single dot (.)
*
*
* -
*
* A double dot (..)
*
*
*
*
* -
*
* Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML
* model consists of two S3 objects s3://mybucket/model/weights and
* s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as
* the value of S3Uri and S3Prefix as the value of S3DataType,
* then it will result in name clash between /opt/ml/model/weights (a regular file) and
* /opt/ml/model/weights/ (a directory).
*
*
* -
*
* Do not organize the model artifacts in S3 console using
* folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the
* folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker
* restrictions on model artifact file names, leading to model deployment failure.
*
*
* @see ModelCompressionType
* @return Returns a reference to this object so that method calls can be chained together.
* @see ModelCompressionType
*/
Builder compressionType(String compressionType);
/**
*
* Specifies how the ML model data is prepared.
*
*
* If you choose Gzip and choose S3Object as the value of S3DataType,
* S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to
* decompress and untar the object during model deployment.
*
*
* If you choose None and chooose S3Object as the value of S3DataType,
* S3Uri identifies an object that represents an uncompressed ML model to deploy.
*
*
* If you choose None and choose S3Prefix as the value of S3DataType,
* S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model
* to deploy.
*
*
* If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model
* directory for use by your inference code:
*
*
* -
*
* If you choose S3Object as the value of S3DataType, then SageMaker will split the
* key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of
* the file holding the content of the S3 object.
*
*
* -
*
* If you choose S3Prefix as the value of S3DataType, then for each S3 object under
* the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the
* remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3
* object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the
* last part as filename of the file holding the content of the S3 object.
*
*
* -
*
* Do not use any of the following as file names or directory names:
*
*
* -
*
* An empty or blank string
*
*
* -
*
* A string which contains null bytes
*
*
* -
*
* A string longer than 255 bytes
*
*
* -
*
* A single dot (.)
*
*
* -
*
* A double dot (..)
*
*
*
*
* -
*
* Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model
* consists of two S3 objects s3://mybucket/model/weights and
* s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value
* of S3Uri and S3Prefix as the value of S3DataType, then it will result
* in name clash between /opt/ml/model/weights (a regular file) and
* /opt/ml/model/weights/ (a directory).
*
*
* -
*
* Do not organize the model artifacts in S3 console using folders.
* When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you
* provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model
* artifact file names, leading to model deployment failure.
*
*
*
*
* @param compressionType
* Specifies how the ML model data is prepared.
*
* If you choose Gzip and choose S3Object as the value of
* S3DataType, S3Uri identifies an object that is a gzip-compressed TAR
* archive. SageMaker will attempt to decompress and untar the object during model deployment.
*
*
* If you choose None and chooose S3Object as the value of
* S3DataType, S3Uri identifies an object that represents an uncompressed ML
* model to deploy.
*
*
* If you choose None and choose S3Prefix as the value of S3DataType,
* S3Uri identifies a key name prefix, under which all objects represents the uncompressed
* ML model to deploy.
*
*
* If you choose None, then SageMaker will follow rules below when creating model data files under
* /opt/ml/model directory for use by your inference code:
*
*
* -
*
* If you choose S3Object as the value of S3DataType, then SageMaker will split
* the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the
* filename of the file holding the content of the S3 object.
*
*
* -
*
* If you choose S3Prefix as the value of S3DataType, then for each S3 object
* under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix,
* and use the remainder as the path (relative to /opt/ml/model) of the file holding the
* content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as
* directory names and the last part as filename of the file holding the content of the S3 object.
*
*
* -
*
* Do not use any of the following as file names or directory names:
*
*
* -
*
* An empty or blank string
*
*
* -
*
* A string which contains null bytes
*
*
* -
*
* A string longer than 255 bytes
*
*
* -
*
* A single dot (.)
*
*
* -
*
* A double dot (..)
*
*
*
*
* -
*
* Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML
* model consists of two S3 objects s3://mybucket/model/weights and
* s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as
* the value of S3Uri and S3Prefix as the value of S3DataType,
* then it will result in name clash between /opt/ml/model/weights (a regular file) and
* /opt/ml/model/weights/ (a directory).
*
*
* -
*
* Do not organize the model artifacts in S3 console using
* folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the
* folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker
* restrictions on model artifact file names, leading to model deployment failure.
*
*
* @see ModelCompressionType
* @return Returns a reference to this object so that method calls can be chained together.
* @see ModelCompressionType
*/
Builder compressionType(ModelCompressionType compressionType);
/**
*
* Specifies the access configuration file for the ML model. You can explicitly accept the model end-user
* license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and
* complying with any applicable license terms and making sure they are acceptable for your use case before
* downloading or using a model.
*
*
* @param modelAccessConfig
* Specifies the access configuration file for the ML model. You can explicitly accept the model end-user
* license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing
* and complying with any applicable license terms and making sure they are acceptable for your use case
* before downloading or using a model.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder modelAccessConfig(ModelAccessConfig modelAccessConfig);
/**
*
* Specifies the access configuration file for the ML model. You can explicitly accept the model end-user
* license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and
* complying with any applicable license terms and making sure they are acceptable for your use case before
* downloading or using a model.
*
* This is a convenience method that creates an instance of the {@link ModelAccessConfig.Builder} avoiding the
* need to create one manually via {@link ModelAccessConfig#builder()}.
*
*
* When the {@link Consumer} completes, {@link ModelAccessConfig.Builder#build()} is called immediately and its
* result is passed to {@link #modelAccessConfig(ModelAccessConfig)}.
*
* @param modelAccessConfig
* a consumer that will call methods on {@link ModelAccessConfig.Builder}
* @return Returns a reference to this object so that method calls can be chained together.
* @see #modelAccessConfig(ModelAccessConfig)
*/
default Builder modelAccessConfig(Consumer modelAccessConfig) {
return modelAccessConfig(ModelAccessConfig.builder().applyMutation(modelAccessConfig).build());
}
/**
*
* Configuration information for hub access.
*
*
* @param hubAccessConfig
* Configuration information for hub access.
* @return Returns a reference to this object so that method calls can be chained together.
*/
Builder hubAccessConfig(InferenceHubAccessConfig hubAccessConfig);
/**
*
* Configuration information for hub access.
*
* This is a convenience method that creates an instance of the {@link InferenceHubAccessConfig.Builder}
* avoiding the need to create one manually via {@link InferenceHubAccessConfig#builder()}.
*
*
* When the {@link Consumer} completes, {@link InferenceHubAccessConfig.Builder#build()} is called immediately
* and its result is passed to {@link #hubAccessConfig(InferenceHubAccessConfig)}.
*
* @param hubAccessConfig
* a consumer that will call methods on {@link InferenceHubAccessConfig.Builder}
* @return Returns a reference to this object so that method calls can be chained together.
* @see #hubAccessConfig(InferenceHubAccessConfig)
*/
default Builder hubAccessConfig(Consumer hubAccessConfig) {
return hubAccessConfig(InferenceHubAccessConfig.builder().applyMutation(hubAccessConfig).build());
}
}
static final class BuilderImpl implements Builder {
private String s3Uri;
private String s3DataType;
private String compressionType;
private ModelAccessConfig modelAccessConfig;
private InferenceHubAccessConfig hubAccessConfig;
private BuilderImpl() {
}
private BuilderImpl(S3ModelDataSource model) {
s3Uri(model.s3Uri);
s3DataType(model.s3DataType);
compressionType(model.compressionType);
modelAccessConfig(model.modelAccessConfig);
hubAccessConfig(model.hubAccessConfig);
}
public final String getS3Uri() {
return s3Uri;
}
public final void setS3Uri(String s3Uri) {
this.s3Uri = s3Uri;
}
@Override
public final Builder s3Uri(String s3Uri) {
this.s3Uri = s3Uri;
return this;
}
public final String getS3DataType() {
return s3DataType;
}
public final void setS3DataType(String s3DataType) {
this.s3DataType = s3DataType;
}
@Override
public final Builder s3DataType(String s3DataType) {
this.s3DataType = s3DataType;
return this;
}
@Override
public final Builder s3DataType(S3ModelDataType s3DataType) {
this.s3DataType(s3DataType == null ? null : s3DataType.toString());
return this;
}
public final String getCompressionType() {
return compressionType;
}
public final void setCompressionType(String compressionType) {
this.compressionType = compressionType;
}
@Override
public final Builder compressionType(String compressionType) {
this.compressionType = compressionType;
return this;
}
@Override
public final Builder compressionType(ModelCompressionType compressionType) {
this.compressionType(compressionType == null ? null : compressionType.toString());
return this;
}
public final ModelAccessConfig.Builder getModelAccessConfig() {
return modelAccessConfig != null ? modelAccessConfig.toBuilder() : null;
}
public final void setModelAccessConfig(ModelAccessConfig.BuilderImpl modelAccessConfig) {
this.modelAccessConfig = modelAccessConfig != null ? modelAccessConfig.build() : null;
}
@Override
public final Builder modelAccessConfig(ModelAccessConfig modelAccessConfig) {
this.modelAccessConfig = modelAccessConfig;
return this;
}
public final InferenceHubAccessConfig.Builder getHubAccessConfig() {
return hubAccessConfig != null ? hubAccessConfig.toBuilder() : null;
}
public final void setHubAccessConfig(InferenceHubAccessConfig.BuilderImpl hubAccessConfig) {
this.hubAccessConfig = hubAccessConfig != null ? hubAccessConfig.build() : null;
}
@Override
public final Builder hubAccessConfig(InferenceHubAccessConfig hubAccessConfig) {
this.hubAccessConfig = hubAccessConfig;
return this;
}
@Override
public S3ModelDataSource build() {
return new S3ModelDataSource(this);
}
@Override
public List> sdkFields() {
return SDK_FIELDS;
}
}
}