public static interface AlgorithmSpecification.Builder extends CopyableBuilder<AlgorithmSpecification.Builder,AlgorithmSpecification>
Modifier and Type | Method and Description |
---|---|
AlgorithmSpecification.Builder |
trainingImage(String trainingImage)
The registry path of the Docker image that contains the training algorithm.
|
AlgorithmSpecification.Builder |
trainingInputMode(String trainingInputMode)
The input mode that the algorithm supports.
|
AlgorithmSpecification.Builder |
trainingInputMode(TrainingInputMode trainingInputMode)
The input mode that the algorithm supports.
|
copy
applyMutation, build
AlgorithmSpecification.Builder trainingImage(String trainingImage)
The registry path of the Docker image that contains the training algorithm. For information about using your own algorithms, see Docker Registry Paths for Algorithms Provided by Amazon SageMaker .
trainingImage
- The registry path of the Docker image that contains the training algorithm. For information about
using your own algorithms, see Docker Registry
Paths for Algorithms Provided by Amazon SageMaker .AlgorithmSpecification.Builder trainingInputMode(String trainingInputMode)
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see
Algorithms. If an algorithm supports
the File
input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML
storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports
the Pipe
input mode, Amazon SageMaker streams data directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accomodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
trainingInputMode
- The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms
support, see Algorithms. If an
algorithm supports the File
input mode, Amazon SageMaker downloads the training data from
S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training
container. If an algorithm supports the Pipe
input mode, Amazon SageMaker streams data
directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accomodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
TrainingInputMode
,
TrainingInputMode
AlgorithmSpecification.Builder trainingInputMode(TrainingInputMode trainingInputMode)
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see
Algorithms. If an algorithm supports
the File
input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML
storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports
the Pipe
input mode, Amazon SageMaker streams data directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accomodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
trainingInputMode
- The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms
support, see Algorithms. If an
algorithm supports the File
input mode, Amazon SageMaker downloads the training data from
S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training
container. If an algorithm supports the Pipe
input mode, Amazon SageMaker streams data
directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accomodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
TrainingInputMode
,
TrainingInputMode
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