20250126_AWS SageMaker linear learner distributed training.md

To apply distributed training for the AWS SageMaker Linear Learner algorithm, you would typically rely on SageMaker's built-in distributed training capabilities. The Linear Learner algorithm supports distributed training by scaling across multiple instances and using multiple GPUs or CPU cores.

How to Apply Distributed Training for Linear Learner Algorithm in SageMaker

1. Using SageMaker Pre-built Containers with Distributed Training

SageMaker Linear Learner algorithm provides a straightforward approach to use distributed training across multiple instances by setting the instance_count parameter to more than 1.

Steps:

1. Create a SageMaker Estimator:

   • You can specify the number of instances (instance_count) and instance type (instance_type). The Linear Learner algorithm automatically handles the distribution of data and training across these instances.

2. Specify instance_count:

   • Set instance_count > 1 to trigger distributed training. You don’t have to configure the communication backend yourself, as SageMaker will manage this for you.

3. Distributed Training Details:

   • SageMaker Linear Learner uses Horovod for distributed training. With instance_count > 1, SageMaker automatically sets up the communication layer and manages synchronization of model updates across instances.

4. Training Job:

   • When you submit the training job, SageMaker automatically distributes the data, and each instance will train on a subset of the data. The model updates are then aggregated across the instances, and the model is synced.

Code Example:

import sagemaker
from sagemaker import get_execution_role
from sagemaker.estimator import Estimator

role = get_execution_role()

# Create the Estimator with Linear Learner algorithm
linear_learner_estimator = Estimator(
    image_uri='174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:latest',  # Use the correct container URI for your region
    role=role,
    instance_count=2,  # Specify multiple instances for distributed training
    instance_type='ml.m5.large',  # Choose the appropriate instance type
    hyperparameters={
        'predictor_type': 'regressor',
        'mini_batch_size': 200,  # Customize as needed
        'epochs': 10,
        'feature_dim': 784  # Specify the dimensionality of your data
    }
)

# Start the training job
linear_learner_estimator.fit({'train': 's3://path/to/train/data', 'validation': 's3://path/to/validation/data'})

Key Details:

• SageMaker handles data distribution: With instance_count > 1, SageMaker automatically divides the dataset across instances for parallel processing.
• Horovod support: SageMaker Linear Learner uses Horovod under the hood to synchronize model updates across instances. You don't need to explicitly configure Horovod yourself.

When SageMaker Decides to Use Distributed Training:

• Distributed training is automatically triggered when the instance_count is greater than 1.
• SageMaker will scale out training across multiple instances (and across multiple GPUs if available) to speed up the training process.

2. Important Considerations:

• Data Parallelism: Each instance will train on a subset of the data, and the model parameters will be updated and synchronized across instances.
• Hyperparameters: You can adjust hyperparameters like mini_batch_size, epochs, and feature_dim to optimize your training job.
• Scaling: SageMaker Linear Learner works well for parallelizing training jobs with distributed data, but it's important to monitor scaling efficiency, especially when using very large datasets.

3. How SageMaker Optimizes Linear Learner for Distributed Training:

• Automatic Data Sharding: When using multiple instances, SageMaker will automatically shard the data and distribute it across the available instances.
• Model Update Synchronization: The updates to the model's weights are synchronized across instances in each iteration to ensure consistency in the model.

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Summary:

To apply distributed training for SageMaker Linear Learner, you can simply:

• Set instance_count > 1 when creating the Estimator.
• SageMaker will automatically handle the distribution of data and synchronization of the model across instances using Horovod.
• This approach simplifies the process since you don’t need to manually set up the communication layer or manage synchronization—SageMaker takes care of it for you.

This makes distributed training in SageMaker Linear Learner easy and efficient, especially when dealing with large datasets or requiring high performance.