Configuring Auto-Scaling for DeepSeek-R1 Models on Amazon SageMaker

Configuring auto-scaling for DeepSeek-R1 models on Amazon SageMaker involves several steps to ensure that your endpoints scale dynamically based on incoming request volume. Here's a detailed guide on how to achieve this:

1. Understanding Auto-Scaling in SageMaker**

Auto-scaling in SageMaker allows your model endpoints to automatically adjust the number of instances based on the volume of incoming requests. This feature is crucial for maintaining performance during peak hours while optimizing costs during periods of low activity.

2. Setting Up Auto-Scaling**

To configure auto-scaling for a DeepSeek-R1 model, you need to set up an Auto Scaling policy. This policy defines when to scale up or down based on specific metrics, such as the number of incoming requests or latency.

3. Choosing Metrics for Auto-Scaling**

Common metrics used for auto-scaling include:
- Request latency: Scale up if requests are taking too long to process.
- Incoming request count: Scale up if the number of incoming requests exceeds a certain threshold.

4. Configuring Auto-Scaling Policies**

You can configure these policies using the AWS Management Console, AWS CLI, or SDKs like Python's Boto3. For example, you might set a policy to scale up when the average latency exceeds a certain threshold and scale down when it drops below another threshold.

5. Implementing Auto-Scaling with SageMaker Endpoints**

When deploying a DeepSeek-R1 model using SageMaker, you can enable auto-scaling directly through the SageMaker endpoint configuration. SageMaker supports automatic load balancing and auto-scaling, allowing your model to scale dynamically based on incoming requests.

6. Using Hugging Face TGI for Deployment**

If you are deploying DeepSeek-R1 models using Hugging Face Text Generation Inference (TGI) on SageMaker, ensure that your endpoint is configured to support auto-scaling. This involves specifying the initial instance count and instance type during deployment.

Example Code Snippet for Deployment with Auto-Scaling

Here's an example of how you might deploy a DeepSeek-R1 model with auto-scaling enabled using the SageMaker SDK:

python
import sagemaker

from sagemaker.huggingface import HuggingFaceModel

# Get the execution role
role = sagemaker.get_execution_role()

# Create a SageMaker session
session = sagemaker.Session()

# Define the model and its environment variables
deploy_image_uri = get_huggingface_llm_image_uri("huggingface", version="3.0.1")

deepseek_tgi_model = HuggingFaceModel(
    image_uri=deploy_image_uri,
    env={
        "HF_MODEL_ID": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
        # Other environment variables...
    },
    role=role,
    sagemaker_session=session,
    name="deepseek-r1-llma-8b-model"
)

# Deploy the model with auto-scaling
predictor = deepseek_tgi_model.deploy(
    endpoint_name="deepseek-r1-llma-8b-endpoint",
    initial_instance_count=1,
    instance_type="ml.g6.2xlarge",
    # Enable auto-scaling by setting appropriate policies in the AWS console or CLI
    wait=False
)

After deploying the model, you can configure auto-scaling policies through the AWS Management Console or using AWS CLI commands.

7. Monitoring and Adjusting Auto-Scaling Policies**

Monitor your endpoint's performance and adjust the auto-scaling policies as needed to ensure optimal performance and cost efficiency.

By following these steps, you can effectively configure auto-scaling for DeepSeek-R1 models on SageMaker, ensuring that your endpoints adapt dynamically to changing request volumes.

Citations:
[1] https://aws.amazon.com/blogs/machine-learning/optimize-hosting-deepseek-r1-distilled-models-with-hugging-face-tgi-on-amazon-sagemaker-ai/
[2] https://www.byteplus.com/en/topic/409084
[3] https://www.youtube.com/watch?v=OczrlyNtw60
[4] https://aws.amazon.com/blogs/machine-learning/deploy-deepseek-r1-distilled-models-on-amazon-sagemaker-using-a-large-model-inference-container/
[5] https://www.reddit.com/r/aws/
[6] https://www.oneclickitsolution.com/centerofexcellence/aiml/deploy-deepseek-r1-amazon-sagemaker-guide
[7] https://repost.aws/questions/QUT4xru2SdTSqtoNyt1XV3VA/configuring-auto-scaling-for-sagemaker-async-inference
[8] https://huggingface.co/blog/deepseek-r1-aws

Can you explain the process of configuring auto-scaling for DeepSeek-R1 on SageMaker