Deploying finetuned models

Deploying your finetuned LLM is a critical step in bringing your custom finetuned model into a place where it can be used as part of a real-world use case. This process involves careful planning and consideration of various factors to ensure optimal performance, reliability, and cost-effectiveness. In this section, we'll explore the key aspects of LLM deployment and discuss different options available to you.

Deployment Considerations

Before diving into specific deployment options, you should understand the various factors that influence the deployment process. One of the primary considerations is the memory and machine requirements for your finetuned model. LLMs are typically resource-intensive, requiring substantial RAM, processing power and specialized hardware. This choice of hardware can significantly impact both performance and cost, so it's crucial to strike the right balance based on your specific use case.

Real-time considerations play a vital role in deployment planning, especially for applications that require immediate responses. This includes preparing for potential failover scenarios if your finetuned model encounters issues, conducting thorough benchmarks and load testing, and modeling expected user load and usage patterns. Additionally, you'll need to decide between streaming and non-streaming approaches, each with its own set of trade-offs in terms of latency and resource utilization.

Optimization techniques, such as quantization, can help reduce the resource footprint of your model. However, these Optimizations often come with additional steps in your workflow and require careful evaluation to ensure they don't negatively impact model performance. Rigorous evaluation becomes crucial in quantifying the extent to which you can optimize without compromising accuracy or functionality.

Deployment Options and Trade-offs

When it comes to deploying your finetuned LLM, several options are available, each with its own set of advantages and challenges:

1. Roll Your Own: This approach involves setting up and managing your own infrastructure. While it offers the most control and customization, it also requires expertise and resources to maintain. For this, you'd usually create some kind of Docker-based service (a FastAPI endpoint, for example) and deploy this on your infrastructure, with you taking care of all of the steps along the way.

2. Serverless Options: Serverless deployments can provide scalability and cost-efficiency, as you only pay for the compute resources you use. However, be aware of the "cold start" phenomenon, which can introduce latency for infrequently accessed models.

3. Always-On Options: These deployments keep your model constantly running and ready to serve requests. While this approach minimizes latency, it can be more costly as you're paying for resources even during idle periods.

4. Fully Managed Solutions: Many cloud providers and AI platforms offer managed services for deploying LLMs. These solutions can simplify the deployment process but may come with less flexibility and potentially higher costs.

When choosing a deployment option, consider factors such as your team's expertise, budget constraints, expected load patterns, and specific use case requirements like speed, throughput, and accuracy needs.

Deployment with vLLM and ZenML

vLLM is a fast and easy-to-use library for running large language models (LLMs) at high throughputs and low latency. ZenML comes with a vLLM integration that makes it easy to deploy your finetuned model using vLLM. You can use a pre-built step that exposes a VLLMDeploymentService that can be used as part of your deployment pipeline.

from zenml import pipeline
from typing import Annotated
from steps.vllm_deployer import vllm_model_deployer_step
from zenml.integrations.vllm.services.vllm_deployment import VLLMDeploymentService


@pipeline()
def deploy_vllm_pipeline(
    model: str,
    timeout: int = 1200,
) -> Annotated[VLLMDeploymentService, "my_finetuned_llm"]:
   # ...
   # assume we have previously trained and saved our model
   service = vllm_model_deployer_step(
      model=model,
      timeout=timeout,
   )
   return service

In this code snippet, the model argument can be a path to a local model or it can be a model ID on the Hugging Face Hub. This will then deploy the model locally using vLLM and you can then use the VLLMDeploymentService for batch inference requests using the OpenAI-compatible API.

For more details on how to use this deployer, see the vLLM integration documentation.

Cloud-Specific Deployment Options

For AWS deployments, Amazon SageMaker stands out as a fully managed machine learning platform that offers deployment of LLMs with options for real-time inference endpoints and automatic scaling. If you prefer a serverless approach, combining AWS Lambda with API Gateway can host your model and trigger it for real-time responses, though be mindful of potential cold start issues. For teams seeking more control over the runtime environment while still leveraging AWS's managed infrastructure, Amazon ECS or EKS with Fargate provides an excellent container orchestration solution, though do note that with all of these options you're taking on a level of complexity that might become costly to manage in-house.

On the GCP side, Google Cloud AI Platform offers similar capabilities to SageMaker, providing managed ML services including model deployment and prediction. For a serverless option, Cloud Run can host your containerized LLM and automatically scale based on incoming requests. Teams requiring more fine-grained control over compute resources might prefer Google Kubernetes Engine (GKE) for deploying containerized models.

Architectures for Real-Time Customer Engagement

Ensuring your system can engage with customers in real-time, for example, requires careful architectural consideration. One effective approach is to deploy your model across multiple instances behind a load balancer, using auto-scaling to dynamically adjust the number of instances based on incoming traffic. This setup provides both responsiveness and scalability.

To further enhance performance, consider implementing a caching layer using solutions like Redis. This can store frequent responses, reducing the load on your model and improving response times for common queries. For complex queries that may take longer to process, an asynchronous architecture using message queues (such as Amazon SQS or Google Cloud Pub/Sub) can manage request backlogs and prevent timeouts, ensuring a smooth user experience even under heavy load.

For global deployments, edge computing services like AWS Lambda@Edge or CloudFront Functions can be invaluable. These allow you to deploy lighter versions of your model closer to end-users, significantly reducing latency for initial responses and improving the overall user experience.

Reducing Latency and Increasing Throughput

Optimizing your deployment for low latency and high throughput is crucial for real-time engagement. Start by focusing on model optimization techniques such as quantization to reduce model size and inference time. You might also explore distillation techniques to create smaller, faster models that approximate the performance of larger ones without sacrificing too much accuracy.

Hardware acceleration can provide a significant performance boost. Leveraging GPU instances for inference, particularly for larger models, can dramatically reduce processing time.

Implementing request batching allows you to process multiple inputs in a single forward pass, increasing overall throughput. This can be particularly effective when combined with parallel processing techniques, utilizing multi-threading or multi-processing to handle multiple requests concurrently. This would make sense if you were operating at serious scale, but this is probably unlikely in the short-term when you are just getting started.

Finally, implement detailed monitoring and use profiling tools to identify bottlenecks in your inference pipeline. This ongoing process of measurement and optimization will help you continually refine your deployment, ensuring it meets the evolving demands of your users.

By thoughtfully implementing these strategies and maintaining a focus on continuous improvement, you can create a robust, scalable system that provides real-time engagement with low latency and high throughput, regardless of whether you're deploying on AWS, GCP, or a multi-cloud environment.

Monitoring and Maintenance

Once your finetuned LLM is deployed, ongoing monitoring and maintenance become crucial. Key areas to watch include:

1. Evaluation Failures: Regularly run your model through evaluation sets to catch any degradation in performance.

2. Latency Metrics: Monitor response times to ensure they meet your application's requirements.

3. Load and Usage Patterns: Keep an eye on how users interact with your model to inform scaling decisions and potential Optimizations.

4. Data Analysis: Regularly analyze the inputs and outputs of your model to identify trends, potential biases, or areas for improvement.

It's also important to consider privacy and security when capturing and logging responses. Ensure that your logging practices comply with relevant data protection regulations and your organization's privacy policies.

By carefully considering these deployment options and maintaining vigilant monitoring practices, you can ensure that your finetuned LLM performs optimally and continues to meet the needs of your users and organization.