Difference between Serving Techniques: BentoML and Seldon Core BentoML 和 Seldon Core 之间的服务技术差异

Difference between Serving Techniques: BentoML and Seldon Core
BentoML 和 Seldon Core 之间的服务技术差异

https://yadnyesh.medium.com/difference-between-serving-techniques-bentoml-and-seldon-core-b89f0840f518

 

BentoML and Seldon Core are both powerful tools for serving machine learning models in production environments. While they share similarities in their objectives, there are distinct differences in their approaches, handling, operations, inner structures, and performance characteristics.
BentoML 和 Seldon Core 都是用于生产环境部署机器学习模型的强大工具。虽然它们在目标上有很多相似之处，但在方法、处理、操作、内部结构和性能特性上存在明显的差异。

Handling:  处理：

BentoML:  BentoML：

• BentoML focuses on creating a containerized environment for serving machine learning models. It allows packaging trained models along with their dependencies into Docker containers, enabling easy deployment and serving.
  BentoML 专注于为机器学习模型提供容器化环境。它允许将训练好的模型及其依赖项打包到 Docker 容器中，从而实现轻松部署和提供服务。
• BentoML offers flexibility in handling various machine learning frameworks, allowing users to deploy models built with TensorFlow, PyTorch, Scikit-learn, and others.
  BentoML 提供了处理各种机器学习框架的灵活性，允许用户部署使用 TensorFlow、PyTorch、Scikit-learn 等构建的模型。
• It emphasizes simplicity and ease of use, providing a straightforward interface for packaging and deploying models without extensive configuration.
  它强调简洁易用，提供了一个简单的接口，用于打包和部署模型，而无需进行大量的配置。
• BentoML may face challenges in handling high request loads, as it might experience crashes under heavy traffic without proper load balancing mechanisms.
  BentoML 在处理高请求数量时可能会遇到挑战，因为在高流量情况下缺乏适当的负载均衡机制可能导致其崩溃。

Seldon Core:  Seldon Core：

• Seldon Core provides a comprehensive platform for deploying and managing machine learning models at scale. It offers advanced features for model deployment, scaling, monitoring, and governance.
  Seldon Core 提供了一个全面的平台，用于大规模部署和管理机器学习模型。它提供了模型部署、扩展、监控和治理的高级功能。
• Seldon Core leverages Kubernetes for orchestration, allowing seamless scaling of model deployments based on demand. It provides robust support for high availability and fault tolerance.
  Seldon Core 利用 Kubernetes 进行编排，可以根据需求无缝扩展模型部署。它提供了强大的高可用性和容错支持。
• Unlike BentoML, Seldon Core offers sophisticated features for model versioning, A/B testing, and canary deployments, enabling advanced deployment strategies and experimentation.
  与 BentoML 不同，Seldon Core 提供了复杂的模型版本管理、A/B 测试和金丝雀部署功能，从而支持高级部署策略和实验。
• Seldon Core’s architecture is designed to handle high request rates efficiently, with built-in mechanisms for load balancing and horizontal scaling. It excels in serving models reliably even under heavy workloads.
  Seldon Core 的架构设计用于高效处理高请求率，并内置了负载均衡和水平扩展机制。即使在重负载下，它也能可靠地提供模型服务。

Operations:  操作：

BentoML:  BentoML：

• BentoML simplifies the process of packaging and deploying machine learning models, making it suitable for smaller-scale deployments and rapid prototyping.
  BentoML 简化了机器学习模型的打包和部署过程，使其适用于小型部署和快速原型设计。
• It provides a user-friendly interface for managing model versions and deployments, allowing users to easily update and roll back models as needed.
  它提供了一个用户友好的界面来管理模型版本和部署，使用户能够轻松地根据需要更新和回滚模型。
• BentoML’s operational capabilities are more focused on simplicity and ease of use, which may limit its suitability for complex production environments with stringent requirements.
  BentoML 的运营能力更侧重于简洁和易用性，这可能使其不适合具有严格要求的复杂生产环境。

Seldon Core:  Seldon Core：

• Seldon Core caters to enterprise-grade deployments with a comprehensive set of operational features. It offers fine-grained control over model deployments, monitoring, and governance.
  Seldon Core 为企业级部署提供了一整套运营功能，支持对模型部署、监控和治理进行精细控制。
• The platform supports advanced operational workflows, including model governance, compliance, and auditing, making it suitable for regulated industries and large-scale deployments.
  该平台支持高级运营工作流，包括模型治理、合规性和审计，使其适用于受监管的行业和大规模部署。
• Seldon Core integrates with various monitoring and logging systems, enabling comprehensive observability into model performance and behavior in production environments.
  Seldon Core 与多种监控和日志系统集成，能够在生产环境中全面观察模型的性能和行为。

Inner Structure:  内部结构：

BentoML:  BentoML：

• Internally, BentoML utilizes lightweight containers to encapsulate machine learning models along with their dependencies and serving logic.
  内部，BentoML 使用轻量级容器来封装机器学习模型及其依赖项和服务逻辑。
• It follows a modular architecture, allowing users to extend and customize functionality through plugins and extensions.
  它采用模块化架构，允许用户通过插件和扩展来扩展和自定义功能。
• BentoML’s architecture prioritizes simplicity and modularity, aiming for lightweight and flexible deployments.
  BentoML 的架构注重简洁和模块化，旨在实现轻量级和灵活的部署。

Seldon Core:  Seldon Core：

• Seldon Core’s architecture is built on top of Kubernetes, leveraging its orchestration capabilities for managing model deployments at scale.
  Seldon Core 的架构基于 Kubernetes，利用其编排能力大规模管理模型部署。
• It consists of multiple components, including the Seldon Deployment Manager, Seldon Core API Server, and model servers, orchestrated within Kubernetes clusters.
  它由多个组件组成，包括 Seldon 部署管理器、Seldon Core API 服务器和模型服务器，并在 Kubernetes 集群中进行编排。
• Seldon Core’s architecture is designed for scalability, resilience, and extensibility, supporting complex deployment scenarios and integration with various ecosystem tools.
  Seldon Core 的架构设计旨在实现可扩展性、可靠性和可扩展性，支持复杂的部署场景，并与各种生态系统工具进行集成。

Performance:  性能：

Load Balancing Testing:  负载均衡测试：

• Load balancing testing reveals differences in how BentoML and Seldon Core handle high request rates:
  负载均衡测试揭示了 BentoML 和 Seldon Core 在处理高请求率时的差异：
• Seldon Core demonstrates superior performance in handling a high volume of requests, thanks to its built-in mechanisms for load balancing and horizontal scaling.
  Seldon Core 由于内置了负载均衡和水平扩展机制，在处理大量请求时表现出色。
• BentoML may experience crashes or degraded performance under heavy loads, especially without proper load balancing configurations.
  BentoML 在高负载下可能会出现崩溃或性能下降的情况，尤其是在没有适当的负载均衡配置时。

Flexibility vs. Scalability:
灵活性 vs. 扩展性：

• BentoML prioritizes flexibility and ease of use, making it suitable for smaller-scale deployments and rapid experimentation.
  BentoML 优先考虑灵活性和易用性，使其适合小型部署和快速实验。
• Seldon Core emphasizes scalability, resilience, and advanced operational capabilities, making it ideal for large-scale, mission-critical deployments in enterprise environments.
  Seldon Core 强调可扩展性、韧性和高级运维能力，使其成为企业环境中大规模、关键任务部署的理想选择。

Ecosystem Integration:  生态系统集成：

• Both BentoML and Seldon Core integrate with popular machine learning frameworks and tools, but Seldon Core offers deeper integration with Kubernetes and ecosystem components, providing a more comprehensive solution for production deployments.
  BentoML 和 Seldon Core 都可以与流行的机器学习框架和工具集成，但 Seldon Core 提供了与 Kubernetes 和生态系统组件更深层次的集成，从而为生产部署提供了更为全面的解决方案。

Community and Support:  社区与支持：

• Consider the community size, support offerings, and ecosystem partnerships associated with each platform, as these factors can impact long-term viability and adoption in enterprise environments.
  考虑每个平台的社区规模、支持服务以及生态系统合作伙伴关系，因为这些因素会影响其在企业环境中的长期可行性和采用率。

Here’s a simple example demonstrating how you can implement a machine learning model serving using BentoML and Seldon Core
这是一个简单的示例，演示了如何使用 BentoML 和 Seldon Core 实现机器学习模型服务

BentoML Implementation:  BentoML 实现：

# Importing required libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from bentoml import BentoService, api, env, artifacts
from bentoml.adapters import DataframeInput
from bentoml.frameworks.sklearn import SklearnModelArtifact
# Defining BentoML service
@env(infer_pip_packages=True)
@artifacts([SklearnModelArtifact('model')])
class RandomForestClassifierService(BentoService):
    # Define an API endpoint for model inference
    @api(input=DataframeInput(), batch=True)
    def predict(self, df: pd.DataFrame):
        return self.artifacts.model.predict(df)
        
# Instantiate the model and save it using BentoML
model = RandomForestClassifier()
model.fit(X_train, y_train)
bento_service = RandomForestClassifierService()
bento_service.pack('model', model)
saved_path = bento_service.save()
print("Model saved in BentoML format:", saved_path)

Seldon Core Implementation (Kubernetes YAML):
Seldon Core 实现（Kubernetes YAML）:

apiVersion: machinelearning.seldon.io/v1alpha3
kind: SeldonDeployment
metadata:
  name: my-model-deployment
spec:
  predictors:
  - graph:
      children: []
      implementation: SKLEARN_SERVER
      modelUri: gs://your-bucket/your-model
      name: classifier
      parameters:
        - name: method
          type: STRING
          value: predict
    name: default
    replicas: 1

In the Seldon Core implementation, modelUri should point to the location where your serialized model is stored, such as a cloud storage bucket. Replace gs://your-bucket/your-model with the appropriate path.
在 Seldon Core 的实现中， modelUri 应指向您的序列化模型存储的位置，例如云存储桶。将 gs://your-bucket/your-model 替换为合适的路径。

In summary, while BentoML and Seldon Core share common goals of serving machine learning models in production, they differ significantly in their handling, operations, inner structures, and performance characteristics. Choosing between the two depends on the specific requirements, scale, and complexity of the deployment environment, with Seldon Core offering a more robust solution for enterprise-grade deployments requiring scalability, reliability, and advanced operational features
总之，虽然 BentoML 和 Seldon Core 都旨在在生产环境中提供机器学习模型，但在处理方式、操作、内部结构和性能特性上存在显著差异。选择哪一种取决于部署环境的具体需求、规模和复杂性，Seldon Core 为需要可扩展性、可靠性和高级运维功能的企业级部署提供了更 robust 的解决方案。