Proposal: Integration of Training Operator into Intel Cloud Native AI Pipeline

[leyao-daily]

Integration of Training Operator into Intel Cloud Native AI Pipeline

Executive Summary

This proposal presents a strategic vision for enhancing Intel's Cloud Native AI Pipeline (CNAP), which has currently developed components focused on inference, by the integration of the Kubeflow Training Operator into Intel's Cloud Native AI Pipeline. The proposed integration aims to extend the capabilities of the pipeline beyond inference, adding scalable, efficient, and flexible machine learning training functionalities. This expansion is designed to address the growing demand for end-to-end solutions in machine learning workflows, from data preprocessing and model training to inference and deployment. Key benefits include improved resource utilization, streamlined machine learning workflows, and cross-framework compatibility. This proposal outlines the motivation, detailed integration plan, testing strategies, and risk assessment to ensure a successful implementation.

Introduction

The Intel Cloud Native AI Pipeline is a robust platform designed for efficient AI and machine learning tasks. Kubeflow, an open-source project, offers a Training Operator that facilitates scalable and flexible machine learning operations on Kubernetes. Integrating Kubeflow's Training Operator into the Intel Cloud Native AI Pipeline aims to leverage the strengths of both platforms for advanced machine learning capabilities.

Motivation for Integration

1. Enhanced Scalability and Efficiency: To manage and scale machine learning tasks more effectively in the cloud-native environment.
2. Flexibility and Extensibility: To support a broader range of machine learning frameworks and use cases.
3. Leveraging Community Innovations: To benefit from the open-source community's continuous innovations and support.

Benefits of Integration

1. Seamless Transition from Training to Inference: Facilitates a streamlined workflow within the same pipeline, allowing models trained using the Kubeflow Training Operator to be directly deployed for inference in the Intel Cloud Native AI Pipeline.
2. Enhanced Resource Utilization: Leverages Intel's hardware optimizations for both training and inference tasks, ensuring optimal performance and energy efficiency.
3. Cross-framework Compatibility: Supports a diverse range of machine learning frameworks, making the pipeline more versatile and adaptable to various use cases.

Potential Benefits to Intel

1. Optimized Performance on Intel Architecture: Integration with Kubeflow's Training Operator can lead to more efficient utilization of Intel CPUs, GPUs, and other hardware accelerators. By tailoring machine learning workflows to leverage Intel-specific optimizations like Intel Deep Learning Boost (DL Boost), there could be substantial improvements in computational efficiency and model training speed.
2. Enhanced Data Processing Capabilities: The integration can capitalize on Intel's advanced data processing units (DPUs) and networking technologies to accelerate data throughput and reduce latency, crucial for large-scale machine learning tasks.
3. Energy Efficiency: By optimizing workloads for Intel hardware, the integrated system could achieve higher energy efficiency, reducing the overall power consumption and operational costs in data centers.
4. Scalability with Intel Infrastructure: Leveraging Intel's scalable infrastructure, the integrated solution can offer enhanced performance as the computational demands grow, ensuring that the AI pipeline remains efficient and effective even at larger scales.

Integration Plan

Investigate the Training Framework

We compare the following popular training framework from multi aspects, and I recommend integrate training operator the standalone component of Kubeflow to bring the training functionality to Intel Cloud Native AI Pipeline.

Criteria	Kubeflow	MLflow	MLRun	Horovod
Compatibility with Kubernetes (k8s)	High (Kubernetes-native)	Moderate (Can be deployed on Kubernetes)	High (Can be deployed on Kubernetes)	Moderate (Can be deployed on Kubernetes with some configurations)
Focus on Training	High (Dedicated training operators for TF and PyTorch)	Low (Primarily for experiment tracking, not focused on training)	High (Built-in or custom model training services)	High (Designed for distributed training)
Ease of Integration	High (Modular design allows for lightweight integration)	Moderate (Requires additional setup for full MLOps lifecycle)	High (Integrates into development and CI/CD environment)	Moderate (Requires some configurations for distributed training)
User Interface for Interaction	Available (Web-based dashboard)	Available (Web-based UI for experiment tracking)	Not explicitly mentioned	Not explicitly mentioned
Lightweight	Moderate (Individual components can be lightweight)	Yes (Single Python package)	Not explicitly mentioned	Not explicitly mentioned
Support for Fine-tuning Existing Models	Yes	Yes (Model Registry for managing models)	Yes (Supports training at scale with multiple parameters)	Not explicitly mentioned
Scalability	High (Designed for distributed training)	Not explicitly mentioned	Not explicitly mentioned	High (Designed for distributed training across many GPUs/nodes)
Cloud-Native Design	High (Designed for cloud-native deployments on k8s)	Moderate (Can be deployed on cloud but not designed specifically as cloud-native)	Moderate (Can be deployed on cloud)	Moderate (Can be deployed on cloud with some configurations)

Key Insights:

1. Kubeflow provide comprehensive Kubernetes-native solutions with a strong focus on training, making them suitable for cloud-native ML/DL deployments.
2. Horovod is well-regarded for distributed training across multiple GPUs/nodes, aligning well with scalability requirements, though it may require additional configurations for a cloud-native setup.
3. MLRun is a flexible and integrable solution with built-in or custom model training services, offering a balance between ease of integration and training-focused functionalities.
4. MLflow, while being lightweight and providing a user interface, is more geared towards experiment tracking rather than training, making it less suitable if training is a priority.

Component Analysis and Compatibility Check

System Architecture Review:

• The Intel Cloud Native AI Pipeline is structured as a microservices-based architecture, leveraging Kubernetes for orchestration. It consists of modular components including data ingestion, preprocessing, model training, and deployment services.

• Potential integration points for the Kubeflow Training Operator are primarily within the model training service. This is where the operator can manage machine learning training jobs, optimizing them for distributed processing.

• Assess how data flows through the pipeline, particularly focusing on how it is ingested, preprocessed, and fed into the training modules. This will help in understanding how the Kubeflow operator will interact with data in the pipeline.

Compatibility Assessment

• Framework and Language Dependencies:
  • The Kubeflow Training Operator primarily supports TensorFlow, PyTorch, and MXNet. The assessment will verify the compatibility of these frameworks with the existing AI pipeline, especially Intel-specific optimizations for machine learning frameworks.
• Resource Requirements:
  • CPU/GPU
  • CXL/QAT/DSA
  • Memory
• Kubernetes Configurations and Customizations:
  • All components in CNAP have been evaluated on the latest stable Kubernetes release, includes checking version compatibilities, custom resource definitions (CRDs), and any custom Kubernetes extensions or operators used in CNAP.
• Scalability and Performance Impact:
  • For phase 1, the integration won't introduce bottlenecks or reduce the overall efficiency of the system. We also should keep enhancing the performance and bridge the gaps for both the existing and new components in the future phase implementations.
• Conflict Identification and Resolution Strategies:
  • Independent components for CNAP and Training Operator won't bring the dependencies conflict and mis-configurations.

API Integration and Customization

• Phase 1 Design:
  • Integrate as a helm chart with the latest standard training operator
  • Deliver the sample codes and examples from source repo and give out the BKM scripts and docs.
• Phase 2 Design:
  • Integrate with the current UI
  • Integrate with the current Data flow
• Phase 3 Design:
  • Event driven - fine tuning
• Customization for Intel Optimizations: Adapt the Kubeflow Training Operator to leverage Intel-specific hardware and software optimizations, enhancing performance and efficiency.

System Modification and Configuration

System Modifications

To successfully integrate the Kubeflow Training Operator into the existing Intel Cloud Native AI Pipeline, specific system modifications are necessary across different phases:

Phase 1: Initial Integration

• Helm Chart Implementation: Modify the pipeline's deployment process to include a Helm chart for the Kubeflow Training Operator. This will ensure smooth deployment and management of the training operator within the Kubernetes environment.
• Infrastructure Adjustments: Adjust the underlying infrastructure to support the additional load and functionalities brought by the training operator. This may include scaling Kubernetes nodes, configuring network policies, and setting up appropriate storage solutions.

Phase 2: UI and Data Flow Integration

• UI Integration: Modify the existing user interface of the Intel Cloud Native AI Pipeline to incorporate controls and monitoring tools for the Kubeflow Training Operator. This will involve updating the dashboard to display training metrics and statuses.
• Data Flow Integration: Ensure seamless data flow between the existing components of the pipeline and the Kubeflow Training Operator. This includes configuring data ingestion and preprocessing modules to feed data into the training operator and receive trained models for inference.

Phase 3: Advanced Integration and Fine-Tuning

• Event-Driven Architecture: Implement an event-driven architecture to enable real-time responses and adjustments in the training process. This involves integrating message queues or event streams to trigger and control training jobs based on specific events or conditions.
• Fine-Tuning Mechanisms: Develop mechanisms for fine-tuning training processes, including dynamic resource allocation, hyperparameter tuning, and model optimization strategies.

Customization for Intel Optimizations

• Adapt the Kubeflow Training Operator to leverage Intel-specific hardware optimizations, such as Intel Deep Learning Boost and other processor features. This may involve modifying the training operator's codebase or developing plugins to enable these optimizations.

Configuration Details

The configuration process will vary for each phase, ensuring that the system remains operational and performs optimally:

Phase 1 Configuration

• Helm Chart Configuration: Set up and customize the Helm chart for the Kubeflow Training Operator, including specifying resource limits, environment variables, and other deployment settings.
• Documentation and Scripts: Provide comprehensive documentation and Best Known Methods (BKM) scripts to guide users through the setup and deployment process.

Phase 2 Configuration

• UI Configuration: Update the UI configuration files and scripts to incorporate new features related to the training operator. Test the UI to ensure that it accurately reflects the state and performance of the training processes.
• Data Flow Configuration: Configure the data ingestion and preprocessing modules to ensure compatibility with the training operator. This includes setting up data formats, pipelines, and triggers for model training.

Phase 3 Configuration

• Event-Driven Setup: Configure the message queues or event streams and integrate them with the training operator. Set up rules and triggers for automated training job initiation and adjustments.
• Fine-Tuning Configuration: Implement configuration options for fine-tuning, allowing users to customize training parameters, resource allocation, and optimization strategies.

Throughout these phases, continuous testing and validation are crucial to ensure that the modifications and configurations lead to a stable, efficient, and scalable system. Regular updates to documentation and training materials will also be necessary to keep users informed about new features and best practices.

Testing and Validation Strategy

• Unit and Integration Testing: TBD
• Performance Benchmarking: TBD

Risk Assessment and Mitigation

• TBD

Timeline

• TBD

Conclusion

This proposal outlines a proposal plan for integrating the Kubeflow Training Operator into the Intel Cloud Native AI Pipeline. The integration is expected to bring significant benefits in terms of scalability, efficiency, and flexibility, enhancing the pipeline's capabilities for handling complex machine learning tasks. With careful planning, rigorous testing, and effective risk management, this integration can set a new standard for cloud-native AI and machine learning pipelines.

[leyao-daily]

This proposal represents a preliminary assessment and a potential approach following a brief period of research. It serves as an initial summary of our findings and considerations. However, it's important to emphasize that this integration is a complex and multifaceted endeavor, requiring in-depth analysis and extensive exploration. We foresee the need for additional comprehensive evaluations to fully understand the implications, challenges, and opportunities this integration presents. As we move forward, a more detailed investigation and broader consultation for the project will be crucial to ensure the feasibility and success of this initiative.