As AI and machine learning workloads increasingly depend on stateful data management and real-time access to large datasets, the role of a robust storage layer becomes pivotal in the architecture of these systems. Efficient data storage and management are crucial for running generative AI workloads in production. Portworx, a cloud-native storage solution, offers robust features that enhance the performance and scalability of your GenAI stack.
Why Optimize Storage for GenAI?
Generative AI apps require efficient processing and storage of massive datasets. These applications work with enormous amounts of data to generate new content. Poor optimization may slow data access and processing lead to delayed responses and reduced output quality. Inefficient storage utilization results in higher expenses for maintaining unnecessary capacity.
An optimized storage implement delivers the following benefits:
- Ensure high availability of AI models and data, keeping systems operational and accessible at all times
- Minimize downtime by reducing periods when the AI system is unavailable or non-functional
- Provide scalability for growing data and computational needs, allowing seamless expansion as requirements increase
- Enhance speed and efficiency of data retrieval for faster access to stored information for AI processing
- Improve data processing with more efficient handling and transformation of data
- Accelerate model training for quicker and more effective AI model development and refinement
- Boost application responsiveness and capabilities, enabling AI systems to react faster and handle more complex tasks
- Contribute to cost-efficiency by optimizing resource usage and lowering overall operational costs
How Portworx Empowers the Generative AI Stack
Portworx by Pure Storage provides a comprehensive storage solution tailored for Kubernetes environments that require high availability, scalability, and durability—key elements that are critical for a stateful Generative AI (GenAI) stack.
High Availability (HA): In the realm of GenAI, where models continuously learn and infer from data, downtime can be costly. Portworx ensures high availability through its ability to automatically failover to a healthy node in the event of a hardware failure, thus minimizing disruptions. This capability is crucial for maintaining continuous operations in production AI environments, where even brief periods of inaccessibility can lead to significant delays and financial losses.
Scalability: As AI models evolve and the datasets they operate on grow, the underlying infrastructure must scale seamlessly. Portworx supports horizontal scaling, enabling the storage layer to expand dynamically with the needs of the AI applications. This is particularly important for embedding and reranker models within the AI stack, which require rapid scaling capabilities to accommodate varying workload demands.
Additionally, Portworx Autopilot enhances this scalability by automating storage management tasks, such as provisioning and expanding storage volumes based on usage patterns. With Autopilot, resources are automatically optimized, ensuring high performance and availability without manual intervention, making it ideal for environments where AI workloads can fluctuate unpredictably.
Durability: Portworx ensures data durability through its snapshot and backup features, which protect data against corruption and loss. These features are essential for stateful workloads, such as vector databases that store critical AI-generated data. Portworx Backup goes beyond merely capturing data; it is application-aware and container-granular, preserving app configurations and Kubernetes objects associated with the data. This capability is crucial for Kubernetes-based AI applications, ensuring that all components of the application can be quickly restored to a known good state. This level of durability is invaluable, particularly when dealing with complex machine learning models that require consistent and accurate data sets to function properly.
Performance: Portworx is engineered to deliver high-performance storage that meets the intensive I/O requirements of generative AI workloads. It optimizes I/O paths to reduce latency and increase throughput through several advanced features. Portworx provides configurable I/O profiles tailored to specific application needs, ensuring optimal performance for different workloads. Additionally, it supports application I/O optimization by dynamically adjusting storage resources based on real-time demands. These capabilities are essential for training and inference tasks that rely on quick data retrieval and processing, enabling the efficient operation of large language models (LLMs) and other computationally intensive models within the Kubernetes ecosystem.
Data Locality and Hyperconvergence: Portworx enhances Kubernetes storage management by offering data locality features that ensure data is kept close to the pods that need it, minimizing latency and maximizing performance. Its Volume Placement Strategy (VPS), facilitated by the Scheduler for Orchestrating Replicas & Kubernetes (STORK), is instrumental in this process. STORK optimizes the placement of pods and associated storage across the Kubernetes cluster, guaranteeing that pods are scheduled on nodes with the required data affinity. This node affinity ensures that workloads have faster access to their data, thereby enhancing performance.
Security and Compliance: With the increasing importance of data security in AI applications, Portworx provides robust security features, including data-at-rest encryption and integrated key management, ensuring that sensitive information is protected in line with compliance standards. This is crucial for applications handling proprietary or sensitive data, reinforcing trust in the system’s ability to secure vital information.
Choosing the Right Storage Class
Choosing the right storage class in Portworx is pivotal to achieving optimal performance and efficiency for your GenAI stack. Portworx provides flexibility in defining storage classes tailored to the specific needs of different workloads. Here are key considerations and configurations for selecting the appropriate storage class:
High IOPS Storage Class: Suitable for workloads requiring high input/output operations per second, such as databases and real-time data processing.
Low Latency Storage Class: Ideal for applications where minimal delay is crucial, like live AI inference and interactive applications.
Standard Storage Class: A balanced option for general-purpose workloads.
SharedV4 Storage Class: Necessary for scenarios where multiple pods need simultaneous access to the same volume, such as model catalogs in GenAI
The below storage class specification is ideal for running database workloads:
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
name: high-iops-sc
provisioner: kubernetes.io/portworx-volume
parameters:
repl: "3"
io_profile: "db"
priority_io: "high"
fs: "ext4"
In the same way, the storage class for the running model catalog would follow a similar structure, as shown below:
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
name: portworx-rwx-rep2
provisioner: pxd.portworx.com
parameters:
repl: "2"
sharedv4: "true"
reclaimPolicy: Retain
allowVolumeExpansion: true
Optimizing storage for a Generative AI stack using Portworx involves selecting the right storage class and configuring PVs and PVCs appropriately for each layer. By focusing on the specific needs of the model catalog, vector database, SQL database, NoSQL database, and application orchestration, you can ensure high availability, performance, and scalability of your GenAI applications. Implementing these best practices will lead to a more efficient and cost-effective AI infrastructure.
Summary
Efficient data storage is critical for running generative AI (GenAI) workloads. Portworx, a cloud-native storage solution, enhances the performance and scalability of the GenAI stack through high availability, scalability, durability, and performance features. It supports horizontal scaling, automates failover for high availability, and ensures data durability with snapshot and backup capabilities. Portworx also offers application I/O optimization and configurable profiles to reduce latency and increase throughput. Additionally, it provides data locality features, robust security, and flexible storage class definitions tailored to different workloads. Optimizing storage with Portworx ensures efficient, scalable, and cost-effective AI infrastructure.
