WekaFS
WekaFS is a high-performance, scalable file system optimized for AI/ML/DL workloads. It delivers high throughput and IOPS for small files while minimizing latency.
file-system
high-performance
scalable
aimldl-workloads
WekaFS: High-Performance File System for AI/ML/DL
WekaFS is a high-performance, scalable file system optimized for AI/ML/DL workloads. It delivers high throughput and IOPS for small files while minimizing latency.
What is WekaFS?
WekaFS is a parallel file system designed specifically to address the storage challenges of artificial intelligence, machine learning, and deep learning workloads. It delivers high performance and scalability for data-intensive workloads that access large numbers of small files.
Key capabilities and benefits of WekaFS include:
- High throughput and IOPS specifically for small file workloads
- Low latency data access
- Dynamic scalability to tens of thousands of nodes
- Native support for leading AI frameworks like TensorFlow, PyTorch, and MATLAB
- Optimized for both on-premises and cloud infrastructures
- Advanced data protection and resilience features
WekaFS is built on a modular architecture that can leverage NVMe-based storage for the highest performance. It accelerates AI model development pipelines by providing a high performance parallel file system backend optimized for machine learning workflows.
WekaFS Features
Features
- Distributed file system optimized for AI/ML workloads
- Delivers high throughput and IOPS for small files
- Minimizes latency
- Scales performance linearly
- Supports POSIX interfaces
- Integrates with Kubernetes
- Optimized for NVMe flash storage
Pricing
- Subscription-Based
Pros
High performance for AI/ML workloads
Scales linearly
Low latency
Easy to use with POSIX APIs
Kubernetes integration
Works well with NVMe flash storage
Cons
Less suited for general purpose workloads
Requires learning curve for configuration
Limited ecosystem compared to HDFS
Official Links
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