Ceph Applications in AI Training and Data Lake Architectures

🔰 Introduction

As enterprises increasingly adopt Artificial Intelligence (AI) and Big Data Analytics,
storage systems are no longer just about capacity or reliability.
They must now deliver performance, scalability, consistency, and data unification across diverse workloads.

Ceph, an open-source distributed storage platform, offers exactly that flexibility.
It simultaneously supports Block (RBD), File (CephFS), and Object (RGW) storage modes —
making it an ideal backbone for modern AI training pipelines and data lake architectures.

This article explores how Ceph supports these environments through:
1️⃣ Data lake tiering and design
2️⃣ AI data pipelines and workflows
3️⃣ The role of CephFS, RBD, and RGW in each stage
4️⃣ Real-world architecture examples and best-practice recommendations

🧩 1. Storage Challenges in AI and Data Lakes

From data ingestion to model deployment,
AI workflows involve massive data movement and complex processing cycles.

A typical AI data pipeline looks like this:

Data Source → Pre-processing → Feature Engineering → Model Training → Validation → Deployment → Continuous Learning

This process involves:

Huge volumes of unstructured data (images, video, logs, text, audio)
High-frequency parallel I/O during multi-GPU training
Cross-team collaboration between data engineers, ML engineers, and DevOps

Thus, an AI-ready storage system must deliver:

Requirement	Description
High concurrency	Support parallel read/write across GPU nodes
Linear scalability	Scale out to petabyte-level capacity
Fault tolerance	Automatic replication and self-healing
Unified namespace	Simplified collaboration across workloads
Multi-protocol support	S3, POSIX, and block interfaces on one platform

Ceph was built to meet exactly these requirements.

⚙️ 2. Ceph’s Role in a Multi-Layer Data Lake

A well-designed data lake often follows a multi-tier architecture:

Layer	Purpose	Ceph Module
Raw Layer	Raw logs, IoT streams, unstructured data	RGW (Object Storage)
Processed Layer	Curated, pre-processed, or feature-engineered data	CephFS (File System)
Serving Layer	High-performance storage for AI training and inference	RBD (Block Storage)

RGW (RADOS Gateway)
Provides an S3-compatible object interface, suitable for ingestion from Spark, Hadoop, or MinIO clients.
CephFS
Offers a POSIX-compliant shared file system for GPU nodes and collaborative AI workflows.
RBD (RADOS Block Device)
Delivers low-latency, high-IOPS block storage for training environments and containerized ML workloads.

☁️ 3. Ceph + AI Training Architecture

                   ┌────────────────────────────┐
                   │        Data Sources        │
                   │   IoT / Logs / Sensors     │
                   └──────────────┬─────────────┘
                                  │
                         S3 API (RGW)
                                  │
             ┌───────────────────────────────────┐
             │           Ceph Cluster            │
             │───────────────────────────────────│
             │  RGW  → Object Layer (Raw Data)   │
             │  CephFS → File Layer (Processed)  │
             │  RBD   → Block Layer (Training)   │
             └───────────────────────────────────┘
                                  │
          ┌────────────────────────────────────┐
          │    AI Compute Cluster (GPU Nodes)  │
          │ TensorFlow / PyTorch / DeepSpeed   │
          │ Mount CephFS + Access RBD Volumes  │
          └────────────────────────────────────┘
                                  │
                    Models & Results → Saved to RGW

This unified architecture allows data to seamlessly flow between ingestion, transformation, and training —
all within the same Ceph infrastructure.

🧠 4. Integrating Ceph into the AI Training Workflow

1️⃣ Training Phase

GPU compute nodes mount CephFS as the training dataset directory.
Training workloads read data in parallel batches.
Model checkpoints are saved to RBD volumes or CephFS.

2️⃣ Model Versioning and Storage

Trained model artifacts (.pt, .h5, etc.) are stored in RGW (S3).
Integrate with MLflow, Kubeflow, or HuggingFace Hub for version tracking.

3️⃣ Parallel Access Optimization

Enable multi-MDS scaling for heavy read/write workloads:

ceph fs set cephfs max_mds 4
ceph mds set allow_multimds true

This enables horizontal scaling across multiple metadata servers for high concurrency.

⚡ 5. Real-World Applications of Ceph in AI and Data Lakes

Use Case	Ceph Components	Key Benefits
Enterprise Data Lake	RGW + CephFS	Unified namespace with S3 and POSIX access
AI Training Platform	RBD + CephFS	High-speed data I/O and multi-GPU parallelism
MLOps (Kubeflow / MLflow)	RBD + RGW	Containerized model registry and artifact management
Document or File Lake	CephFS + RGW	Unified file and object access
Multi-Site DR / Replication	RGW Multi-Site / RBD Mirror	Geo-replicated fault tolerance and disaster recovery

🔍 6. Performance and Optimization Guidelines

Parameter	Recommendation
Network	≥ 25 GbE, preferably RoCE or Infiniband
Storage Type	NVMe/SSD for hot AI data; HDD for archival
CephFS Tuning	Enable metadata caching and read-ahead
RADOS Pool Strategy	Separate pools for raw, processed, and training data
Monitoring	Use Prometheus + Grafana to track GPU I/O and latency
Disaster Recovery	Combine RBD Mirror and RGW Multi-Site for full redundancy

🔒 7. Integration with Proxmox Environments

Within Proxmox clusters, Ceph can serve as:

RBD backend for virtual machines
CephFS datastore for Proxmox Backup Server (PBS)
Shared dataset storage for AI training clusters

This unified storage layer allows VM environments, backup systems, and AI workloads
to operate seamlessly on a common Ceph infrastructure — simplifying both data flow and operations.

✅ Conclusion

Ceph is more than a distributed storage system —
it is the core data platform for AI, RAG (Retrieval-Augmented Generation),
and enterprise data lake environments.

Through:

RGW for unstructured data ingestion
CephFS for collaborative feature and training datasets
RBD for high-performance GPU compute storage

organizations can build an open-source, scalable, and resilient AI infrastructure that provides:

⚙️ Elastic performance
🌐 Unified data access
🔁 Built-in high availability and disaster recovery

💬 Coming next:
“Integrating Ceph with DeepSeek and RAG Architectures” —
exploring how Ceph underpins LLM training, document indexing,
and real-time knowledge retrieval within enterprise AI ecosystems.