💻 programming

3FS

3FS is a high-performance distributed file system designed for AI training and inference workloads.

#AI
#machine learning
#high performance computing
#storage
#Distributed file system
3FS

Product Details

3FS is a high-performance distributed file system designed for AI training and inference workloads. It leverages modern SSD and RDMA networks to provide a shared storage layer to simplify distributed application development. Its core advantages lie in high performance, strong consistency and support for multiple workloads, which can significantly improve the efficiency of AI development and deployment. The system is suitable for large-scale AI projects, especially in the data preparation, training and inference phases.

Main Features

1
High Performance: Combines the throughput of thousands of SSDs with the network bandwidth of hundreds of storage nodes to support massive parallel access.
2
Strong consistency: Adopt CRAQ protocol to ensure strong data consistency and simplify application development.
3
Multiple workload support: suitable for scenarios such as data preparation, training sample loading, checkpoint saving, and inference caching.
4
Easy to use: Provides standard file interface, no need to learn new storage API.
5
High scalability: supports large-scale cluster deployment to meet the needs of AI projects of different sizes.

How to Use

1
1. Clone the 3FS repository from GitHub: `git clone https://github.com/deepseek-ai/3fs`.
2
2. Initialize the submodule: `cd 3fs && git submodule update --init --recursive`.
3
3. Install dependencies, such as CMake, libuv, liblz4, etc. (see the documentation for specific dependencies).
4
4. Build 3FS: `cmake -S . -B build -DCMAKE_BUILD_TYPE=RelWithDebInfo`, then run `cmake --build build`.
5
5. Deploy the test cluster and configure the storage nodes and clients according to the deployment guide.

Target Users

3FS is suitable for AI developers and research teams who need high-performance storage solutions, especially those dealing with large-scale data sets and complex model training. It can significantly improve data processing efficiency and reduce development and deployment costs.

Examples

In large-scale AI training, 3FS is used to store and quickly access training data, significantly improving training speed.

During the inference phase, 3FS’s KVCache function provides efficient caching support for LLM inference and reduces computing overhead.

3FS is used in the data preparation phase to efficiently manage the intermediate output of the data pipeline and optimize the data processing process.

Quick Access

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Categories

💻 programming
› Development and Tools
› Model training and deployment

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