Profiling Data In DeepSeek Infra : Analyzes the computation and communication overlap strategies in V3/R1, providing performance analysis data for deep learning frameworks.

Profiling Data In DeepSeek Infra

Model Training and Deployment Research Tools #Deep Learning #Performance Analysis #Distributed Computing #Communication Overlap #PyTorch Standard Picks Open Source

Overview :

DeepSeek Profile Data is a project focused on performance analysis of deep learning frameworks. It captures performance data of training and inference frameworks through PyTorch Profiler, helping researchers and developers better understand computation and communication overlap strategies and underlying implementation details. This data is crucial for optimizing large-scale distributed training and inference tasks, significantly improving system efficiency and performance. This project is a significant contribution from the DeepSeek team in the field of deep learning infrastructure, aiming to promote the community's exploration of efficient computing strategies.

Target Users :

This product primarily targets deep learning researchers, distributed system developers, and academics and industry professionals interested in high-performance computing and communication strategies. It provides them with detailed performance analysis data to help optimize model training and inference processes, improving overall system efficiency.

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Use Cases

Researchers can use this data to optimize the distributed training strategies of large-scale pre-trained models.

Developers can refer to this data to improve the communication and computation efficiency in the inference framework.

Academic teams can use this data to research new Mixture-of-Experts model routing strategies.

Features

Provides performance analysis data for the training and inference phases to help optimize model training and inference efficiency.

Supports intuitive visualization of performance analysis results through the tracing tool in Chrome or Edge browser.

Simulates a balanced MoE routing strategy to provide performance analysis benchmarks for Mixture-of-Experts models.

Demonstrates the overlapping strategy of forward and backward propagation in the DualPipe framework to improve parallel computing efficiency.

Provides performance analysis of the pre-filling and decoding phases to optimize communication and computation strategies for large-scale inference tasks.

How to Use

1. Visit the project homepage and download the performance analysis data files for the training and inference phases.

2. Open Chrome or Edge browser and enter chrome://tracing or edge://tracing to access the performance analysis tool.

3. Load the downloaded performance analysis data file and view the detailed performance analysis results.

4. Based on the analysis results, optimize the model training and inference strategies, and adjust the overlapping methods of communication and computation.

5. Refer to the project documentation to understand performance optimization suggestions and best practices for different phases.