Cambricon MLU Development with BANGPy and Neuware Training Course
Cambricon MLUs (Machine Learning Units) are specialized AI processors optimized for inference and training in both edge computing and data center environments.
This instructor-led, live training (available online or onsite) is designed for intermediate developers seeking to build and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.
Upon completion of this training, participants will be able to:
- Set up and configure the BANGPy and Neuware development environments.
- Develop and optimize Python- and C++-based models for Cambricon MLUs.
- Deploy models to edge and data center devices running the Neuware runtime.
- Integrate machine learning workflows with MLU-specific acceleration features.
Course Format
- Interactive lectures and discussions.
- Hands-on development and deployment using BANGPy and Neuware.
- Guided exercises focused on optimization, integration, and testing.
Customization Options
- To request customized training tailored to your specific Cambricon device model or use case, please contact us to arrange.
Course Outline
Introduction to Cambricon and MLU Architecture
- Overview of Cambricon’s AI chip portfolio.
- MLU architecture and instruction pipeline.
- Supported model types and use cases.
Installing the Development Toolchain
- Installing BANGPy and Neuware SDK.
- Setting up environments for Python and C++.
- Ensuring model compatibility and preprocessing.
Model Development with BANGPy
- Managing tensor structures and shapes.
- Constructing computation graphs.
- Supporting custom operations in BANGPy.
Deploying with Neuware Runtime
- Converting and loading models.
- Controlling execution and inference.
- Best practices for edge and data center deployment.
Performance Optimization
- Memory mapping and layer tuning.
- Execution tracing and profiling.
- Identifying and resolving common bottlenecks.
Integrating MLU into Applications
- Using Neuware APIs for application integration.
- Supporting streaming and multi-model scenarios.
- Implementing hybrid CPU-MLU inference scenarios.
End-to-End Project and Use Case
- Lab: Deploying a vision or NLP model.
- Performing edge inference with BANGPy integration.
- Testing for accuracy and throughput.
Summary and Next Steps
Requirements
- A solid understanding of machine learning model structures.
- Experience with Python and/or C++.
- Familiarity with concepts related to model deployment and acceleration.
Audience
- Embedded AI developers.
- Machine learning engineers deploying solutions to edge or data center environments.
- Developers working with Chinese AI infrastructure.
Open Training Courses require 5+ participants.
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