Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to CANN and Ascend AI Processors
- Understanding CANN and its function within Huawei’s AI compute stack
- Overview of Ascend processor architectures (including 310, 910, and others)
- Review of supported AI frameworks and the associated toolchain
Model Conversion and Compilation
- Employing the ATC tool for converting models from TensorFlow, PyTorch, and ONNX
- Generating and verifying OM model files
- Addressing unsupported operators and resolving typical conversion problems
Deploying with MindSpore and Other Frameworks
- Implementing model deployment using MindSpore Lite
- Integrating OM models via Python APIs or C++ SDKs
- Utilizing the Ascend Model Manager
Performance Optimization and Profiling
- Grasping AI Core, memory, and tiling optimization techniques
- Profiling model execution using CANN-specific tools
- Applying best practices to enhance inference speed and resource efficiency
Error Handling and Debugging
- Identifying and resolving common deployment errors
- Interpreting logs and utilizing error diagnosis tools
- Conducting unit tests and functional validation of deployed models
Edge and Cloud Deployment Scenarios
- Deploying to Ascend 310 for edge-based applications
- Integrating with cloud-based APIs and microservices
- Examining real-world case studies in computer vision and NLP
Summary and Next Steps
Requirements
- Proficiency with Python-based deep learning frameworks like TensorFlow or PyTorch
- A solid grasp of neural network architectures and model training workflows
- Basic command-line (CLI) and scripting experience on Linux systems
Target Audience
- AI engineers focused on model deployment
- Machine learning practitioners aiming to leverage hardware acceleration
- Deep learning developers creating inference solutions
14 Hours
