NVIDIA GPU Programming - Extended Training Course

Huawei Ascend comprises a series of AI processors engineered for high-performance inference and training tasks.

This guided, live training session (available online or on-site) targets intermediate-level AI engineers and data scientists eager to create and enhance neural network models utilizing Huawei's Ascend platform alongside the CANN toolkit.

Upon completion of this course, participants will be capable of:

• Configuring and establishing the CANN development environment.
• Creating AI applications through MindSpore and CloudMatrix workflows.
• Enhancing performance on Ascend NPUs via tiling techniques and custom operators.
• Deploying models across cloud or edge infrastructure.

Course Format

• Engaging lectures paired with interactive discussions.
• Practical application of the CANN toolkit and Huawei Ascend within sample projects.
• Supervised exercises emphasizing model construction, training, and deployment.

Options for Course Customization

• To arrange a tailored training session adapted to your specific datasets or infrastructure, please reach out to us.

CANN (Compute Architecture for Neural Networks) represents Huawei’s AI compute stack designed to deploy and optimize AI models on Ascend AI processors.

This instructor-led, live training—available either online or onsite—is tailored for intermediate-level AI developers and engineers who aim to efficiently deploy trained AI models onto Huawei Ascend hardware. The curriculum leverages the CANN toolkit alongside tools such as MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be equipped to:

• Comprehend the CANN architecture and its significance within the AI deployment pipeline.
• Convert and adapt models from widely used frameworks into formats compatible with Ascend.
• Utilize tools like ATC, OM model conversion, and MindSpore for both edge and cloud inference tasks.
• Identify deployment challenges and optimize performance on Ascend hardware.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical lab exercises using CANN tools alongside Ascend simulators or physical devices.
• Real-world deployment scenarios based on existing AI models.

Customization Options

• For inquiries regarding customized training for this course, please reach out to us to make arrangements.

CloudMatrix represents Huawei's unified platform for AI development and deployment, engineered to facilitate scalable, production-ready inference pipelines. <\/p>

This instructor-led live training, available online or on-site, targets beginner to intermediate AI professionals looking to deploy and monitor AI models using the CloudMatrix platform integrated with CANN and MindSpore. <\/p>

Upon completion of this training, participants will be equipped to: <\/p>

• Utilize CloudMatrix for packaging, deploying, and serving models. <\/li>
• Convert and optimize models specifically for Ascend chipsets. <\/li>
• Establish pipelines for both real-time and batch inference workloads. <\/li>
• Monitor deployments and fine-tune performance within production environments. <\/li> <\/ul>

  Course Format <\/p>

  • Interactive lectures and discussions. <\/li>
  • Practical, hands-on experience with CloudMatrix using real-world deployment scenarios. <\/li>
  • Guided exercises centered on model conversion, optimization, and scaling. <\/li> <\/ul>

    Customization Options <\/p>

    • For customized training tailored to your specific AI infrastructure or cloud environment, please contact us to arrange a session. <\/li> <\/ul>

Biren AI Accelerators are high-performance GPUs designed for AI and HPC workloads with support for large-scale training and inference.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level developers who wish to program and optimize applications using Biren’s proprietary GPU stack, with practical comparisons to CUDA-based environments.

By the end of this training, participants will be able to:

• Understand Biren GPU architecture and memory hierarchy.
• Set up the development environment and use Biren’s programming model.
• Translate and optimize CUDA-style code for Biren platforms.
• Apply performance tuning and debugging techniques.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Biren SDK in sample GPU workloads.
• Guided exercises focused on porting and performance tuning.

Course Customization Options

• To request a customized training for this course based on your application stack or integration needs, please contact us to arrange.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to optimize both inference and training tasks for edge and data center environments.

This instructor-led, live training (available online or on-site) targets intermediate developers aiming to build and deploy AI models utilizing the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completing this training, participants will be capable of:

• Setting up and configuring development environments for BANGPy and Neuware.
• Creating and optimizing Python- and C++-based models tailored for Cambricon MLUs.
• Deploying these models to edge and data center devices powered by the Neuware runtime.
• Integrating machine learning workflows with acceleration features specific to MLU hardware.

Course Format

• Interactive lectures and discussions.
• Practical application of BANGPy and Neuware for development and deployment.
• Guided exercises centered on optimization, integration, and testing.

Customization Options

• To arrange a customized version of this course tailored to your specific Cambricon device model or use case, please contact us.

CANN (Compute Architecture for Neural Networks) is Huawei’s toolkit for AI computing, designed to compile, optimize, and deploy AI models on Ascend processors.

This guided training session, available online or in-person, targets AI developers at the beginner level who want to grasp how CANN integrates into the model lifecycle—from training through to deployment—and how it interacts with frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completion of this course, participants will be able to:

• Grasp the purpose and architectural design of the CANN toolkit.
• Configure a development environment using CANN alongside MindSpore.
• Convert and deploy a straightforward AI model onto Ascend hardware.
• Acquire foundational knowledge to support future projects involving CANN optimization or integration.

Course Format

• Interactive lectures and discussions.
• Practical hands-on labs focused on simple model deployment.
• Step-by-step guidance through the CANN toolchain and integration points.

Customization Options

• For tailored training arrangements, please contact us directly.

Huawei's Ascend CANN toolkit facilitates robust AI inference on edge devices, including the Ascend 310. It offers crucial tools for compiling, optimizing, and deploying models in environments with limited computing power and memory.

This instructor-led, live training (available online or on-site) is designed for intermediate-level AI developers and integrators looking to deploy and optimize models on Ascend edge devices using the CANN toolchain.

Upon completion of this training, participants will be able to:

• Prepare and convert AI models for the Ascend 310 using CANN tools.
• Construct lightweight inference pipelines utilizing MindSpore Lite and AscendCL.
• Optimize model performance for environments with constrained compute and memory resources.
• Deploy and monitor AI applications in real-world edge use cases.

Course Format

• Interactive lectures and demonstrations.
• Hands-on lab work focusing on edge-specific models and scenarios.
• Live deployment examples on virtual or physical edge hardware.

Course Customization Options

• To request customized training for this course, please contact us to arrange it.

Huawei provides a tightly integrated AI development and deployment environment, spanning from the low-level CANN SDK to the high-level MindSpore framework, all optimized for Ascend hardware.

This instructor-led live training (available online or onsite) is designed for technical professionals at beginner to intermediate levels who want to understand how CANN and MindSpore components collaborate to support AI lifecycle management and infrastructure decision-making.

Upon completing this training, participants will be able to:

• Grasp the layered architecture of Huawei’s AI compute stack.
• Recognize how CANN facilitates model optimization and hardware-level deployment.
• Assess the MindSpore framework and toolchain relative to industry alternatives.
• Position Huawei's AI stack within enterprise or cloud/on-premises environments.

Course Format

• Interactive lectures and discussions.
• Live system demonstrations and case-based walkthroughs.
• Optional guided labs exploring the model flow from MindSpore to CANN.

Customization Options

• For customized training arrangements for this course, please contact us.

The CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI compute platform, empowering developers to refine and optimize the performance of deployed neural networks on Ascend AI processors.

This instructor-led training session, available both online and onsite, targets advanced AI developers and system engineers seeking to enhance inference performance through CANN’s sophisticated toolset. Key areas covered include the Graph Engine, TIK, and custom operator development.

Upon completion of this training, participants will be equipped to:

• Gain insight into CANN's runtime architecture and performance lifecycle.
• Leverage profiling tools and the Graph Engine for detailed performance analysis and optimization.
• Develop and optimize custom operators utilizing TIK and TVM.
• Address memory bottlenecks and significantly improve model throughput.

Course Format

• Interactive lectures and group discussions.
• Practical hands-on labs featuring real-time profiling and operator tuning.
• Optimization exercises based on edge-case deployment scenarios.

Customization Options

• To arrange a customized training program for this course, please contact us directly.

The CANN SDK (Compute Architecture for Neural Networks) delivers robust deployment and optimization capabilities for real-time AI applications in computer vision and natural language processing (NLP), particularly on Huawei Ascend hardware.

This instructor-led live training, available online or on-site, is designed for intermediate-level AI practitioners looking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.

Upon completion of this training, participants will be able to:

• Deploy and optimize computer vision (CV) and NLP models using CANN and AscendCL.
• Utilize CANN utilities to convert models and integrate them into live processing pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment.

Course Format

• Interactive lectures and practical demonstrations.
• Hands-on labs focusing on model deployment and performance profiling.
• Live design of pipelines using real-world CV and NLP use cases.

Customization Options

• To request a customized training session for this course, please contact us to make arrangements.

By leveraging CANN TIK (Tensor Instruction Kernel) alongside Apache TVM, developers can achieve advanced optimization and customization of AI model operators for Huawei Ascend hardware.

This instructor-led live training (available online or onsite) is designed for experienced system developers who want to create, deploy, and fine-tune custom operators for AI models utilizing CANN’s TIK programming model and TVM compiler integration.

Upon completion of this training, participants will be able to:

• Write and test custom AI operators using the TIK DSL for Ascend processors.
• Integrate custom operations into the CANN runtime and execution graph.
• Utilize TVM for operator scheduling, auto-tuning, and benchmarking.
• Debug and optimize instruction-level performance for custom computation patterns.

Course Format

• Interactive lectures accompanied by practical demonstrations.
• Hands-on coding exercises for operators using TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Customization Options

• To arrange customized training for this course, please contact us.

Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, provide alternatives to CUDA that are specifically designed for the domestic AI and High-Performance Computing (HPC) markets.

This instructor-led live training, available both online and onsite, is designed for advanced GPU programmers and infrastructure specialists looking to migrate and optimize their existing CUDA applications for deployment on Chinese hardware platforms.

Upon completion of this training, participants will be able to:

• Evaluate the compatibility of current CUDA workloads with Chinese chip alternatives.
• Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
• Compare performance metrics and identify optimization opportunities across different platforms.
• Address practical challenges related to cross-architecture support and deployment.

Course Format

• Interactive lectures and discussions.
• Hands-on labs involving code translation and performance comparisons.
• Guided exercises focused on multi-GPU adaptation strategies.

Customization Options

• To request customized training for this course tailored to your specific platform or CUDA project, please contact us to arrange.

Ascend, Biren, and Cambricon represent top-tier AI hardware platforms in China, each providing distinct acceleration and profiling tools designed for production-scale AI workloads.

This instructor-led live training, available online or onsite, targets advanced AI infrastructure and performance engineers seeking to optimize model inference and training workflows across these diverse Chinese AI chip platforms.

Upon completion of this training, participants will be equipped to:

• Benchmark models across Ascend, Biren, and Cambricon platforms.
• Identify system bottlenecks and inefficiencies in memory and compute.
• Implement optimizations at the graph, kernel, and operator levels.
• Tune deployment pipelines to enhance throughput and reduce latency.

Course Format

• Interactive lectures and discussions.
• Practical application of profiling and optimization tools on each platform.
• Guided exercises focused on real-world tuning scenarios.

Customization Options

• To request a customized version of this course tailored to your specific performance environment or model type, please contact us to arrange.