Adobe LiveCycle Designer Training Course

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

This instructor-led live training session, available both online and onsite, targets intermediate-level data scientists and AI engineers looking to develop and optimize neural network models utilizing Huawei’s CANN toolkit and Ascend platform.

Upon completing this training, attendees will be equipped to:

• Establish and configure the CANN development environment.
• Create AI applications leveraging CloudMatrix workflows and MindSpore.
• Enhance performance on Ascend NPUs through tiling and custom operators.
• Deploy models to either cloud or edge environments.

Course Format

• Practical use of Huawei Ascend and the CANN toolkit within sample applications.
• Interactive discussions and lectures.
• Guided exercises centered on model building, training, and deployment.

Customization Options for the Course

• For tailored training aligned with your specific infrastructure or datasets, please get in touch with us to make arrangements.

CANN (Compute Architecture for Neural Networks) serves as Huawei's comprehensive AI compute stack designed for the efficient deployment and optimization of AI models on Ascend AI processors.

This instructor-led live training session, available either online or onsite, is tailored for intermediate-level AI developers and engineers seeking to efficiently deploy trained AI models onto Huawei Ascend hardware. Participants will utilize the CANN toolkit alongside tools such as MindSpore, TensorFlow, or PyTorch.

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

• Gaining a deep understanding of the CANN architecture and its pivotal role within the AI deployment pipeline.
• Converting and adapting models originating from popular frameworks into formats compatible with Ascend.
• Leveraging tools such as ATC, OM model conversion, and MindSpore for both edge and cloud inference tasks.
• Diagnosing deployment-related issues and optimizing performance specifically on Ascend hardware.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical hands-on lab exercises utilizing CANN tools along with Ascend simulators or physical devices.
• Real-world deployment scenarios based on authentic AI models.

Course Customization Options

• To arrange a customized training session for this course, please reach out to us.

CloudMatrix serves as Huawei’s consolidated platform for AI development and deployment, engineered to facilitate scalable, production-ready inference pipelines.

This instructor-led live training, available online or onsite, targets beginner to intermediate AI professionals seeking to deploy and monitor AI models using CloudMatrix, leveraging its integration with CANN and MindSpore.

Upon completion of this training, participants will gain the ability to:

• Utilize CloudMatrix for packaging, deploying, and serving models.
• Convert and optimize models specifically for Ascend chipsets.
• Establish pipelines for both real-time and batch inference tasks.
• Monitor deployments and optimize performance within production environments.

Course Format

• Interactive lectures and discussions.
• Practical application of CloudMatrix through real-world deployment scenarios.
• Guided exercises concentrating on conversion, optimization, and scaling techniques.

Course Customization Options

• To arrange a customized training session tailored to your specific AI infrastructure or cloud environment, please reach out to us.

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 processors designed for optimized inference and training in both edge and datacenter environments.

This instructor-led live training (available online or on-site) is designed for intermediate developers looking 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 development environments for BANGPy and Neuware.
• Develop and optimize Python and C++ based models for Cambricon MLUs.
• Deploy models to edge and datacenter devices running the Neuware runtime.
• Integrate ML workflows with MLU-specific acceleration features.

Course Format

• Interactive lectures and discussions.
• Hands-on development and deployment using BANGPy and Neuware.
• Guided exercises focusing on optimization, integration, and testing.

Course Customization Options

• To request customized training for this course based on your specific Cambricon device model or use case, please contact us to arrange.

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

This instructor-led training, available online or onsite, is tailored for beginner-level AI developers seeking to grasp how CANN integrates into the model lifecycle, spanning from training to deployment, and its interoperability with frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Comprehending the purpose and architecture of the CANN toolkit.
• Establishing a development environment utilizing CANN and MindSpore.
• Converting and deploying a simple AI model to Ascend hardware.
• Acquiring foundational knowledge to support future CANN optimization or integration projects.

Course Format

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

Customization Options for the Course

• For tailored training arrangements, please contact us to discuss your needs.

Huawei's Ascend CANN toolkit empowers powerful AI inference on edge devices like the Ascend 310. It provides crucial tools for compiling, optimizing, and deploying models in environments with limited compute and memory capacity.

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

Upon completing 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 within constrained compute and memory settings.
• Deploy and monitor AI applications in real-world edge scenarios.

Course Format

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

Course Customization Options

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

Huawei’s AI ecosystem — spanning from the low-level CANN SDK to the high-level MindSpore framework — delivers a seamlessly integrated environment for developing and deploying AI solutions, specifically optimized for Ascend hardware.

This instructor-led, live training (available online or on-site) targets technical professionals at the beginner to intermediate levels who seek to comprehend how CANN and MindSpore components collaborate to manage the AI lifecycle and inform infrastructure strategies.

Upon completing this training, participants will be equipped to:

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

Course Format

• Engaging lectures coupled with interactive discussions.
• Live system demonstrations and case-based walkthroughs.
• Optional guided labs focusing on the model flow from MindSpore to CANN.

Course Customization Options

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

The CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI compute framework, enabling developers to fine-tune and maximize the performance of neural networks deployed on Ascend AI processors.

This instructor-led live training (available online or onsite) is designed for advanced AI developers and system engineers seeking to optimize inference performance through CANN’s sophisticated toolset, including the Graph Engine, TIK, and custom operator development.

Upon completing this training, participants will be able to:

• Grasp CANN's runtime architecture and performance lifecycle.
• Utilize profiling tools and the Graph Engine for performance analysis and optimization.
• Develop and optimize custom operators using TIK and TVM.
• Address memory bottlenecks and enhance model throughput.

Course Format

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

Customization Options

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

The CANN SDK (Compute Architecture for Neural Networks) offers robust tools for deploying and optimizing real-time AI applications in computer vision and natural language processing (NLP), particularly on Huawei Ascend hardware.

This instructor-led training, available online or onsite, targets intermediate AI professionals 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 CV and NLP models utilizing CANN and AscendCL.
• Leverage CANN tools to convert models and integrate them into operational pipelines.
• Enhance inference performance for tasks such as object detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures and live demonstrations.
• Practical lab sessions focused on model deployment and performance profiling.
• Real-time pipeline design using practical CV and NLP use cases.

Customization Options

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

CANN TIK (Tensor Instruction Kernel) and Apache TVM facilitate the sophisticated optimization and customization of AI model operators for Huawei Ascend hardware.

This live, instructor-led training (available online or onsite) is designed for advanced system developers looking to build, deploy, and fine-tune custom operators for AI models by leveraging 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 operators 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 and live demonstrations.
• Hands-on coding exercises for operators using TIK and TVM pipelines.
• Testing and tuning performed on Ascend hardware or simulators.

Course Customization Options

• For information on requesting customized training for this course, please contact us to make arrangements.

Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, provide CUDA alternatives specifically designed for the local AI and HPC markets.

This instructor-led live training (available online or onsite) targets advanced-level GPU programmers and infrastructure specialists who intend to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.

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

• Assess the compatibility of existing 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.

Format of the Course

• Interactive lecture and discussion.
• Hands-on code translation and performance comparison labs.
• Guided exercises focused on multi-GPU adaptation strategies.

Course Customization Options

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

Ascend, Biren, and Cambricon stand out as premier AI hardware platforms in China, providing distinct acceleration and profiling tools tailored for production-scale AI workloads.

This instructor-led live training, available both online and onsite, targets advanced-level AI infrastructure and performance engineers aiming to optimize model inference and training workflows across various Chinese AI chip platforms.

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

• Benchmarking models on Ascend, Biren, and Cambricon platforms.
• Identifying system bottlenecks and memory/compute inefficiencies.
• Implementing graph-level, kernel-level, and operator-level optimizations.
• Tuning deployment pipelines to enhance throughput and reduce latency.

Course Format

• Interactive lectures and discussions.
• Hands-on experience with profiling and optimization tools on each platform.
• Guided exercises focusing on practical tuning scenarios.

Customization Options

• For customized training based on your specific performance environment or model type, please contact us to arrange.