ADI MAX78000 和 MAX78002 AI器件 的模型训练_python_代码_下载

# ADI MAX78000/MAX78002 Model Training and Synthesis June 14, 2022 ADI’s MAX78000/MAX78002 project is comprised of five repositories: 1. **Start here**: **[Top Level Documentation](https://github.com/MaximIntegratedAI/MaximAI_Documentation)** 2. The software development kit (SDK), which contains drivers and example programs ready to run on the evaluation kits (EVkit and Feather): [MAX78000_SDK](https://github.com/MaximIntegratedAI/MAX78000_SDK) *also includes MAX78002 support* 3. The training repository, which is used for deep learning *model development and training*: [ai8x-training](https://github.com/MaximIntegratedAI/ai8x-training) **(described in this document)** 4. The synthesis repository, which is used to *convert a trained model into C code* using the “izer” tool: [ai8x-synthesis](https://github.com/MaximIntegratedAI/ai8x-synthesis) **(described in this document)** 5. The reference design repository, which contains host applications and sample applications for reference designs: [refdes](https://github.com/MaximIntegratedAI/refdes) _Open the `.md` version of this file in a markdown enabled viewer, for example Typora (<http://typora.io>). See <https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet> for a description of Markdown. A [PDF copy of this file](README.pdf) is available in this repository. The GitHub rendering of this document does not show the mathematical formulas. Use the ≡ button to access the table of contents on GitHub._ --- [TOC] ## Part Numbers This document covers several of ADI’s ultra-low power machine learning accelerator systems. They are sometimes referred to by their die types. The following shows the die types and their corresponding part numbers: | Die Type | Part Number(s) | | -------- | ------------------------------ | | *AI84* | *Unreleased test chip* | | **AI85** | **MAX78000** (full production) | | AI87 | MAX78002 (engineering samples) | ## Overview The following graphic shows an overview of the development flow:  ## Installation ### File System Layout Including the SDK, the expected/resulting file system layout will be: ..../ai8x-training/ ..../ai8x-synthesis/ ..../ai8x-synthesis/sdk/ where “....” is the project root, for example `~/Documents/Source/AI`. ### Prerequisites This software requires PyTorch. *For TensorFlow / Keras, please use the `develop-tf` branch.* PyTorch operating system and hardware support are constantly evolving. This document does not cover all possible combinations of operating system and hardware. Instead, this document describes how to install PyTorch on one officially supported platform. #### Platform Recommendation and Full Support Full support and documentation are provided for the following platform: * CPU: 64-bit amd64/x86_64 “PC” with [Ubuntu Linux 20.04 LTS](https://ubuntu.com/download/server) * GPU for hardware acceleration (optional but highly recommended): Nvidia with [CUDA 11](https://developer.nvidia.com/cuda-toolkit-archive) * [PyTorch 1.8.1 (LTS)](https://pytorch.org/get-started/locally/) on Python 3.8.x Limited support and advice for using other hardware and software combinations is available as follows. #### Operating System Support ##### Linux **The only officially supported platform for model training** is Ubuntu Linux 20.04 LTS on amd64/x86_64, either the desktop or the [server version](https://ubuntu.com/download/server). *Note that hardware acceleration/CUDA is <u>not available</u> in PyTorch for Raspberry Pi 4 and other <u>aarch64/arm64</u> devices, even those running Ubuntu Linux 20.04. See also [Development on Raspberry Pi 4 and 400](https://github.com/MaximIntegratedAI/ai8x-synthesis/blob/develop/docs/RaspberryPi.md) (unsupported).* This document also provides instructions for installing on RedHat Enterprise Linux / CentOS 8 with limited support. ##### Windows On Windows 10 version 21H2 or newer, and Windows 11, after installing the Windows Subsystem for Linux (WSL2), Ubuntu Linux 20.04 can be used inside Windows with full CUDA acceleration, please see *[Windows Subsystem for Linux](https://github.com/MaximIntegratedAI/ai8x-synthesis/blob/develop/docs/WSL2.md).* For the remainder of this document, follow the steps for Ubuntu Linux. If WSL2 is not available, it is also possible (but not recommended due to inherent compatibility issues and slightly degraded performance) to run this software natively on Windows. Please see *[Native Windows Installation](https://github.com/MaximIntegratedAI/ai8x-synthesis/blob/develop/docs/Windows.md)*. ##### macOS The software works on macOS, but model training suffers from the lack of hardware acceleration. ##### Virtual Machines (Unsupported) This software works inside a virtual machine running Ubuntu Linux 20.04. However, GPU passthrough is potentially difficult to set up and <u>not always available</u> for Linux VMs, so there may be no CUDA hardware acceleration. Certain Nvidia cards support [vGPU software](https://www.nvidia.com/en-us/data-center/graphics-cards-for-virtualization/); see also [vGPUs and CUDA](https://docs.nvidia.com/cuda/vGPU/), but vGPU features may come at substantial additional cost and vGPU software is not covered by this document. ##### Docker Containers (Unsupported) This software also works inside Docker containers. However, CUDA support inside containers requires Nvidia Docker ([see blog entry](https://developer.nvidia.com/blog/nvidia-docker-gpu-server-application-deployment-made-easy/)) and is not covered by this document. #### PyTorch and Python The officially supported version of [PyTorch is 1.8.1 (LTS)](https://pytorch.org/get-started/locally/) running on Python 3.8.x. Newer versions will typically work, but are not covered by support, documentation, and installation scripts. #### Hardware Acceleration When going beyond simple models, model training does not work well without CUDA hardware acceleration. The network loader (“izer”) does <u>not</u> require CUDA, and very simple models can also be trained on systems without CUDA. * CUDA requires Nvidia GPUs. * There is a PyTorch pre-release with ROCm acceleration for certain AMD GPUs on Linux ([see blog entry](https://pytorch.org/blog/pytorch-for-amd-rocm-platform-now-available-as-python-package/)), but this is not currently covered by the installation instructions in this document, and it is not supported. * At this time, there is neither CUDA nor ROCm nor Neural Engine support on macOS, and therefore no hardware acceleration (there is a pre-release version of PyTorch with M1 acceleration on macOS 12.3 or later, and M1 acceleration will be supported in a future release of these tools). * PyTorch does not include CUDA support for aarch64/arm64 systems. *Rebuilding PyTorch from source is not covered by this document.* ##### Using Multiple GPUs When using multiple GPUs (graphics cards), the software will automatically use all available GPUs and distribute the workload. To prevent this (for example, when the GPUs are not balanced), set the `CUDA_VISIBLE_DEVICES` environment variable. Use the `--gpus` command line argument to set the default GPU. #### Shared (Multi-User) and Remote Systems On a shared (multi-user) system that has previously been set up, only local installation is needed. CUDA and any `apt-get` or `brew` tasks are not necessary, with the exception of the CUDA [Environment Setup](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#environment-setup). The `screen` command (or alternatively, the more powerful `tmux`) can be used inside a remote terminal to disconnect a session from the controlling terminal, so that a long running training session doesn’t abort due to network issues, or local power saving. In addition, screen can log all console output to a text file. Example: ```shell $ ssh targethost targethost$ screen -L # or screen