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AiMR's Model Training\n\n\n\n\n[](https://www.npmjs.com/package/@aimr/asmr-gan-lib)\n[](https://github.com/AI-ASMR/asmr-gan-core/releases)\n[](https://app.codacy.com/gh/AI-ASMR/asmr-gan-core/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)\n[](https://hub.docker.com/repository/docker/stiliyankushev/aimr-asmr-gan/general)\n \n#### Table of Contents\n- [What is this?](#introduction)\n- [Model architecture.](#model)\n- [Getting started.](#getting-started)\n- [Build from source.](#build-from-source)\n- [Repo's file structure.](#file-structure)\n- [Versioning and automation.](#versioning)\n- [Arch linux NVIDIA Container Toolkit.](#arch-nvidia-container)\n- [Windows Support.](#windows)\n\n### What is this? \n\nThis is a mono-repo hosting the source code of a GAN Neural Network model capable of producting (and being trained on) 64x64 images (1 or 3 channels), along with a helper/wrapper library that can be used in any javascript context to load a pre-trained model for immediate use.\n\nDocumentation can be found at [ai-asmr.github.io/asmr-gan-core](https://ai-asmr.github.io/asmr-gan-core/).
\nLatest trained model files can be found [here](https://github.com/AI-ASMR/asmr-gan-core/releases/tag/trained-model).
\nThe trained model files ([model.json](https://firebasestorage.googleapis.com/v0/b/aimr-model-storage.appspot.com/o/model.json?alt=media), [weights.bin](https://firebasestorage.googleapis.com/v0/b/aimr-model-storage.appspot.com/o/weights.bin?alt=media)) are also uploaded to firebase and are publicly readable.\n\n### Model architecture. \n\n![](\"https://raw.githubusercontent.com/AI-ASMR/asmr-gan-core/main/assets/model.png\")
\n\n### Getting started. \n\nThe library can be found at [npm](https://www.npmjs.com/package/@aimr/asmr-gan-lib) and at the [latest release](https://github.com/AI-ASMR/asmr-gan-core/releases/).\nYou can get it via npm like so.\n```shell\nnpm i @aimr/asmr-gan-lib\n```\nOr you can install it locally by downloading the latest release.\n```shell\nnpm i aimr-asmr-gan-lib-.tgz\n```\nOr you can import it via cdn like so:\n```html\n\n```\n\n#### Example usage.\n\n```js\n// or use require\nimport AiMR_GAN from \"@aimr/asmr-gan-lib\";\n\n// import some version of tensorflow in any way you'd like\nimport * as tf from \"@tensorflow/tfjs\";\n\n// load the model and/or cache it for subsequent reloads.\nawait AiMR_GAN.load(tf);\n\n// generate some fake Asmr Images\nconsole.log(await AiMR_GAN.generateChunks(1));\n```\n\nTo train your own model take a look at the [release binaries](https://github.com/AI-ASMR/asmr-gan-core/releases/).\nTo get a quick rundown on how to use it simply run:\n```shell\nasmr-gan-bin- --help\n```\nFor better performance you can also use the binaries via docker like so:\n```shell\n# pull the latest version\nsudo docker pull stiliyankushev/aimr-asmr-gan:latest\n# run the docker instance (pass arguments at the end)\nsudo docker run --gpus all -ti stiliyankushev/aimr-asmr-gan:latest --help\n```\nFull example of docker usage:\n```shell\n# assuming the training data is at \"/home/kushev/Documents/training-data\"\nsudo docker run --mount src=\"/home/kushev/Documents\",target=\"/home\",type=bind \\\n--gpus all -ti stiliyankushev/aimr-asmr-gan:latest \\\n-i /home/training-data -d /home/dataset.bin -c /home/checkpoints -p /home/preview.png -q 10000 -s 512\n```\n\n#### (Optional) Docker Prerequisites.\nRunning the above docker container will automatically use a version of tensorflow that makes use of native C bindings. It'll also try to take advantage of any CUDA enabled GPUs running on the system. The docker container already pre-configures Cuda and Cudnn to work with tensorflow js. What you need to do is have:\n- Nvidia GPU with Cuda support.\n- Running a Linux distro.\n- Nvidia proprietary drivers installed.\n- Installed and configured NVIDIA Container Toolkit. (for arch linux, [follow my guide](#arch-nvidia-container).)\n\n### Build from source. \n\nYou can build both the library and the binary from source using short predefined npm-scripts.\nYou need to install deps first.\n```shell\ngit clone https://github.com/AI-ASMR/asmr-gan-core.git\ncd ./asmr-gan-core\nnpm i\n```\nBuild the library:\n```shell\nnpm run build.lib\n```\nBuild the binaries:\n```shell\nnpm run build.bin\n```\nYou can use the binaries directly from source without building executables.\nThis will attempt to use your (CUDA enabled) GPU (Linux Only),\nsame as with the docker container:\n```shell\nnpm start -- --help\n```\n#### Requirements for CUDA enabled model training.\nRunning the above command will work but might not automatically pick up your GPU. \nThat's why it's advised to use the docker image which comes pre-configured. However, if you'd like to run this locally without docker, here's what you need:\n- Nvidia GPU with Cuda support.\n- Running a Linux distro (preferably supported by tensorflow).\n- Cuda installed (version < v12.0.0).\n- Nvidia linux driver that supports the version of cuda installed.\n- libcudnn installed (version >= 8.9.5).\n\n### File structure. \n\nThis is a basic mono-repo that implements both a library and a running process. Both are their own separate typescript npm projects. Both share [common](https://github.com/AI-ASMR/asmr-gan-core/tree/main/common) assets/files, both share common npm packages listed in the root [package.json](https://github.com/AI-ASMR/asmr-gan-core/blob/main/package.json) and both extend root config files such as [tsconfig.json](https://github.com/AI-ASMR/asmr-gan-core/blob/main/tsconfig.json).\n\n```\n+-- 📁 bin # git release files here.\n+-- 📁 common # shared files between lib and src.\n+-- 📁 lib # sources of library here.\n+-- 📁 src # sources of binary here.\n+-- 📁 tensorboard # tfjs storage used by bin.\n+-- 📁 tests # unit tests here.\n+-- scripts.js # mini build tool used by the repo.\n+-- version.cfg # version tracker.\n+-- package.json\n+-- README.md\n+-- tsconfig.json\n```\n\n### Versioning and automation. \n\nBoth the (npm) library and the (git) versioned binary, as well as the docker container share the same common version number. Versioning is automatically increased via CI/CD in the event of meaningful changes. Once there's a version change CI/CD automatically deploys updates for git releases, npm releases and docker tag releases respectively.\n\nCI/CD implementation can be found here:\n- [version-bump.yml](https://github.com/AI-ASMR/asmr-gan-core/blob/main/.github/workflows/version-bump.yml) - called on meaningful changes.\n- [publish-git-version.yml](https://github.com/AI-ASMR/asmr-gan-core/blob/main/.github/workflows/publish-git-version.yml) - called on (above) version bump.\n- [publish-npm-package.yml](https://github.com/AI-ASMR/asmr-gan-core/blob/main/.github/workflows/publish-npm-package.yml) - called on (above) version bump.\n- [publish-docker-tag.yml](https://github.com/AI-ASMR/asmr-gan-core/blob/main/.github/workflows/publish-docker-tag.yml) - called on (above) version bump.\n- [publish-docs.yml](https://github.com/AI-ASMR/asmr-gan-core/blob/main/.github/workflows/publish-docs.yml) - called on (above) version bump.\n\nThe repository hosts a minimal, scripted and cross-platform build tool used by all github actions, as well as users (via npm-scripts.)\n\nFor more details, [read the documented source](https://github.com/AI-ASMR/asmr-gan-core/blob/main/scripts.js).\n\n### Arch linux NVIDIA Container Toolkit. \n\nThis is a short guide on how to install the NVIDIA Container Toolkit on arch linux. For other Linux distros take a look at their official [guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).\n\nI've created a custom PKGBUILD you need to build and install.\n\nMake a fresh directory:\n```shell\nmkdir ./temp-nvidia \ncd ./temp-nvidia\n```\nDownload the PKGBUILD file:\n```shell\nwget https://raw.githubusercontent.com/AI-ASMR/asmr-gan-core/main/PKGBUILD\n```\nBuild the package:\n```shell\nmakepkg\n```\nInstall all .tar.zst files:\n```shell\nsudo pacman -U \\ \n./libnvidia-container1-1.14.3-1-x86_64.pkg.tar.zst \\\n./libnvidia-container-tools-1.14.3-1-x86_64.pkg.tar.zst \\\n./nvidia-container-runtime-1.14.3-1-x86_64.pkg.tar.zst \\\n./nvidia-container-toolkit-1.14.3-1-x86_64.pkg.tar.zst \\ \n./nvidia-container-toolkit-base-1.14.3-1-x86_64.pkg.tar.zst \\ \n./nvidia-docker2-1.14.3-1-x86_64.pkg.tar.zst\n```\nInstall `libnvidia-container-tools` manually:\n```shell\nsudo pacman -Syu libnvidia-container-tools\n```\nConfigure docker:\n```shell\nsudo nvidia-ctk runtime configure --runtime=docker\n```\nRestart docker afterwards:\n```shell\nsudo systemctl restart docker\n```\nAt this point docker should be configured. Test like so:\n```shell\nsudo docker run --gpus all ubuntu nvidia-smi\n```\nIf `nvidia-smi` works, than everything works as expected.\n\n### Windows Support. \n\nThe easiest way to run this is to use the docker container in WSL(2) and enable NVIDIA CUDA following this [guide](https://learn.microsoft.com/en-us/windows/ai/directml/gpu-cuda-in-wsl).\n","readmeFilename":"README.md","description":" ![Git publish passing](https://github.com/AI-ASMR/asmr-gan-core/actions/workflows/publish-git-version.yml/badge.svg?branch=mai"}