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Installation Steps

Basic Installation

The whole platform architecture is depicted in the following diagram:

Platform Architecture

Minimum system requirements for a local installation are:

  • CPU: at least 8 cores
  • RAM: at least 16 GB
  • GPU: NVIDIA GPU with at least 12 GB VRAM
  • OS: Ubuntu 22.04 or newer
  • Browser: Google Chrome or Microsoft Edge

Before You Start

Make sure that you have cloned the following repository on your local machine:

git clone https://github.com/TEXTaiLES/AmalthAI

  • The AmalthAI_WebApp folder contains the web app code, which is necessary for the platform's UI.

  • The Backend folder contains all the necessary code for the platform's functionality which includes machine learning models, data processing scripts, and deployment configurations. Ensure that the content of the backend folder is also shared across the Kubernetes cluster and the corresponding containers.

Step 1 - Docker Installation

Make sure that you have a local installation of Docker. You can follow the installation process described here:

Docker

Step 2 - Kubernetes Installation

Kubernetes installation is required to orchestrate the different components of the platform. You can follow the official installation tutorial here:

Kubernetes

Step 3 - Kubeflow Installation

Kubeflow installation is also required because experiment initialization is based on it. For installation, follow the instructions here:

Kubeflow

For local development and quick testing, the kind tool can be used.

Ensure that the Kubeflow installation:

  • is accessible from the Python environment, i.e. the Kubeflow Python SDK can connect to the API

  • has at least one directory mounted as a PersistentVolume or a hostPath

  • has GPU access

When the above directory is mounted, make sure that you move every folder from the Backend folder inside that directory so that the three tasks are accessible from the Kubeflow pipelines.

Important Note: Make sure that inside this folder you have created a folder named Datasets where all the datasets will be stored. Inside the Datasets folder, create three subfolders named Segmentation, Classification and Object-Detection for each task respectively.

Step 4 - Docker Images Setup

Machine learning models require appropriate environments to run on. Because the platform is Kubernetes-based, there is need for ready-to-use docker containers.

1) For the semantic segmentation and the classification mode, to run the models, a Docker container based on PyTorch is needed.

First of all, download the basic image using the followning command:

docker pull nvcr.io/nvidia/pytorch:22.12-py3

After pulling the base image, you have to create an updated image with all the necessary libraries installed. To do that, change your directory to Backend/Segmentation/ and utilize the Dockerfile inside that folder by running the following command:

docker build -t segm_cls_image .

The Platform's backend dynamically creates containers on demand for each segmentation, classification and object detection task. These containers are single-use and they are instantiated only for the duration of the task execution and automatically destroyed upon completion.

2) For object detection task, you can use the official Ultralytics Docker image that has all the necessary libraries installed for running YOLO models.

To build this image locally, you can perform the following steps:

docker pull ultralytics/ultralytics:latest-python-export

Kubeflow

Important Note: Make sure that you keep the config.yml file updated inside /AmalthAI_WebApp folder with the correct image names and the shared directory path where the Segmentation, Classification and ObjectDetection folders are located.

Step 5 - Upload docker images into kind cluster

To upload a locally built Docker image to your Kubernetes cluster, you have to run the following command:

kind load docker-image myimage:latest --name name-of-your-cluster

For the AmalthAI platform, you have to upload both the segm_cls_image and the ultralytics/ultralytics:latest-python-export images into the kind cluster.

Step 6 - CVAT Installation

For the annotation purposes of this platform, we utilize CVAT annotation tool. To install it on your system, follow the instructions that are provided here.

Kubeflow

Step 7 - Platform UI Setup

For the platform's UI, you have to create a Docker container with the appropriate libraries to run the web app.

1) First open your terminal inside the /AmalthAI_WebApp folder and run:

docker build -t amalthai .

2) After the build is completed, you can run the container with the following command:

docker run --rm --network=host -v $(pwd):/app -v /var/run/docker.sock:/var/run/docker.sock -v /path/to/backend/shared/folder:/data -v ~/.kube/config:/root/.kube/config amalthai

Then you can easily navigate to the web app, by opening your browser and going to:

http://0.0.0.0:5000