Welcome to the Intel® Geti™ SDK! The Intel® Geti™ platform enables teams to rapidly develop AI models. The platform reduces the time needed to build models by easing the complexities of model development and harnessing greater collaboration between teams. Most importantly, the platform unlocks faster time-to-value for digitization initiatives with AI.

The Intel® Geti™ SDK is a python package which contains tools to interact with an Intel® Geti™ server via the REST API. It provides functionality for:

• Project creation from annotated datasets on disk
• Project downloading (images, videos, configuration, annotations, predictions and models)
• Project creation and upload from a previous download
• Deploying a project for local inference with OpenVINO
• Getting and setting project and model configuration
• Launching and monitoring training jobs
• Media upload and prediction

This repository also contains a set of (tutorial style) Jupyter notebooks that demonstrate how to use the SDK. We highly recommend checking them out to get a feeling for use cases for the package.

Using an environment manager such as miniforge or venv to create a new Python environment before installing the Intel® Geti™ SDK and its requirements is highly recommended.

NOTE: If you have installed multiple versions of Python, use py -3.9 venv -m <env_name> when creating your virtual environment to specify a supported version (in this case 3.9). Once you activate the virtual environment <venv_path>/Scripts/activate, make sure to upgrade pip to the latest version python -m pip install --upgrade pip wheel setuptools.

Make sure to set up your environment using one of the supported Python versions for your operating system, as indicated in the table below.

Once you have created and activated a new environment, follow the steps below to install the package.

Use pip install geti-sdk to install the SDK from the Python Package Index (PyPI). To install a specific version (for instance v1.5.0), use the command pip install geti-sdk==1.5.0

1. Download or clone the repository and navigate to the root directory of the repo in your terminal.

2. Base installation Within this directory, install the SDK using pip install . This command will install the package and its base dependencies in your environment.

3. Notebooks installation (Optional) If you want to be able to run the notebooks, make sure to install the extra requirements using pip install .[notebooks] This will install both the SDK and all other dependencies needed to run the notebooks in your environment

4. Development installation (Optional) If you plan on running the tests or want to build the documentation, you can install the package extra requirements by doing for example pip install -e .[dev]

   The valid options for the extra requirements are [dev, docs, notebooks], corresponding to the following functionality:

   • dev Install requirements to run the test suite on your local machine
   • notebooks Install requirements to run the Juypter notebooks in the notebooks folder in this repository.
   • docs Install requirements to build the documentation for the SDK from source on your machine

The SDK contains example code in various forms to help you get familiar with the package.

• Code examples are short snippets that demonstrate how to perform several common tasks. This also shows how to configure the SDK to connect to your Intel® Geti™ server.

• Jupyter notebooks are tutorial style notebooks that cover pretty much the full SDK functionality. These are the recommended way to get started with the SDK.

• Example scripts are more extensive scripts that cover more advanced usage than the code examples, have a look at these if you don't like Jupyter.

The package provides a main class Geti that can be used for the following use cases

To establish a connection between the SDK running on your local machine, and the Intel® Geti™ platform running on a remote server, the Geti class needs to know the hostname or IP address for the server and it needs to have some form of authentication.

Instantiating the Geti class will establish the connection and perform authentication.

• Personal Access Token

  The recommended authentication method is the 'Personal Access Token'. The token can be obtained by following the steps below:

  1. Open the Intel® Geti™ user interface in your browser
  2. Click on the User menu, in the top right corner of the page. The menu is accessible from any page inside the Intel® Geti™ interface.
  3. In the dropdown menu that follows, click on Personal access token, as shown in the image below.
  4. In the screen that follows, go through the steps to create a token.
  5. Make sure to copy the token value!

  

  Once you created a personal access token, it can be passed to the Geti class as follows:

  from geti_sdk import Geti
  
  geti = Geti(
      host="https://your_server_hostname_or_ip_address",
      token="your_personal_access_token"
  )

• User Credentials

  NOTE: For optimal security, using the token method outlined above is recommended.

  In addition to the token, your username and password can also be used to connect to the server. They can be passed as follows:

  from geti_sdk import Geti
  
  geti = Geti(
      host="https://your_server_hostname_or_ip_address", username="dummy_user", password="dummy_password"
  )

  Here, "dummy_user" and "dummy_password" should be replaced by your username and password for the Geti server.

• SSL certificate validation

  By default, the SDK verifies the SSL certificate of your server before establishing a connection over HTTPS. If the certificate can't be validated, this will results in an error and the SDK will not be able to connect to the server.

  However, this may not be appropriate or desirable in all cases, for instance if your Geti server does not have a certificate because you are running it in a private network environment. In that case, certificate validation can be disabled by passing verify_certificate=False to the Geti constructor. Please only disable certificate validation in a secure environment!

• Project download The following python snippet is a minimal example of how to download a project using Geti:

  from geti_sdk import Geti
  
  geti = Geti(
    host="https://your_server_hostname_or_ip_address", token="your_personal_access_token"
  )
  
  geti.download_project_data(project_name="dummy_project")

  Here, it is assumed that the project with name 'dummy_project' exists on the cluster. The Geti instance will create a folder named 'dummy_project' in your current working directory, and download the project parameters, images, videos, annotations, predictions and the active model for the project (including optimized models derived from it) to that folder.

  The method takes the following optional parameters:

  • target_folder -- Can be specified to change the directory to which the project data is saved.

  • include_predictions -- Set to True to download the predictions for all images and videos in the project. Set to False to not download any predictions.

  • include_active_model -- Set to True to download the active model for the project, and any optimized models derived from it. If set to False, no models are downloaded. False by default.

  NOTE: During project downloading the Geti SDK stores data on local disk. If necessary, please apply additional security control to protect downloaded files (e.g., enforce access control, delete sensitive data securely).

• Project upload The following python snippet is a minimal example of how to re-create a project on an Intel® Geti™ server using the data from a previously downloaded project:

  from geti_sdk import Geti
  
  geti = Geti(
    host="https://your_server_hostname_or_ip_address", token="your_personal_access_token"
  )
  
  geti.upload_project_data(target_folder="dummy_project")

  The parameter target_folder must be a valid path to the directory holding the project data. If you want to create the project using a different name than the original project, you can pass an additional parameter project_name to the upload method.

The Geti instance can be used to either back-up a project (by downloading it and later uploading it again to the same cluster), or to migrate a project to a different cluster (download it, and upload it to the target cluster).

To up- or download all projects from a cluster, simply use the geti.download_all_projects and geti.upload_all_projects methods instead of the single project methods in the code snippets above.

The following code snippet shows how to create a deployment for local inference with OpenVINO:

import cv2

from geti_sdk import Geti

geti = Geti(
host="https://your_server_hostname_or_ip_address", token="your_personal_access_token"
)

# Download the model data and create a `Deployment`
deployment = geti.deploy_project(project_name="dummy_project")

# Load the inference models for all tasks in the project, for CPU inference
deployment.load_inference_models(device='CPU')

# Run inference
dummy_image = cv2.imread('dummy_image.png')
prediction = deployment.infer(image=dummy_image)

# Save the deployment to disk
deployment.save(path_to_folder="dummy_project")

The deployment.infer method takes a numpy image as input.

The deployment.save method will save the deployment to the folder named 'dummy_project', on the local disk. The deployment can be reloaded again later using Deployment.from_folder('dummy_project').

The examples folder contains example scripts, showing various use cases for the package. They can be run by navigating to the examples directory in your terminal, and simply running the scripts like any other python script.

In addition, the notebooks folder contains Jupyter notebooks with example use cases for the geti_sdk. To run the notebooks, make sure that the requirements for the notebooks are installed in your Python environment. If you have not installed these when you were installing the SDK, you can install them at any time using pip install -r requirements/requirements-notebooks.txt

Once the notebook requirements are installed, navigate to the notebooks directory in your terminal. Then, launch JupyterLab by typing jupyter lab. This should open your browser and take you to the JupyterLab landing page, with the SDK notebooks open (see the screenshot below).

NOTE: Both the example scripts and the notebooks require access to a server running the Intel® Geti™ platform.

The Geti class provides the following methods:

• download_project_data -- Downloads a project by project name (Geti-SDK representation), returns an interactive object.

• upload_project_data -- Uploads project (Geti-SDK representation) from a folder.

• download_all_projects -- Downloads all projects found on the server.

• upload_all_projects -- Uploads all projects found in a specified folder to the server.

• export_project -- Exports a project to an archive on disk. This method is useful for creating a backup of a project, or for migrating a project to a different cluster.

• import_project -- Imports a project from an archive on disk. This method is useful for restoring a project from a backup, or for migrating a project to a different cluster.

• export_dataset -- Exports a dataset to an archive on disk. This method is useful for creating a backup of a dataset, or for migrating a dataset to a different cluster.

• import_dataset -- Imports a dataset from an archive on disk. A new project will be created for the dataset. This method is useful for restoring a project from a dataset backup, or for migrating a dataset to a different cluster.

• upload_and_predict_image -- Uploads a single image to an existing project on the server, and requests a prediction for that image. Optionally, the prediction can be visualized as an overlay on the image.

• upload_and_predict_video -- Uploads a single video to an existing project on the server, and requests predictions for the frames in the video. As with upload_and_predict_image, the predictions can be visualized on the frames. The parameter frame_stride can be used to control which frames are extracted for prediction.

• upload_and_predict_media_folder -- Uploads all media (images and videos) from a folder on local disk to an existing project on the server, and download predictions for all uploaded media.

• deploy_project -- Downloads the active model for all tasks in the project as an OpenVINO inference model. The resulting Deployment can be used to run inference for the project on a local machine. Pipeline inference is also supported.

• create_project_single_task_from_dataset -- Creates a single task project on the server, potentially using labels and uploading annotations from an external dataset.

• create_task_chain_project_from_dataset -- Creates a task chain project on the server, potentially using labels and uploading annotations from an external dataset.

For further details regarding these methods, please refer to the method documentation, the code snippets, and example scripts provided in this repo.

Please visit the full documentation for a complete API reference.

• Creating projects. You can pass a variable project_type to control what kind of tasks will be created in the project pipeline. For example, if you want to create a single task segmentation project, you'd pass project_type='segmentation'. For a detection -> segmentation task chain, you can pass project_type=detection_to_segmentation. Please see the scripts in the examples folder for examples on how to do this.

• Creating datasets and retrieving dataset statistics.

• Uploading images, videos, annotations for images and video frames and configurations to a project.

• Downloading images, videos, annotations, models and predictions for all images and videos/video frames in a project. Also downloading the full project configuration is supported.

• Setting configuration for a project, like turning auto train on/off and setting number of iterations for all tasks.

• Deploying a project to load OpenVINO inference models for all tasks in the pipeline, and running the full pipeline inference on a local machine.

• Creating and restoring a backup of an existing project, using the code snippets provided above. Only annotations, media and configurations are backed up, models are not.

• Launching and monitoring training jobs is straightforward with the TrainingClient. Please refer to the notebook 007_train_project for instructions.

• Authorization via Personal Access Token is available for both On-Prem and SaaS users.

• Fetching the active dataset

• Triggering (post-training) model optimization for model quantization and changing models precision.

• Running model tests

• Benchmarking models to measure inference throughput on different hardware. It allows for quick and easy comparison of inference framerates for different model architectures and precision levels for the specified project.

• Model upload
• Prediction upload
• Importing datasets to an existing project: For this, you can use the import functionality from the Intel® Geti™ user interface instead.