Launch training

After having initialized an Experiment in the frame of a Project, it is time to launch the training of the ModelVersion we're aiming to create within this Experiment.

Depending on the way you integrated the Picsellia platform with your infrastructure, there are three different ways to launch the training of a ModelVersion through an Experiment:

• Launch the training script on the Picsellia training engine.
• Launch the training script on your own training resources from the Picsellia platform.
• Launch the training script manually on your own training resources.

Each possibility has its own requirements that you need to fulfill to ensure the success of the ModelVersion training.

1. Launch the training on the Picsellia training engine

You can use the Picsellia training engine, natively integrated with the platform, to execute the training script directly from your Experiment.

The Picsellia training engine is hosted by OVH and composed of NVIDIA Tesla V100 GPUs. The training engine is operated by the Picsellia team.

You can launch the execution of the training script with your current Experiment by clicking on the Launch Experiment button and selecting Picsellia Infra, as shown below:

If you have the necessary permissions and everything has been properly set up, the execution of the training script on the Picsellia training engine will then start.

This execution can be followed in real time through the Telemetry tab, as long as information is logged by the script on Picsellia.

The training script that gets executed is the one contained in the Docker image of the Base architecture selected for the current Experiment. If the script has been written following the guidelines provided in this guide, it will retrieve the DatasetVersion and training parameters to initialize the training step of your model. During the training, in addition to visualizing real-time execution information in the Telemetry tab, the callback potentially defined in the training script will initialize and fill in the Metrics in the Logs tab. At the end of the training, Evaluation can also be logged and computed by the training script, and the outcome files stored as Artifacts on the Picsellia Experiment.

Once again, all those steps are defined in the training script, so they are under the responsibility of the script author. This is why, during your first use of the platform, we advise using a ModelVersion from the Public Registry as Base architecture, as the training scripts contained in the attached Docker image have been properly written by the Picsellia team according to the guidelines defined in the guide.

2. Launch the training script on your own training resources from the Picsellia platform

If you'd rather use your own computing resources to perform the training of your ModelVersion, you can use the Docker command generated for you and launch it on your training infrastructure. The data and training parameters will be pulled from your Picsellia Experiment, and the outcome of the training (Metrics, Artifacts, Evaluation...) will be logged to your Picsellia Experiment by your training script.

You can launch the execution of the training script on your infrastructure with your current Experiment by clicking on the Launch Experiment button and selecting Docker, as shown below:

If you have the necessary permissions, a Docker command will be displayed in a modal — copy this command and paste it into your training environment on your own infrastructure. If everything has been properly set up on your side, the execution of the training script on your infrastructure will then start.

This execution can be followed in real time through the Telemetry tab, as long as information is logged by the script on Picsellia.

The training script that gets executed is the one contained in the Docker image of the Base architecture selected for the current Experiment. If the script has been written following the guidelines provided in this guide, it will retrieve the DatasetVersion and training parameters to initialize the training step of your ModelVersion. During the training, in addition to visualizing real-time execution information in the Telemetry tab, the callback potentially defined in the training script will initialize and fill in the Metrics in the Logs tab. At the end of the training, Evaluation can also be logged and computed by the training script, and the outcome files stored as Artifacts on the Picsellia Experiment.

Once again, all those steps are defined in the training script, so they are under the responsibility of the script author. This is why, during your first use of the platform, we advise using a ModelVersion from the Public Registry as Base architecture, as the training scripts contained in the attached Docker image have been properly written by the Picsellia team according to the guidelines defined in the guide.

This way of launching the training of your ModelVersion lets you use your own computing resources while still leveraging the Picsellia platform to structure and orchestrate your Computer Vision projects.

However, this method requires using, as Base architecture, a ModelVersion that has a Docker image containing the attached training script. This Docker image must also be stored either on a public registry, such as Docker Hub, or on a private container registry provided by the Picsellia team. If you'd rather not Dockerize your code, you can launch the script manually on your own infrastructure, as explained below.

3. Launch the training script manually on your own training resources

If your code is not Dockerized, or if you don't want to publish your Docker image on a container hub, you can also take care of launching the training on your infrastructure yourself.

In this case, you need to make sure your script has been adapted to Picsellia (as detailed in this guide), so that the DatasetVersion and training parameters attached to the Experiment are properly pulled onto the computing resource executing the script. The results of the training (Metrics, Evaluation, and Artifacts) are then stored in your Picsellia Experiment, ensuring the traceability and structure of your Project directly within your Picsellia Organization.