It is now time to create your first
Experiment that will track the training of your
DatasetVersion you're planning to use for
ModelVersion training has been attached to your
Project, you can click on the + button next to the Experiment list to reach the Experiment creation form.
The experiment creation form is divided into 4 sub-parts:
Here again, and as is the case for all Picsellia objects for traceability purposes, you'll be asked to give a name and description to your
In addition, you need to define the Inference Type of the
Experiment you are creating. This Inference Type information will allow Picsellia to ensure the consistency of the
Experiment you are about to perform. For instance, make sure that you are not training an ObjectDetection
ModelVersion with a Classification
The selection of the Base architecture is critical in the
Experiment creation process.
The point here is to select the base model and its associated _training script _that will be used to train the
ModelVersion in the frame of this new
Basically, you can use as a Base architecture any
ModelVersion that is stored in the Model Registry (Public or Private) or an
Experiment already performed in the frame of the current
As ML/AI training is an iterative process, you may or may not want to use exisiting checkpoints, model weights, and configuration files for your
Experiment. In both cases, Picsellia will initialize the to-be-created
Experiment by copying if existing, the following files from the Base architecture:
- Model Files (in case of
ModelVersionas Barse architecture) or Artifacts (in case of
Experimentas Base architecture)
- Associated training Docker Image
By clicking on Attach a Model, a modal with the Model Registry will open.
First of all, you can browse among the Public Registry gathering SOTA models ready-to-be-used and packaged by the Picsellia team or among your Private Registry gathering all
ModelVersion you already have developed or imported by our or any user accessing your Organization.
ModelVersion is selected (from the Public or Private Registry), you can select the
ModelVersion related to be used in the frame of the current
ModelVersion will be attached to the
Experiment after having clicked on the Select button.
More information about
ModelVersion is available on this page.
By clicking on Attach an Experiment, a modal listing the existing
Experiment in the frame of the current
Project will allow you to select the
Experiment to take as a Base architecture for your new
Experiment will be attached to the new
Experiment after having clicked on the Select button.
In summary, the selection of the Base architecture will define the ML base (neural network architecture, weights, training script, and hyperparameters...) that will be used to train a
ModelVersion in our new
The list of
Experimentproposed while choosing the Base architecture is filtered based on the Inference Type choosen in the General Information sub-part of the Experiment creation form.
After selecting the Base architecture, Picsellia will automatically inherit the Hyperparameters of the Base architecture and associated default values.
In this sub-part of the Experiment creation form, you will then be able to modify the value of the inherited Hyperparameters to configure the training as expected. You also have the possibility to add or remove Hyperparameters. But be careful because those Hyperparameters and their associated values will be passed as training parameters to the training script when launching the
ModelVersion training in the frame of the
Experiment, so if some training parameters are missing or unexpected, it might impact the training script execution and cause errors.
ModelVersionfrom Public Registry
We strongly recommend you to not add or remove Hyperparameters if you are using
ModelVersionfrom the Public Registry as Base architecture of your
Last but not least, you need to attach
DatasetVersion to your
Experiment, obviously those
DatasetVersion will be used during the training to train your
By clicking on the +, a modal displaying all
DatasetVersion attached to the
Project will open.
You can from this one, pick up the
DatasetVersion that will be used during the model training, depending on the ability of the training script to handle one or several
DatasetVersion as input.
For each selected
DatasetVersion, you'll be asked to provide an alias. This alias will be passed along with the
DatasetVersion to the training script, it will allow the training script (if written to receive such an alias) to understand the purpose of each
DatasetVersion, for instance, differentiate the training
DatasetVersion, from the validation or test ones.
If you use a
ModelVersion from the Public Registry as Base architecture, you might see a different modal, this is due to the Dataset Constraints.
A Dataset Constraint is a set of constraints in which each constraint predefines the number of
DatasetVersion that can be attached to an
Experimentand define also the alias that must be given to each attached
DatasetVersion. A Dataset Constraint is always related to a
ModelVersion, it means that the Dataset Constraint you will have to fulfill depends on the selected Base architecture.
Dataset Constraints reflects the fact the training script is designed to get a given number
DatasetVersion with particular alis as input. Dataset Constraint mainly aims at preventing the training script of the Base architecture from falling into error because
DatasetVersion attached and associated aliases are not handled.
ModelVersionfrom the Public Registry
At the moment Dataset Constraints are only activated on
ModelVersionfrom the Public Registry. But soon, Picsellia users will be able to create and edit Dataset Constraints on their own
It is also important to note that, if existing,
Dataset Contraintsare inherited from the Base architecture to the
ModelVersionthat will be created through the
Experimentusing this Base architecture.
So at first, after having selected a Base architecture that has Dataset Constraints defined, the
DatasetVersion selection modal will open displaying a dropdown of available constraints for this Base architecture and empty placeholders with associated aliases that correspond to the constraint selected.
Then you can just click on any placeholder to be prompted with the list of available
DatasetVersion ready to add to your
Experiment, without the need to write the alias because it has been pre-filled for you! This way, you ensured that you attached the right number of
DatasetVersion with the proper aliases depending on the Base Architecture selected.
Once the constraint has been filled properly with all the required
DatsetVersion, you can click on Attach.
All the Experiment creation form sub-parts should now be completed, the very last step is to click on create to instantiate your
Once created, you'll land on the Experiment overview, in which you will retrieve all the elements defined during the
Updated about 2 months ago