The web console
Limited access
Where it is
The Artificial Intelligence section of the web console is the front end of everything in this chapter. It signs its requests with your console session, so you see the organization you are logged into and nothing else, and every action it offers is one of the API calls documented on the previous pages. Its menu has two screens: AI capabilities and Schedulers.
AI capabilities
The screen lists the trainers of your organization, one row per model:
| Column | Shows |
|---|---|
| Trainer name | The trainer’s name |
| Model name | The model it trains — also the name of its inferencer and rule |
| Status | Whether the model has been trained and whether its inferencer is running |
| Retraining | Whether the trainer has a schedule, and when it fires next |
| Inferencer | The inferencer’s state and active version, or that none has been created yet |
Each row has three actions:
- Configure inferencer — opens the inferencer’s versions. Pick a version from the list to see the metrics its training recorded, and use the Enabled switch to deploy it or take it down. Saving with a different version selected deactivates the running one and activates the new one. This is the console’s face of the activation call. Greyed out until the first training has created the inferencer.
- See history — the executions of this trainer from the scheduler’s history, one line per step with result, description, start and end. A time-series trainer shows two steps per run: the export and the training. A failed training says why here — Not enough data for APN, a timeout, an image that could not be built.
- Delete — removes the trainer and its schedule. As with the API, the inferencer and its versions stay; remove them from Configure inferencer or through the Inferencers API.
Reload data refreshes the table; Create new AI trainer opens the wizard.
Creating a trainer
The wizard has four steps and ends with the POST described in Trainers.
1. Selection of training plan
The catalogue, grouped by type — anomaly holds the two RADIUS plans, classification the image plan. Each plan shows its description; read Training plans before choosing, because a plan is specific about the data it wants.
2. Configure training data
- The plan’s configuration fields, one input per entry in its
configFields— the APN for the RADIUS plans. - The source type: File or Timeserie. Only the types the plan accepts are enabled; the image plan is file-only.
- With File: a browser of your organization’s file space. Navigate folders, upload a file or an archive into the current one, download or delete files, and select the file — or stay in a folder to select the folder itself, which is what the image plan needs.
- With Timeserie: pick one of the organization’s time series, set the export timeout in seconds (the default is generous; a large series can take a while to export), and map every column the plan expects onto a column of the time series. The identifier column of the series is offered first.
3. Trainer configuration
| Field | Notes |
|---|---|
| Model’s name | Lowercase letters, digits and hyphens; no spaces. It cannot be changed afterwards and names the inferencer and the rule |
| Model description | Free text |
| Trainer’s name | The trainer is a separate object from the model it produces; this name identifies the training task |
| Maximum execution time | Seconds before a training run is killed. Default 1800; raise it for the autoencoder and image plans |
| Automatic re-training | Off, every 30, 60 or 90 days, or a custom cron expression. A period runs at midnight on the day of the month the trainer was created plus three days, every one, two or three months |
Whatever you choose, the wizard asks for an immediate first execution, so a first version is trained as soon as the trainer is created.
4. Summary
Everything you selected, then Create. The trainer appears in the table at once; the first version appears when the training finishes, and See history follows its progress.
A trainer with too little data is not an error yet
If the time series does not yet hold the minimum the plan requires, the trainer is still created. Its first execution fails with an explicit message in the history and, if it has a retraining schedule, it tries again at the next tick — when enough history has accumulated, the model gets trained without anyone touching the trainer.
Schedulers
A compact view of the organization’s schedulations — REST requests, image executions and pipelines together — with identifier, type, cron pattern, last and next execution, and a Delete action. New scheduler opens a wizard for a REST request or an image execution. Trainers appear here too, as the pipeline or image execution named after their model: deleting one from this screen stops the trainer’s retraining as surely as deleting the trainer, but leaves the trainer record behind — prefer the Delete action on the AI capabilities screen.
For the full-featured scheduler widgets — cloning, per-schedulation history, pipeline editing — use the dashboard scheduler browsers.