Managing ML packages

Note:

Starting with 2022.10, importing and exporting ML Packages using scripts is no longer supported.

Check the Import ML Package and Download ML Packages sections for more information on importing and exporting ML Packages using the UI.

Package validations

For serving

For models uploaded with the Enable Training flag inactive, when a model is uploaded, AI Center validates the uploaded .zip file against the requirements described here. The following three checks are performed:

1. A non-empty root folder exists.
2. A requirements.txt file exists.
3. A file named main.py exists in the root folder which implements a class Main. The class is further validated to implement an __init__ and a predict function.

Success or failure along with any errors that caused it are shown on the ML Logs page.

For training

For models uploaded with the Enable Training flag active, in addition to validating the requirements as above, AI Center also validates the uploaded .zip file against the requirements described here. For these packages the following two checks are performed:

1. A non-empty root folder exists.
2. A file named train.py exists in the root folder which implements a class Main. The class is further validated to implement an __init__ function and the following functions: train, evaluate, and save.

Success or failure along with any errors that caused it are shown on the ML Logs page.

Viewing ML package details

Click a package in the list to navigate to its ML Package > [ML Package Name] page.

In the Version tab, view its details: package version, creation time, change log, status, whether or not training is enabled, whether or not recommended GPU is enabled, and arguments.

You can find more information on each entry in the ML Packages Version by clicking on the ⁝ icon and then Details. A dialog box will be displayed with all the information on the package version.

In the Pipeline runs tab, view the details related to the package's pipeline runs: package name, type, version, status, creation time, duration, score, and additional details.

Version control

AI Center also supports versioning and version management of packages. When a package is uploaded, it's displayed as version 1.0 of that package (we say it's Major Version is 1, and Minor Version is 0). This helps with differentiating between packages uploaded by users, and packages retrained via pipelines, the latter only changing their minor version.

Uploading new ML package versions

Follow these steps to upload a new version for an already uploaded package:

1. In the ML Packages page, click ⁝ next to a package and select the Upload new version option.

   Alternatively, in the ML Package > [ML Package Name] page, click Upload new version. The Upload New Package Version for > [ML Package Name] window is displayed, with most of the fields prefilled with the information you provided at the time when you first uploaded that package.

2. Click Upload Package to select the desired .zip file, or drag & drop the file above this field.
3. Optional: Update the existing information in the following fields:
   • Input description
   • Output description
   • Language
4. Optional: In the ChangeLog field, enter what has changed.
5. Select whether the model requires a GPU, by default it is set to No.
6. Select whether to enable training for your model.
7. Click Create to upload the new version for the existing uploaded package or Cancel to abort the process. The Upload Package window is closed and the new version of the package is uploaded. It may take a few minutes before your upload is propagated.
   
   

   

   
   

The new version of the package is not visible directly in the ML Packages page. You can view its information within the ML Package Details page for that package.

Note: When a new version is uploaded on an existing package, that creates a new major version. For example, if I have uploaded my first package, that upload will be version 1.0. When I upload a new version, that version will be 2.0.

ML package versions created by training pipelines

When a training pipeline or a full pipeline executes successfully on a package version, a new minor version is created. For example, if I have uploaded a package (version 1.0), and start a training pipeline, version 1.1 is displayed after completion in the ML Package Details page as below:

Viewing package arguments

In the ML Package > [ML Package Name] page Version tab, click the information icon next to a package version. The Arguments for > [ML Package Name] > [ML Package Version] window is displayed.

The input type, and the input and output descriptions of the selected package version are displayed. Please note that you cannot edit the values.

Deleting ML packages

Packages can only be deleted if they are not deployed within a skill and no pipelines are currently running on those packages.

1. In the ML Packages page, click ⁝ next to a package and select Delete undeployed versions. A confirmation window is displayed.
2. In the confirmation window, click OK to delete all undeployed versions of the selected package. If a package version is part of a skill (it is active), it is NOT going to be deleted. If all the versions are inactive, they are all deleted.

OR

1. In the ML Package > [ML Package Name] page Version tab, click ⁝ next to a package version and select Delete. A confirmation window is displayed.
2. In the confirmation window, click OK to delete the selected version of the package. If a package version is part of a skill (it is active), it is NOT going to be deleted. If this is the only version for the selected package, the package itself is also deleted.