- Release Notes
- Requirements
- Installation
- Getting Started
- Projects
- Datasets
- ML Packages
- Pipelines
- ML Skills
- ML Logs
- Document Understanding in AI Fabric
- Basic Troubleshooting Guide
Managing ML Packages
Before uploading packages, make sure they are built as specified here.
class, break, from,
finally, global, None,
etc. Make sure to choose another name. The listed examples are not complete
since package name is used for class <pkg-name> and
import <pck-name>.
Follow these steps to upload an already created package:
.zip file against the requirements described here. The following three checks are performed:
- A non-empty root folder exists.
- A requirements.txt file exists.
- 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 apredictfunction.
Success or failure along with any errors that caused it are shown in the ML Logs page.
.zip file
against the requirements described here. For these packages the
following two checks are performed:
- A non-empty root folder exists.
- 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, andsave.
Success or failure along with any errors that caused it are shown in the ML Logs page.
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, and arguments.
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.
AI Fabric 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.
Follow these steps to upload a new version for an already uploaded package:
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.
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:
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.
Packages can only be deleted if they are not deployed within a skill and no pipelines are currently running on those packages.
- In the ML Packages page, click ⋮ next to a package and select Delete undeployed versions. A confirmation window is displayed.
- 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
- In the ML Package > [ML Package Name] page Version tab, click ⋮ next to a package version and select Delete. A confirmation window is displayed.
- 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.
