Dataset Integration
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This section covers the integration of the mnist
dataset into Tensorleap. We'll later use this dataset with a classification model.
Below is the full dataset script to be used in the integration. More information about the structure of this script can be found under .
To add a new Dataset Instance:
In the Dataset Editor, enter these properties:
Dataset Name: mnist
Click Save.
After saving the mnist
dataset, the platform will automatically parse the dataset script. This process evaluates the script and ensures that all its functions, including the ability to successfully read the data, are working as expected.
The purpose of this section was to help you define a dataset script and create a dataset instance in Tensorleap.
Now that the mnist
dataset has been integrated into Tensorleap, we can use it with a classification model. That's what we'll do in the next section, where we'll build a classification model.
For more information, see .
Navigate to and click the button.
Script: copy and paste the script from the above
Verify that leapcli
is installed. For more information, see .
The API_ID
, API_KEY
and the ORIGIN
, along with the full command, can easily be found by clicking the button within the view.
By default, the .tensorleap/dataset.py
file has a sample template. Let's replace it with our above. One way to do it is with vim
:
Congrats! You have successfuly created the mnist
Dataset Instance and integrated the . You can view it in the UI in the Resources Management view.
When ready, move on to .