2. Offline model training, logging metrics in Dataloop

Models can be trained offline (i.e. locally, without connecting the model to the platform) with only model metrics being uploaded to the Dataloop platform for versioning and comparisons. This tutorial will walk you through how to upload metrics from model training via the SDK.

The Dataloop entities required are:

  • package

  • codebase reference

  • model (with a valid dataset ID)

2.1. Create Dataloop entities

First you need to create a dummy package, a dummy codebase reference, and a model with a valid dataset ID. The code below shows how to do this, and remember to replace <project_name> and <dataset_id> with the appropriate strings to reference your project and dataset.

import dtlpy as dl
import os
project = dl.projects.get(project_name='<project_id>')
package = project.packages.push(package_name='dummy-model-package',
model = package.models.create(model_name='My Model',
                              description='model for offline model logging',

Now that you’ve created the necessary Dataloop entities, metrics can be uploaded to the platform with model.add_log_samples function.

Here is an example uploading some dummy training data:

epoch = np.linspace(0, 9, 10)
epoch_metric = np.linspace(0, 9, 10)
for x_metric, y_metric in zip(epoch, epoch_metric):
    model.add_log_samples(samples=dl.LogSample(figure='tutorial plot',
                                               legend='some metric',

Metrics plots will appear under the “metrics” tab of your chosen model. The above code example will look like this:

https://github.com/dataloop-ai/dtlpy-documentation/blob/main/assets/images/model_management/tutorial_model_metrics.pngScreenshot of model metrics plot

Once you’ve uploaded multiple model metrics, you can compare them by checking all the relevant boxes on the left that you would like to compare.