Quickstart
A jump-start guide to using Deep Lake.
How to Get Started with Activeloop Deep Lake in Under 5 Minutes
Version control, query, and train models while streaming your deep-learning datasets from a cloud of your choice.
Installing Deep Lake
Deep Lake can be installed through pip. By default, Deep Lake does not install dependencies for audio, video, google-cloud, and other features. Details on all installation options are available here.
Fetching Your First Deep Lake Dataset
Let's load the Visdrone dataset, a rich dataset with many object detections per image. Datasets hosted on Activeloop Platform are typically identified by host organization name followed by the dataset name: activeloop/visdrone-det-train
.
Reading Samples From a Deep Lake Dataset
Data is not immediately read into memory because Deep Lake operates lazily. You can fetch data by calling the .numpy()
or .data()
methods:
Visualizing a Deep Lake Dataset
Deep Lake enables users to visualize and interpret large datasets. The tensor layout for a dataset can be inspected using:
The dataset can be visualized in Activeloop Platform, or using an iframe in a Jupyter notebook:
Visualizing datasets in Activeloop Platform will unlock more features and faster performance compared to visualization in Jupyter notebooks.
Create your own Datasets
You can perform all of the steps above and more with your own datasets! Please check out the links below to learn more:
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