What is Deep Lake?

9 min readFeb 4, 2024

A Deep Lake is a data lake(database) specialized for deep learning use cases where the raw data includes images, videos, audio, and other unstructured data. The raw data are then materialized into deep learning native sensorial storage format and streamed to model training across the network.

Significance of Deep Lake

Now, why is it useful? Well, here are a few ways it can make our lives easier:

1. Storing Data and Vectors for LLM Applications:
When we’re building applications using LLM , we need to handle a lot of data and vectors. Deep Lake is like a special storage space that’s really good at handling this kind of information.

2. Managing Datasets for Training Models:
When we’re training our AI models, especially those involved in deep learning, we need to manage datasets effectively. Deep Lake helps us do just that. It’s like our assistant in keeping everything organized.

3. Making Life Easier for Enterprise-Grade LLM Products:
If we’re working on big products using LLM, Deep Lake is like our superhero. It offers storage for all kinds of data — embeddings, audio, text, videos, images, pdfs, annotations, and more. Plus, it does cool things like searching for vectors in the data, streaming data while we train models (which is like teaching our AI), and keeping track of different versions of our data.

4. Works with Any Size of Data, Serverless, and Cloud-Friendly:
No matter how big our data is, Deep Lake can handle it. It’s also ‘serverless,’ meaning we don’t need to worry about managing servers. And guess what? It lets us store all our data in our own cloud — like our personal storage space for everything related to AI in law.

5. Used by Some Big Names:
Deep Lake isn’t just a random tool; it’s trusted by some big players like Intel, Airbus, Matterport, ZERO Systems, Red Cross, Yale, and Oxford. They use it because it makes dealing with AI much smoother.

Features of Deep Lake

1.Multimodal Sensor inputs:

DeepLake is described as multimodal, it means that it has the capability to handle and process information from different modalities. Modalities, in this context, typically refer to different types of data or sensory inputs, such as text, images, and possibly other forms like audio.

For example, a multimodal model like DeepLake could potentially analyze and generate content based on both textual and visual input. This versatility allows it to understand and respond to information in a more holistic manner, combining insights from various sources.

2.Serverless

Multi-Cloud Support (S3, GCP, Azure):Deep Lake is like a superhero that can work with different clouds — like S3, GCP, and Azure. It’s like saying it’s fluent in multiple cloud languages, so no matter where your data is stored, Deep Lake can handle it.

Here the MLOPs can be done on these Cloud Supports.

3 All in one

a. Query: Rapid Query with Tensor Query Language

Imagine you want to create a dataset of 1,000 images and labels, filter them based on weather conditions and objects in the scene, and then sort them by prediction error. That’s a complex task, and normally it would take a data scientist a good few hours and a ton of code to pull it off.

But here comes Tensor Query Language, a superhero in the Deep Lake world. With just one SQL command, you can perform advanced queries involving multidimensional arrays and tensors. Need to filter images based on weather and objects, adjust sizes, and order by prediction error? No problem. It’s like having a powerful tool that speaks the language of tensors and makes your data scientist life way easier.

SELECT 100 500 100 500 100 100 0 0
WHERE contains (categories,'bicycle') and weather == 'raining'
ORDER BY desc
LIMIT 1000

This is an example of the TQL you can perform on the images dataset

b.Native Compression with Lazy NumPy-like Indexing:

Now, imagine if Deep Lake could compress your data, making it take up less space, while still keeping it easily accessible when you need it. That’s the magic of Native Compression with Lazy NumPy-like Indexing. Think of it like having a super-organized filing system that saves space without making your data hard to find.

c. Materialize the Dataset

After finalizing your dataset, and you’re all set to dive into training your model. But here’s the thing — dealing with a bunch of files and copying your dataset to your computer for the actual training can be a real pain. Sometimes, your super-powerful GPUs end up twiddling their thumbs, waiting for the data to be copied over, and that’s not cool.

Now, enter the concept of “materialization of the dataset.” It’s like turning your virtual view of the dataset into something that’s ready to be used by your deep learning model. Imagine it as a magical transformation that takes your dataset and turns it into a format that’s just perfect for your GPU.

The cool part is that Deep Lake is all about making this process super-efficient. Instead of waiting around while your data is being copied from storage to your local machine, Deep Lake enables you to stream the data directly into your GPU. It’s like a fast and direct highway for your data, ensuring that your powerful GPUs are always doing something meaningful, without wasting time idly.

Each sample is a row. It is stored in a set of tensors.

d. Dataset Version Control:
Think of this as a way to keep track of different ‘editions’ of our datasets. It’s like saving different versions of a video game — you can go back to an older version if you want. Deep Lake helps us do this with our datasets.

Like Git versioning we have branches as you can see in the below example:

An example of using a branch in Deep lake

e. Dataloaders for Popular Deep Learning Frameworks:

We have Deep Learning hardware's like Nvidia GPUs or Intel CPUs to run our computations. These machines are optimized for speed, making our models learn and predict at lightning speed.

But here’s the catch — it’s not the computations themselves that are slowing us down; it’s the way we’re passing the data to these super-fast models.

Enter Python, our trusty programming language. Now, Python is fantastic for many things, but when it comes to dealing with lots of data and making sure it gets to our models without any hiccups, well, it’s showing some limitations. We’re facing challenges with multiprocessing and multithreading in Python. In simpler terms, when we try to send data to our powerful hardware for processing, Python struggles to do it efficiently, especially when we’re trying to do multiple things at once.

Here we have Deep Lake in our arsenal! It’s like the superhero sidekick to our deep learning frameworks like PyTorch, Tensorflow, or JAX. What makes it special is that it’s designed to work seamlessly with these frameworks. Deep Lake’s got this awesome data loader. Imagine it as a super-efficient delivery system. While our models are busy learning and training on those GPUs, the data loader is making sure the necessary information is flowing smoothly from our storage to the GPUs. It’s like a well-orchestrated dance, ensuring that our models always have the data they need, right when they need it.

f. Integrations with Powerful Tools:
Deep Lake is like a team player; it can work seamlessly with other powerful tools. It’s like having a favorite app that connects with all your other favorite apps to make things even more awesome.

Intergrating Modern data stack and MLOPs

f. 100+ Most-Popular Image, Video, and Audio Datasets Available in Seconds:
Imagine having access to over a hundred of the most popular image, video, and audio datasets in just a snap. Deep Lake makes it super quick and easy to get our hands on the data we need for our projects.

g. Instant Visualization Support in the Deep Lake App:
Deep Lake isn’t just about handling data behind the scenes; it also has a cool app that lets us see and understand our data instantly. It’s like having a window into the world of our datasets.

Deep Lake vs Data Lake

In Deep Lake we store different types of information like pictures, videos, annotations (extra notes or explanations), and tables of data. This Deep Lake is a bit special because it organizes all this information in a way that makes it easy and fast for powerful computer programs, especially those used for deep learning, to understand and use.

Deep lake is a subset of data lake. In a data lake you’d store lots of information, but in Deep Lake, it’s like having different folders for pictures, videos, notes, and tables. And the best part is, when we need to use this information for deep learning (which is like super-smart computer learning), Deep Lake can quickly send this data to the special programs without wasting any time or power.

Deep Lake vs Huggingface

1. Deep Lake:
Think of Deep Lake as your go-to buddy for anything related to pictures and videos — like identifying objects in an image or understanding what’s happening in a video. It’s the expert in computer vision, making it a perfect match for tasks that involve seeing and interpreting visual information.

2. HuggingFace
Now, HuggingFace is more like the expert in understanding the language of words. It’s like having a friend who’s really good with written or spoken language. HuggingFace is all about natural language processing (NLP), which means it helps us make sense of words, sentences, and text data.

So, imagine Deep Lake as your visual friend, great with images and videos, and HuggingFace as your language friend, amazing with words and text.

Now, here’s the interesting part,HuggingFace has this cool set of tools and tricks, like HuggingFace Transforms, that are like magic spells for understanding and working with language. However, these tools are not quite the same as what Deep Lake offers. Deep Lake has its own set of superpowers, but they are more tuned to dealing with images and visual stuff.

3 Vs as per Gartner to use Data Lake:

When we’re dealing with a lot of data, like a massive collection of information. Gartner talks about three big challenges with handling this huge amount of data. They call them the 3 “V”s — Volume, Variety, and Velocity.

a. Volume

Volume is about dealing with a lot, I mean, a massive amount of data. It’s way more than what we had to handle before.

b. Variety

Variety is like having different types of data — some are structured, like tables, and others are more like random bits and pieces.

c. Velocity

Velocity is about how fast this data is coming at us, like in real-time, and we need to make sense of it quickly.

Now, traditional ways of storing data struggle with these challenges. But here comes the hero: Data Lakes! Think of Data Lakes as super-smart storage systems, especially in the cloud. They help us handle huge amounts of data (Volume), store all kinds of data neatly (Variety), and deal with data coming in real fast (Velocity).

Imagine, instead of having separate places for different types of data, we put everything in one big lake, making it easier to find and use. And guess what? We don’t have to wait for a long time to analyze the data; we can do it right away, in real-time!

Data Lakes break down barriers between different data sources, so we can discover new things hidden in our data. These lakes usually sit on top of cloud services like Amazon S3 or Azure Blob Storage, making them even more powerful and flexible. So, when it comes to handling a massive load of data in the modern world, Data Lakes are like our tech superheroes!