Getting Started with Hub


  1. Install Hub

    pip3 install hub
  2. Register and authenticate to upload datasets to Activeloop store

    activeloop register
    activeloop login
    # alternatively, add username and password as arguments (use on platforms like Kaggle)
    activeloop login -u username -p password
  3. Load a dataset

    import hub
    ds = hub.Dataset("activeloop/cifar10_train")
    print(ds["label", :10].compute())
    print(ds["id", 1234].compute())
    print(ds["image", 4321].compute())
  4. Create a dataset

    import numpy as np
    import hub
    from hub.schema import ClassLabel, Image
    my_schema = {
        "image": Image((28, 28)),
        "label": ClassLabel(num_classes=10),
    url = "./data/examples/quickstart" # write your {username}/{dataset_name} to make it remotely accessible
    ds = hub.Dataset(url, shape=(1000,), schema=my_schema)
    for i in range(len(ds)):
        ds["image", i] = np.ones((28, 28), dtype="uint8")
        ds["label", i] = 3
    print(ds["image", 5].compute())
    print(ds["label", 100:110].compute())

    This code creates dataset in “./data/examples/new_api_intro” folder with overwrite mode. Dataset has a thousand samples. In each sample there is an image and a label. Once the dataset is ready, you may read, write and loop over it.

    You can also transfer a dataset from TFDS (as below) and convert it from/to Tensorflow or PyTorch.

    import hub
    import tensorflow as tf
    out_ds = hub.Dataset.from_tfds('mnist', split='test+train', num=1000)
    res_ds ="username/mnist") # res_ds is now a usable hub dataset

Data Storage

Every dataset needs to specify where it is located. Hub Datasets use its first positional argument to declare its url.


If url parameter has the form of username/dataset, the dataset will be stored in our cloud storage.

url = 'username/dataset'
ds = hub.Dataset(url, shape=(1000,), schema=my_schema)

This is the default way to work with Hub datasets. Besides, you can also create or load a dataset locally or in S3, MinIO, Google Cloud Storage and Azure. In case you choose other remote storage platforms, you will need to provide the corresponding credentials as a token argument during Dataset creation or loading. It can be a filepath to your credentials or a dict.

Local storage

To store datasets locally, let the url parameter be a local path.

url = './datasets/'
ds = hub.Dataset(url, shape=(1000,), schema=my_schema)


url = 's3://new_dataset'  # your s3 path
ds = hub.Dataset(url, shape=(1000,), schema=my_schema, token={"aws_access_key_id": "...",
                                                             "aws_secret_access_key": "...",


url = 's3://new_dataset'  # minio also uses *s3://* prefix
ds = hub.Dataset(url, shape=(1000,), schema=my_schema, token={"aws_access_key_id": "your_minio_access_key",
                                                              "aws_secret_access_key": "your_minio_secret_key",
                                                              "endpoint_url": "your_minio_url:port",

Google Cloud Storage

url = 'gcs://new_dataset' # your google storage (gs://) path
ds = hub.Dataset(url, shape=(1000,), schema=my_schema, token="/path/to/credentials")


url = '' # Azure link
ds = hub.Dataset(url, shape=(1000,), schema=my_schema, token="/path/to/credentials")


Schema is a dictionary that describes what a dataset consists of. Every dataset is required to have a schema. This is how you can create a simple schema:

from hub.schema import ClassLabel, Image, BBox, Text

my_schema = {
    'kind': ClassLabel(names=["cows", "horses"]),
    'animal': Image(shape=(512, 256, 3)),
    'eyes': BBox(),
    'description': Text(max_shape=(100,))


Shape is another required attribute of a dataset. It simply specifies how large a dataset is. The rules associated with shapes are derived from numpy. As you might have noticed, shape is a universal attribute that is also present in schemas, however it is no longer required. If a schema does not have a well-definied shape, max_shape might be required.

Dataset Access, Modification and Deletion

In order to access the data from the dataset, you should use .compute() on a portion of the dataset: ds['key', :5].compute().

You can modify the data to the dataset with a regular assignment operator or by performing more sophisticated transforms.

You can delete your dataset with .delete() or through Activeloop’s app on in a dataset overview tab.

Flush, Commit and Close

Since Hub implements caching, you need to tell the program to push the final changes to permanent storage. Hub Datasets have three methods that let you do that.

The most fundamental method, .flush() saves changes from cache to the dataset final storage and does not invalidate dataset object. It means that you can continue working on your data and pushing it later on.

.commit() saves the changes into a new version of a dataset that you may go back to later on if you want to.

In rare cases, you may also use .close() to invalidate the dataset object after saving the changes.

If you prefer flushing to be taken care for you, wrap your operations on the dataset with the with statement in this fashion:

with hub.Dataset(...) as ds:

Windows FAQ

Q: Running activeloop commands results in an error with a message stating that 'activeloop' is not recognized as an internal or external command, operable program or batch file. What should I do to use such commands?

A: If you are having troubles running activeloop commands on Windows, it usually means there are issues with your PATH environmental variable and activeloop commands are only affected by this underlying problem. Regardless, there are several ways in which you can still be able to use the CLI.

Option 1. You may try running hub as a module, i.e. py -m hub and add arguments as necessary.

Option 2. You may try adding Python scripts to your path. First, you need to find out where your Python installation is located. Start from running: py --list-paths If your Python interpreter is not on the list but you can run it (despite not knowing its path), you should paste the following excerpt to Python console to find out its location:

import os
import sys

Once you know the path to the directory with the Python version you are using, adapt it to match the pattern in the command below. If you are unsure whether it is correct, check if the path exists. Finally, run this command in the command prompt (CMD):

setx /m PATH "%PATH%;C:\path\to\Python\Python3X\Scripts\"

Then refresh your CMD with:

start & exit

Now, you should be able to run activeloop commands.