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Step 2: Creating Hub Datasets Manually
Step 3: Understanding Compression
Step 4: Improving Performance via "with" Syntax
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Step 6: Using Activeloop Storage
Step 7: Connecting Hub Datasets to ML Frameworks
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Step 7: Connecting Hub Datasets to ML Frameworks
Connecting Hub Datasets to machine learning frameworks such as PyTorch and TensorFlow.
You can connect Hub Datasets to popular ML frameworks such as PyTorch and TensorFlow using minimal boilerplate code.

PyTorch

You can train a model by creating a PyTorch DataLoader from a Hub Dataset using ds.pytorch() .
from Hub import Dataset
from torch.utils.data import DataLoader
​
ds = Dataset('./dataset_path') # Hub Dataset
dataloader= ds.pytorch() #PyTorch Dataloader
​
for data in dataloader:
print(data)
# Training Loop

TensorFlow - Coming Soon

Similarly, you can convert a Hub Dataset to a TensorFlow Dataset via the tf.Data API.
ds # Hub Dataset object, to be used for training
ds = ds.tensorflow() # A TensorFlow Dataset
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PyTorch
TensorFlow - Coming Soon