Metadata¶
Metadata provides key-value storage for datasets and columns.
Dataset Metadata¶
deeplake.Metadata
¶
Bases: ReadOnlyMetadata
Writable access to dataset and column metadata for ML workflows.
Stores important information about datasets like:
- Model parameters and hyperparameters
- Preprocessing statistics
- Data splits and fold definitions
- Version and training information
Changes are persisted immediately without requiring commit()
.
Examples:
Storing model metadata:
ds.metadata["model_name"] = "resnet50"
ds.metadata["hyperparameters"] = {
"learning_rate": 0.001,
"batch_size": 32
}
Setting preprocessing stats:
ds["images"].metadata["mean"] = [0.485, 0.456, 0.406]
ds["images"].metadata["std"] = [0.229, 0.224, 0.225]
# Set dataset metadata
ds.metadata["description"] = "Training dataset"
ds.metadata["version"] = "1.0"
ds.metadata["params"] = {
"image_size": 224,
"mean": [0.485, 0.456, 0.406],
"std": [0.229, 0.224, 0.225]
}
# Read dataset metadata
description = ds.metadata["description"]
params = ds.metadata["params"]
# List all metadata keys
for key in ds.metadata.keys():
print(f"{key}: {ds.metadata[key]}")
Column Metadata¶
deeplake.ReadOnlyMetadata
¶
Read-only access to dataset and column metadata for ML workflows.
Stores important information about datasets like: - Model parameters and hyperparameters - Preprocessing statistics (mean, std, etc.) - Data splits and fold definitions - Version and training information
Examples:
Accessing model metadata:
metadata = ds.metadata
model_name = metadata["model_name"]
model_params = metadata["hyperparameters"]
Reading preprocessing stats:
# Set column metadata
ds["images"].metadata["mean"] = [0.485, 0.456, 0.406]
ds["images"].metadata["std"] = [0.229, 0.224, 0.225]
ds["labels"].metadata["class_names"] = ["cat", "dog", "bird"]
# Read column metadata
mean = ds["images"].metadata["mean"]
class_names = ds["labels"].metadata["class_names"]
ds.commit() # Commit the changes to the dataset