Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.onyx-robotics.com/llms.txt

Use this file to discover all available pages before exploring further.

onyx.save_dataset(
    name: str,
    dataset: OnyxDataset,
    source_datasets: List[Dict[str, Optional[str]]] = [],
    time_format: Literal["datetime", "s", "ms", "us", "ns", "none"] = "s"
)
Uploads an OnyxDataset to the Onyx Engine, making it available for model training.

​
Parameters

​
name
str
required
The name for the new dataset. Must be a non-empty string.
​
dataset
OnyxDataset
required
The OnyxDataset object containing the dataframe and metadata.
​
source_datasets
List[Dict[str, Optional[str]]]
default:"[]"
Source datasets used to create this dataset, for lineage tracking. Each dictionary should have:
  • name (str): Name of the source dataset
  • version_id (str, optional): Specific version, or latest if not provided
​
time_format
Literal
default:"s"
The time format of your data. Options:
  • "s" - Seconds (default)
  • "ms" - Milliseconds
  • "us" - Microseconds
  • "ns" - Nanoseconds
  • "datetime" - Python datetime objects
  • "none" - No time column

​
Returns

None. Prints a confirmation message when upload completes.

​
Raises

  • Exception: If the dataset dataframe is empty
  • Exception: If the name is an empty string
  • Exception: If a source dataset is not found in the Engine

​
Example

import pandas as pd
from onyxengine import Onyx
from onyxengine.data import OnyxDataset

# Initialize the client
onyx = Onyx()

# Create a dataset from a dataframe
df = pd.read_csv('my_data.csv')
dataset = OnyxDataset(
    dataframe=df,
    features=['acceleration', 'velocity', 'position', 'control'],
    dt=0.01
)

# Upload to the Engine
onyx.save_dataset(
    name='my_training_data',
    dataset=dataset,
    time_format='s'
)

​
With Source Tracking

from onyxengine import Onyx

onyx = Onyx()

# Load raw data
raw = onyx.load_dataset('raw_sensor_data')

# Process the data
processed_df = process(raw.dataframe)
processed = OnyxDataset(
    dataframe=processed_df,
    features=['accel', 'vel', 'pos'],
    dt=0.01
)

# Save with lineage tracking
onyx.save_dataset(
    name='processed_data',
    dataset=processed,
    source_datasets=[{'name': 'raw_sensor_data'}]
)

​
Notes

  • The dataset is saved locally before uploading (in ~/.onyx/datasets/)
  • Float64 columns are automatically converted to float32 for efficiency
  • The dataset becomes available for training once processing completes