vineyard-ml 0.16.1

Vineyard integration with machine learning frameworks

vineyard-ml: Accelerating Data Science Pipelines

Vineyard has been tightly integrated with the data preprocessing pipelines in widely-adopted machine learning frameworks like PyTorch, TensorFlow, and MXNet. Shared objects in vineyard, e.g., vineyard::Tensor, vineyard::DataFrame, vineyard::Table, etc., can be directly used as the inputs of the training and inference tasks in these frameworks.

Examples

The following examples shows how DataFrame in vineyard can be used as the input of Dataset for PyTorch:

import os

import numpy as np
import pandas as pd

import torch
import vineyard

# connected to vineyard, see also: https://v6d.io/notes/getting-started.html
client = vineyard.connect(os.environ['VINEYARD_IPC_SOCKET'])

# generate a dummy dataframe in vineyard
df = pd.DataFrame({
    # multi-dimensional array as a column
    'data': vineyard.data.dataframe.NDArrayArray(np.random.rand(1000, 10)),
    'label': np.random.rand(1000)
})
object_id = client.put(df)

# take it as a torch dataset
from vineyard.contrib.ml.torch import torch_context
with torch_context():
    # ds is a `torch.utils.data.TensorDataset`
    ds = client.get(object_id)

# or, you can use datapipes from torchdata
from vineyard.contrib.ml.torch import datapipe
pipe = datapipe(ds)

# use the datapipes in your training loop
for data, label in pipe:
    # do something
    pass

Reference and Implementation

• torch: including PyTorch datasets, torcharrow and torchdata.
• tensorflow
• mxnet

For more details about vineyard itself, please refer to the Vineyard project.