PyTorch/Deep Learning Frameworks#
DocArray can be easily integrated into PyTorch, Tensorflow, PaddlePaddle frameworks.
.tensor attributes in Document class can contain PyTorch sparse/dense tensor, Tensorflow sparse/dense tensor or PaddlePaddle dense tensor.
It means that if you store the Document on disk in
protobuf with/o compression, or transit the Document over the network in
protobuf with/o compression, the data type of
.tensor is preserved.
import numpy as np import paddle import torch from docarray import Document, DocumentArray emb = np.random.random([10, 3]) da = DocumentArray( [ Document(embedding=emb), Document(embedding=torch.tensor(emb).to_sparse()), Document(embedding=torch.tensor(emb)), Document(embedding=paddle.to_tensor(emb)), ] ) da.save_binary('test.protobuf.gz')
Now let’s load them again and check the data type:
from docarray import DocumentArray for d in DocumentArray.load_binary('test.protobuf.gz'): print(type(d.embedding))
<class 'numpy.ndarray'> <class 'torch.Tensor'> <class 'torch.Tensor'> <class 'paddle.Tensor'>
Load, map, batch in one-shot#
There is a very common pattern in the deep learning engineering: loading big data, mapping it via some function for preprocessing on CPU, and batching it to GPU for intensive deep learning stuff.
There are many pitfalls in this pattern when not implemented correctly, to name a few:
data may not fit into memory;
mapping via CPU only utilizes a single-core;
data-draining problem: GPU is not fully utilized as data is blocked by the slow CPU preprocessing step.
DocArray provides a high-level function
dataloader() that allows you to do this in one-shot, avoiding all pitfalls. The following figure illustrates this function:
Say we have a one million 32 x 32 color images, which takes 3.14GB on the disk with
compress='gz'. To process it:
import time from docarray import DocumentArray def cpu_job(da): time.sleep(2) print('cpu job done') return da def gpu_job(da): time.sleep(1) print('gpu job done') for da in DocumentArray.dataloader( 'da.protobuf.gz', func=cpu_job, batch_size=64, num_worker=4 ): gpu_job(da)
cpu job done cpu job done cpu job done cpu job done GPU job done cpu job done cpu job done GPU job done cpu job done cpu job done GPU job done cpu job done GPU job done cpu job donecpu job done