This a dataloader that for multiple training jobs in deep learning. The goal of this project is to avoid redundant data prep work and boost training efficiency.

• Rust
• Python
• Opencv
• PyTorch

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Install packages for server

• Install the opencv-rust
• Install the tensorflow-rust

Install packages for client

pip install -r requirements.txt 

Build the target of server

  cargo build --release

1. Run the server

./server/target/release/joader

2. Create a dataset with some keys and conditions

from dataset.dataset import Dataset as JDataset, DatasetType
channel = grpc.insecure_channel('127.0.0.1:4321')
ds = JDataset(name=name, location=location, ty=DatasetType.LMDB)
for k in keys:
    ds.add_item([str(k).encode()])
ds.create(channel)
channel.close()

3. Register the job for loading data and read data

job = Job.new(dataset_name, name='job', ip='127.0.0.1:4321')
for _ in range(dataset_len):
    data = job.next

4. Train the model with PyTorch

More examples are in client/test and unitests are in each file in server

Distributed under the MIT License. See LICENSE.txt for more information.