Skip to main content

Waffle hub

Project description

Waffle is a framework that lets you use lots of different deep learning tools through just one interface. When it comes to MLOps (machine learning operations), you need to be able to keep up with all the new ideas in deep learning as quickly as possible. But it's hard to do that if you have to write all the code yourself. That's why we started a project to bring together different tools into one framework.

Experience the power of multiple deep learning frameworks at your fingertips with Waffle's seamless integration, unlocking limitless possibilities for your machine learning projects.

Prerequisites

We've tested Waffle on the following environments:

OS Python PyTorch Device Backend Pass
Ubuntu 20.04 3.9, 3.10 1.13.1 CPU, GPU All Waffle Hub cpu test
Windows 3.9, 3.10 1.13.1 CPU, GPU All Waffle Hub cpu test
Ubuntu 20.04 3.9 1.13.1 Multi GPU Ultralytics Waffle Hub multi-gpu(ddp) test on self-hosted runner

We recommend using above environments for the best experience.

Installation

  1. Install pytorch and torchvision
  2. Install Waffle Hub
    • pip install -U waffle-hub

Example Usage

We provide both python module and CLI for Waffle Hub.

Following examples do the exact same thing.

Python Module

from waffle_hub.dataset import Dataset
dataset = Dataset.sample(
  name = "mnist_classification",
  task = "classification",
)
dataset.split(
  train_ratio = 0.8,
  val_ratio = 0.1,
  test_ratio = 0.1
)
export_dir = dataset.export("YOLO")

from waffle_hub.hub import Hub
hub = Hub.new(
  name = "my_classifier",
  task = "classification",
  model_type = "yolov8",
  model_size = "n",
  categories = dataset.get_category_names(),
)
hub.train(
  dataset = dataset,
  epochs = 30,
  batch_size = 64,
  image_size=64,
  device="cpu"
)
hub.inference(
  source=export_dir,
  draw=True,
  device="cpu"
)

CLI

wd sample --name mnist_classification --task classification
wd split --name mnist_classification --train-ratio 0.8 --val-ratio 0.1 --test-ratio 0.1
wd export --name mnist_classification --data-type YOLO

wh new --name my_classifier --task classification --model-type yolov8 --model-size n --categories [1,2]
wh train --name my_classifier --dataset mnist_classification --epochs 30 --batch-size 64 --image-size 64 --device cpu
wh inference --name my_classifier --source datasets/mnist_classification/exports/YOLO --draw --device cpu

See our documentation for more information!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

waffle_hub-0.2.10.tar.gz (83.3 kB view details)

Uploaded Source

Built Distribution

waffle_hub-0.2.10-py3-none-any.whl (94.4 kB view details)

Uploaded Python 3

File details

Details for the file waffle_hub-0.2.10.tar.gz.

File metadata

  • Download URL: waffle_hub-0.2.10.tar.gz
  • Upload date:
  • Size: 83.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for waffle_hub-0.2.10.tar.gz
Algorithm Hash digest
SHA256 71007887d9e39e3d614c812c23496b0675c325528ede40491d9782d20add1366
MD5 bb361e8e36f26635d211b2f93f064109
BLAKE2b-256 0182b2985adccc5449da008aff82d2cd36d0cccbc2bb93e2ebffb2b1cc23c040

See more details on using hashes here.

File details

Details for the file waffle_hub-0.2.10-py3-none-any.whl.

File metadata

  • Download URL: waffle_hub-0.2.10-py3-none-any.whl
  • Upload date:
  • Size: 94.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for waffle_hub-0.2.10-py3-none-any.whl
Algorithm Hash digest
SHA256 1191570c2a12ebdc9fc46f7fe9283728af800bd4d88febd4d5628f526bdf4ece
MD5 b335aa990e0f2be5f2167bef79d324e0
BLAKE2b-256 a70f35da234ba0a7bb7cd14d271444347b0b9e33f40da6dd91928481999a50d9

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page