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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.\n\nExperience the power of multiple deep learning frameworks at your fingertips with Waffle's seamless integration, unlocking limitless possibilities for your machine learning projects.\n\n# Prerequisites\nWe've tested Waffle on the following environments:\n| OS | Python | PyTorch | Device | Backend | Pass |\n|:---:|:---:|:---:|:---:|:---:|:---:|\n| Ubuntu 20.04 | 3.9, 3.10 | 1.13.1 | CPU, GPU | All | [![Waffle Hub cpu test](https://github.com/snuailab/waffle_hub/actions/workflows/ci.yaml/badge.svg)](https://github.com/snuailab/waffle_hub/actions/workflows/ci.yaml) |\n| Windows | 3.9, 3.10 | 1.13.1 | CPU, GPU | All | [![Waffle Hub cpu test](https://github.com/snuailab/waffle_hub/actions/workflows/ci.yaml/badge.svg)](https://github.com/snuailab/waffle_hub/actions/workflows/ci.yaml) |\n| Ubuntu 20.04 | 3.9 | 1.13.1 | Multi GPU | Ultralytics |[![Waffle Hub multi-gpu(ddp) test on self-hosted runner](https://github.com/snuailab/waffle_hub/actions/workflows/ddp.yaml/badge.svg)](https://github.com/snuailab/waffle_hub/actions/workflows/ddp.yaml) |\n\n\nWe recommend using above environments for the best experience.\n\n# Installation\n1. Install pytorch and torchvision\n    - [PyTorch and TorchVision](https://pytorch.org/get-started/locally/) (We recommend using 1.13.1)\n2. Install Waffle Hub\n    - `pip install -U waffle-hub`\n\n# Example Usage\nWe provide both python module and CLI for Waffle Hub.\n\nFollowing examples do the exact same thing.\n\n## Python Module\n```python\nfrom waffle_hub.dataset import Dataset\ndataset = Dataset.sample(\n  name = \"mnist_classification\",\n  task = \"classification\",\n)\ndataset.split(\n  train_ratio = 0.8,\n  val_ratio = 0.1,\n  test_ratio = 0.1\n)\nexport_dir = dataset.export(\"YOLO\")\n\nfrom waffle_hub.hub import Hub\nhub = Hub.new(\n  name = \"my_classifier\",\n  task = \"classification\",\n  model_type = \"yolov8\",\n  model_size = \"n\",\n  categories = dataset.get_category_names(),\n)\nhub.train(\n  dataset = dataset,\n  epochs = 30,\n  batch_size = 64,\n  image_size=64,\n  device=\"cpu\"\n)\nhub.inference(\n  source=export_dir,\n  draw=True,\n  device=\"cpu\"\n)\n```\n\n## CLI\n```bash\nwd sample --name mnist_classification --task classification\nwd split --name mnist_classification --train-ratio 0.8 --val-ratio 0.1 --test-ratio 0.1\nwd export --name mnist_classification --data-type YOLO\n\nwh new --name my_classifier --task classification --model-type yolov8 --model-size n --categories [1,2]\nwh train --name my_classifier --dataset mnist_classification --epochs 30 --batch-size 64 --image-size 64 --device cpu\nwh inference --name my_classifier --source datasets/mnist_classification/exports/YOLO --draw --device cpu\n```\n\nSee our [documentation](https://snuailab.github.io/waffle/) for more information!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsnuailab%2Fwaffle_hub","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsnuailab%2Fwaffle_hub","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsnuailab%2Fwaffle_hub/lists"}