Learn how to export your PyTorch Lightning models to the ONNX format so that you can run them on a variety of devices and platforms.
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This guide walks through how to export a PyTorch Lightning model to ONNX format. Once the model is in ONNX format, it can be used with a variety of tools and platforms for inference.
ONNX is a standard format for representing deep learning models that allows them to be easily transferred between different frameworks and tools. By exporting to ONNX format, you can use your PyTorch Lightning model with a variety of other frameworks and tools for inference.
To export a PyTorch Lightning model to ONNX format, you will need to install the pytorch-onnx package. This package can be installed via pip:
pip install pytorch-onnx
Once the package is installed, you can export your model using the onnx.export() function. This function takes in three arguments:
-The PyTorch Lightning model that you want to export (required)
-The input shape of your model (required)
-The file path where you want to save the exported model (required)
For example, if you have a PyTorch Lightning model named “MyModel” and you want to save the exported model to the “models” directory, you would use the following code:
onnx.export(MyModel, input_shape=(1, 3, 224, 224), file_path=”models/MyModel.onnx”)
What is PyTorch Lightning?
PyTorch Lightning is a lightweight framework for PyTorch that helps users scale models and automate training. With PyTorch Lightning, users can easily export their models to the ONNX format. This tutorial will show you how to export PyTorch Lightning models to ONNX.
What is ONNX?
ONNX is an open source format for deep learning models that allows them to be interoperable between different frameworks and tools. PyTorch Lightning is a popular deep learning framework that can be used with ONNX to export models for inference. This guide will show you how to export your PyTorch Lightning models to ONNX.
Why Export PyTorch Lightning Models to ONNX?
Pytorch Lightning is a great tool for training Pytorch models, but one of its drawbacks is that it doesn’t natively support exporting models to other formats. However, you can export Pytorch Lightning models to the ONNX format using the onnxruntime library.
ONNX is a standard format for representing deep learning models that can be used by a variety of different frameworks and tools. By exporting your Pytorch Lightning model to ONNX, you’ll be able to use it with any tool that supports the ONNX format.
Additionally, by exporting your model to ONNX you’ll be able to take advantage of optimizers and other tools that only support the ONNX format. So if you’re looking to get the most out of your Pytorch Lightning models, exporting them to ONNX is a great option.
How to Export PyTorch Lightning Models to ONNX?
PyTorch Lightning is a great tool for prototyping and developing deep learning models. It makes working with PyTorch much simpler and easier, and has a lot of new features that make deep learning development faster and easier.
One of the great things about PyTorch Lightning is that it can export models to the ONNX format. ONNX is a standard format for representing deep learning models that can be used by a variety of different frameworks and tools.
In this tutorial, we’ll show you how to export a PyTorch Lightning model to ONNX. We’ll also show you how to use the exported model in another framework, such as TensorFlow or Caffe2.
### Step 1: Install PyTorch Lightning
If you don’t already have PyTorch Lightning installed, you can install it with pip:
pip install pytorch-lightning
### Step 2: Train your Model with PyTorch Lightning
First, we need to train a model with PyTorch Lightning. For this tutorial, we’ll use the MNIST dataset and train a simple convolutional neural network (CNN).
You can find the full code for this in the `mnist_cnn.py` file in the `examples/` directory of the PyTorch Lightning repository. We won’t go into too much detail about how this model works here, but feel free to take a look at the code if you’re interested.
Here’s what training this model with PyTorch Lightning looks like:
os.environ[‘CUDA_VISIBLE_DEVICES’]=’0′ ## set gpu device number to 0 if needed (1) ”’ Set path according to your system”’ ‘/content/drive/My Drive/Lightning_logs/’ from pytorch_lightning import Trainer (2) from pytorch_lightning import loggers from test_tube import Experiment ”’You will need experiment for saving logs”’ exp = Experiment(save_dir=’/content/drive/My Drive /Lightning_logs’) logger = loggers.TensorBoardLogger(‘experiment’, exp) trainer = Trainer(max_epochs=5, logger=logger) trainer.fit(model) ”’If needed uncomment following line otherwise data parallel will take care of it.”’ trainer.save_checkpoint(“path where u want ur checkpoint file “) ”’I’ve chosen my google drive as storage so I have provided full path.”’ “` If You are not using google colab or any other cloud services then skip line 1 . comments start from ## . Other steps are same . at last Don’t forget to save checkpoint file which will be used later while exporting onnx format . This way I ran my code on colab . I hope same will work on your system too provided u installed all dependencies which are required for running pytorh-lightning . Now , let’s take look what each line does : **Line 1** : Sets device number if cuda is available otherwise removes that line from code . In my case I am using colab which provides free gpu services so i am setting device number as 0but If you are not using any cloud services then skip Line 1 or if cuda isnt available on system then also skip that line because by default cpu will be used for training which is time taking process training mnist dataset on cpu took arround 9hr but gpu only 4mins **Line 2** : Imports Trainer class which will be used further lines down in script **Line 3** : Loggers help in visualization part while training like mlflow , neptune , Sacred , comet ml etc In our case we’ve imported TensorBoardLogger because later in script we created an Experiment object which needs logger object **Line 4** : Imports Experiment class from test tube library which provides many functionalities like creating experiment object , drought example hyper parameter values etc Which also needs logger object as input **Line 5** & 6 : As comments suggest these lines creates an experiment object like mlflow experiments and provides path where logs should get saved Test tube also helps us saving hyperparameters values using hyperparam tool objects but unfortunately it doesn’t support pytorh-lightning yet hopefully they might consider adding17 functionality soon **Line 7 & 8** : These lines creates logger object of type tensorboard which writes logs in tensorboard style format during training process We generally use mlflow ui
We’ve seen how to export PyTorch Lightning models to ONNX format. We’ve also looked at some of the benefits of using ONNX format, including its compatibility with a variety of frameworks and its support for hardware acceleration.
Keyword: How to Export PyTorch Lightning Models to ONNX