PyTorch

Note
Neptune integrates with both pure PyTorch and many libraries from the PyTorch Ecosystem. You may want to check out the following integrations:
​PyTorch Lightning​
​Fastai and Fastai2​
​Catalyst​
​PyTorch Ignite​
​Skorch​
For the pure PyTorch integration, read on.
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​See code examples on Github​
​See  results in Neptune​
​Run example in Google Colab​
What will you learn?
You will learn how to use Neptune + PyTorch to help you keep track of your model training metadata.
With Neptune + PyTorch you can:
Log Model Configuration
Log hyperparameters
Log loss & metrics
Log training code and git information
Log images and the predictions
Log artifacts(i.e. model weights, dataset version)  
Log 2-D/3-D tensors as images or 1-D tensors as metrics.
You can log other metadata types like interactive charts, video, audio, and more. See What can you log and display?​
Quickstart
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​See code examples on Github​
​See  results in Neptune​
​Run example in Google Colab​
In this guide I will show you how to:
Install Neptune
Connect Neptune to your PyTorch model training code and create a Run
Log the training loss and metrics
Upload training scripts and .git info to Neptune 
Explore metadata that you logged on the Neptune UI
To see the full code example click on the links above, this guide only shows mainly Neptune-specific code snippets.
Step 0: Before you start
Make sure that:
You have Python 3.6+ in your system,
You are already registered user, so that you can log metadata to your private projects.
You have your NEPTUNE_API_TOKEN set to the environment variable: check this docs page.
Install neptune-client, torch, and torchvision
Depending on your operating system open a terminal or CMD and run this command:
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pip install neptune-client torch torchvision
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For more help see installing neptune-client.
This integration is tested with numpy==1.19.5, torch==1.8.1, torchvision==0.10.0, and neptune-client==0.9.16.
You need minimal familiarity with PyTorch. Go through the PyTorch tutorial to get started.
Step 1: Initialize a Neptune Run
Place this code snippet at the beginning of your script or notebook cell
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import neptune.new as neptune
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run = neptune.init(project = '<YOUR_WORKSPACE/YOUR_PROJECT>', 
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                         api_token = '<YOURR_API_TOKEN>', 
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                         source_files=['*.py'])
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This opens a new Run in Neptune that allows you to log various objects.
You need to authenticate yourself and open an existing project.  Here is how:
You can use the api_token='ANONYMOUS' and project='common/pytorch-integration' to explore without having to create a Neptune account. 
Namespaces
You can use namespace to helps you organize all your model training metadata into  folders. For more see Namespace handler.
Step 2: Log config & hyperparameters
You can assign your configuration variables and hyperparameters to Fields of a Run using "=". There are a few benefits from doing this, precisely:
Makes it easier to read your code 
And distinguish single value or Atom Fields (i.e. hyperparameters) from a multi value or Series Fields (i.e. losses and metrics) which you see next.
  For more see the Parameters and configurations or Field types page.
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run['config/dataset/path'] = data_dir
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run['config/dataset/transforms'] = data_tfms # dict() object
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run['config/dataset/size'] = dataset_size # dict() object
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run['config/model'] = type(model).__name__
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run['config/criterion'] = type(criterion).__name__
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run['config/optimizer'] = type(optimizer).__name__
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run['config/params'] = hparams # dict() object
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Here is how it will look like in the UI.
Step 3: Log losses and metrics
To log metrics and losses use .log() method in your training loop:
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for epoch in range(epochs): 
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    for i, (x, y) in enumerate(trainloader, 0):
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        # Log batch loss
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        run["training/batch/loss"].log(loss)
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        # Log batch accuracy
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        run["training/batch/acc"].log(acc)
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Step 4: Stop logging to the Neptune Run
Once you are done logging, you should stop tracking the run using the stop() method. This is needed only while logging from a notebook environment. While logging through a script, Neptune automatically stops tracking once the script has completed execution.
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run.stop()
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Step 5: Run your training script or notebook cell and monitor your training in Neptune UI
Run your script or notebook cell as you normally would.
After running your script or notebook cell you will get a link similar to:
https://app.neptune.ai/o/common/org/pytorch-integration/e/PYTOR1-66
with common/pytorch-integration replaced by your project, and PYROR-66 replaced by your run.
Click on the link to open the Run in Neptune to watch your model training live.  
Initially, it may be empty but keep the tab with the Run open to see your experiment metadata update in real-time. 
More Options
Log model Architecture & Weights
It's always helpful to you or a team member to store the model arch and best weight file. Here are a few reasons why:
Allows for a quick understanding of the model structure 
Enables reproducibility
And helps in testing against another model, dataset, or moving it to production.
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run['model_arch'].upload(f"./model_arch.txt")
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run['model_weights'].upload(f"./model.pth")
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Log Images and Predictions
Plotting images and predictions is always helpful in computer vision tasks because it allows you to visually inspect the model's predictions.
With Neptune + PyTorch you can log torch tensors and they will be displayed as images in the Neptune UI. 
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from neptune.new.types import File
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# Log image with predictions
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img = torch.rand(30, 30, 3)
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description = {"Predicted": pred, "Ground Truth": gt}
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run['torch_tensor'].upload(File.as_image(img),
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                                description = description)
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To learn what ML metadata types are supported, see What you can log and display.
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​See code examples on Github​
​See  results in Neptune​
​Run example in Google Colab​
How to ask for help?
Please visit the Getting help page. Everything regarding support is there.
Other pages you may like
You may want to check out the following integrations from the Pytorch ecosystem:
Catalyst
fastai
skorch
PyTorch Ignite