In this tutorial, we will use TensorBoard and PyTorch to visualize the graph of a model we trained with PyTorch, with TensorBoard’s graphs and evaluation metrics.
PyTorch is an open source library that provides fast and flexible deep machine learning algorithms, on top of the powerful TensorFlow back-end. One of the major problems with PyTorch is the lack of a visual interface to help you track and debug the model’s training progress.
In this blog post, we will learn how to visualize graph of a PyTorch model with TensorBoard. We will create a new model and make graph of it. Then we will inspect graph of the model by TensorBoard.
PyTorch executes everything in a diagram. TensorBoard can visualize these model graphs, so you can see what they look like. TensorBoard is an integrated TensorFlow viewer that allows you to perform many tasks, from visualizing the structure of your model to tracking training progress. In this article we will learn how to use TensorBoard to visualize the PyTorch model, and we will also look at the structure of the model we created. To install TensorBoard for PyTorch, use the following command: pip install tensorboard Once TensorBoard is installed, it allows you to store PyTorch models and metrics in the catalog for viewing in the TensorBoard user interface. Scalars, images, histograms, graphs, and integration visualizations are supported for PyTorch models.
ModelModel
We will define a simple model architecture in this tutorial. model = nn.Sequential( nn.Conv2d(3, 32, kernel_size=3, padding=1), nn.ReLU(), nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.MaxPool2d(2, 2), # Output: 64 x 16 x 16 nn.BatchNorm2d(64), nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.MaxPool2d(2, 2), # Output: 128 x 8 x 8 nn.BatchNorm2d(128), nn.Flatten(), nn.Linear(8*8*128, 1024), nn.ReLU(), nn.Linear(1024, 10)) A printout of the model will give you an idea of the different layers involved and their properties.
You won’t get as much detail about the model as you would with model.summary() in Keras, since PyTorch doesn’t have model.summary(). Torch-summary provides information in addition to that provided by print(your_model) in PyTorch, similar to the model.summary() API in Tensorflow for viewing models. the torchsummary import summary …….. summary(model,input_size=(3,32,32)) 
The summary must accept the size entered, and the batch size is set to -1, which is any batch size we specify.
Inclusion of model overview
The TensorBoard is a web interface that reads and displays data from a file. To make this task easier, PyTorch has a helper class called SummaryWriter. The SummaryWriter class is your main input for storing data for display in TensorBoard. from torch.utils.tensorboard import SummaryWriter …… writer=SummaryWriter(‘/content/logsdir’) The SummaryWriter class provides a high-level API for creating an event file in a specified directory and adding summaries and events to that file. The class updates the contents of the file asynchronously. This allows the training program to call methods directly from the training to add data to the file without slowing down the training.
Starting a TensorBoard
When we run the TensorBoard command, we pass an argument that tells TensorBoard where the data is located. To start TensorBoard, we need to run the TensorBoard command in the terminal. tensorboard –logdir = content/logsdir This starts a local server that serves the TensorBoard user interface based on the data written to disk by SummaryWriter. If you go to https://localhost:6006, you should see the following.
Here we see a graph of our network. One of the strengths of TensorBoard is the ability to visualize complex model structures. Let’s visualize the model we built. Double-click Sequential to see its extension and a detailed view of the individual operations that make up the model.
With a double click we can zoom out. Note that the graph is inverted; the data goes from bottom to top, and is therefore inverted with respect to the code. You can see that the graph closely matches the PyTorch model definition, with additional edges to other compute nodes. We can expand any of these blocks by clicking on the plus sign to see more details. For example, if I unfold the Conv2d block, we see that it is composed of several subcomponents. We can scroll the screen to zoom in or out, and click and drag to pan. 
You can also display the metadata by clicking on the node. This allows you to see inputs, outputs, shapes and other details.
Run this code in Google Colab
PyTorch is a deep learning framework for Python. It’s a pure Python library that’s easy to understand, and it also supports the efficient execution of complex neural networks on the GPU. The TensorBoard is a monitoring and visualization tool that provides information about your running neural network. It’s useful for debugging, and the visual graphs help you understand what’s happening inside the running neural network.. Read more about conda install tensorboard pytorch and let us know what you think.
Frequently Asked Questions
Can I use TensorBoard with PyTorch?
PyTorch is a high-level Python library for high-performance, production-grade machine learning. But what if you want to visualize your model graph? You can’t do it with Bokeh because it is not available for PyTorch, and you can’t do it with Numpy because it is not available for PyTorch. For that reason, TensorFlow has a visualization API named TensorBoard that is available for PyTorch as well. This tutorial shows how you can visualize your model graph using TensorBoard. PyTorch is an open source machine learning library that offers a new and intuitive way of developing deep learning models. The library is supported by Google and Facebook, and it is used to build AI and deep learning models used for recommendation systems, computer vision, natural language processing, image recognition and other applications. While the library comes with its own built-in Jupyter notebook, we often want to visualize the deep learning model graph and save it as a file for future reference. In this blog post, I will show you how to visualize the deep learning model graph using tensorboard, a popular open source visualization tool used to monitor deep learning models.
How do you visualize a graph in TensorBoard?
There is a lot of replay on TensorBoard in the machine learning community, since it is one of the most commonly used products for visualizing ML models. However, I think it’s still not that well known. So, I decided to write this blog post to show how to use TensorBoard to visualize a PyTorch model graph. In TensorBoard, every step of GPU computation is visualized on a graph. You can inspect all of the nodes of the graph and break the computation down into individual operations. This helps you understand what is happening and why.
How do you use TensorBoard with PyTorch?
Deep Learning is an incredibly powerful tool when it comes to machine learning and image processing, especially when it comes to computer vision. PyTorch, which is a Python based neural networks library, is quickly becoming a go-to choice for machine learning and deep learning enthusiasts. The main benefit of using a deep learning library is that it is very easy to implement and train a machine learning model. The downside is that it’s hard to interpret what the model is actually doing. What if you could visualize the data flowing from your TensorFlow model using a graph? What if you could get an overview of your network? What if you could see your model make predictions?
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