See here for more details on using tf.data to optimize your input pipelines”. In this case, let’s cache the training dataset and prefetch the data to ensure that there is always data available for the GPU to process.

Use the tf.data API to optimize the input pipeline. If you’re using GPU the documentation of TensorBoard says “As a general rule of thumb, it is a good idea to always keep the device (GPU/TPU) active. The trace-viewer cracks, parallels and executes all operations in CPU and/or GPU to train our neural network. You may control the graph by clicking in the button w to zoom in, s to zoom out, d to scroll right and e to scroll left. The following image shows the powerboard. Now let’s have some fun by cracking our heads to understand the bottlenecks using the trace-viewer and powerboard tools. I’ll create the “teste” directory inside of tmp, as follows: evaluate(test_images, test_labels, verbose = 2)īefore you run the script let’s make a directory in tmp files, so go to /tmp by doing cd /tmp and make a directory inside. Validation_data =(test_images, test_labels)) fit(train_images, train_labels, epochs = 2,batch_size = 64, SparseCategoricalCrossentropy(from_logits = True), Train_images, test_images = train_images / 255.0, test_images / 255.0Ĭlass_names = [ 'airplane', 'automobile', 'bird', 'cat', 'deer', # Normalize pixel values to be between 0 and 1

(train_images, train_labels), (test_images, test_labels) = datasets. If you’re having trouble installing tensorflow follow the link to a blog post that teaches you how to install tensorflow in the Power architecture:įor the installation of PowerBoard Plugin acess the site, or use the following pip command:įrom tensorflow.keras import datasets, layers, models PowerBoard is a plugin designed to show the power that is consumed while the neural network is trained, doing that allows the trace-viewer a better understanding of the model and helps to debug the bottlenecks. In this blog post, we’ll show how to use the plugin we’ve created, which adds a new feature to the TensorBoard, this plugin assists on debugging bottlenecks in conjunction with the trace-viewer. TensorBoard is a tool to view models which were created in TensorFlow, the TensorBoard is a toolkit that allows graphic visualization of your models, making it easier to understand the used model, debug bottlenecks and, as a result, optimize it. Investigating tools for deep learning and machine learning we found TensorBoard.

The architecture Power was designed for use in artificial intelligence and deep learning. First we need to talk about the motivation behind this plugin.

Tensorflow board view values