Introduction The distributed package included in PyTorch(i.e., torch.distributed) enables researchers and practitioners to easily parallelize their computations across processes and clusters of machines. To do so, it leverages message passing semantics allowing each process to communicate data to any of the other processes. Setup In order to get started we need the ability to run multiple proces..

import tensorflow as tf import numpy as np from tensorflow import keras The Layer class: the combination of state (weights) and some computation A layer encapsulates both a state (the layer's "weights") and a transformation from inputs to outputs (a "call", the layer's forward pass). class CustomLinear1(keras.layers.Layer): def __init__(self, d_in, d_out): super().__init__() w_init = tf.random_n..

The main idea is that a deep learning model is usually a directed acyclic graph(DAG) of layers. So the functional API is a way to build graphs of layers. import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers Create model by funtional API To build this model using the functional API: Creating an input node(keras.input) which return information about the shape an..

You have learned about tensors, variables, gradient tape, and modules. In this guide, you will fit these all together to train models. import tensorflow as tf import matplotlib.pyplot as plt Solving machine learning problems Solving a machine learning problem usually consists of the following steps: Obtain training data Define the model Define a loss function Run through the training data, calcu..

To do machine learning in TensorFlow, you are likely to need to define, save, and restore a model. A model is, abstractly: A function that computes something on tensors (a forward pass) Some variables that can be updated in response to training import tensorflow as tf from datetime import datetime %load_ext tensorboard Defining models and layers in TensorFlow In TensorFlow, most high-level imple..

This guide goes beneath the surface of TensorFlow and Keras to demonstrate how TensorFlow works. In this guide, you'll learn How TensorFlow allows you to make simple to your code to get graphs How graphs are stored and represented How you can use them to accelerate your models This is a big-picture overview that covers how tf.function allows you to switch from eager execution to graph execution...