Here’s an example: say you have a huge dataset with millions of rows, but your computer only has one CPU core to handle it all at once. This would mean that the program would take forever to run because it can only do one thing at a time (like reading data from disk or calculating some fancy math).
But if we use LlamaModel Configuration for Tensor Parallelism, we can split up our dataset into smaller chunks and process them simultaneously on multiple CPU cores. This means that the program will run much faster because it’s doing more things at once!
Here’s an example of how to set this up in Python:
# Import necessary libraries
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
# Load the dataset and split it into training/validation sets
# load_dataset() is a function that loads the dataset and returns the training and testing data
(x_train, y_train), (x_test, y_test) = load_dataset()
# Define a simple neural network model with one hidden layer of 10 neurons
# Sequential() creates a sequential model, Dense() adds a fully connected layer with 10 neurons, Dropout() adds a dropout layer to prevent overfitting
model = Sequential([
Dense(10, input_shape=(784,)), # input layer with 784 input nodes (28x28 pixels)
Dropout(0.2), # dropout layer with 20% dropout rate
Dense(10), # hidden layer with 10 neurons
Dropout(0.2), # dropout layer with 20% dropout rate
Dense(10) # output layer with 10 neurons (corresponding to 10 classes)
])
# Compile the model with a loss function and optimizer
# compile() configures the model for training with specified loss function and optimizer
model.compile(loss='categorical_crossentropy', optimizer='adam')
# Set up Tensor Parallelism for faster training on multiple GPUs or CPUs
# MirroredStrategy() creates a strategy to distribute the model across multiple devices
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
# Train the model using the training data and validation data
# fit() trains the model for a fixed number of epochs (iterations on a dataset)
# batch_size specifies the number of samples per gradient update
# validation_data is used to evaluate the loss and any model metrics at the end of each epoch
history = model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))
In this example, we’re loading a dataset (which could be anything from images to text data), defining a simple neural network model with one hidden layer of 10 neurons, and then compiling it using the `compile()` function. We’re also setting up Tensor Parallelism for faster training on multiple GPUs or CPUs by creating a `MirroredStrategy()`. This allows us to split our data into smaller chunks (called “tensors”) and process them simultaneously on multiple devices, which can significantly speed up the training time!
Hope that helps clarify things!