It works by using regular old CPUs instead of expensive GPUs to process data. This means that it’s great for people who don’t have access to fancy hardware or want to save money on their computing costs.
Here’s an example of how you might use ggml: let’s say you have a dataset with some numbers in it, and you want to train a model to predict what the next number will be based on the previous ones. With ggml, you can do this by loading your data into memory (which is super fast because of its clever algorithms), training your model using regular old CPUs instead of fancy GPUs, and then saving your trained model for later use.
Here’s some code to get you started:
# Import the necessary libraries
import ggml as gm # Import the ggml library and alias it as "gm"
from sklearn.datasets import load_boston # Import the load_boston function from the sklearn.datasets library
from sklearn.model_selection import train_test_split # Import the train_test_split function from the sklearn.model_selection library
# Load the data and split it into training and testing sets
data = load_boston() # Load the boston dataset using the load_boston function and assign it to the variable "data"
X, y = data['data'], data['target'] # Assign the features (X) and target (y) data to their respective variables
X_train, X_test, y_train, y_test = train_test_split(X, y) # Split the data into training and testing sets using the train_test_split function and assign them to their respective variables
# Define a simple linear regression model using ggml's API
model = gm.Model() # Create a new model using the Model class from the ggml library and assign it to the variable "model"
model.add('input', 'x') # Add an input layer to the model with the name "x"
model.add('dense', {'units': 1}) # Add a dense layer with 1 unit to the model
model.compile({'loss': 'mean_squared_error'}, optimizer='adam') # Compile the model with the mean squared error as the loss function and the adam optimizer
# Train the model on the training data using regular old CPUs instead of fancy GPUs
history = model.fit(X_train, y_train) # Train the model on the training data and assign the training history to the variable "history"
# Evaluate the trained model on the testing data and print out some results
y_pred = model.predict(X_test) # Use the trained model to make predictions on the testing data and assign them to the variable "y_pred"
print('Mean squared error:', history['loss'][-1]) # Print out the mean squared error from the last epoch of the training history
As you can see, using ggml is super easy! Just load your data, define a simple linear regression model (or any other type of machine learning algorithm), and then train it on regular old CPUs instead of fancy GPUs. And the best part? It’s all open source and free to use! So why not give it a try today?