This notebook demonstrates loading data and training a simple TensorFlow model on the ShaSentra Labs platform. The environment is connected to a T4 GPU instance.
# Load your dataset directly from integrated storage
import pandas as pd
df = pd.read_csv('/shasentra_storage/my_data.csv')
df.head()
# Train a simple model using GPU resources
import tensorflow as tf
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
# The platform automatically detects and uses available GPUs
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)