ShaSentra LabsMVP

Integrated JupyterLab Environment

Go from idea to production-ready model, all within a familiar, powerful notebook interface connected to our high-performance compute infrastructure.

View Interactive Demo
my-experiment.ipynb
GPU: T4 - Active
15.2 / 100 GB
Python 3 (ipykernel)

Experiment Setup

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)