Enhancing data analysis with Deepnote and Scala

Deepnote supports a variety of programming languages through different kernels. Scala, a powerful language for data science and machine learning, can be integrated into Deepnote using the Almond kernel. We will provide detailed instructions for setting up a Scala kernel with Almond in Deepnote and demonstrate how to use TensorFlow with Scala.

Setting up the Scala kernel with Almond

To run Scala in Deepnote, you’ll need to create a custom environment that includes the Almond kernel. Follow these steps to set up the Scala kernel:

Dockerfile

FROM almondsh/almond:latest

ENV DEFAULT_KERNEL_NAME "scala213"

Tensorflow with Scala

import $ivy.`org.platanios::tensorflow:0.4.0`

import org.platanios.tensorflow.api._

// Start a new session
val session = Session()

// Define a constant tensor
val tensor = tf.constant(Tensor(3.0))

// Run the session to evaluate the tensor
val result = session.run(fetches = tensor)

// Print the result
println(result.scalar)

Example notebook

Conclusion

By following the steps outlined in this guide, you can successfully set up and run a Scala kernel with Almond in Deepnote. This allows you to leverage Scala’s powerful features for data science and machine learning tasks, including using TensorFlow with Scala.

If you encounter further issues, please get in touch with our support. Happy conquering the world with AI in Deepnote!