How do I install dl4clj since it's not on Clojars?

You must fork or clone the GitHub repository and build it locally, as artifacts aren't published. The README provides Maven repository setup for local use, but there's no Leiningen or Clojars support yet.

dl4clj vs using Deeplearning4j directly in Clojure?

dl4clj wraps DL4J with a Clojure-friendly DSL, reducing Java interop boilerplate for a more functional workflow. However, direct DL4J usage might be better for teams needing the latest updates or finer control over Java objects.

How to configure a multi-layer neural network with dl4clj?

Use the nn/builder function with layer maps, as shown in the examples. Specify network parameters, layer defaults, and individual layers in a declarative style, leveraging the dual API for code or object returns.

Is dl4clj suitable for production machine learning projects?

No, due to its unreleased status, incomplete tests, and ongoing refactoring. It's best for experimentation or prototyping in Clojure environments until it reaches a stable version with full documentation.

What data preprocessing capabilities does dl4clj have?

It supports importing from CSV files, converting Clojure data structures to INDArrays, and applying normalizers like standardization. Examples show record readers and dataset iterators for streamlined workflows.

Can I use dl4clj for distributed training with Apache Spark?

Yes, but cautiously. Spark support is listed as being refactored and may be unstable. The README includes Spark examples, but they require direct object handling and may lack comprehensive testing.

dl4clj — Clojure Wrapper for Deeplearning4j

What is dl4clj?

dl4clj is a Clojure wrapper for Deeplearning4j, a popular Java-based deep learning library. It provides an idiomatic Clojure interface for building, training, and evaluating neural networks, including support for data import, model configuration, and distributed training via Spark. The project solves the problem of integrating Java-centric deep learning tools into Clojure applications, offering a seamless functional programming experience.

Target Audience

Clojure developers and data scientists who want to leverage deep learning capabilities within the JVM ecosystem without leaving the Clojure environment. It's particularly useful for teams already using Clojure for data processing or backend services.

Value Proposition

Developers choose dl4clj because it provides a clean, expressive Clojure API over the robust Deeplearning4j framework, eliminating Java interop boilerplate. Its dual code/object return system allows for both pure functional workflows and direct integration with existing DL4J pipelines.

Overview

dl4clj is a Clojure port of the Deeplearning4j (DL4J) library, enabling Clojure developers to build, train, and evaluate deep learning models with a functional and expressive API. It bridges the gap between Java-based deep learning tools and the Clojure ecosystem, offering seamless integration for machine learning workflows.

Key Features

Neural Networks DSL — Define and configure neural network layers using a Clojure-friendly domain-specific language.
Data Import & Preprocessing — Load datasets from files (e.g., CSV) and convert Clojure data structures into INDArrays and datasets.
Model Training & Evaluation — Train models with early stopping, transfer learning, and evaluate performance using classification metrics.
Distributed Training — Support for Spark-based distributed training with parameter averaging and cluster management.
Model Configuration — Build single-layer and multi-layer network configurations with customizable optimizers, activations, and regularization.

Philosophy

dl4clj emphasizes a dual API that returns either code representations or direct Java objects, giving developers flexibility to work with pure data structures or interoperate with the underlying DL4J runtime as needed.

dl4clj

What is dl4clj?

Overview

Key Features

Philosophy

Related Projects

Found a gem we're missing?

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions