---
type: concept
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    - ai-runtime-databricks-on-aws.md
  confidence: 0.85
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  aliases:
    - genie-code-for-deep-learning
    - GCFDL
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  citations:
    - file: ai-runtime-databricks-on-aws.md
title: Genie Code for Deep Learning
description: An AI assistant feature that supports deep learning workloads on AI Runtime with code generation, debugging, and optimization suggestions
tags:
  - ai-assistant
  - code-generation
  - debugging
timestamp: "2026-06-19T17:31:38.295Z"
---

```yaml
---
title: Genie Code for Deep Learning
summary: An AI-assisted coding tool within Databricks that supports deep learning workloads on AI Runtime, helping with training code generation, library error resolution, optimization suggestions, and debugging.
sources:
  - ai-runtime-databricks-on-aws.md
kind: concept
createdAt: "2026-06-18T14:23:02.425Z"
updatedAt: "2026-06-18T14:23:02.425Z"
tags:
  - databricks
  - ai-assistance
  - code-generation
aliases:
  - genie-code-for-deep-learning
  - GCFDL
confidence: 1
provenanceState: extracted
inferredParagraphs: 2
---

# Genie Code for Deep Learning

**Genie Code for Deep Learning** is a feature within [[AI Runtime]] that provides AI-assisted support for deep learning workloads. It can help with generating training code, resolving library installation errors, suggesting optimizations, and debugging common issues encountered during model development on Databricks. ^[ai-runtime-databricks-on-aws.md]

## Overview

Genie Code is available as part of [[AI Runtime]], which is in [Public Preview](https://docs.databricks.com/aws/en/release-notes/release-types) for single-node tasks. ^[ai-runtime-databricks-on-aws.md] It acts as an intelligent coding assistant integrated into the notebook environment, capable of understanding the context of a deep learning task and offering targeted guidance.

For general information on using Genie Code for data science, see the Databricks documentation on [Use Genie Code for data science](https://docs.databricks.com/aws/en/notebooks/ds-agent). ^[ai-runtime-databricks-on-aws.md]

## Capabilities

Genie Code for deep learning supports the following types of assistance ^[ai-runtime-databricks-on-aws.md]:

- **Generating training code** — Producing skeleton or full training loops, data loading pipelines, and model definitions for common deep learning frameworks.
- **Resolving library installation errors** — Diagnosing package conflicts or missing dependencies and suggesting correct installation commands or environment configurations.
- **Suggesting optimizations** — Recommending performance improvements such as mixed-precision training, gradient accumulation, or better data loading strategies.
- **Debugging common issues** — Identifying mistakes in tensor shapes, loss functions, or optimizer settings and proposing fixes.

All of these capabilities are accessible directly from the Genie Agent interface in Databricks notebooks.

## Prerequisites

To use Genie Code for deep learning, you must have an active AI Runtime compute resource (notebook or job) connected. The feature is part of the Genie Code offering for data science, which requires the appropriate workspace enablement.

## Related Concepts

- [[AI Runtime]] — The GPU-accelerated compute offering that supports deep learning workloads.
- Genie Agent — The broader AI assistant for data science and engineering tasks on Databricks.
- Deep Learning — The underlying technique that Genie Code for deep learning supports.
- [[Workload YAML for Distributed Training|Distributed Training]] — Multi-GPU training strategies available on AI Runtime that Genie Code can help set up.
- [[MLflow]] — Experiment tracking and model registry integration on Databricks.

## Sources

- ai-runtime-databricks-on-aws.md
```

# Citations

1. [ai-runtime-databricks-on-aws.md](/references/ai-runtime-databricks-on-aws-a734dca1.md)
