---
type: concept
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  pageDirectory: concepts
  sources:
    - best-practices-for-deep-learning-on-databricks-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - resource-and-environment-management-for-deep-learning
    - Environment Management for Deep Learning and Resource
    - RAEMFDL
  citations:
    - file: best-practices-for-deep-learning-on-databricks-databricks-on-aws.md
title: Resource and Environment Management for Deep Learning
description: Techniques for customizing deep learning environments on Databricks at notebook, cluster, and job levels, including cluster policies to guide data scientists.
tags:
  - databricks
  - environment-management
  - devops
timestamp: "2026-06-19T09:09:34.083Z"
---

# Resource and Environment Management for Deep Learning

**Resource and Environment Management for Deep Learning** refers to the practices and tools used to configure, customize, and maintain consistent computing environments and infrastructure for deep learning workloads. Effective resource management ensures that data scientists and engineers can develop, train, and deploy models efficiently while controlling costs and maintaining reproducibility.

## Overview

Deep learning workflows require specialized hardware (GPUs), specific software libraries (TensorFlow, PyTorch, Keras), and consistent runtime configurations. Databricks provides infrastructure and tooling to manage these resources across development, training, and production phases.^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

## Customizing the Development Environment

### Notebook-Scoped Libraries

Notebook-scoped Python or R libraries allow individual users to install specific library versions for a particular notebook without affecting other users on the same cluster. This enables experimentation with different library versions or dependencies while maintaining isolation.^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

### Cluster-Level Libraries

Libraries can be installed at the cluster level to standardize versions for a team or project. This ensures all team members use the same library versions, reducing compatibility issues and supporting reproducibility.^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

### Job-Level Environments

Databricks jobs can be configured to ensure that repeated tasks run in consistent, unchanging environments. This is critical for production workflows where environment drift can cause unexpected behavior or failures.^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

## Cluster Policies

Cluster policies provide governance over cluster configuration choices. Administrators can create policies that guide data scientists toward appropriate configurations, such as:

- Using a [Single Node cluster](/concepts/single-node-clusters-for-pytorch.md) for development work
- Using an autoscaling cluster for large training jobs

This helps control costs while giving users the flexibility they need.^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

## GPU Resource Management

### A100 GPU Considerations

[A100 GPUs](/concepts/a100-gpu-support-on-databricks.md) are an efficient choice for many deep learning tasks, including training [Large Language Models (LLMs)](/concepts/large-language-models-llms-on-databricks.md), natural language processing, object detection, and recommendation engines. However, A100 GPUs typically have limited availability in cloud environments. Databricks recommends contacting your cloud provider for resource allocation or reserving capacity in advance to ensure availability.^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

### GPU Scheduling

To maximize GPU utilization for distributed training and inference, optimize GPU scheduling configurations. Proper scheduling ensures that GPU resources are used efficiently across concurrent workloads.^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

### Single Node vs. Distributed Training

A [Single Node](/concepts/single-node-ai-runtime.md) (driver-only) GPU cluster is typically fastest and most cost-effective for model development. One node with 4 GPUs is likely to be faster than 4 worker nodes with 1 GPU each, because distributed training incurs network communication overhead. Single Node clusters are ideal during fast, iterative development and for training on small to medium-sized datasets. When datasets become large enough to make training slow on a single machine, consider moving to distributed training.^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

## Databricks Runtime for Machine Learning

[Databricks Runtime for Machine Learning](/concepts/databricks-runtime-for-machine-learning.md) provides a pre-built deep learning infrastructure that includes:

- Common deep learning libraries (TensorFlow, PyTorch, Keras)
- Pre-configured GPU support including drivers and supporting libraries
- All capabilities of the Databricks workspace (cluster management, library management, Git integration, automation, and MLflow integration)

This runtime simplifies environment setup and ensures consistent configurations.^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

## Best Practices

- **Start with Single Node clusters** for development, then scale to distributed training as needed.^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]
- **Use cluster policies** to enforce resource governance and cost controls.^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]
- **Plan GPU capacity in advance**, especially for high-demand GPU types like A100s.^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]
- **Separate preprocessing from inference** to allow different hardware for each job (e.g., CPUs for ETL, GPUs for inference).^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]
- **Use [MLflow](/concepts/mlflow.md) tracking** and [autologging](/concepts/mlflow-autologging.md) to monitor experiments and maintain reproducibility.^[best-practices-for-deep-learning-on-databricks-databricks-on-aws.md]

## Related Concepts

- [Databricks Runtime for Machine Learning](/concepts/databricks-runtime-for-machine-learning.md)
- Cluster Policies
- GPU Scheduling
- [Distributed Training](/concepts/workload-yaml-for-distributed-training.md)
- [Single Node Clusters](/concepts/single-node-clusters-for-pytorch.md)
- [MLflow Tracking](/concepts/mlflow-tracking.md)
- [Deep learning best practices on Databricks](/concepts/deep-learning-best-practices-on-databricks.md)

## Sources

- best-practices-for-deep-learning-on-databricks-databricks-on-aws.md

# Citations

1. [best-practices-for-deep-learning-on-databricks-databricks-on-aws.md](/references/best-practices-for-deep-learning-on-databricks-databricks-on-aws-4a84d373.md)
