domino-environments

name	domino-environments
description	Create and customize Domino Compute Environments - Docker containers defining tools, packages, and configurations. Covers Dockerfile customization, package installation, IDE configuration, DSE (Domino Standard Environments), and troubleshooting build failures. Use when installing dependencies, customizing environments, or fixing environment issues.

Domino Compute Environments Skill

Description

This skill helps users create, customize, and manage Domino Compute Environments - Docker-based containers that define the tools, packages, and configurations for workspaces, jobs, and other executions.

Activation

Activate this skill when users want to:

• Create or customize a compute environment
• Install packages or dependencies
• Configure Dockerfile instructions
• Understand environment best practices
• Troubleshoot environment build issues

What is a Compute Environment?

A Domino Compute Environment is a Docker container image that contains:

• Operating system and base tools
• Programming languages (Python, R)
• IDEs (Jupyter, VS Code, RStudio)
• Libraries and packages
• Custom configurations

Domino Standard Environments (DSEs)

Domino provides pre-built environments with common tools:

Environment	Includes
Domino Standard Environment	Python 3.9, R 4.1, Jupyter, VS Code
Domino Spark Environment	Spark 3.x, PySpark
Domino Ray Environment	Ray for distributed computing
Domino GPU Environment	CUDA, cuDNN, GPU libraries

Creating a Custom Environment

Via Domino UI

1. Go to Environments in Domino
2. Click Create Environment
3. Configure:
   • Name: Descriptive name
   • Base Image: Start from DSE or custom image
   • Dockerfile Instructions: Add customizations
4. Click Build

Dockerfile Instructions

Add instructions to customize the environment. Do NOT include FROM statement.

# Install system packages
RUN apt-get update && apt-get install -y \
    libpq-dev \
    graphviz \
    && rm -rf /var/lib/apt/lists/*

# Install Python packages
RUN pip install --no-cache-dir \
    pandas==2.0.0 \
    scikit-learn==1.3.0 \
    tensorflow==2.13.0

# Install R packages
RUN R -e "install.packages(c('tidyverse', 'caret'), repos='https://cloud.r-project.org')"

# Set environment variables
ENV MODEL_PATH=/mnt/artifacts/model.pkl

Package Installation Methods

Method 1: Dockerfile Instructions (Recommended)

Best for packages that should always be available.

RUN pip install pandas numpy scikit-learn

Pros: Fast startup, consistent environment Cons: Requires environment rebuild for changes

Method 2: requirements.txt

Packages installed at execution startup.

# requirements.txt in project root
pandas>=2.0.0
numpy>=1.24.0
scikit-learn>=1.3.0

Pros: No rebuild needed, per-project customization Cons: Slower startup

Method 3: Pre/Post-run Scripts

Custom scripts that run at execution start.

#!/bin/bash
# pre-run.sh
pip install -q custom-package

Method 4: Runtime Installation

Install during execution (temporary).

!pip install package-name

Dockerfile Best Practices

Combine RUN Commands

# Good: Single layer
RUN pip install pandas numpy scikit-learn matplotlib

# Bad: Multiple layers
RUN pip install pandas
RUN pip install numpy
RUN pip install scikit-learn

Clean Up

RUN apt-get update && apt-get install -y \
    package1 \
    package2 \
    && rm -rf /var/lib/apt/lists/*

Pin Versions

# Good: Reproducible
RUN pip install pandas==2.0.0 scikit-learn==1.3.0

# Bad: May break
RUN pip install pandas scikit-learn

Use --no-cache-dir

RUN pip install --no-cache-dir package-name

Environment Variables

Set in Dockerfile

ENV MY_VAR=value
ENV DATA_PATH=/mnt/data

Set in Domino UI

1. Go to Environment settings
2. Add environment variables
3. Variables available in all executions

Access in Code

import os
value = os.environ.get('MY_VAR', 'default')

GPU Environments

CUDA Setup

# Ensure base image has CUDA
# Add GPU-specific packages
RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

TensorFlow GPU

RUN pip install tensorflow[and-cuda]

Verify GPU Access

import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU count: {torch.cuda.device_count()}")

IDE Configuration

Custom Jupyter Config

RUN mkdir -p /home/domino/.jupyter && \
    echo "c.NotebookApp.token = ''" >> /home/domino/.jupyter/jupyter_notebook_config.py

VS Code Extensions

Pre-install extensions:

RUN code-server --install-extension ms-python.python

Environment Revisions

Domino tracks environment versions:

• Each build creates a new revision
• Can roll back to previous revisions
• Executions can specify which revision to use

View Revisions

Go to Environment > Revisions tab

Use Specific Revision

Select revision when launching workspace or job.

Troubleshooting Builds

Build Fails

1. Check Dockerfile syntax
2. Verify base image exists
3. Check network access for package downloads
4. Review build logs for specific errors

Common Errors

Package not found:

# Wrong
RUN pip install sklearn

# Right
RUN pip install scikit-learn

Permission denied:

# Run as root if needed
USER root
RUN apt-get update && apt-get install -y package
USER domino

Timeout during build:

• Reduce number of packages
• Use pre-built wheels
• Check network connectivity

Test Locally

# Build and test locally before adding to Domino
docker build -t test-env -f Dockerfile .
docker run -it test-env python -c "import pandas; print(pandas.__version__)"

Best Practices Summary

1. Start from DSE: Use Domino Standard Environment as base
2. Minimize layers: Combine RUN commands
3. Pin versions: Ensure reproducibility
4. Document changes: Comment Dockerfile instructions
5. Test before building: Verify packages work together
6. Clean up: Remove cache and temporary files
7. Regular updates: Keep packages current for security

Documentation Reference

• Customize your Environment
• Add packages to Environments
• Domino Standard Environments
• Best Practices for Environments