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Documentation Index

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Use this file to discover all available pages before exploring further.

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Overview

Neurenix provides comprehensive Docker integration for containerizing models and applications, making deployment and scaling easier across different environments.

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Quick Start

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Build a Docker Image

from neurenix.docker import ImageBuilder

builder = ImageBuilder()

# Create a Neurenix-ready Dockerfile
builder.create_neurenix_dockerfile(
    output_path="./Dockerfile",
    cuda=False,  # Set to True for GPU support
    python_version="3.9",
    additional_packages=["fastapi", "uvicorn"]
)

# Build the image
image_id = builder.build(
    tag="neurenix-model:latest",
    context="."
)

​
Run a Container

from neurenix.docker import Container, ContainerConfig

config = ContainerConfig(
    image="neurenix-model:latest",
    ports={"8000/tcp": "8000"},
    environment={
        "MODEL_PATH": "/app/model.nx",
        "DEVICE": "cpu"
    },
    volumes={
        "/app/models": {
            "./models": "rw"
        }
    }
)

container = Container()
container_id = container.create(config)
container.start()

print(f"Container {container_id} started")

​
ImageBuilder

The ImageBuilder class helps create and build Docker images.

​
Creating Custom Dockerfiles

from neurenix.docker import ImageBuilder

builder = ImageBuilder()

commands = [
    "apt-get update && apt-get install -y git",
    "pip install --no-cache-dir numpy scipy",
    "mkdir -p /app/models",
    "COPY model.nx /app/models/",
    "COPY serve.py /app/"
]

dockerfile_path = builder.create_dockerfile(
    base_image="python:3.9-slim",
    commands=commands,
    output_path="./Dockerfile",
    workdir="/app",
    env={"PYTHONUNBUFFERED": "1"},
    expose=["8000"],
    cmd=["python", "serve.py"]
)

​
Creating Neurenix-Specific Dockerfiles

# CPU-only deployment
builder.create_neurenix_dockerfile(
    output_path="./Dockerfile",
    cuda=False,
    python_version="3.9",
    neurenix_version="1.0.0",  # Specify version
    additional_packages=["fastapi", "uvicorn", "onnx"],
    entrypoint=["python"],
    cmd=["serve.py"]
)

# GPU deployment with CUDA
builder.create_neurenix_dockerfile(
    output_path="./Dockerfile.cuda",
    cuda=True,
    python_version="3.9",
    additional_packages=["cupy-cuda116"],
    entrypoint=["python"],
    cmd=["serve.py"]
)

​
Building Images

builder = ImageBuilder(path="./my-app")

# Basic build
image_id = builder.build(tag="myapp:latest")

# Build with arguments
image_id = builder.build(
    tag="myapp:v1.0",
    dockerfile="./Dockerfile",
    context=".",
    build_args={
        "NEURENIX_VERSION": "1.0.0",
        "PYTHON_VERSION": "3.9"
    },
    no_cache=False,
    pull=True,
    platform="linux/amd64",
    labels={
        "version": "1.0",
        "maintainer": "team@example.com"
    }
)

​
Image Management

The Image class provides operations for managing Docker images.

​
Working with Images

from neurenix.docker import Image

image = Image("neurenix-model:latest")

# Check if image exists
if image.exists():
    print("Image found")
else:
    image.pull()  # Pull from registry

# Tag an image
image.tag("myregistry.com/neurenix-model:v1.0")

# Push to registry
image.push()

# Inspect image
info = image.inspect()
print(f"Created: {info['Created']}")
print(f"Size: {info['Size']} bytes")

# Save to file
image.save("neurenix-model.tar")

# Load from file
image.load("neurenix-model.tar")

# Remove image
image.remove(force=True)

​
Container Management

The Container class provides comprehensive container lifecycle management.

​
Container Configuration

from neurenix.docker import ContainerConfig

config = ContainerConfig(
    image="neurenix-model:latest",
    command=["python", "serve.py"],
    entrypoint=["python"],
    environment={
        "MODEL_PATH": "/app/model.nx",
        "PORT": "8000",
        "LOG_LEVEL": "info"
    },
    volumes={
        "/app/models": {"./models": "ro"},
        "/app/data": {"./data": "rw"}
    },
    ports={
        "8000/tcp": "8000",
        "8001/tcp": "8001"
    },
    working_dir="/app",
    user="neurenix",
    network="bridge",
    hostname="neurenix-server",
    memory="2g",
    cpus=2.0
)

​
GPU Support

# Enable GPU access
config = ContainerConfig(
    image="neurenix-model:cuda",
    gpu=True,  # Enable all GPUs
    runtime="nvidia",
    environment={"CUDA_VISIBLE_DEVICES": "0,1"}
)

container = Container()
container.create(config)
container.start()

​
Container Operations

from neurenix.docker import Container

container = Container()

# Create container
container_id = container.create(config)
print(f"Created: {container_id}")

# Start container
container.start()

# Stop container
container.stop(timeout=10)

# Get logs
logs = container.logs(tail="100")
print(logs)

# Follow logs in real-time
logs = container.logs(follow=True)

# Execute command in container
output = container.exec(["ls", "-la", "/app"])
print(output)

# Interactive execution
container.exec("bash", interactive=True)

# Inspect container
info = container.inspect()
print(f"Status: {info['State']['Status']}")
print(f"IP: {info['NetworkSettings']['IPAddress']}")

# Wait for container to exit
exit_code = container.wait()
print(f"Exit code: {exit_code}")

# Copy files to/from container
container.copy_to("./model.nx", "/app/model.nx")
container.copy_from("/app/logs/app.log", "./app.log")

# Remove container
container.remove(force=True)

​
Complete Deployment Example

Here’s a complete workflow for deploying a Neurenix model:

​
1. Create Application Structure

my-neurenix-app/
├── Dockerfile
├── requirements.txt
├── serve.py
├── model.nx
└── models/

​
2. Create Serving Script

# serve.py
import os
from neurenix import load_model
from neurenix.api_support import create_rest_server

model_path = os.getenv("MODEL_PATH", "model.nx")
port = int(os.getenv("PORT", "8000"))

print(f"Loading model from {model_path}")
model = load_model(model_path)

print(f"Starting server on port {port}")
server = create_rest_server(host="0.0.0.0", port=port)
server.add_model("model", model)
server.start()

# Keep running
import time
try:
    while True:
        time.sleep(1)
except KeyboardInterrupt:
    server.stop()

​
3. Build and Deploy

from neurenix.docker import ImageBuilder, Container, ContainerConfig

# Create Dockerfile
builder = ImageBuilder()
builder.create_neurenix_dockerfile(
    output_path="./Dockerfile",
    additional_packages=["fastapi", "uvicorn"],
    cmd=["python", "serve.py"]
)

# Build image
image_id = builder.build(
    tag="my-neurenix-app:latest",
    context="."
)

# Create and start container
config = ContainerConfig(
    image="my-neurenix-app:latest",
    ports={"8000/tcp": "8000"},
    environment={
        "MODEL_PATH": "/app/model.nx",
        "PORT": "8000"
    },
    volumes={
        "/app": {".": "rw"}
    },
    memory="2g",
    cpus=2.0
)

container = Container()
container.create(config)
container.start()

print("Model server running at http://localhost:8000")

​
Docker Compose Integration

Create a docker-compose.yml for multi-container deployments: 
version: '3.8'

services:
  neurenix-api:
    build: .
    image: neurenix-model:latest
    ports:
      - "8000:8000"
    environment:
      - MODEL_PATH=/app/models/model.nx
      - DEVICE=cuda
    volumes:
      - ./models:/app/models:ro
      - ./logs:/app/logs:rw
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    restart: unless-stopped

  nginx:
    image: nginx:alpine
    ports:
      - "80:80"
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf:ro
    depends_on:
      - neurenix-api

​
Registry Operations

from neurenix.docker import Registry, RegistryAuth

# Authenticate to registry
auth = RegistryAuth(
    username="myuser",
    password="mypass",
    registry="myregistry.com"
)

registry = Registry(auth)

# Push image
registry.push("neurenix-model:latest")

# Pull image
registry.pull("myregistry.com/neurenix-model:v1.0")

​
Best Practices

  1. Multi-Stage Builds: Use multi-stage Dockerfiles to reduce image size
  2. Layer Caching: Order Dockerfile commands from least to most frequently changing
  3. Security: Run containers as non-root users
  4. Resource Limits: Set memory and CPU limits to prevent resource exhaustion
  5. Health Checks: Implement health check endpoints for container orchestration
  6. Logging: Use structured logging and mount log volumes
  7. Secrets Management: Use Docker secrets or environment variables for sensitive data
  8. Image Tagging: Use semantic versioning for image tags

​
Troubleshooting

​
Check Docker Installation

The container classes automatically check for Docker: 
try:
    container = Container()
except RuntimeError as e:
    print(f"Docker error: {e}")
    # Install Docker or start Docker daemon

​
Debug Container Issues

# View detailed logs
logs = container.logs(tail="all")
print(logs)

# Inspect container state
info = container.inspect()
print(f"State: {info['State']}")
print(f"Exit Code: {info['State']['ExitCode']}")

# Execute diagnostic commands
container.exec(["df", "-h"])  # Check disk space
container.exec(["free", "-m"])  # Check memory
container.exec(["nvidia-smi"])  # Check GPU (if available)

​
Next Steps

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