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Disha-Singh-Singraur/
shadow-docket

Name: shadow-docket
Availability: InStock

openenv

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Myenv Environment

A simple test environment that echoes back messages. Perfect for testing the env APIs as well as demonstrating environment usage patterns.

Quick Start

The simplest way to use the Myenv environment is through the MyenvEnv class:

python

from MyEnv import MyenvAction, MyenvEnv

try:
    # Create environment from Docker image
    MyEnvenv = MyenvEnv.from_docker_image("MyEnv-env:latest")

    # Reset
    result = MyEnvenv.reset()
    print(f"Reset: {result.observation.echoed_message}")

    # Send multiple messages
    messages = ["Hello, World!", "Testing echo", "Final message"]

    for msg in messages:
        result = MyEnvenv.step(MyenvAction(message=msg))
        print(f"Sent: '{msg}'")
        print(f"  → Echoed: '{result.observation.echoed_message}'")
        print(f"  → Length: {result.observation.message_length}")
        print(f"  → Reward: {result.reward}")

finally:
    # Always clean up
    MyEnvenv.close()

That's it! The MyenvEnv.from_docker_image() method handles:

Starting the Docker container
Waiting for the server to be ready
Connecting to the environment
Container cleanup when you call close()

Building the Docker Image

Before using the environment, you need to build the Docker image:

bash

# From project root
docker build -t MyEnv-env:latest -f server/Dockerfile .

Deploying to Hugging Face Spaces

You can easily deploy your OpenEnv environment to Hugging Face Spaces using the openenv push command:

bash

# From the environment directory (where openenv.yaml is located)
openenv push

# Or specify options
openenv push --namespace my-org --private

The openenv push command will:

Validate that the directory is an OpenEnv environment (checks for openenv.yaml)
Prepare a custom build for Hugging Face Docker space (enables web interface)
Upload to Hugging Face (ensuring you're logged in)

Prerequisites

Authenticate with Hugging Face: The command will prompt for login if not already authenticated

Options

--directory, -d: Directory containing the OpenEnv environment (defaults to current directory)
--repo-id, -r: Repository ID in format 'username/repo-name' (defaults to 'username/env-name' from openenv.yaml)
--base-image, -b: Base Docker image to use (overrides Dockerfile FROM)
--private: Deploy the space as private (default: public)

Examples

bash

# Push to your personal namespace (defaults to username/env-name from openenv.yaml)
openenv push

# Push to a specific repository
openenv push --repo-id my-org/my-env

# Push with a custom base image
openenv push --base-image ghcr.io/meta-pytorch/openenv-base:latest

# Push as a private space
openenv push --private

# Combine options
openenv push --repo-id my-org/my-env --base-image custom-base:latest --private

After deployment, your space will be available at: https://huggingface.co/spaces/<repo-id>

The deployed space includes:

Web Interface at /web - Interactive UI for exploring the environment
API Documentation at /docs - Full OpenAPI/Swagger interface
Health Check at /health - Container health monitoring
WebSocket at /ws - Persistent session endpoint for low-latency interactions

Environment Details

Action

MyenvAction: Contains a single field

message (str) - The message to echo back

Observation

MyenvObservation: Contains the echo response and metadata

echoed_message (str) - The message echoed back
message_length (int) - Length of the message
reward (float) - Reward based on message length (length × 0.1)
done (bool) - Always False for echo environment
metadata (dict) - Additional info like step count

Reward

The reward is calculated as: message_length × 0.1

"Hi" → reward: 0.2
"Hello, World!" → reward: 1.3
Empty message → reward: 0.0

Advanced Usage

Connecting to an Existing Server

If you already have a Myenv environment server running, you can connect directly:

python

from MyEnv import MyenvEnv

# Connect to existing server
MyEnvenv = MyenvEnv(base_url="<ENV_HTTP_URL_HERE>")

# Use as normal
result = MyEnvenv.reset()
result = MyEnvenv.step(MyenvAction(message="Hello!"))

Note: When connecting to an existing server, MyEnvenv.close() will NOT stop the server.

Using the Context Manager

The client supports context manager usage for automatic connection management:

python

from MyEnv import MyenvAction, MyenvEnv

# Connect with context manager (auto-connects and closes)
with MyenvEnv(base_url="http://localhost:8000") as env:
    result = env.reset()
    print(f"Reset: {result.observation.echoed_message}")
    # Multiple steps with low latency
    for msg in ["Hello", "World", "!"]:
        result = env.step(MyenvAction(message=msg))
        print(f"Echoed: {result.observation.echoed_message}")

The client uses WebSocket connections for:

Lower latency: No HTTP connection overhead per request
Persistent session: Server maintains your environment state
Efficient for episodes: Better for many sequential steps

Concurrent WebSocket Sessions

The server supports multiple concurrent WebSocket connections. To enable this, modify server/app.py to use factory mode:

python

# In server/app.py - use factory mode for concurrent sessions
app = create_app(
    MyenvEnvironment,  # Pass class, not instance
    MyenvAction,
    MyenvObservation,
    max_concurrent_envs=4,  # Allow 4 concurrent sessions
)

Then multiple clients can connect simultaneously:

python

from MyEnv import MyenvAction, MyenvEnv
from concurrent.futures import ThreadPoolExecutor

def run_episode(client_id: int):
    with MyenvEnv(base_url="http://localhost:8000") as env:
        result = env.reset()
        for i in range(10):
            result = env.step(MyenvAction(message=f"Client {client_id}, step {i}"))
        return client_id, result.observation.message_length

# Run 4 episodes concurrently
with ThreadPoolExecutor(max_workers=4) as executor:
    results = list(executor.map(run_episode, range(4)))

Development & Testing

Direct Environment Testing

Test the environment logic directly without starting the HTTP server:

bash

# From the server directory
python3 server/MyEnv_environment.py

This verifies that:

Environment resets correctly
Step executes actions properly
State tracking works
Rewards are calculated correctly

Running Locally

Run the server locally for development:

uvicorn server.app:app --reload

Project Structure

MyEnv/
├── .dockerignore         # Docker build exclusions
├── __init__.py            # Module exports
├── README.md              # This file
├── openenv.yaml           # OpenEnv manifest
├── pyproject.toml         # Project metadata and dependencies
├── uv.lock                # Locked dependencies (generated)
├── client.py              # MyenvEnv client
├── models.py              # Action and Observation models
└── server/
    ├── __init__.py        # Server module exports
    ├── MyEnv_environment.py  # Core environment logic
    ├── app.py             # FastAPI application (HTTP + WebSocket endpoints)
    └── Dockerfile         # Container image definition

Disha-Singh-Singraur/shadow-docket

Myenv Environment

Quick Start

Building the Docker Image

Deploying to Hugging Face Spaces

Prerequisites

Options

Examples

Environment Details

Action

Observation

Reward

Advanced Usage

Connecting to an Existing Server

Using the Context Manager

Concurrent WebSocket Sessions

Development & Testing

Direct Environment Testing

Running Locally

Project Structure

Disha-Singh-Singraur/
shadow-docket