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https://github.com/ehsaniara/joblet

Joblet is a micro-container runtime for running Linux jobs with: Process and filesystem isolation (PID namespace, chroot) Fine-grained CPU, memory, and IO throttling (cgroups v2) Secure job execution with mTLS and RBAC Built-in scheduler, SSE log streaming, and multi-core pinning Ideal for: Agentic AI Workloads (Untrusted code)
https://github.com/ehsaniara/joblet

cgroups containerization golang linux linux-namespaces namespaces process-isolation resource-management system system-call-isolation

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Joblet is a micro-container runtime for running Linux jobs with: Process and filesystem isolation (PID namespace, chroot) Fine-grained CPU, memory, and IO throttling (cgroups v2) Secure job execution with mTLS and RBAC Built-in scheduler, SSE log streaming, and multi-core pinning Ideal for: Agentic AI Workloads (Untrusted code)

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README 

          
# Joblet / RNX - Secure Linux Process Execution Platform



[![Tests](https://github.com/ehsaniara/joblet/actions/workflows/ci.yml/badge.svg)](https://github.com/ehsaniara/joblet/actions/workflows/ci.yml)

[![Go Report Card](https://goreportcard.com/badge/github.com/ehsaniara/joblet)](https://goreportcard.com/report/github.com/ehsaniara/joblet)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)






**Joblet** is a secure Linux job execution platform that simplifies running isolated processes with comprehensive

resource management. It handles process isolation, resource limits, GPU acceleration, and monitoring automatically,

letting you focus on your applications rather than infrastructure complexity.



> Complete Documentation: [/docs/README.md](docs/README.md)



> Provides sandboxed code execution with GPU support, resource controls, and complete process

> isolation for AI workloads.



## Key Capabilities



- **🔒 Isolation**: Run in secure sandboxes, separate namespaces, preventing interference between

  workloads

- **⚡ GPU Acceleration**: Native NVIDIA GPU support with automatic CUDA env. setup for AI/ML workloads

- **🎛️ Resource Management**: CPU, memory, I/O, and GPU resource limits with fine-grained control

- **🌐 Network Isolation**: Networking options from full isolation to custom networks for secure

  communication

- **💾 Storage Solutions**: Persistent volumes for data retention and temporary storage with automatic cleanup

- **⏰ Job Scheduling**: Execute jobs immediately, schedule for specific times, or set delayed execution

- **🛡️ Enterprise Security**: mTLS encryption with certificate-based authentication and role-based

  access control

- **🐍 Runtime Environments**: Build Python ML, Java, and custom runtime environments from declarative YAML specifications



## Getting Started



> **☁️ Cloud Deployment**: Joblet can be deployed directly on AWS EC2 with automated installation DynamoDB and

> CloudWatch

> integration. See the [AWS Deployment Guide](docs/AWS_DEPLOYMENT.md) for one-click deployment with user data scripts.



### A. Joblet Server Installation (Linux)



#### Option 1: Package Installation (Ubuntu/Debian)



```bash

# Download and install the latest release

wget $(curl -s https://api.github.com/repos/ehsaniara/joblet/releases/latest | grep "browser_download_url.*_amd64\.deb" | cut -d '"' -f 4)

sudo dpkg -i joblet_*_amd64.deb



# Start the service

sudo systemctl start joblet

sudo systemctl enable joblet

```



#### Option 2: Manual Installation (Any Linux Distribution)



```bash

# Download and extract the release

curl -L -o rnx-linux-amd64.tar.gz $(curl -s https://api.github.com/repos/ehsaniara/joblet/releases/latest | grep "browser_download_url.*linux-amd64.tar.gz" | cut -d '"' -f 4)

tar -xzf rnx-linux-amd64.tar.gz

cd rnx-linux-amd64



# Run the installation script

sudo ./install.sh

sudo systemctl start joblet

```



### B. RNX Client Installation (Cross-Platform)



#### macOS (Homebrew)



```bash

# Add the Joblet tap

brew tap ehsaniara/joblet https://github.com/ehsaniara/joblet

brew install rnx              # Installs RNX CLI



# Copy configuration from server

scp user@joblet-server:/opt/joblet/config/rnx-config.yml ~/.rnx/

```



#### Windows



```powershell

# Download the Windows binary

Invoke-WebRequest -Uri "https://github.com/ehsaniara/joblet/releases/latest/download/rnx-windows-amd64.exe" -OutFile "rnx.exe"



# Add to PATH

New-Item -ItemType Directory -Force -Path "$env:USERPROFILE\bin"

Move-Item rnx.exe "$env:USERPROFILE\bin\"

[Environment]::SetEnvironmentVariable("Path", $env:Path + ";$env:USERPROFILE\bin", [EnvironmentVariableTarget]::User)



# Copy configuration from Joblet server to %USERPROFILE%\.rnx\rnx-config.yml

```



### C. Verify Installation



```bash

# Test the connection and run your first job

rnx job list                           # List current jobs

rnx job run echo "Hello from Joblet!" # Execute a simple test job

rnx monitor status                     # Check server health



```



### D. Python SDK Usage (Alternative to CLI)



```python

# Install: pip install joblet-sdk

from joblet import JobletClient



with JobletClient(host="your-joblet-server") as client:

    # Run a job programmatically

    job = client.jobs.run_job(

        command="echo",

        args=["Hello from Python!"],

        name="python-job"

    )

    print(f"Job started: {job['job_uuid']}")

```



## 🚀 Usage Examples



### Command Line Interface



```bash

# Basic job execution with resource isolation

rnx job run echo "Hello World"

rnx job run --max-cpu=50 --max-memory=512 python3 script.py



# Build and use runtime environments

rnx runtime build ./examples/python-3.11-ml/runtime.yaml   # Build Python ML runtime

rnx runtime build ./examples/java-21/runtime.yaml          # Build Java 21 runtime

rnx job run --runtime=python-3.11-ml python analysis.py    # Use Python ML runtime

rnx job run --runtime=openjdk-21 java App.java             # Use Java runtime



# GPU acceleration for compute-intensive workloads

rnx job run --gpu=1 --gpu-memory=8GB python train_model.py

rnx job run --gpu=2 --runtime=python-3.11-ml python distributed_training.py



# File uploads and environment configuration

rnx job run --upload=data.csv --upload=script.py python3 script.py



# Environment variables (visible and secure)

rnx job run --env=NODE_ENV=production node app.js

rnx job run --secret-env=API_KEY=secret python app.py       # Hidden from logs



# Network isolation modes

rnx job run --network=none secure_task.sh                   # No network access

rnx job run --network=isolated wget https://api.com         # External access only

rnx job run --network=bridge api_server.py                  # Inter-job communication



# Persistent storage management

rnx volume create mydata --size=1GB

rnx job run --volume=mydata python3 process_data.py



# Job scheduling options

rnx job run --schedule="1hour" backup.sh

rnx job run --schedule="2025-12-25T00:00:00" maintenance.py



# Execution timeout (terminates job if exceeded)

rnx job run --timeout=5m long_running_task.sh

rnx job run --timeout=30s --max-memory=256 quick_check.sh

```



## 🎨 Web Administration Interface



The Joblet Admin UI is available as a standalone package

at [joblet-admin repository](https://github.com/ehsaniara/joblet-admin):



- **System Monitoring**: Real-time CPU, memory, disk, and network metrics with visual dashboards

- **Job Management**: Comprehensive job listing, filtering, control, and log streaming capabilities

- **Workflow Visualization**: Interactive dependency graphs and execution timelines

- **System Administration**: Intuitive volume, network, and runtime environment management

- **Direct gRPC**: Connects directly to Joblet server via gRPC



To use the admin interface:



```bash

# Clone the joblet-admin repository

git clone https://github.com/ehsaniara/joblet-admin

cd joblet-admin



# Install and run

npm install

npm run dev



# Access at http://localhost:3000

```



**Learn more**: See the [Admin UI Documentation](docs/ADMIN_UI.md)



## System Requirements



### Joblet Server (Linux)



- **Operating System**: Linux kernel 4.6+ (Ubuntu 18.04+, CentOS 8+, or equivalent distributions)

- **Hardware**: Multi-core CPU, 1GB+ RAM, 5GB+ disk space (scales with workload)

- **Network**: Port 50051 accessible for gRPC communication

- **Privileges**: Root access required for namespace and cgroup management

- **Dependencies**: cgroups v2, iptables, iproute2, bridge-utils (standard on most distributions)

- **GPU Support**: NVIDIA drivers (optional, required only for GPU workloads)



### RNX Client (Cross-Platform)



- **Operating Systems**: Linux, macOS 10.15+, Windows 10+

- **Resources**: Minimal - 50MB+ RAM, network connectivity to server port 50051

- **Distribution**: Single binary executable with no additional dependencies



## Documentation



- **[Quick Start Guide](docs/QUICKSTART.md)** - Get up and running quickly with step-by-step instructions

- **[Installation Guide](docs/INSTALLATION.md)** - Comprehensive installation procedures for all platforms

- **[AWS Deployment Guide](docs/AWS_DEPLOYMENT.md)** - Deploy Joblet on AWS EC2 with CloudWatch integration

- **[GPU Support Guide](docs/GPU_SUPPORT.md)** - Complete guide to NVIDIA GPU acceleration and CUDA integration

- **[Runtime System Guide](docs/RUNTIME_SYSTEM.md)** - Build and manage runtime environments from YAML specifications

- **[Job Execution Guide](docs/JOB_EXECUTION.md)** - Detailed job management and execution patterns

- **[RNX CLI Reference](docs/RNX_CLI_REFERENCE.md)** - Complete command reference with examples

- **[Security Guide](docs/SECURITY.md)** - Security configuration and best practices

- **[Working Examples](examples/)** - Production-ready runtime configurations and job examples



## Related Projects



- **[Joblet Admin UI](https://github.com/ehsaniara/joblet-admin)**

- **[Joblet Proto](https://github.com/ehsaniara/joblet-proto)**

- **[Joblet Python SDK](https://github.com/ehsaniara/joblet-sdk-python/)**

- **[Joblet MCP Server](https://github.com/ehsaniara/joblet-mcp-server)**



**Developing a custom client?** Start with the [protocol definitions](https://github.com/ehsaniara/joblet-proto) to

generate language-specific bindings.



## License



MIT License - see [LICENSE](LICENSE) file for details.