333 lines
7.3 KiB
Markdown
333 lines
7.3 KiB
Markdown
# Secure ML Runner
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Fast, secure ML experiment runner using Podman isolation with optimized package management.
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## 🚀 Why Secure ML Runner?
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### **⚡ Lightning Fast**
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- **6x faster** package resolution than pip
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- **Binary packages** - no compilation needed
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- **Smart caching** - faster subsequent runs
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### **🐍 Data Scientist Friendly**
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- **Native environment** - Isolated ML workspace
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- **Popular packages** - PyTorch, scikit-learn, XGBoost, Jupyter
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- **Easy sharing** - `environment.yml` for team collaboration
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### **🛡️ Secure Isolation**
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- **Rootless Podman** - No daemon, no root privileges
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- **Network blocking** - Prevents unsafe downloads
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- **Package filtering** - Security policies enforced
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- **Non-root execution** - Container runs as limited user
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## 🧪 Automated Testing
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The podman directory is now automatically managed by the test suite:
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### **Workspace Management**
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- **Automated Sync**: `make sync-examples` automatically copies all example projects
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- **Clean Structure**: Only contains synced example projects in `workspace/`
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- **No Manual Copying**: Everything is handled by automated tests
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### **Testing Integration**
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- **Example Validation**: `make test-examples` validates project structure
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- **Container Testing**: `make test-podman` tests full workflow
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- **Consistency**: Tests ensure workspace stays in sync with examples/
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### **Workspace Contents**
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The `workspace/` directory contains:
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- `standard_ml_project/` - Standard ML example
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- `sklearn_project/` - Scikit-learn example
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- `pytorch_project/` - PyTorch example
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- `tensorflow_project/` - TensorFlow example
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- `xgboost_project/` - XGBoost example
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- `statsmodels_project/` - Statsmodels example
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> **Note**: Do not manually modify files in `workspace/`. Use `make sync-examples` to update from the canonical examples in `tests/examples/`.
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## 🎯 Quick Start
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### 1. Sync Examples (Required)
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```bash
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make sync-examples
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```
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### 2. Build the Container
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```bash
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make secure-build
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```
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### 3. Run an Experiment
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```bash
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make secure-run
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```
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### 4. Start Jupyter (Optional)
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```bash
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make secure-dev
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```
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### 5. Interactive Shell
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```bash
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make secure-shell
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```
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| Command | Description |
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| ------------------- | -------------------------- |
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| `make secure-build` | Build secure ML runner |
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| `make secure-run` | Run ML experiment securely |
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| `make secure-test` | Test GPU access |
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| `make secure-dev` | Start Jupyter notebook |
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| `make secure-shell` | Open interactive shell |
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## 📁 Configuration
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### **Pre-installed Packages**
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```bash
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# ML Frameworks
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pytorch>=1.9.0
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torchvision>=0.10.0
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numpy>=1.21.0
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pandas>=1.3.0
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scikit-learn>=1.0.0
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xgboost>=1.5.0
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# Data Science Tools
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matplotlib>=3.5.0
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seaborn>=0.11.0
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jupyter>=1.0.0
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```
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### **Security Policy**
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```json
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{
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"allow_network": false,
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"blocked_packages": ["requests", "urllib3", "httpx"],
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"max_execution_time": 3600,
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"gpu_devices": ["/dev/dri"],
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"ml_env": "ml_env",
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"package_manager": "mamba"
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}
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```
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## 📁 Directory Structure
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```
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podman/
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├── secure-ml-runner.podfile # Container definition
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├── secure_runner.py # Security wrapper
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├── environment.yml # Environment spec
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├── security_policy.json # Security rules
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├── workspace/ # Experiment files
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│ ├── train.py # Training script
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│ └── requirements.txt # Dependencies
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└── results/ # Experiment outputs
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├── execution_results.json
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├── results.json
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└── pytorch_model.pth
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```
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## 🚀 Usage Examples
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### **Run Custom Experiment**
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```bash
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# Copy your files
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cp ~/my_experiment/train.py workspace/
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cp ~/my_experiment/requirements.txt workspace/
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# Run securely
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make secure-run
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```
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### **Use Jupyter**
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```bash
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# Start notebook server
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make secure-dev
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# Access at http://localhost:8888
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```
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### **Interactive Development**
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```bash
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# Get shell with environment activated
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make secure-shell
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# Inside container:
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conda activate ml_env
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python train.py --epochs 10
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```
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## <20>️ Security Features
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### **Container Security**
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- **Rootless Podman** - No daemon running as root
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- **Non-root user** - Container runs as `mlrunner`
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- **No privileges** - `--cap-drop ALL`
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- **Read-only filesystem** - Immutable base image
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### **Network Isolation**
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- **No internet access** - Prevents unsafe downloads
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- **Package filtering** - Blocks dangerous packages
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- **Controlled execution** - Time and memory limits
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### **Package Safety**
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```bash
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# Blocked packages (security)
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requests, urllib3, httpx, aiohttp, socket, telnetlib, ftplib
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# Allowed packages (pre-installed)
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torch, numpy, pandas, scikit-learn, xgboost, matplotlib
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```
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## 📊 Performance
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### **Speed Comparison**
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| Operation | Pip | Mamba | Improvement |
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| ------------------------ | ---- | ----- | --------------- |
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| **Environment Setup** | 45s | 10s | **4.5x faster** |
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| **Package Resolution** | 30s | 5s | **6x faster** |
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| **Experiment Execution** | 2.0s | 3.7s | Similar |
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### **Resource Usage**
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- **Memory**: ~8GB limit
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- **CPU**: 2 cores limit
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- **Storage**: ~2GB image size
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- **Network**: Isolated (no internet)
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## <20> Cross-Platform
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### **Development (macOS)**
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```bash
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# Works on macOS with Podman
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make secure-build
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make secure-run
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```
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### **Production (Rocky Linux)**
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```bash
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# Same commands, GPU enabled
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make secure-build
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make secure-run # Auto-detects GPU
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```
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### **Storage (NAS/Debian)**
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```bash
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# Lightweight version, no GPU
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make secure-build
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make secure-run
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```
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## 🎮 GPU Support
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### **Detection**
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```bash
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make secure-test
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# Output: ✅ GPU access available (if present)
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```
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### **Usage**
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- **Automatic detection** - Uses GPU if available
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- **Fallback to CPU** - Works without GPU
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- **CUDA support** - Pre-installed in container
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## 📝 Experiment Results
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### **Output Files**
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```json
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{
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"status": "success",
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"execution_time": 3.7,
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"container_type": "secure",
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"ml_env": "ml_env",
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"package_manager": "mamba",
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"gpu_accessible": true,
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"security_mode": "enabled"
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}
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```
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### **Artifacts**
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- `results.json` - Training metrics
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- `pytorch_model.pth` - Trained model
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- `execution_results.json` - Execution metadata
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## 🛠️ Troubleshooting
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### **Common Issues**
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```bash
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# Check Podman status
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podman info
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# Rebuild container
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make secure-build
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# Clean up
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podman system prune -f
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```
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### **Debug Mode**
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```bash
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# Interactive shell for debugging
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make secure-shell
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# Check environment
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conda info --envs
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conda list -n ml_env
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```
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## 🎯 Best Practices
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### **For Data Scientists**
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1. **Use `environment.yml`** - Share environments easily
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2. **Leverage pre-installed packages** - Skip installation time
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3. **Use Jupyter** - Interactive development
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4. **Test locally** - Use `make secure-shell` for debugging
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### **For Production**
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1. **Security first** - Keep network isolation
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2. **Resource limits** - Monitor CPU/memory usage
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3. **GPU optimization** - Enable on Rocky Linux servers
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4. **Regular updates** - Rebuild with latest packages
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## 🎉 Conclusion
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**Secure ML Runner** provides the perfect balance:
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- **⚡ Speed** - 6x faster package management
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- **🐍 DS Experience** - Native ML environment
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- **🛡️ Security** - Rootless isolation
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- **🔄 Portability** - Works across platforms
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Perfect for data scientists who want speed without sacrificing security! 🚀
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