tetra_rp 0.5.0
A Python library for distributed inference and serving of machine learning models
License: MIT
Requires Python: <3.14,>=3.9

# Tetra: Serverless computing for AI workloads

Tetra is a Python SDK that streamlines the development and deployment of AI workflows on Runpod's [Serverless infrastructure](http://docs.runpod.io/serverless/overview). Write Python functions locally, and Tetra handles the infrastructure, provisioning GPUs and CPUs, managing dependencies, and transferring data, allowing you to focus on building AI applications.

You can find a repository of prebuilt Tetra examples at [runpod/tetra-examples](https://github.com/runpod/tetra-examples).

> [!Note]
> **New feature - Consolidated template management:** `PodTemplate` overrides now seamlessly integrate with `ServerlessResource` defaults, providing more consistent resource configuration and reducing deployment complexity.

## Table of contents

- [Requirements](#requirements)
- [Getting started](#getting-started)
- [Key concepts](#key-concepts)
- [How it works](#how-it-works)
- [Use cases](#use-cases)
- [Advanced features](#advanced-features)
- [Configuration](#configuration)
- [Workflow examples](#workflow-examples)
- [Troubleshooting](#troubleshooting)

## Getting started

Before you can use Tetra, you'll need:

- Python 3.9 (or higher) installed on your local machine.
- A Runpod account with API key ([sign up here](https://runpod.io/console)).
- Basic knowledge of Python and async programming.

### Step 1: Install Tetra

```bash
pip install tetra_rp
```

### Step 2: Set your API key

Generate an API key from the [Runpod account settings](https://docs.runpod.io/get-started/api-keys) page and set it as an environment variable:

```bash
export RUNPOD_API_KEY=[YOUR_API_KEY]
```

Or save it in a `.env` file in your project directory:

```bash
echo "RUNPOD_API_KEY=[YOUR_API_KEY]" > .env
```

### Step 3: Write your first Tetra function

Add the following code to a new Python file:

```python
import asyncio
from tetra_rp import remote, LiveServerless

# Configure GPU resources
gpu_config = LiveServerless(name="tetra-quickstart")

@remote(
    resource_config=gpu_config,
    dependencies=["torch", "numpy"]
)
def gpu_compute(data):
    import torch
    import numpy as np
    
    # This runs on a GPU in Runpod's cloud
    tensor = torch.tensor(data, device="cuda")
    result = tensor.sum().item()
    
    return {
        "result": result,
        "device": torch.cuda.get_device_name(0)
    }

async def main():
    # This runs locally
    result = await gpu_compute([1, 2, 3, 4, 5])
    print(f"Sum: {result['result']}")
    print(f"Computed on: {result['device']}")

if __name__ == "__main__":
    asyncio.run(main())
```

Run the example:

```bash
python your_script.py
```

## Key concepts

### Remote functions

Tetra's `@remote` decorator marks functions for execution on Runpod's infrastructure. Everything inside the decorated function runs remotely, while code outside runs locally.

```python
@remote(resource_config=config, dependencies=["pandas"])
def process_data(data):
    # This code runs remotely
    import pandas as pd
    df = pd.DataFrame(data)
    return df.describe().to_dict()

async def main():
    # This code runs locally
    result = await process_data(my_data)
```

### Resource configuration

Tetra provides fine-grained control over hardware allocation through configuration objects:

```python
from tetra_rp import LiveServerless, GpuGroup, CpuInstanceType, PodTemplate

# GPU configuration
gpu_config = LiveServerless(
    name="ml-inference",
    gpus=[GpuGroup.AMPERE_80],  # A100 80GB
    workersMax=5,
    template=PodTemplate(containerDiskInGb=100)  # Extra disk space
)

# CPU configuration
cpu_config = LiveServerless(
    name="data-processor",
    instanceIds=[CpuInstanceType.CPU5C_4_16],  # 4 vCPU, 16GB RAM
    workersMax=3
)
```

### Dependency management

Specify Python packages in the decorator, and Tetra installs them automatically:

```python
@remote(
    resource_config=gpu_config,
    dependencies=["transformers==4.36.0", "torch", "pillow"]
)
def generate_image(prompt):
    # Import inside the function
    from transformers import pipeline
    import torch
    from PIL import Image
    
    # Your code here
```

### Parallel execution

Run multiple remote functions concurrently using Python's async capabilities:

```python
# Process multiple items in parallel
results = await asyncio.gather(
    process_item(item1),
    process_item(item2),
    process_item(item3)
)
```

## How it works

Tetra orchestrates workflow execution through a sophisticated multi-step process:

1. **Function identification**: The `@remote` decorator marks functions for remote execution, enabling Tetra to distinguish between local and remote operations.
2. **Dependency analysis**: Tetra automatically analyzes function dependencies to construct an optimal execution order, ensuring data flows correctly between sequential and parallel operations.
3. **Resource provisioning and execution**: For each remote function, Tetra:
   - Dynamically provisions endpoint and worker resources on Runpod's infrastructure.
   - Serializes and securely transfers input data to the remote worker.
   - Executes the function on the remote infrastructure with the specified GPU or CPU resources.
   - Returns results to your local environment for further processing.
4. **Data orchestration**: Results flow seamlessly between functions according to your local Python code structure, maintaining the same programming model whether functions run locally or remotely.

## Use cases

Tetra is well-suited for a diverse range of AI and data processing workloads:

- **Multi-modal AI pipelines**: Orchestrate unified workflows combining text, image, and audio models with GPU acceleration.
- **Distributed model training**: Scale training operations across multiple GPU workers for faster model development.
- **AI research experimentation**: Rapidly prototype and test complex model combinations without infrastructure overhead.
- **Production inference systems**: Deploy sophisticated multi-stage inference pipelines for real-world applications.
- **Data processing workflows**: Efficiently process large datasets using CPU workers for general computation and GPU workers for accelerated tasks.
- **Hybrid GPU/CPU workflows**: Optimize cost and performance by combining CPU preprocessing with GPU inference.

## Advanced features

### Custom Docker images

`LiveServerless` resources use a fixed Docker image that's optimized for Tetra runtime, and supports full remote code execution. For specialized environments that require a custom Docker image, use `ServerlessEndpoint` or `CpuServerlessEndpoint`:

```python
from tetra_rp import ServerlessEndpoint

custom_gpu = ServerlessEndpoint(
    name="custom-ml-env",
    imageName="pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime",
    gpus=[GpuGroup.AMPERE_80]
)
```

Unlike `LiveServerless`, these endpoints only support dictionary payloads in the form of `{"input": {...}}` (similar to a traditional [Serverless endpoint request](https://docs.runpod.io/serverless/endpoints/send-requests)), and cannot execute arbitrary Python functions remotely.

### Persistent storage

Attach network volumes for model caching:

```python
config = LiveServerless(
    name="model-server",
    networkVolumeId="vol_abc123",  # Your volume ID
    template=PodTemplate(containerDiskInGb=100)
)
```

### Environment variables

Pass configuration to remote functions:

```python
config = LiveServerless(
    name="api-worker",
    env={"HF_TOKEN": "your_token", "MODEL_ID": "gpt2"}
)
```

## Configuration

### GPU configuration parameters

The following parameters can be used with `LiveServerless` (full remote code execution) and `ServerlessEndpoint` (Dictionary payload only) to configure your Runpod GPU endpoints:

| Parameter          | Description                                     | Default       | Example Values                      |
|--------------------|-------------------------------------------------|---------------|-------------------------------------|
| `name`             | (Required) Name for your endpoint               | `""`          | `"stable-diffusion-server"`         |
| `gpus`             | GPU pool IDs that can be used by workers        | `[GpuGroup.ANY]` | `[GpuGroup.ADA_24]` for RTX 4090 |
| `gpuCount`         | Number of GPUs per worker                       | 1             | 1, 2, 4                             |
| `workersMin`       | Minimum number of workers                       | 0             | Set to 1 for persistence            |
| `workersMax`       | Maximum number of workers                       | 3             | Higher for more concurrency         |
| `idleTimeout`      | Minutes before scaling down                     | 5             | 10, 30, 60                          |
| `env`              | Environment variables                           | `None`        | `{"HF_TOKEN": "xyz"}`               |
| `networkVolumeId`  | Persistent storage ID                           | `None`        | `"vol_abc123"`                      |
| `executionTimeoutMs`| Max execution time (ms)                        | 0 (no limit)  | 600000 (10 min)                     |
| `scalerType`       | Scaling strategy                                | `QUEUE_DELAY` | `REQUEST_COUNT`                     |
| `scalerValue`      | Scaling parameter value                         | 4             | 1-10 range typical                  |
| `locations`        | Preferred datacenter locations                  | `None`        | `"us-east,eu-central"`              |
| `imageName`        | Custom Docker image (`ServerlessEndpoint` only)   | Fixed for LiveServerless | `"pytorch/pytorch:latest"`, `"my-registry/custom:v1.0"` |

### CPU configuration parameters

The same GPU configuration parameters above apply to `LiveServerless` (full remote code execution) and `CpuServerlessEndpoint` (dictionary payload only), with these additional CPU-specific parameters:

| Parameter          | Description                                     | Default       | Example Values                      |
|--------------------|-------------------------------------------------|---------------|-------------------------------------|
| `instanceIds`      | CPU Instance Types (forces a CPU endpoint type) | `None`        | `[CpuInstanceType.CPU5C_2_4]`       |
| `imageName`        | Custom Docker image (`CpuServerlessEndpoint` only) | Fixed for `LiveServerless` | `"python:3.11-slim"`, `"my-registry/custom:v1.0"` |

### Resource class comparison

| Feature | LiveServerless | ServerlessEndpoint | CpuServerlessEndpoint |
|---------|----------------|-------------------|----------------------|
| **Remote code execution** | ✅ Full Python function execution | ❌ Dictionary payloads only | ❌ Dictionary payloads only |
| **Custom Docker images** | ❌ Fixed optimized images | ✅ Any Docker image | ✅ Any Docker image |
| **Use case** | Dynamic remote functions | Traditional API endpoints | Traditional CPU endpoints |
| **Function returns** | Any Python object | Dictionary only | Dictionary only |
| **@remote decorator** | Full functionality | Limited to payload passing | Limited to payload passing |

### Available GPU types

Some common GPU groups available through `GpuGroup`:

- `GpuGroup.ANY` - Any available GPU (default)
- `GpuGroup.ADA_24` - NVIDIA GeForce RTX 4090
- `GpuGroup.AMPERE_80` - NVIDIA A100 80GB
- `GpuGroup.AMPERE_48` - NVIDIA A40, RTX A6000
- `GpuGroup.AMPERE_24` - NVIDIA RTX A5000, L4, RTX 3090


### Available CPU instance types
- `CpuInstanceType.CPU3G_1_4` - (cpu3g-1-4) 3rd gen general purpose, 1 vCPU, 4GB RAM
- `CpuInstanceType.CPU3G_2_8` - (cpu3g-2-8) 3rd gen general purpose, 2 vCPU, 8GB RAM
- `CpuInstanceType.CPU3G_4_16` - (cpu3g-4-16) 3rd gen general purpose, 4 vCPU, 16GB RAM
- `CpuInstanceType.CPU3G_8_32` - (cpu3g-8-32) 3rd gen general purpose, 8 vCPU, 32GB RAM
- `CpuInstanceType.CPU3C_1_2` - (cpu3c-1-2) 3rd gen compute-optimized, 1 vCPU, 2GB RAM
- `CpuInstanceType.CPU3C_2_4` - (cpu3c-2-4) 3rd gen compute-optimized, 2 vCPU, 4GB RAM
- `CpuInstanceType.CPU3C_4_8` - (cpu3c-4-8) 3rd gen compute-optimized, 4 vCPU, 8GB RAM
- `CpuInstanceType.CPU3C_8_16` - (cpu3c-8-16) 3rd gen compute-optimized, 8 vCPU, 16GB RAM
- `CpuInstanceType.CPU5C_1_2` - (cpu5c-1-2) 5th gen compute-optimized, 1 vCPU, 2GB RAM
- `CpuInstanceType.CPU5C_2_4` - (cpu5c-2-4) 5th gen compute-optimized, 2 vCPU, 4GB RAM
- `CpuInstanceType.CPU5C_4_8` - (cpu5c-4-8) 5th gen compute-optimized, 4 vCPU, 8GB RAM
- `CpuInstanceType.CPU5C_8_16` - (cpu5c-8-16) 5th gen compute-optimized, 8 vCPU, 16GB RAM

## Workflow examples

### Basic GPU workflow

```python
import asyncio
from tetra_rp import remote, LiveServerless

# Simple GPU configuration
gpu_config = LiveServerless(name="example-gpu-server")

@remote(
    resource_config=gpu_config,
    dependencies=["torch", "numpy"]
)
def gpu_compute(data):
    import torch
    import numpy as np
    
    # Convert to tensor and perform computation on GPU
    tensor = torch.tensor(data, device="cuda")
    result = tensor.sum().item()
    
    # Get GPU info
    gpu_info = torch.cuda.get_device_properties(0)
    
    return {
        "result": result,
        "gpu_name": gpu_info.name,
        "cuda_version": torch.version.cuda
    }

async def main():
    result = await gpu_compute([1, 2, 3, 4, 5])
    print(f"Result: {result['result']}")
    print(f"Computed on: {result['gpu_name']} with CUDA {result['cuda_version']}")

if __name__ == "__main__":
    asyncio.run(main())
```

### Advanced GPU workflow with template configuration

```python
import asyncio
from tetra_rp import remote, LiveServerless, GpuGroup, PodTemplate

# Advanced GPU configuration with consolidated template overrides
sd_config = LiveServerless(
    gpus=[GpuGroup.AMPERE_80],  # A100 80GB GPUs
    name="example_image_gen_server",
    template=PodTemplate(containerDiskInGb=100),  # Large disk for models
    workersMax=3,
    idleTimeout=10
)

@remote(
    resource_config=sd_config,
    dependencies=["diffusers", "transformers", "torch", "accelerate", "safetensors"]
)
def generate_image(prompt, width=512, height=512):
    import torch
    from diffusers import StableDiffusionPipeline
    import io
    import base64
    
    # Load pipeline (benefits from large container disk)
    pipeline = StableDiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        torch_dtype=torch.float16
    )
    pipeline = pipeline.to("cuda")
    
    # Generate image
    image = pipeline(prompt=prompt, width=width, height=height).images[0]
    
    # Convert to base64 for return
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue()).decode()
    
    return {"image": img_str, "prompt": prompt}

async def main():
    result = await generate_image("A serene mountain landscape at sunset")
    print(f"Generated image for: {result['prompt']}")
    # Save image locally if needed
    # img_data = base64.b64decode(result["image"])
    # with open("output.png", "wb") as f:
    #     f.write(img_data)

if __name__ == "__main__":
    asyncio.run(main())
```

### Basic CPU workflow

```python
import asyncio
from tetra_rp import remote, LiveServerless, CpuInstanceType

# Simple CPU configuration
cpu_config = LiveServerless(
    name="example-cpu-server",
    instanceIds=[CpuInstanceType.CPU5G_2_8],  # 2 vCPU, 8GB RAM
)

@remote(
    resource_config=cpu_config,
    dependencies=["pandas", "numpy"]
)
def cpu_data_processing(data):
    import pandas as pd
    import numpy as np
    import platform
    
    # Process data using CPU
    df = pd.DataFrame(data)
    
    return {
        "row_count": len(df),
        "column_count": len(df.columns) if not df.empty else 0,
        "mean_values": df.select_dtypes(include=[np.number]).mean().to_dict(),
        "system_info": platform.processor(),
        "platform": platform.platform()
    }

async def main():
    sample_data = [
        {"name": "Alice", "age": 30, "score": 85},
        {"name": "Bob", "age": 25, "score": 92},
        {"name": "Charlie", "age": 35, "score": 78}
    ]
    
    result = await cpu_data_processing(sample_data)
    print(f"Processed {result['row_count']} rows on {result['platform']}")
    print(f"Mean values: {result['mean_values']}")

if __name__ == "__main__":
    asyncio.run(main())
```

### Advanced CPU workflow with template configuration

```python
import asyncio
import base64
from tetra_rp import remote, LiveServerless, CpuInstanceType, PodTemplate

# Advanced CPU configuration with template overrides
data_processing_config = LiveServerless(
    name="advanced-cpu-processor",
    instanceIds=[CpuInstanceType.CPU5C_4_16, CpuInstanceType.CPU3C_4_8],  # Fallback options
    template=PodTemplate(
        containerDiskInGb=20,  # Extra disk space for data processing
        env=[{"key": "PYTHONPATH", "value": "/workspace"}]  # Custom environment
    ),
    workersMax=5,
    idleTimeout=15,
    env={"PROCESSING_MODE": "batch", "DEBUG": "false"}  # Additional env vars
)

@remote(
    resource_config=data_processing_config,
    dependencies=["pandas", "numpy", "scipy", "scikit-learn"]
)
def advanced_data_analysis(dataset, analysis_type="full"):
    import pandas as pd
    import numpy as np
    from sklearn.preprocessing import StandardScaler
    from sklearn.decomposition import PCA
    import platform
    
    # Create DataFrame
    df = pd.DataFrame(dataset)
    
    # Perform analysis based on type
    results = {
        "platform": platform.platform(),
        "dataset_shape": df.shape,
        "memory_usage": df.memory_usage(deep=True).sum()
    }
    
    if analysis_type == "full":
        # Advanced statistical analysis
        numeric_cols = df.select_dtypes(include=[np.number]).columns
        if len(numeric_cols) > 0:
            # Standardize data
            scaler = StandardScaler()
            scaled_data = scaler.fit_transform(df[numeric_cols])
            
            # PCA analysis
            pca = PCA(n_components=min(len(numeric_cols), 3))
            pca_result = pca.fit_transform(scaled_data)
            
            results.update({
                "correlation_matrix": df[numeric_cols].corr().to_dict(),
                "pca_explained_variance": pca.explained_variance_ratio_.tolist(),
                "pca_shape": pca_result.shape
            })
    
    return results

async def main():
    # Generate sample dataset
    sample_data = [
        {"feature1": np.random.randn(), "feature2": np.random.randn(), 
         "feature3": np.random.randn(), "category": f"cat_{i%3}"}
        for i in range(1000)
    ]
    
    result = await advanced_data_analysis(sample_data, "full")
    print(f"Processed dataset with shape: {result['dataset_shape']}")
    print(f"Memory usage: {result['memory_usage']} bytes")
    print(f"PCA explained variance: {result.get('pca_explained_variance', 'N/A')}")

if __name__ == "__main__":
    asyncio.run(main())
```

### Hybrid GPU/CPU workflow

```python
import asyncio
from tetra_rp import remote, LiveServerless, GpuGroup, CpuInstanceType, PodTemplate

# GPU configuration for model inference
gpu_config = LiveServerless(
    name="ml-inference-gpu",
    gpus=[GpuGroup.AMPERE_24],  # RTX 3090/A5000
    template=PodTemplate(containerDiskInGb=50),  # Space for models
    workersMax=2
)

# CPU configuration for data preprocessing
cpu_config = LiveServerless(
    name="data-preprocessor",
    instanceIds=[CpuInstanceType.CPU5C_4_16],  # 4 vCPU, 16GB RAM
    template=PodTemplate(
        containerDiskInGb=30,
        env=[{"key": "NUMPY_NUM_THREADS", "value": "4"}]
    ),
    workersMax=3
)

@remote(
    resource_config=cpu_config,
    dependencies=["pandas", "numpy", "scikit-learn"]
)
def preprocess_data(raw_data):
    import pandas as pd
    import numpy as np
    from sklearn.preprocessing import StandardScaler
    
    # Data cleaning and preprocessing
    df = pd.DataFrame(raw_data)
    
    # Handle missing values
    df = df.fillna(df.mean(numeric_only=True))
    
    # Normalize numeric features
    numeric_cols = df.select_dtypes(include=[np.number]).columns
    if len(numeric_cols) > 0:
        scaler = StandardScaler()
        df[numeric_cols] = scaler.fit_transform(df[numeric_cols])
    
    return {
        "processed_data": df.to_dict('records'),
        "shape": df.shape,
        "columns": list(df.columns)
    }

@remote(
    resource_config=gpu_config,
    dependencies=["torch", "transformers", "numpy"]
)
def run_inference(processed_data):
    import torch
    import numpy as np
    
    # Simulate ML model inference on GPU
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    # Convert to tensor
    data_array = np.array([list(item.values()) for item in processed_data["processed_data"]])
    tensor = torch.tensor(data_array, dtype=torch.float32).to(device)
    
    # Simple neural network simulation
    with torch.no_grad():
        # Simulate model computation
        result = torch.nn.functional.softmax(tensor.mean(dim=1), dim=0)
        predictions = result.cpu().numpy().tolist()
    
    return {
        "predictions": predictions,
        "device_used": str(device),
        "input_shape": tensor.shape
    }

async def ml_pipeline(raw_dataset):
    """Complete ML pipeline: CPU preprocessing -> GPU inference"""
    print("Step 1: Preprocessing data on CPU...")
    preprocessed = await preprocess_data(raw_dataset)
    print(f"Preprocessed data shape: {preprocessed['shape']}")
    
    print("Step 2: Running inference on GPU...")
    results = await run_inference(preprocessed)
    print(f"Inference completed on: {results['device_used']}")
    
    return {
        "preprocessing": preprocessed,
        "inference": results
    }

async def main():
    # Sample dataset
    raw_data = [
        {"feature1": np.random.randn(), "feature2": np.random.randn(), 
         "feature3": np.random.randn(), "label": i % 2}
        for i in range(100)
    ]
    
    # Run the complete pipeline
    results = await ml_pipeline(raw_data)
    
    print("\nPipeline Results:")
    print(f"Data processed: {results['preprocessing']['shape']}")
    print(f"Predictions generated: {len(results['inference']['predictions'])}")
    print(f"GPU device: {results['inference']['device_used']}")

if __name__ == "__main__":
    asyncio.run(main())
```

### Multi-stage ML pipeline example

```python
import os
import asyncio
from tetra_rp import remote, LiveServerless

# Configure Runpod resources
runpod_config = LiveServerless(name="multi-stage-pipeline-server")

# Feature extraction on GPU
@remote(
    resource_config=runpod_config,
    dependencies=["torch", "transformers"]
)
def extract_features(texts):
    import torch
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
    model = AutoModel.from_pretrained("bert-base-uncased")
    model.to("cuda")
    
    features = []
    for text in texts:
        inputs = tokenizer(text, return_tensors="pt").to("cuda")
        with torch.no_grad():
            outputs = model(**inputs)
        features.append(outputs.last_hidden_state[:, 0].cpu().numpy().tolist()[0])
    
    return features

# Classification on GPU
@remote(
    resource_config=runpod_config,
    dependencies=["torch", "sklearn"]
)
def classify(features, labels=None):
    import torch
    import numpy as np
    from sklearn.linear_model import LogisticRegression
    
    features_np = np.array(features[1:] if labels is None and isinstance(features, list) and len(features)>0 and isinstance(features[0], dict) else features)
    
    if labels is not None:
        labels_np = np.array(labels)
        classifier = LogisticRegression()
        classifier.fit(features_np, labels_np)
        
        coefficients = {
            "coef": classifier.coef_.tolist(),
            "intercept": classifier.intercept_.tolist(),
            "classes": classifier.classes_.tolist()
        }
        return coefficients
    else:
        coefficients = features[0]
        
        classifier = LogisticRegression()
        classifier.coef_ = np.array(coefficients["coef"])
        classifier.intercept_ = np.array(coefficients["intercept"])
        classifier.classes_ = np.array(coefficients["classes"])
        
        # Predict
        predictions = classifier.predict(features_np)
        probabilities = classifier.predict_proba(features_np)
        
        return {
            "predictions": predictions.tolist(),
            "probabilities": probabilities.tolist()
        }

# Complete pipeline
async def text_classification_pipeline(train_texts, train_labels, test_texts):
    train_features = await extract_features(train_texts)
    test_features = await extract_features(test_texts)
    
    model_coeffs = await classify(train_features, train_labels)
    
    # For inference, pass model coefficients along with test features
    # The classify function expects a list where the first element is the model (coeffs)
    # and subsequent elements are features for prediction.
    predictions = await classify([model_coeffs] + test_features)
    
    return predictions
```

### More examples

You can find many more examples in the [tetra-examples repository](https://github.com/runpod/tetra-examples).

You can also install the examples as a submodule:

```bash
git clone https://github.com/runpod/tetra-examples.git
cd tetra-examples
python -m examples.example
python -m examples.image_gen
python -m examples.matrix_operations
```

## Troubleshooting

### Authentication errors

Verify your API key is set correctly:

```bash
echo $RUNPOD_API_KEY  # Should show your key
```

### Import errors in remote functions

Remember to import packages inside remote functions:

```python
@remote(dependencies=["requests"])
def fetch_data(url):
    import requests  # Import here, not at top of file
    return requests.get(url).json()
```

### Performance optimization

- Set `workersMin=1` to keep workers warm and avoid cold starts
- Use `idleTimeout` to balance cost and responsiveness
- Choose appropriate GPU types for your workload

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

<p align="center">
  <a href="https://github.com/yourusername/tetra">Tetra</a> •
  <a href="https://runpod.io">Runpod</a>
</p>