> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langtrace.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Cerebras

> Learn how to use Langtrace with Cerebras for AI workload observability

# Cerebras Integration

This guide demonstrates how to integrate Langtrace with Cerebras to gain deep observability into your AI workloads. Cerebras, a leader in AI hardware and cloud solutions, enables unprecedented performance for large language models. By combining Cerebras with Langtrace, you can monitor, debug, and optimize your AI pipelines effectively.

## Prerequisites

Before you begin, make sure you have:

1. A Cerebras API key
2. Python 3.7 or later installed
3. The Langtrace SDK installed and configured

## Installation

Install the required packages:

```bash theme={null}
pip install langtrace-python-sdk cerebras-cloud-sdk python-dotenv
```

## Configuration

Set up your environment variables:

```bash theme={null}
export CEREBRAS_API_KEY='your-cerebras-api-key'
export LANGTRACE_API_KEY='your-langtrace-api-key'
```

Or use a `.env` file:

```plaintext theme={null}
CEREBRAS_API_KEY=your-cerebras-api-key
LANGTRACE_API_KEY=your-langtrace-api-key
```

## Usage

### Direct Cerebras SDK Integration

Here's how to use Langtrace with the Cerebras SDK:

```python theme={null}
from langtrace_python_sdk import langtrace
from cerebras.cloud.sdk import Cerebras
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Initialize Langtrace
langtrace.init()

# Initialize Cerebras client
client = Cerebras()

def completion_example(stream=False):
    completion = client.chat.completions.create(
        messages=[{
            "role": "user",
            "content": "Why is fast inference important?",
        }],
        model="llama3.1-8b",
        stream=stream,
    )

    if stream:
        for chunk in completion:
            print(chunk)
    else:
        return completion

# Execute the completion
completion_example()
```

### OpenAI-Compatible Endpoint

Cerebras also provides an OpenAI-compatible endpoint:

```python theme={null}
from langtrace_python_sdk import langtrace
from dotenv import load_dotenv
from openai import OpenAI
import os

# Load environment variables
load_dotenv()

# Initialize Langtrace
langtrace.init()

# Initialize OpenAI client with Cerebras endpoint
openai_client = OpenAI(
    base_url="https://api.cerebras.ai/v1",
    api_key=os.getenv("CEREBRAS_API_KEY"),
)

def openai_cerebras_example(stream=False):
    completion = openai_client.chat.completions.create(
        messages=[{
            "role": "user",
            "content": "Why is fast inference important?",
        }],
        model="llama3.1-8b",
        stream=stream,
    )

    if stream:
        for chunk in completion:
            print(chunk)
    else:
        return completion

# Execute the completion
openai_cerebras_example()
```

## Observability Features

Langtrace provides comprehensive observability for your Cerebras workloads:

1. **Performance Monitoring**
   * Track latency and throughput metrics
   * Monitor resource utilization
   * Identify bottlenecks in your pipeline

2. **Error Tracking**
   * Trace errors in distributed workloads
   * Debug complex pipelines
   * Monitor both synchronous and asynchronous operations

3. **Resource Optimization**
   * Analyze hardware resource usage
   * Optimize model execution
   * Fine-tune performance parameters

4. **User Experience Metrics**
   * Track completion times
   * Monitor streaming response performance
   * Measure end-to-end request latency

## Best Practices

1. **Initialize Early**
   Always initialize Langtrace before creating the Cerebras client to ensure all operations are traced.

2. **Use Environment Variables**
   Store sensitive credentials in environment variables or `.env` files.

3. **Enable Streaming**
   For long-running operations, use streaming to monitor progress in real-time.

4. **Monitor Resource Usage**
   Regularly check resource utilization metrics to optimize performance.

## Troubleshooting

Common issues and solutions:

1. **Authentication Errors**
   * Verify your API keys are correctly set
   * Ensure environment variables are loaded

2. **Performance Issues**
   * Check Langtrace dashboards for bottlenecks
   * Monitor resource utilization metrics
   * Verify model configuration settings

## Additional Resources

* [Cerebras Documentation](https://docs.cerebras.ai/)
* [Langtrace Documentation](https://docs.langtrace.ai/)
* [Cerebras API Reference](https://api.cerebras.ai/)