fabric-compute-sdk 1.0.5

Official Python SDK for Fabric - Distributed AI Compute Network

Fabric SDK

Official Python SDK for Fabric - Distributed AI Compute Network

Submit AI workloads to the Fabric network programmatically.

Installation

pip install fabric-compute-sdk

Quick Start

from fabric_sdk import FabricClient

# Initialize client
client = FabricClient(
    api_url="https://api.fabric.carmel.so",
    email="[e-mail]",
    password="your_password"
)

# Submit a job
job = client.submit_job(
    workload_type="llm_inference",
    params={
        "prompt": "Explain quantum computing in simple terms",
        "max_length": 200,
        "temperature": 0.7,
        "use_gpu": True
    },
    job_name="My LLM Inference Job"
)

print(f"Job submitted: {job['id']}")

# Wait for completion
result = client.wait_for_job(job['id'], timeout=300)
print(f"Job completed in {result['duration_seconds']}s")
print(f"Cost: ${result['actual_cost']}")

Features

• Automatic Authentication - JWT token management
• Job Submission - Submit 28 production workload types
• Job Monitoring - Track progress and get results
• Credit Management - Check balance and purchase credits
• Node Discovery - List available compute nodes
• Auto-Retry - Built-in network resilience
• Type Hints - Full TypeScript-style typing support

Supported Workload Types (26 Total)

Compute & Simulation (5)

• cpu_compute_benchmark, gpu_compute_benchmark
• eigenvalue_decomposition, financial_forecast_simulation, agent_simulation

Data Processing (5)

• data_cleaning, feature_extraction, csv_vectorization
• data_augmentation, outlier_detection

AI Inference (7)

• llm_inference, llm_inference_batch, image_classification
• embedding_generation, sentiment_analysis
• text_summarization, question_answering

Media Processing (5)

• video_transcode, audio_to_text, video_object_detection
• image_resize_batch, video_summarization

ML Training (4)

• random_forest_training, svm_training
• xgboost_training, neural_network_training

Custom (1)

• custom_python

For detailed parameter documentation for each workload type, see DEVELOPER_GUIDE.md.

Documentation

• Developer Guide - Complete SDK API reference
• Publishing to PyPI - How to release new versions

License

MIT License - See LICENSE