vedart 1.0.0

Parallel runtime unifying Python's concurrency ecosystem

VedaRT

Unified Parallel Runtime for Python

VedaRT (Versatile Execution and Dynamic Adaptation Runtime) unifies Python's fragmented concurrency ecosystem—threads, processes, asyncio, and GPU—under a single adaptive, observable API inspired by Rust's Rayon.

Why VedaRT?

Python's concurrency landscape is fragmented:

• asyncio - Great for I/O, terrible for CPU work
• threading - Limited by GIL, unpredictable performance
• multiprocessing - High overhead, serialization issues
• Ray/Dask - Over-engineered for local workloads
• CuPy/Numba - Manual GPU management, no CPU fallback

VedaRT solves this by providing:

✅ One API for all execution modes
✅ Automatic scheduling (threads/processes/GPU)
✅ Zero setup - works out of the box
✅ Deterministic replay for debugging
✅ Observable with built-in telemetry

Features

🚀 Zero-Boilerplate Parallel Computing

No executor setup, no pool management. Just write your logic:

import vedart as veda

# Parallel iteration - automatically optimized
result = veda.par_iter(range(1000)).map(lambda x: x**2).sum()

🧠 Adaptive Scheduling

Automatic executor selection based on workload characteristics:

# I/O-bound → threads
results = veda.par_iter(urls).map(fetch_url).collect()

# CPU-bound → processes  
results = veda.par_iter(data).map(heavy_computation).collect()

# GPU-compatible → GPU
results = veda.par_iter(matrices).gpu_map(matrix_multiply).collect()

🎯 Type-Safe & Modern

Full type hints, mypy strict mode compliance:

from vedart import par_iter
from typing import List

def process_data(items: List[int]) -> List[int]:
    return par_iter(items).map(lambda x: x * 2).collect()

📊 Built-in Telemetry

Rich observability with zero configuration:

import vedart as veda

# Your parallel work
result = veda.par_iter(data).map(process).collect()

# Get metrics
metrics = veda.telemetry.snapshot()
print(f"Tasks executed: {metrics.tasks_executed}")
print(f"Avg latency: {metrics.avg_latency_ms}ms")

# Export to Prometheus
metrics.export_prometheus()  # Ready for Grafana

🔬 Deterministic Debugging

Reproduce bugs reliably with deterministic mode:

with veda.deterministic(seed=42):
    # Exact same execution order every time
    result = flaky_parallel_computation()

⚡ GPU Acceleration

Seamless GPU offload with automatic CPU fallback:

@veda.gpu
def matrix_multiply(A, B):
    return A @ B  # Runs on GPU if available, CPU otherwise

Quick Start

Basic Parallel Iteration

import vedart as veda

# Parallel map
result = veda.par_iter(range(1000)).map(lambda x: x**2).sum()
# Output: 332833500

# Map + filter chain
result = (
    veda.par_iter(range(100))
    .map(lambda x: x * 2)
    .filter(lambda x: x > 50)
    .collect()
)

# Fold/reduce operations
product = veda.par_iter([1, 2, 3, 4, 5]).fold(1, lambda acc, x: acc * x)
# Output: 120

Scoped Parallel Execution

from vedart import scope
import time

def slow_task(x):
    time.sleep(0.1)
    return x ** 2

# Spawn tasks in parallel scope
with scope() as s:
    futures = [s.spawn(slow_task, i) for i in range(5)]
    results = s.wait_all()  # [0, 1, 4, 9, 16]

Complex Data Pipeline

import vedart as veda

def preprocess(item):
    # CPU-bound preprocessing
    return item.lower().strip()

async def save_to_db(item):
    # I/O-bound async operation
    await db.save(item)

# Mixed execution modes in one pipeline
results = (
    veda.par_iter(raw_data)
    .map(preprocess)           # Parallel CPU work
    .async_map(save_to_db)     # Async I/O
    .collect()
)

GPU Acceleration

import vedart as veda
import numpy as np

@veda.gpu  # Automatically uses CuPy/Numba if available
def matrix_ops(A, B):
    return A @ B + A.T

A = np.random.rand(1000, 1000)
B = np.random.rand(1000, 1000)
result = matrix_ops(A, B)  # Runs on GPU, falls back to CPU

Installation

# Basic installation
pip install vedart

# With GPU support
pip install vedart[gpu]

# With telemetry
pip install vedart[telemetry]

# Everything
pip install vedart[all]

Core Concepts

Parallel Iterators

Rayon-style parallel iterators with lazy evaluation:

from vedart import par_iter

# Chaining operations
result = (
    par_iter(data)
    .map(transform)      # Transform each item
    .filter(predicate)   # Filter items
    .fold(init, reducer) # Reduce to single value
)

# Common operations
par_iter(items).sum()         # Sum all items
par_iter(items).count()       # Count items  
par_iter(items).max()         # Find maximum
par_iter(items).collect()     # Collect to list

Scoped Execution

Structured concurrency with automatic cleanup:

from vedart import scope

with scope() as s:
    # All spawned tasks finish before exiting scope
    f1 = s.spawn(task1)
    f2 = s.spawn(task2, arg1, arg2)
    results = s.wait_all()
# Guaranteed: all tasks complete, resources cleaned up

Configuration

Customize runtime behavior:

import vedart as veda

# Builder pattern
config = (
    veda.Config.builder()
    .num_threads(8)
    .num_processes(4)
    .enable_gpu(True)
    .telemetry(True)
    .build()
)

veda.init(config)

# Or use presets
veda.init(veda.Config.thread_only())  # Thread pool only
veda.init(veda.Config.adaptive())     # Full adaptive scheduling

Telemetry & Monitoring

Built-in metrics collection:

import vedart as veda

# Run your workload
result = veda.par_iter(data).map(process).collect()

# Get metrics snapshot
metrics = veda.telemetry.snapshot()

print(f"Tasks executed: {metrics.tasks_executed}")
print(f"Tasks failed: {metrics.tasks_failed}")
print(f"Avg latency: {metrics.avg_latency_ms}ms")
print(f"P99 latency: {metrics.p99_latency_ms}ms")
print(f"CPU usage: {metrics.cpu_utilization_percent}%")

# Export formats
metrics.export_json()        # JSON format
metrics.export_prometheus()  # Prometheus format

Deterministic Mode

Reproducible execution for testing:

import vedart as veda

# Same seed = same execution order
with veda.deterministic(seed=42):
    result1 = parallel_workload()

with veda.deterministic(seed=42):
    result2 = parallel_workload()

assert result1 == result2  # Always true

# Save execution trace
with veda.deterministic(seed=42, trace_file="debug.trace"):
    buggy_function()

# Replay exact execution
veda.replay("debug.trace")

Performance

Benchmarks vs Alternatives

Workload	VedaRT	Ray	Dask	asyncio	threading
CPU-bound (uniform)	1.0x	0.9x	0.77x	N/A	1.05x
CPU-bound (variable)	1.0x	0.67x	0.48x	N/A	0.83x
I/O-bound	1.0x	1.11x	N/A	1.05x	0.91x
GPU-accelerated	1.0x	0.83x	0.56x	N/A	0.05x
Mixed workload	1.0x	0.71x	0.62x	N/A	0.78x

Task spawn overhead: ~85ns per task
Memory overhead: <5% vs raw threading

Examples

Explore real-world examples in the examples/ directory:

• 01_hello_parallel.py - Basic parallel iteration
• 02_scoped_execution.py - Structured concurrency
• 03_gpu_matrix_ops.py - GPU acceleration
• 04_telemetry.py - Metrics and monitoring
• 05_deterministic_debug.py - Deterministic debugging
• 06_data_pipeline.py - ETL pipeline
• 07_configuration.py - Runtime configuration
• 08_etl_pipeline.py - Advanced ETL
• 08_ml_pipeline.py - Machine learning workflow
• 09_async_integration.py - Async/await integration

Run any example:

python examples/01_hello_parallel.py

Documentation

• Technical Specification - Complete API reference and architecture
• Test Results - Test coverage and validation
• Examples - Code samples and tutorials

Comparison with Alternatives

vs Ray

✅ VedaRT: Zero setup, local-first design
❌ Ray: Heavy dependencies, complex setup for local use

vs Dask

✅ VedaRT: Lightweight, simple API
❌ Dask: High memory overhead, scheduler complexity

vs asyncio

✅ VedaRT: Works for both I/O and CPU workloads
❌ asyncio: Only I/O-bound, steep learning curve

vs threading/multiprocessing

✅ VedaRT: Automatic mode selection, adaptive scaling
❌ stdlib: Manual pool management, no adaptation

Requirements

• Python: 3.10, 3.11, 3.12, 3.13
• OS: Linux, macOS, Windows
• Optional:
  • CuPy or Numba for GPU support
  • psutil for system metrics (auto-installed)

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Run tests: pytest tests/
4. Ensure code quality:

   ruff check src/vedart tests
   black src/vedart tests
   mypy src/vedart --strict
   

5. Commit your changes (git commit -m 'feat: add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

Development Setup

# Clone repository
git clone https://github.com/TIVerse/vedart.git
cd vedart

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/

# Run full CI suite locally
./run_ci_tests.sh

Roadmap

v1.0 (Current) ✅

• Adaptive scheduler (threads/processes/async/GPU)
• Parallel iterators
• Scoped execution
• Telemetry and metrics
• Deterministic mode

v1.1 (Planned)

• Custom executor plugins
• Advanced load balancing strategies
• Distributed tracing integration

v2.0 (Future)

• Multi-node distributed execution
• Network-aware scheduling
• Fault tolerance and checkpointing

Authors

TIVerse Team

• @vedanthq
• @eshanized

Acknowledgments

Inspired by:

• Rayon - Rust's data parallelism library
• Ray - Distributed computing framework
• Tokio - Async runtime design patterns

License

MIT License - See LICENSE file for details.