162227dc71
Add README.md documentation for: - Main SDK overview with quick start guides - JavaScript/TypeScript SDK - Python SDK - Go SDK - Rust SDK - Java SDK - Kotlin SDK - Swift SDK - Flutter/Dart SDK - C SDK - C++ SDK - C#/.NET SDK - Ruby SDK Each README includes: - Installation instructions - Quick start examples - Tensor operations - Matrix operations (matmul, conv2d, attention) - LLM inference (single and streaming) - Configuration options - Error handling - Type definitions
187 lines
3.2 KiB
Markdown
187 lines
3.2 KiB
Markdown
# Synor Compute SDK for Python
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Access distributed heterogeneous compute at 90% cost reduction.
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## Installation
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```bash
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pip install synor-compute
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# or
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poetry add synor-compute
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```
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## Quick Start
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```python
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import asyncio
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from synor_compute import SynorCompute, Tensor
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async def main():
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client = SynorCompute('your-api-key')
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# Matrix multiplication on GPU
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a = Tensor.random((512, 512))
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b = Tensor.random((512, 512))
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result = await client.matmul(a, b, precision='fp16', processor='gpu')
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print(f"Execution time: {result.execution_time_ms}ms")
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print(f"Cost: ${result.cost}")
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asyncio.run(main())
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```
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## NumPy Integration
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```python
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import numpy as np
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from synor_compute import Tensor
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# Create from NumPy
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arr = np.random.randn(100, 100).astype(np.float32)
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tensor = Tensor.from_numpy(arr)
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# Convert back to NumPy
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result_np = tensor.numpy()
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```
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## Tensor Operations
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```python
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# Create tensors
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zeros = Tensor.zeros((3, 3))
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ones = Tensor.ones((2, 2))
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random = Tensor.random((10, 10))
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randn = Tensor.randn((100,)) # Normal distribution
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# Operations
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reshaped = tensor.reshape((50, 200))
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transposed = tensor.T
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# Math operations
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mean = tensor.mean()
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std = tensor.std()
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```
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## Matrix Operations
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```python
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# Matrix multiplication
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result = await client.matmul(a, b,
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precision='fp16',
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processor='gpu',
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strategy='speed'
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)
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# 2D Convolution
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conv = await client.conv2d(input_tensor, kernel,
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stride=(1, 1),
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padding=(1, 1)
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)
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# Flash Attention
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attention = await client.attention(query, key, value,
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num_heads=8,
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flash=True
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)
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```
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## LLM Inference
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```python
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# Single response
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response = await client.inference(
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'llama-3-70b',
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'Explain quantum computing',
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max_tokens=512,
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temperature=0.7
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)
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print(response.result)
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# Streaming response
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async for chunk in client.inference_stream('llama-3-70b', 'Write a poem'):
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print(chunk, end='', flush=True)
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```
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## Configuration
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```python
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from synor_compute import SynorCompute, Config
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config = Config(
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api_key='your-api-key',
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base_url='https://api.synor.io/compute/v1',
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default_processor='gpu',
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default_precision='fp16',
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default_strategy='balanced',
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timeout=30.0,
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debug=False
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)
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client = SynorCompute(config)
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```
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## Synchronous API
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For non-async contexts:
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```python
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from synor_compute import SynorComputeSync
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client = SynorComputeSync('your-api-key')
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result = client.matmul(a, b) # Blocking call
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```
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## Job Management
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```python
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# Submit async job
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job = await client.submit_job('matmul', {'a': a, 'b': b})
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# Poll for status
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status = await client.get_job_status(job.job_id)
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# Wait for completion
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result = await client.wait_for_job(job.job_id, timeout=60.0)
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# Cancel job
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await client.cancel_job(job.job_id)
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```
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## Error Handling
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```python
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from synor_compute import SynorError
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try:
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result = await client.matmul(a, b)
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except SynorError as e:
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print(f"API Error: {e.message} (status: {e.status_code})")
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```
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## Type Hints
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Full type hint support:
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```python
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from synor_compute.types import (
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ProcessorType,
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Precision,
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BalancingStrategy,
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JobStatus,
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MatMulOptions,
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InferenceOptions,
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JobResult
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)
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```
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## Testing
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```bash
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pytest
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# or
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python -m pytest tests/
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```
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## License
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MIT
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