89fc542da4
Adds comprehensive model management and training capabilities: synor-compute (Rust): - ModelRegistry with pre-registered popular models - LLMs: Llama 3/3.1, Mistral, Mixtral, Qwen, DeepSeek, Phi, CodeLlama - Embedding: BGE, E5 - Image: Stable Diffusion XL, FLUX.1 - Speech: Whisper - Multi-modal: LLaVA - ModelInfo with parameters, format, precision, context length - Custom model upload and registration - Model search by name/category Flutter SDK: - Model registry APIs: listModels, getModel, searchModels - Custom model upload with multipart upload - Training APIs: train(), fineTune(), trainStream() - TrainingOptions: framework, epochs, batch_size, learning_rate - TrainingProgress for real-time updates - ModelUploadOptions and ModelUploadResult Example code for: - Listing available models by category - Fine-tuning pre-trained models - Uploading custom Python/ONNX models - Streaming training progress This enables users to: 1. Use pre-registered models like 'llama-3-70b' 2. Upload their own custom models 3. Fine-tune models on custom datasets 4. Track training progress in real-time
103 lines
2.9 KiB
Dart
103 lines
2.9 KiB
Dart
/// Synor Compute SDK for Flutter/Dart
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///
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/// A high-performance SDK for distributed heterogeneous computing.
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/// Supports CPU, GPU, TPU, NPU, LPU, FPGA, DSP, WebGPU, and WASM processors.
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///
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/// ## Quick Start
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///
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/// ```dart
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/// import 'package:synor_compute/synor_compute.dart';
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///
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/// void main() async {
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/// // Create client
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/// final client = SynorCompute(apiKey: 'your-api-key');
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///
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/// // Matrix multiplication
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/// final a = Tensor.rand([512, 512]);
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/// final b = Tensor.rand([512, 512]);
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/// final result = await client.matmul(a, b, options: MatMulOptions(
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/// precision: Precision.fp16,
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/// processor: ProcessorType.gpu,
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/// ));
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///
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/// print('Result shape: ${result.result!.shape}');
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/// print('Execution time: ${result.executionTimeMs}ms');
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///
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/// // LLM Inference
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/// final response = await client.inference(
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/// 'llama-3-70b',
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/// 'Explain quantum computing',
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/// options: InferenceOptions(maxTokens: 256),
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/// );
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/// print(response.result);
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///
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/// // Streaming inference
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/// await for (final token in client.inferenceStream(
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/// 'llama-3-70b',
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/// 'Write a haiku about computing',
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/// )) {
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/// stdout.write(token);
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/// }
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///
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/// // Clean up
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/// client.dispose();
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/// }
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/// ```
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///
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/// ## Features
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///
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/// - **Matrix Operations**: matmul, conv2d, attention, elementwise, reduce
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/// - **LLM Inference**: Standard and streaming inference
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/// - **Tensor Management**: Upload, download, and delete tensors
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/// - **Job Management**: Submit, poll, cancel, and list jobs
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/// - **Pricing**: Get real-time pricing for all processor types
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/// - **Usage Statistics**: Track compute usage and costs
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///
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/// ## Supported Processors
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///
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/// | Processor | Best For |
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/// |-----------|----------|
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/// | CPU | General compute, small batches |
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/// | GPU | Large matrix operations, training |
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/// | TPU | Tensor operations, inference |
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/// | NPU | Neural network inference |
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/// | LPU | Large language model inference |
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/// | FPGA | Custom operations, low latency |
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/// | DSP | Signal processing |
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/// | WebGPU | Browser-based compute |
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/// | WASM | Portable compute |
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library synor_compute;
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export 'src/types.dart'
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show
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Precision,
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ProcessorType,
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Priority,
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JobStatus,
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BalancingStrategy,
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DType,
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SynorConfig,
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MatMulOptions,
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Conv2dOptions,
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AttentionOptions,
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InferenceOptions,
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PricingInfo,
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UsageStats,
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SynorException,
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// Model types
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ModelCategory,
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ModelFormat,
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MlFramework,
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ModelInfo,
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ModelUploadOptions,
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ModelUploadResult,
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// Training types
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TrainingOptions,
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TrainingResult,
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TrainingProgress;
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export 'src/tensor.dart' show Tensor;
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export 'src/job.dart' show JobResult, JobStatusUpdate, Job, JobBatch;
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export 'src/client.dart' show SynorCompute;
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