Skip to content

on_boarding.md

Latest commit

 

History

History
93 lines (57 loc) · 7.94 KB

on_boarding.md

File metadata and controls

93 lines (57 loc) · 7.94 KB
Raw
graph LR
    Structured_Data_Input_Preprocessing["Structured Data Input & Preprocessing"]
    Fold_Blocks_API_Computation_Graph_Definition_["Fold Blocks API (Computation Graph Definition)"]
    Block_to_Loom_Compiler["Block-to-Loom Compiler"]
    Loom_Dynamic_Batching_Engine_Runtime_["Loom Dynamic Batching Engine (Runtime)"]
    TensorFlow_Backend_Custom_Operations["TensorFlow Backend & Custom Operations"]
    Model_Training_Evaluation_Orchestration["Model Training & Evaluation Orchestration"]
    Structured_Data_Input_Preprocessing -- "Provides prepared structured data." --> Loom_Dynamic_Batching_Engine_Runtime_
    Fold_Blocks_API_Computation_Graph_Definition_ -- "Defines computation graphs." --> Block_to_Loom_Compiler
    Block_to_Loom_Compiler -- "Generates Loom-executable graph." --> Loom_Dynamic_Batching_Engine_Runtime_
    Loom_Dynamic_Batching_Engine_Runtime_ -- "Dispatches batched operations." --> TensorFlow_Backend_Custom_Operations
    TensorFlow_Backend_Custom_Operations -- "Returns tensor results." --> Loom_Dynamic_Batching_Engine_Runtime_
    Model_Training_Evaluation_Orchestration -- "Requests batched data." --> Loom_Dynamic_Batching_Engine_Runtime_
    Model_Training_Evaluation_Orchestration -- "Initiates model compilation." --> Block_to_Loom_Compiler
    Model_Training_Evaluation_Orchestration -- "Manages computation sessions." --> Loom_Dynamic_Batching_Engine_Runtime_
    Loom_Dynamic_Batching_Engine_Runtime_ -- "Provides batched data." --> Model_Training_Evaluation_Orchestration
    Model_Training_Evaluation_Orchestration -- "Manages TensorFlow sessions and graph execution." --> TensorFlow_Backend_Custom_Operations
    click Fold_Blocks_API_Computation_Graph_Definition_ href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/fold/Fold_Blocks_API_Computation_Graph_Definition_.md" "Details"
    click Block_to_Loom_Compiler href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/fold/Block_to_Loom_Compiler.md" "Details"
    click Loom_Dynamic_Batching_Engine_Runtime_ href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/fold/Loom_Dynamic_Batching_Engine_Runtime_.md" "Details"
    click TensorFlow_Backend_Custom_Operations href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/fold/TensorFlow_Backend_Custom_Operations.md" "Details"
    click Model_Training_Evaluation_Orchestration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/fold/Model_Training_Evaluation_Orchestration.md" "Details"
Loading

CodeBoardingDemoContact

Details

The TensorFlow Fold architecture is designed to efficiently process variable-structured data within TensorFlow's static graph paradigm. It achieves this through a layered approach, starting with Structured Data Input & Preprocessing which prepares raw data. The Fold Blocks API allows users to declaratively define computation graphs, which are then translated by the Block-to-Loom Compiler into an optimized representation. The core Loom Dynamic Batching Engine dynamically batches these variable-sized inputs for efficient execution on the TensorFlow Backend & Custom Operations, which leverages custom C++ operations for performance. Finally, the Model Training & Evaluation Orchestration component oversees the entire lifecycle, managing data flow, compilation, and TensorFlow session execution for training and evaluation. This design ensures a clear separation of concerns, enabling flexible model definition and high-performance execution of structured data.

Structured Data Input & Preprocessing

Responsible for ingesting and initially preparing variable-structured input data, converting it into a format suitable for Fold's internal processing. This component handles data loading, parsing, and initial structuring.

Related Classes/Methods:

Fold Blocks API (Computation Graph Definition) [Expand]

Provides the high-level, declarative API for users to define complex computation graphs using composable "blocks." This is the primary interface for users to specify their deep learning models and data transformations.

Related Classes/Methods:

Block-to-Loom Compiler [Expand]

The crucial intermediary that translates the high-level Fold Blocks API definitions into a lower-level, optimized representation executable by the Loom dynamic batching engine. This component bridges the gap between user-defined logic and the runtime.

Related Classes/Methods:

Loom Dynamic Batching Engine (Runtime) [Expand]

The core runtime engine that dynamically batches variable-sized structured inputs into fixed-size tensors. It manages the execution of operations defined by the Block Compiler, effectively bridging the gap to TensorFlow's static graph model.

Related Classes/Methods:

TensorFlow Backend & Custom Operations [Expand]

The low-level layer responsible for executing the batched operations within TensorFlow. This includes custom C++ operations essential for the performance-critical aspects of the dynamic batching process, interfacing directly with TensorFlow's C++ API.

Related Classes/Methods:

Model Training & Evaluation Orchestration [Expand]

Manages the end-to-end lifecycle of a Fold model, including setting up training loops, managing TensorFlow sessions, optimizing parameters, and evaluating model performance.

Related Classes/Methods:

FAQ