How dharmik7878/tensorflow works
TensorFlow is an end-to-end machine-learning framework with Python and C++ APIs. A typical user workflow builds a computation from tensor operations, runs it on available devices, and uses results in training or inference; models can also be saved, restored, or converted for deployment. The tree additionally shows distributed execution, gradients, and accelerator-specific paths. The excerpts are selective, so the graph represents documented capabilities and visible subsystems without claiming uninspected modules are directly wired together.
Main entry points: standard_ops.h Tensor operations · __init__.py Python API · ops.cc C++ API · central_storage_strategy.py Distributed strategy
Components 19
User APIs
- Python API
tensorflow/__init__.py - C API
tensorflow/c/c_api.cc - C++ API
tensorflow/cc/framework/ops.cc
Execution and training
- Tensor operations
tensorflow/cc/ops/standard_ops.h - Gradient computation
tensorflow/cc/framework/gradients.cc - Distributed strategy
tensorflow/python/distribute/central_storage_strategy.py - Session execution
tensorflow/cc/client/client_session.cc
Model lifecycle
- SavedModel loading
tensorflow/cc/saved_model/loader.cc - SavedModel API
tensorflow/c/experimental/saved_model/core/tf_saved_model_api.cc
Compilation and devices
- XLA compilation
tensorflow/compiler/jit/mark_for_compilation_pass.cc - TFRT execution
tensorflow/compiler/mlir/tfrt/transforms/tfrt_pipeline_options.h - TensorRT operation
tensorflow/compiler/tf2tensorrt/ops/trt_engine_op.cc - TensorRT execution
tensorflow/compiler/tf2tensorrt/kernels/trt_engine_op.cc - StableHLO conversion
tensorflow/compiler/mlir/lite/python/stablehlo_tfl_pipeline.cc - TPU embedding state
tensorflow/core/tpu/kernels/tpu_embedding_engine_state_interface.h
Interop and storage
- Filesystem plugins
tensorflow/c/experimental/filesystem/modular_filesystem.cc - Benchmark result storage
tensorflow/lite/experimental/acceleration/mini_benchmark/fb_storage.h
Other components
- Application developer
- TensorRT
Connections 19
- Application developer → Python API: uses
- Application developer → C API: uses
- Application developer → C++ API: uses
- Python API → Tensor operations: builds computations
- C++ API → Tensor operations: builds computations
- Tensor operations → Session execution: executes graph
- Tensor operations → Gradient computation: computes gradients
- Application developer → Distributed strategy: trains across devices
- Distributed strategy → Session execution: runs computation
- Application developer → SavedModel loading: loads model
- SavedModel API → SavedModel loading: loads model
- Tensor operations → XLA compilation: compiles eligible graphs
- Tensor operations → TFRT execution: lowers for execution
- TensorRT operation → TensorRT execution: dispatches execution
- TensorRT execution → TensorRT: builds and runs engine
- Application developer → StableHLO conversion: converts model
- Application developer → Filesystem plugins: accesses files
- Application developer → Benchmark result storage: records benchmark results
- TPU embedding state → TensorRT