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# Profiling and Performance Analysis
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Profiling tools for analyzing training performance, identifying bottlenecks, and optimizing model training efficiency across different hardware configurations.
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## Capabilities
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### PyTorch Profiler
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Integration with PyTorch's built-in profiler for detailed performance analysis.
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```python { .api }
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class PyTorchProfiler:
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    def __init__(
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        self,
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        dirpath: Optional[str] = None,
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        filename: Optional[str] = None,
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        group_by_input_shapes: bool = False,
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        emit_nvtx: bool = False,
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        export_to_chrome: bool = True,
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        row_limit: int = 20,
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        sort_by_key: Optional[str] = None,
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        record_functions: Set[str] = None,
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        record_shapes: bool = False,
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        profile_memory: bool = False,
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        with_stack: bool = False,
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        with_flops: bool = False,
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        with_modules: bool = False,
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        **profiler_kwargs
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    ):
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        """Initialize PyTorch profiler."""
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```
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### Advanced Profiler
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Comprehensive profiling with detailed timing information.
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```python { .api }
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class AdvancedProfiler:
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    def __init__(
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        self,
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        dirpath: Optional[str] = None,
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        filename: Optional[str] = None,
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        line_count_restriction: float = 1.0
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    ):
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        """Initialize advanced profiler."""
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```
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### Simple Profiler
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Basic profiling for quick performance analysis.
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```python { .api }
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class SimpleProfiler:
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    def __init__(
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        self,
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        dirpath: Optional[str] = None,
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        filename: Optional[str] = None,
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        extended: bool = True
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    ):
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        """Initialize simple profiler."""
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```
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### Pass Through Profiler
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No-operation profiler that disables profiling without code changes.
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```python { .api }
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class PassThroughProfiler:
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    def __init__(self):
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        """Initialize pass-through profiler that performs no profiling."""
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```
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### XLA Profiler
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Profiler for TPU training with XLA optimization analysis.
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```python { .api }
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class XLAProfiler:
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    def __init__(
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        self,
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        port: int = 9012,
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        record_shapes: bool = False,
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        profile_memory: bool = False
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    ):
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        """Initialize XLA profiler for TPU performance analysis."""
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```
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### Base Profiler
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Base class for implementing custom profilers.
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```python { .api }
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class Profiler:
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    def __init__(self):
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        """Initialize base profiler."""
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    def start(self, action_name: str) -> None:
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        """Start profiling an action."""
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    def stop(self, action_name: str) -> None:
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        """Stop profiling an action."""
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    def summary(self) -> str:
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        """Get profiling summary."""
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    def describe(self) -> None:
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        """Print profiling description."""
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```