stormlog.profiler

Core Stormlog for PyTorch.

Classes

GPUMemoryProfiler([device, track_tensors, ...])	Comprehensive GPU memory profiler for PyTorch operations.
MemorySnapshot(timestamp, allocated_memory, ...)	Represents a memory snapshot at a specific point in time.
ProfileResult(function_name, execution_time, ...)	Results from profiling a function or operation.
TensorTracker()	Tracks tensor creation and deletion for memory profiling.

class stormlog.profiler.MemorySnapshot(timestamp, allocated_memory, reserved_memory, max_memory_allocated, max_memory_reserved, active_memory, inactive_memory, cpu_memory, device_id=0, operation=None, stack_trace=None)

Bases: object

Represents a memory snapshot at a specific point in time.

Parameters:

• timestamp (float)

• allocated_memory (int)

• reserved_memory (int)

• max_memory_allocated (int)

• max_memory_reserved (int)

• active_memory (int)

• inactive_memory (int)

• cpu_memory (int)

• device_id (int)

• operation (str | None)

• stack_trace (str | None)

timestamp: float

allocated_memory: int

reserved_memory: int

max_memory_allocated: int

max_memory_reserved: int

active_memory: int

inactive_memory: int

cpu_memory: int

device_id: int = 0

operation: str | None = None

stack_trace: str | None = None

to_dict()

Convert snapshot to dictionary.

Return type:

Dict[str, Any]

class stormlog.profiler.ProfileResult(function_name, execution_time, memory_before, memory_after, memory_peak, memory_allocated, memory_freed, tensors_created, tensors_deleted, call_count=1)

Bases: object

Results from profiling a function or operation.

Parameters:

• function_name (str)

• execution_time (float)

• memory_before (MemorySnapshot)

• memory_after (MemorySnapshot)

• memory_peak (MemorySnapshot)

• memory_allocated (int)

• memory_freed (int)

• tensors_created (int)

• tensors_deleted (int)

• call_count (int)

function_name: str

execution_time: float

memory_before: MemorySnapshot

memory_after: MemorySnapshot

memory_peak: MemorySnapshot

memory_allocated: int

memory_freed: int

tensors_created: int

tensors_deleted: int

call_count: int = 1

memory_diff()

Calculate memory difference between before and after.

Return type:

int

peak_memory_usage()

Get peak memory usage during execution.

Return type:

int

to_dict()

Convert result to dictionary.

Return type:

Dict[str, Any]

class stormlog.profiler.GPUMemoryProfiler(device=None, track_tensors=True, track_cpu_memory=True, collect_stack_traces=False)

Bases: object

Comprehensive GPU memory profiler for PyTorch operations.

Parameters:

• device (str | int | torch.device | None)

• track_tensors (bool)

• track_cpu_memory (bool)

• collect_stack_traces (bool)

profile_function(func, *args, **kwargs)

Profile a single function call.

Parameters:

• func (Callable[[...], Any]) – Function to profile

• *args (Any) – Arguments to pass to function

• **kwargs (Any) – Keyword arguments to pass to function

Returns:

ProfileResult with profiling information

Return type:

ProfileResult

profile_context(name='context')

Context manager for profiling a block of code.

Parameters:

name (str) – Name for the profiled context

Yields:

ProfileResult after the context exits

Return type:

Any

start_monitoring(interval=0.1)

Start continuous memory monitoring.

Parameters:

interval (float) – Monitoring interval in seconds

Return type:

None

stop_monitoring()

Stop continuous memory monitoring.

Return type:

None

get_summary()

Get a summary of all profiling results.

Return type:

Dict[str, Any]

clear_results()

Clear all profiling results and reset state.

Return type:

None

class stormlog.profiler.TensorTracker

Bases: object

Tracks tensor creation and deletion for memory profiling.

count_tensors()

Count current number of tracked tensors.

Return type:

int