torch_geometric.profile

profileit	A decorator to facilitate profiling a function, e.g., obtaining training runtime and memory statistics of a specific model on a specific dataset.
timeit	A context decorator to facilitate timing a function, e.g., obtaining the runtime of a specific model on a specific dataset.
get_stats_summary	Creates a summary of collected runtime and memory statistics.
trace_handler
rename_profile_file
torch_profile
count_parameters	Given a torch.nn.Module, count its trainable parameters.
get_model_size	Given a torch.nn.Module, get its actual disk size in bytes.
get_data_size	Given a torch_geometric.data.Data object, get its theoretical memory usage in bytes.
get_cpu_memory_from_gc	Returns the used CPU memory in bytes, as reported by the Python garbage collector.
get_gpu_memory_from_gc	Returns the used GPU memory in bytes, as reported by the Python garbage collector.
get_gpu_memory_from_nvidia_smi	Returns the free and used GPU memory in megabytes, as reported by nivdia-smi.

profileit()

A decorator to facilitate profiling a function, e.g., obtaining training runtime and memory statistics of a specific model on a specific dataset. Returns a Stats object with the attributes time, max_active_cuda, max_reserved_cuda, max_active_cuda, nvidia_smi_free_cuda, nvidia_smi_used_cuda.

@profileit()
def train(model, optimizer, x, edge_index, y):
    optimizer.zero_grad()
    out = model(x, edge_index)
    loss = criterion(out, y)
    loss.backward()
    optimizer.step()
    return float(loss)

loss, stats = train(model, x, edge_index, y)

class timeit(log: bool = True, avg_time_divisor: int = 0)

A context decorator to facilitate timing a function, e.g., obtaining the runtime of a specific model on a specific dataset.

@torch.no_grad()
def test(model, x, edge_index):
    return model(x, edge_index)

with timeit() as t:
    z = test(model, x, edge_index)
time = t.duration

reset()

Prints the duration and resets current timer.

get_stats_summary(stats_list: List[Stats])

Creates a summary of collected runtime and memory statistics. Returns a StatsSummary object with the attributes time_mean, time_std, max_active_cuda, max_reserved_cuda, max_active_cuda, min_nvidia_smi_free_cuda, max_nvidia_smi_used_cuda.

Parameters

stats_list (List[Stats]) – A list of Stats objects, as returned by profileit().

trace_handler(p)

rename_profile_file(*args)

torch_profile()

count_parameters(model: Module) → int

Given a torch.nn.Module, count its trainable parameters.

Parameters

model (torch.nn.Model) – The model.

get_model_size(model: Module) → int

Given a torch.nn.Module, get its actual disk size in bytes.

Parameters

model (torch model) – The model.

get_data_size(data: BaseData) → int

Given a torch_geometric.data.Data object, get its theoretical memory usage in bytes.

Parameters

data (torch_geometric.data.Data or torch_geometric.data.HeteroData) – The Data or HeteroData graph object.

get_cpu_memory_from_gc() → int

Returns the used CPU memory in bytes, as reported by the Python garbage collector.

get_gpu_memory_from_gc(device: int = 0) → int

Returns the used GPU memory in bytes, as reported by the Python garbage collector.

Parameters

device (int, optional) – The GPU device identifier. (default: 1)

get_gpu_memory_from_nvidia_smi(device: int = 0, digits: int = 2) → Tuple[float, float]

Returns the free and used GPU memory in megabytes, as reported by nivdia-smi.

Note

nvidia-smi will generally overestimate the amount of memory used by the actual program, see here.

Parameters

• device (int, optional) – The GPU device identifier. (default: 1)

• digits (int) – The number of decimals to use for megabytes. (default: 2)