Returns a dictionary of CUDA memory allocator statistics for a given device.

The return value of this function is a dictionary of statistics, each of which is a non-negative integer.

Core statistics:

  • "allocated.{all,large_pool,small_pool}.{current,peak,allocated,freed}": number of allocation requests received by the memory allocator.

  • "allocated_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}": amount of allocated memory.

  • "segment.{all,large_pool,small_pool}.{current,peak,allocated,freed}": number of reserved segments from cudaMalloc().

  • "reserved_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}": amount of reserved memory.

  • "active.{all,large_pool,small_pool}.{current,peak,allocated,freed}": number of active memory blocks.

  • "active_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}": amount of active memory.

  • "inactive_split.{all,large_pool,small_pool}.{current,peak,allocated,freed}": number of inactive, non-releasable memory blocks.

  • "inactive_split_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}": amount of inactive, non-releasable memory.

For these core statistics, values are broken down as follows.

Pool type:

  • all: combined statistics across all memory pools.

  • large_pool: statistics for the large allocation pool (as of October 2019, for size >= 1MB allocations).

  • small_pool: statistics for the small allocation pool (as of October 2019, for size < 1MB allocations).

Metric type:

  • current: current value of this metric.

  • peak: maximum value of this metric.

  • allocated: historical total increase in this metric.

  • freed: historical total decrease in this metric.

In addition to the core statistics, we also provide some simple event counters:

  • "num_alloc_retries": number of failed cudaMalloc calls that result in a cache flush and retry.

  • "num_ooms": number of out-of-memory errors thrown.

The caching allocator can be configured via ENV to not split blocks larger than a defined size (see Memory Management section of the Cuda Semantics documentation). This helps avoid memory framentation but may have a performance penalty. Additional outputs to assist with tuning and evaluating impact:

  • "max_split_size": blocks above this size will not be split.

  • "oversize_allocations.{current,peak,allocated,freed}": number of over-size allocation requests received by the memory allocator.

  • "oversize_segments.{current,peak,allocated,freed}": number of over-size reserved segments from cudaMalloc().


device (torch.device or int, optional) – selected device. Returns statistics for the current device, given by current_device(), if device is None (default).


See Memory management for more details about GPU memory management.


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