API Reference¶

High-level API¶

exception rmm.rmm.RMMError(errcode, msg)¶: Bases: Exception

class rmm.rmm.RMMNumbaManager(*args, **kwargs)¶

Bases: numba.cuda.cudadrv.driver.HostOnlyCUDAMemoryManager

External Memory Management Plugin implementation for Numba. Provides on-device allocation only.

See http://numba.pydata.org/numba-doc/latest/cuda/external-memory.html for details of the interface being implemented here.

Attributes

interface_version: Returns an integer specifying the version of the EMM Plugin interface supported by the plugin implementation.

Methods

`defer_cleanup`()	Returns a context manager that disables cleanup of mapped or pinned host memory in the current context whilst it is active.
`get_ipc_handle`(memory)	Get an IPC handle for the MemoryPointer memory with offset modified by the RMM memory pool.
`get_memory_info`()	Returns `(free, total)` memory in bytes in the context.
`initialize`()	Perform any initialization required for the EMM plugin instance to be ready to use.
`memalloc`(size)	Allocate an on-device array from the RMM pool.
`memhostalloc`(size[, mapped, portable, wc])	Implements the allocation of pinned host memory.
`mempin`(owner, pointer, size[, mapped])	Implements the pinning of host memory.
`reset`()	Clears up all host memory (mapped and/or pinned) in the current context.

memallocmanaged

get_ipc_handle(memory)¶: Get an IPC handle for the MemoryPointer memory with offset modified by the RMM memory pool.

get_memory_info()¶

Returns (free, total) memory in bytes in the context. May raise NotImplementedError, if returning such information is not practical (e.g. for a pool allocator).

Returns: Memory info
Return type: MemoryInfo

initialize()¶

Perform any initialization required for the EMM plugin instance to be ready to use.

Returns: None

property interface_version¶: Returns an integer specifying the version of the EMM Plugin interface supported by the plugin implementation. Should always return 1 for implementations of this version of the specification.

memalloc(size)¶: Allocate an on-device array from the RMM pool.

rmm.rmm.is_initialized()¶: Returns true if RMM has been initialized, false otherwise

rmm.rmm.reinitialize(pool_allocator=False, managed_memory=False, initial_pool_size=None, maximum_pool_size=None, devices=0, logging=False, log_file_name=None)¶

Finalizes and then initializes RMM using the options passed. Using memory from a previous initialization of RMM is undefined behavior and should be avoided.

Parameters

pool_allocatorbool, default False: If True, use a pool allocation strategy which can greatly improve performance.
managed_memorybool, default False: If True, use managed memory for device memory allocation
initial_pool_sizeint, default None: When pool_allocator is True, this indicates the initial pool size in bytes. By default, 1/2 of the total GPU memory is used. When pool_allocator is False, this argument is ignored if provided.
maximum_pool_sizeint, default None: When pool_allocator is True, this indicates the maximum pool size in bytes. By default, the total available memory on the GPU is used. When pool_allocator is False, this argument is ignored if provided.
devicesint or List[int], default 0: GPU device IDs to register. By default registers only GPU 0.
loggingbool, default False: If True, enable run-time logging of all memory events (alloc, free, realloc). This has significant performance impact.
log_file_namestr: Name of the log file. If not specified, the environment variable RMM_LOG_FILE is used. A TypeError is thrown if neither is available.

rmm.rmm.rmm_cupy_allocator(nbytes)¶

A CuPy allocator that makes use of RMM.

Examples

>>> import rmm
>>> import cupy
>>> cupy.cuda.set_allocator(rmm.rmm_cupy_allocator)

Memory Resources¶

class rmm.mr.BinningMemoryResource(MemoryResource upstream_mr, int8_t min_size_exponent=-1, int8_t max_size_exponent=-1)¶

Bases: rmm._lib.memory_resource.MemoryResource

Allocates memory from a set of specified “bin” sizes based on a specified allocation size.

If min_size_exponent and max_size_exponent are specified, initializes with one or more FixedSizeMemoryResource bins in the range [2^min_size_exponent, 2^max_size_exponent].

Call add_bin to add additional bin allocators.

Parameters

upstream_mrMemoryResource: The memory resource to use for allocations larger than any of the bins
min_size_exponentsize_t: The base-2 exponent of the minimum size FixedSizeMemoryResource bin to create.
max_size_exponentsize_t: The base-2 exponent of the maximum size FixedSizeMemoryResource bin to create.

Methods

add_bin(self, size_t allocation_size[, …])

Adds a bin of the specified maximum allocation size to this memory resource.

add_bin(self, size_t allocation_size, bin_resource=None)¶

Adds a bin of the specified maximum allocation size to this memory resource. If specified, uses bin_resource for allocation for this bin. If not specified, creates and uses a FixedSizeMemoryResource for allocation for this bin.

Allocations smaller than allocation_size and larger than the next smaller bin size will use this fixed-size memory resource.

Parameters

allocation_sizesize_t: The maximum allocation size in bytes for the created bin
bin_resourceMemoryResource: The resource to use for this bin (optional)

class rmm.mr.CudaMemoryResource(device=None)¶

Bases: rmm._lib.memory_resource.MemoryResource

Memory resource that uses cudaMalloc/Free for allocation/deallocation

class rmm.mr.FixedSizeMemoryResource(MemoryResource upstream, size_t block_size=1048576, size_t blocks_to_preallocate=128)¶

Bases: rmm._lib.memory_resource.MemoryResource

Memory resource which allocates memory blocks of a single fixed size.

Parameters

upstreamMemoryResource: The MemoryResource from which to allocate blocks for the pool.
block_sizeint, optional: The size of blocks to allocate (default is 1MiB).
blocks_to_preallocateint, optional: The number of blocks to allocate to initialize the pool.

Notes

Supports only allocations of size smaller than the configured block_size.

class rmm.mr.LoggingResourceAdaptor(MemoryResource upstream, log_file_name=None)¶

Bases: rmm._lib.memory_resource.MemoryResource

Memory resource that logs information about allocations/deallocations performed by an upstream memory resource.

Parameters

upstreamMemoryResource: The upstream memory resource.
log_file_namestr: Path to the file to which logs are written.

Methods

`flush`(self)
`get_file_name`(self)
`get_upstream`(self)

flush(self)¶

get_file_name(self)¶

get_upstream(self) → MemoryResource ¶

class rmm.mr.ManagedMemoryResource¶

Bases: rmm._lib.memory_resource.MemoryResource

Memory resource that uses cudaMallocManaged/Free for allocation/deallocation.

class rmm.mr.MemoryResource¶: Bases: object

class rmm.mr.PoolMemoryResource(MemoryResource upstream, initial_pool_size=None, maximum_pool_size=None)¶

Bases: rmm._lib.memory_resource.MemoryResource

Coalescing best-fit suballocator which uses a pool of memory allocated from an upstream memory resource.

Parameters

upstreamMemoryResource: The MemoryResource from which to allocate blocks for the pool.
initial_pool_sizeint,optional: Initial pool size in bytes. By default, an implementation defined pool size is used.
maximum_pool_sizeint, optional: Maximum size in bytes, that the pool can grow to.

rmm.mr.disable_logging()¶: Disable logging if it was enabled previously using rmm.initialize() or rmm.enable_logging().

rmm.mr.enable_logging(log_file_name=None)¶: Enable logging of run-time events.

rmm.mr.get_current_device_resource()¶

Get the memory resource used for RMM device allocations on the current device.

If the returned memory resource is used when a different device is the active CUDA device, behavior is undefined.

rmm.mr.get_current_device_resource_type()¶: Get the memory resource type used for RMM device allocations on the current device.

rmm.mr.get_per_device_resource(int device)¶

Get the default memory resource for the specified device.

If the returned memory resource is used when a different device is the active CUDA device, behavior is undefined.

Parameters

deviceint: The ID of the device for which to get the memory resource.

rmm.mr.get_per_device_resource_type(int device)¶

Get the memory resource type used for RMM device allocations on the specified device.

Parameters

deviceint: The device ID

rmm.mr.is_initialized()¶: Check whether RMM is initialized

rmm.mr.set_current_device_resource(MemoryResource mr)¶

Set the default memory resource for the current device.

Parameters

mrMemoryResource: The memory resource to set. Must have been created while the current device is the active CUDA device.

rmm.mr.set_per_device_resource(int device, MemoryResource mr)¶

Set the default memory resource for the specified device.

Parameters

deviceint: The ID of the device for which to get the memory resource.
mrMemoryResource: The memory resource to set. Must have been created while device was the active CUDA device.

API Reference¶

High-level API¶

Memory Resources¶

Module contents¶