Improve docs formatting and update links.

README.md

@@ -708,7 +708,7 @@ See [here](#memoryresource-objects) for more information on changing the current
 ### Using RMM with Numba
 
 You can configure Numba to use RMM for memory allocations using the
-Numba [EMM Plugin](http://numba.pydata.org/numba-doc/latest/cuda/external-memory.html#setting-the-emm-plugin).
+Numba [EMM Plugin](https://numba.readthedocs.io/en/stable/cuda/external-memory.html).
 
 This can be done in two ways:
 

python/docs/basics.md

@@ -146,7 +146,7 @@ allocations by setting the CuPy CUDA allocator to
 ### Using RMM with Numba
 
 You can configure Numba to use RMM for memory allocations using the
-Numba [EMM Plugin](http://numba.pydata.org/numba-doc/latest/cuda/external-memory.html#setting-the-emm-plugin).
+Numba [EMM Plugin](https://numba.readthedocs.io/en/stable/cuda/external-memory.html).
 
 This can be done in two ways:
 

python/rmm/_lib/memory_resource.pyx

@@ -236,19 +236,19 @@ cdef class CudaMemoryResource(DeviceMemoryResource):
 
     def __init__(self):
         """
-        Memory resource that uses cudaMalloc/Free for allocation/deallocation
+        Memory resource that uses ``cudaMalloc``/``cudaFree`` for allocation/deallocation.
         """
         pass
 
 
 cdef class CudaAsyncMemoryResource(DeviceMemoryResource):
     """
-    Memory resource that uses cudaMallocAsync/Free for
+    Memory resource that uses ``cudaMallocAsync``/``cudaFreeAsync`` for
     allocation/deallocation.
 
     Parameters
     ----------
-    initial_pool_size : int,optional
+    initial_pool_size : int, optional
         Initial pool size in bytes. By default, half the available memory
         on the device is used.
     release_threshold: int, optional
@@ -312,7 +312,7 @@ cdef class ManagedMemoryResource(DeviceMemoryResource):
 
     def __init__(self):
         """
-        Memory resource that uses cudaMallocManaged/Free for
+        Memory resource that uses ``cudaMallocManaged``/``cudaFree`` for
         allocation/deallocation.
         """
         pass
@@ -361,7 +361,7 @@ cdef class PoolMemoryResource(UpstreamResourceAdaptor):
         upstream_mr : DeviceMemoryResource
             The DeviceMemoryResource from which to allocate blocks for the
             pool.
-        initial_pool_size : int,optional
+        initial_pool_size : int, optional
             Initial pool size in bytes. By default, half the available memory
             on the device is used.
         maximum_pool_size : int, optional
@@ -551,7 +551,7 @@ cdef class CallbackMemoryResource(DeviceMemoryResource):
         integer representing the number of bytes to free.
 
     Examples
-    -------
+    --------
     >>> import rmm
     >>> base_mr = rmm.mr.CudaMemoryResource()
     >>> def allocate_func(size):
@@ -695,6 +695,9 @@ cdef class StatisticsResourceAdaptor(UpstreamResourceAdaptor):
         Gets the current, peak, and total allocated bytes and number of
         allocations.
 
+        The dictionary keys are ``current_bytes``, ``current_count``,
+        ``peak_bytes``, ``peak_count``, ``total_bytes``, and ``total_count``.
+
         Returns:
             dict: Dictionary containing allocation counts and bytes.
         """

python/rmm/rmm.py

@@ -66,24 +66,23 @@ def reinitialize(
     logging : bool, default False
         If True, enable run-time logging of all memory events
         (alloc, free, realloc).
-        This has significant performance impact.
+        This has a significant performance impact.
     log_file_name : str
         Name of the log file. If not specified, the environment variable
-        RMM_LOG_FILE is used. A ValueError is thrown if neither is available.
+        ``RMM_LOG_FILE`` is used. A ValueError is thrown if neither is available.
         A separate log file is produced for each device,
         and the suffix `".dev{id}"` is automatically added to the log file
         name.
 
     Notes
     -----
-    Note that if you use the environment variable CUDA_VISIBLE_DEVICES
-    with logging enabled, the suffix may not be what you expect. For
-    example, if you set CUDA_VISIBLE_DEVICES=1, the log file produced
-    will still have suffix `0`. Similarly, if you set
-    CUDA_VISIBLE_DEVICES=1,0 and use devices 0 and 1, the log file
-    with suffix `0` will correspond to the GPU with device ID `1`.
-    Use `rmm.get_log_filenames()` to get the log file names
-    corresponding to each device.
+    Note that if you use the environment variable ``CUDA_VISIBLE_DEVICES`` with
+    logging enabled, the suffix may not be what you expect. For example, if you
+    set ``CUDA_VISIBLE_DEVICES=1``, the log file produced will still have
+    suffix ``0``. Similarly, if you set ``CUDA_VISIBLE_DEVICES=1,0`` and use
+    devices 0 and 1, the log file with suffix ``0`` will correspond to the GPU
+    with device ID ``1``. Use `rmm.get_log_filenames()` to get the log file
+    names corresponding to each device.
     """
     for func, args, kwargs in reversed(_reinitialize_hooks):
         func(*args, **kwargs)
@@ -101,7 +100,7 @@ def reinitialize(
 
 def is_initialized():
     """
-    Returns true if RMM has been initialized, false otherwise
+    Returns True if RMM has been initialized, False otherwise.
     """
     return rmm.mr.is_initialized()
 
@@ -111,7 +110,7 @@ class RMMNumbaManager(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
+    See https://numba.readthedocs.io/en/stable/cuda/external-memory.html for
     details of the interface being implemented here.
     """
 
@@ -206,7 +205,7 @@ def finalizer():
 
 # Enables the use of RMM for Numba via an environment variable setting,
 # NUMBA_CUDA_MEMORY_MANAGER=rmm. See:
-# http://numba.pydata.org/numba-doc/latest/cuda/external-memory.html#environment-variable
+# https://numba.readthedocs.io/en/stable/cuda/external-memory.html#environment-variable
 _numba_memory_manager = RMMNumbaManager
 
 try: