This is a list of changes introduced in each release that users should be aware of when migrating from older versions. Most changes are carefully designed not to break existing code; however changes that may possibly break them are highlighted with a box.
Dropping Support of Python 2.7 and 3.4¶
Starting from CuPy v7, Python 2.7 and 3.4 are no longer supported as it reaches its end-of-life (EOL) in January 2020 (2.7) and March 2019 (3.4). Python 3.5.1 is the minimum Python version supported by CuPy v7. Please upgrade the Python version if you are using affected versions of Python to any later versions listed under Installation.
Binary Packages Ignore
Prior to CuPy v6,
LD_LIBRARY_PATH environment variable can be used to override cuDNN / NCCL libraries bundled in the binary distribution (also known as wheels).
In CuPy v6,
LD_LIBRARY_PATH will be ignored during discovery of cuDNN / NCCL; CuPy binary distributions always use libraries that comes with the package to avoid errors caused by unexpected override.
cupyx.scipy namespace has been introduced to provide CUDA-enabled SciPy functions.
cupy.sparse module has been renamed to
cupy.sparse will be kept as an alias for backward compatibility.
Dropped Support for CUDA 7.0 / 7.5¶
CuPy v5 no longer supports CUDA 7.0 / 7.5.
The version number has been bumped from v2 to v4 to align with the versioning of Chainer. Therefore, CuPy v3 does not exist.
Default Memory Pool¶
Prior to CuPy v4, memory pool was only enabled by default when CuPy is used with Chainer. In CuPy v4, memory pool is now enabled by default, even when you use CuPy without Chainer. The memory pool significantly improves the performance by mitigating the overhead of memory allocation and CPU/GPU synchronization.
When you monitor GPU memory usage (e.g., using
nvidia-smi), you may notice that GPU memory not being freed even after the array instance become out of scope.
This is expected behavior, as the default memory pool “caches” the allocated memory blocks.
To access the default memory pool instance, use
You can access the statistics and free all unused memory blocks “cached” in the memory pool.
import cupy a = cupy.ndarray(100, dtype=cupy.float32) mempool = cupy.get_default_memory_pool() # For performance, the size of actual allocation may become larger than the requested array size. print(mempool.used_bytes()) # 512 print(mempool.total_bytes()) # 512 # Even if the array goes out of scope, its memory block is kept in the pool. a = None print(mempool.used_bytes()) # 0 print(mempool.total_bytes()) # 512 # You can clear the memory block by calling `free_all_blocks`. mempool.free_all_blocks() print(mempool.used_bytes()) # 0 print(mempool.total_bytes()) # 0
You can even disable the default memory pool by the code below. Be sure to do this before any other CuPy operations.
import cupy cupy.cuda.set_allocator(None) cupy.cuda.set_pinned_memory_allocator(None)
CuPy v4 now requires NVIDIA GPU with Compute Capability 3.0 or larger. See the List of CUDA GPUs to check if your GPU supports Compute Capability 3.0.
As CUDA Stream is fully supported in CuPy v4,
cupy.cuda.RandomState.set_stream, the function to change the stream used by the random number generator, has been removed.
See the discussion in #306 for more details.
cupyx namespace has been introduced to provide features specific to CuPy (i.e., features not provided in NumPy) while avoiding collision in future.
See CuPy-specific Functions for the list of such functions.
Update of Docker Images¶
CuPy official Docker images (see Installation Guide for details) are now updated to use CUDA 8.0 and cuDNN 6.0. This change was introduced because CUDA 7.5 does not support NVIDIA Pascal GPUs.
To use these images, you may need to upgrade the NVIDIA driver on your host. See Requirements of nvidia-docker for details.