This is the release note of v5.0.0b2. See here for the complete list of solved issues and merged PRs.

Highlights

• CuPy now supports DLPack to improve interoperability between frameworks. You can convert between cupy.ndarray and DLPack tensor using array.toDlpack() and cupy.fromDlpack(tensor). See the documentation for details.
• CuPy ndarray now implements __array_ufunc__ protocol to improve interoperability with NumPy. It makes NumPy ufuncs applicable to CuPy ndarrays directly (for example, numpy.exp(cupy.ones(3)) will call cupy.exp to compute the exponential, and return CuPy ndarray).
• CuPy now supports CUDA 9.2 and NumPy 1.14.
• More NumPy/SciPy compatible methods have been implemented: cupy.linalg.matrix_power, cupy.random.laplace, cupy.corrcoef, cupy.cov, cupy.i0, cupy.sinc, cupyx.scipy.special.* and more.
• cupy.einsum has been rewritten to use cuBLAS. This significantly reduces the memory usage and also improves the performance.
• Allocation strategy of pinned memory has been improved to reduce host memory usage.
• Fixed bugs in multiple functions with arrays with complex dtypes.

New Features

• Support DLPack (#1082)
• Implement cupy.corrcoef and cupy.cov (#1110, thanks @tsurumeso!)
• Allow more natural fusion notation (#1167)
• Implement special functions (#1233)
• Implement __array_ufunc__ (#1247, thanks @martindurant!)
• Improve performance of batch normalization (#1260)
• Add complex dtype to sparse matrix (#1277, thanks @chengts95!)
• Add cuDNN API for tensor operations and reduction (#1319, thanks @kashif!)
• Add distribution laplace (#1321)
• Implement cupy.linalg.matrix_power (#1374, thanks @ericmjl!)

Enhancements

• Reduce Python function call in ElementwiseKernel (#725)
• Check cuDNN convolution algorithm (#890)
• Remove memory copy in diag function (#1129)
• Fix linalg.matrix_rank casting for Windows (#1217)
• Use cuBLAS in cupy.einsum (#1218)
• Add divmod function to cupy namespace (#1286)
• Improve ndarray initializing performance (#1341)
• Fix type of return value of fused function (#1349)
• Support composition of fused functions (#1350)
• Support weak reference to CuPy array (#1355)
• Remove cupy_stdint.h (#1361)
• Round-up pooled memory allocation size with clp2 (#1372)
• Reduce GPU memory usage in (de)convotion (#1381)
• Support 'f' and 'c' in the order option of ndarray (#1385)

Bug Fixes

• Fix OutOfMemoryError raised even when there are sufficient large freeable chunks (#1256, thanks @hyabe!)
• Fix astype for complex dtypes (#1279)
• Fix real and imag for zero-dim arrays (#1280)
• Support rounding for complex types (#1282)
• Support expm1, log1p, log2 for complex type (#1283)
• Fix unary functions in misc for complex (#1284)
• Fix binary functions in misc to support complex types (#1285)
• Fix view of zerodim ndarray (#1287)
• Catch C++ exceptions from Thrust (#1289)
• Fix real, imag of non-contiguous complex arrays (#1303)
• Fix rint syntax error (#1311)
• Skip einsum test for NumPy versions with broken einsum (#1334)
• Fix type of reduction (#1354)
• Fix to accept longer order names (#1393)

Documentation

• Add NumPy 1.14 to supported versions (#1139)
• Update README to encourage use of wheels (#1208)
• Improve sparse docs to show conversion from/to SciPy (#1213)
• Force displaying known methods which are mis-recognized as attributes by Sphinx (#1250)
• Expand reference on differences in zero-dimensional arrays (#1254)
• Fix typo in sparse matrix docs (#1307)
• Split LICENSE file (#1325)
• Fix typo in profiler docs (#1327)
• Reorganize license file (#1330)
• Fix typos in zeros and zeros_like (#1357)
• Update requirements for v5.0.0b2 / v4.2 release (#1369)

Installation

• Use define macro in setup.py (#1121)
• Support bundling dependent DLLs for Windows wheel support (#1253)
• Remove deprecated imp.load_source in setup.py (#1329, thanks @vilyaair!)
• Add license file to wheel (#1333)

Tests

• Sparse complex ufunc (#1312)
• Add scipy_name to testing helper functions (#1339)