Anaconda Enterprise: Documentation

# Release Notes

## Version 2.1.0

This release of Accelerate is a feature release, containing two additions:

- Profiling tools:
  - An enhanced version of the Python profiler that captures function arguments, including shapes and dtypes of Numpy arrays.
  - Integration of tools for visualising profiles in Jupyter notebooks, allowing interactive experimentation.
- Accelerated UFuncs, which provide a speed improvement over Numpy’s built-in UFuncs by using Intel MKL.

## Version 2.0.2

This update adds support for updated versions of Numba and MKL:

- Numba 0.23 is now supported, and is a requirement.
- MKL 11.3.1, standard in Anaconda 2.5, is now supported.

## Version 2.0.1

This minor update to Accelerate adds no functional changes, but provides additional clarification of the the relationship between the Accelerate and NumbaPro conda packages upon installation.

## Version 2.0

NumbaPro has been deprecated, and its code generation features have been moved into open-source Numba. The CUDA library functions have been moved into Accelerate, along with some Intel MKL functionality. High-level functions and access to additional native library implementations will be added in future releases of Accelerate, and there will be no further updates to NumbaPro.

A NumbaPro compatibility layer (listed as release 0.22.0 of NumbaPro) provides access to the new Accelerate packages through the old NumbaPro package names. This avoids the need to change any existing code immediately for use with Accelerate. A warning will be generated upon import of the compatibility layer, to highlight the deprecation of the NumbaPro package.

### CUDA Libraries

CUDA library functionality is equivalent to that in NumbaPro 0.21, with the following packages renamed:

NumbaPro Package | Accelerate package
--- | ---
`numbapro.cudalib.cublas` | `accelerate.cuda.blas`
`numbapro.cudalib.cufft` | `accelerate.cuda.fft`
`numbapro.cudalib.curand` | `accelerate.cuda.rand`
`numbapro.cudalib.cusparse` | `accelerate.cuda.sparse`
`numbapro.cudalib.sorting` | `accelerate.cuda.sorting`

### Code Generation

The `vectorize` targets `parallel` and `cuda` can now be accessed with Numba, as can the `cuda.reduce` decorator. Printing of integers and floating point values from CUDA kernels is also possible in Numba, and no longer requires NumbaPro or Accelerate to be imported.

### Intel MKL

The MKL FFT implementation is available in the package `accelerate.mkl.fftpack`, and MKL service functions are available in the `accelerate.mkl` package.