Thinc

⚠️ Backwards incompatibilities

* Thinc v7.0 drops support for Python 2.7 on Windows. Python 2.7 remains supported on Linux and OSX. Support could be restored in future. We're currently unable to build our new dependency, [`blis`](https://github.com/explosion/cython-blis), for Windows on Python 2.7. If you can assist with this, please let us know.

✨ New features and improvements

* Use [`blis`](https://github.com/explosion/cython-blis) for matrix multiplication. Previous versions delegated matrix multiplication to platform-specific libraries via numpy. This led to inconsistent results, especially around multi-threading. We now provide a standalone package, with the Blis linear algebra routines. Importantly, we've built Blis to be **single-threaded**. This makes it much easier to do efficient inference, as the library will no longer spawn threads underneath you.

* Use [`srsly`](https://github.com/explosion/srsly) for serialization. We now provide a single package with forks of our preferred serialisation libraries – specifically, `msgpack`, `ujson` and `cloudpickle`. This allows us to provide a single binary wheel for these dependencies, and to maintain better control of our dependency tree, preventing breakages.

* Update versions of `cymem`, `preshed` and `murmurhash`. Thinc is compiled against our memory pool and hash table libraries, `cymem` and `preshed`. Changing these build-time dependencies requires Thinc to be recompiled. This is one reason the major version number needed to be incremented for this release.

🔴 Bug fixes

* Fix issue explosion/spaCy2995: Pin `msgpack` to version `<0.6.0`, to avoid the low message-length limit introduced in v0.6.0, which breaks spaCy. We will relax the pin once spaCy is updated to set the `max_xx_len` argument to `msgpack.dumps()`

✨ New features and improvements

* Update dependencies to be able to provide binary wheels.
* Move GPU ops to separate package, [`thinc_gpu_ops`](https://github.com/explosion/thinc_gpu_ops).
* Support pip specifiers for GPU installation, e.g. `pip install thinc[cuda92]`.

🔴 Bug fixes

* Update `murmurhash` pin to accept newer version.

✨ New features and improvements

You can now require GPU capability using the pip "extras" syntax. Thinc also now expects CUDA to be installed at `/usr/local/cuda` by default. If you've installed it elsewhere, you can specify the location with the CUDA_HOME environment variable. Once Thinc is able to find CUDA, you can tell pip to install Thinc with cupy, as follows:

* `thinc[cuda]`: Install cupy from source (compatible with a range of cuda versions)
* `thinc[cuda80]`: Install the cupy-cuda80 wheel
* `thinc[cuda90]`: Install the cupy-cuda90 wheel
* `thinc[cuda91]`: Install the cupy-cuda91 wheel

If you're installing Thinc from a local wheel file, the syntax for adding an "extras" specifier is a bit unintuitive. The trick is to make the file path into a URL, so you can use an `egg` clause, as follows:

bash
pip install file://path/to/wheelegg=thinc[cuda]

✨ New features and improvements

* Thinc now vendorizes OpenBLAS's `cblas_sgemm` function, and delegates matrix multiplications to it by default. The provided function is single-threaded, making it easy to call Thinc from multiple processes. The default sgemm function can be overridden using the `THINC_BLAS` environment variable --- see below.
* `thinc.neural.util.get_ops` now understands device integers, e.g. `0` for GPU 0, as well as strings like `"cpu"` and `"cupy"`.
* Update `StaticVectors` model, to make use of spaCy v2.0's [`Vectors`](https://spacy.io/api/vectors) class.
* New `.gemm()` method on NumpyOps and CupyOps classes, allowing matrix and vector multiplication to be handled with a simple function. Example usage:

**Customizing the matrix multiplication backend**

Previous versions of Thinc have relied on numpy for matrix multiplications. When numpy is installed via wheel using pip (the default), numpy will usually be linked against a suboptimal matrix multiplication kernel. This made it difficult to ensure that Thinc was well optimized for the target machine.

To fix this, Thinc now provides its own matrix multiplications, by bundling the source code for OpenBLAS's sgemm kernel within the library. To change the default BLAS library, you can specify an environment variable, giving the location of the shared library you want to link against:

bash
THINC_BLAS=/opt/openblas/lib/libopenblas.so pip install thinc --no-cache-dir --no-binary
export LD_LIBRARY_PATH=/opt/openblas/lib
On OSX:
export DYLD_LIBRARY_PATH=/opt/openblas/lib


If you want to link against the Intel MKL instead of OpenBLAS, the easiest way is to install Miniconda. For instance, if you installed miniconda to `/opt/miniconda', the command to install Thinc linked against MKL would be:

bash
THINC_BLAS=/opt/miniconda/numpy-mkl/lib/libmkl_rt.so pip install thinc --no-cache-dir --no-binary
export LD_LIBRARY_PATH=/opt/miniconda/numpy-mkl/lib
On OSX:
export DYLD_LIBRARY_PATH=/opt/miniconda/numpy-mkl/lib


If the library file ends in a .a extension, it is linked statically; if it ends in .so, it's linked dynamically. Make sure you have the directory on your `LD_LIBRARY_PATH` at runtime if you use the dynamic linking.

🔴 Bug fixes

* Fix pickle support for `FeatureExtracter` class.
* Fix unicode error in Quora dataset loader.
* Fix batch normalization bugs. Now supports batch "renormalization" correctly.
* Models now reliably distinguish predict vs. train modes, using the convention `drop=None`. Previously, layers such as `BatchNorm` relied on having their `predict()` method called, which didn't work they were called by layers which didn't implement a `predict()` method. We now set `drop=None` to make this more reliable.
* Fix bug that caused incorrect data types to be produced by `FeatureExtracter`.

👥 Contributors

Thanks to dvsrepo, justindujardin, alephmelo and darkdreamingdan for the pull requests and contributions.

✨ New features and improvements

* Update `cytoolz` version pin to make Thinc compatible with Python 3.7.
* Only install old `pathlib` backport on Python 2 (see 69).
* Use `msgpack` instead of `msgpack-python`.
* Drop `termcolor` dependency.