Building and Installing¶

There are several ways to build and install TensorStore, depending on the intended use case.

Python API¶

The TensorStore Python API requires Python 3.8 or later (Python 2 is not supported).

Installation from PyPI package¶

The Python bindings can be installed directly from the tensorstore PyPI package using pip. It is recommended to first create a virtual environment.

To install the latest published version, use:

# Use -vv option to show progress
python3 -m pip install tensorstore -vv

py -3 -m pip install tensorstore -vv

This is the simplest and fastest way to install the TensorStore Python bindings if you aren’t intending to make changes to the TensorStore source code.

If a pre-built binary package is available for your specific platform and Python version, it will be used and no additional build tools are required. Otherwise, the package will be built from the source distribution and the normal build dependencies are required.

Installation from local checkout¶

If you intend to make changes to the TensorStore source code while simultaneously using TensorStore as a dependency, you can create a virtual environment and then install from a local checkout of the git repository:

git clone https://github.com/google/tensorstore
cd tensorstore
python3 setup.py develop

This invokes Bazel to build the TensorStore C++ extension module. You must have the required build dependencies.

After making changes to the C++ source code, you must re-run:

python3 setup.py develop

to rebuild the extension module. Rebuilds are incremental and will be much faster than the initial build.

Note that while it also works to invoke python3 -m pip install -e . or python3 -m pip install ., that will result in Bazel being invoked from a temporary copy of the source tree, which prevents incremental rebuilds.

The build is affected by the following environment variables:

TENSORSTORE_BAZELISK¶

Path to Bazelisk script that is invoked in order to execute the build. By default the bundled bazelisk.py is used, but this environment variable allows that to be overridden in order to pass additional options, etc.

BAZELISK_HOME¶

Path to cache directory used by Bazelisk for downloaded Bazel versions. Defaults to a platform-specific cache directory.

TENSORSTORE_BAZEL_COMPILATION_MODE¶

Bazel compilation mode to use. Defaults to opt (optimized build).

TENSORSTORE_BAZEL_STARTUP_OPTIONS¶

Additional Bazel startup options to specify when building. Multiple options may be separated by spaces; options containing spaces or other special characters should be encoded according to Posix shell escaping rules as implemented by shlex.split().

This may be used to specify a non-standard cache directory:

TENSORSTORE_BAZEL_STARTUP_OPTIONS="--output_user_root /path/to/bazel_cache"

TENSORSTORE_BAZEL_BUILD_OPTIONS¶

Additional Bazel build options to specify when building. The encoding is the same as for TENSORSTORE_BAZEL_STARTUP_OPTIONS.

ARCHFLAGS¶

macOS only. Specifies the CPU architecture to target for cross-compilation. May be -arch x86_64 or -arch arm64. Universal2 builds (specified by -arch arm64 -arch x86_64 are not supported).

MACOSX_DEPLOYMENT_TARGET¶

macOS only. Specifies the minimum required macOS version to target. Must not be earlier than 10.14. If not specified, defaults to the same macOS version required by the Python binary itself, or 10.14 if later.

TENSORSTORE_PREBUILT_DIR¶

If specified, building is skipped, and instead setup.py expects to find the pre-built extension module in the specified directory, from a prior invocation of build_ext:

python3 setup.py build_ext -b /tmp/prebuilt
TENSORSTORE_PREBUILT_DIR=/tmp/prebuilt pip wheel .

IPython shell without installing¶

python3 bazelisk.py run -c opt //python/tensorstore:shell

Publishing a PyPI package¶

To build a source package:

python3 setup.py sdist

To build a binary package:

python3 setup.py bdist_wheel

The packages are written to the dist/ sub-directory.

C++ API¶

The C++ API is supported for both Bazel and CMake projects. In either case, it must be added as a dependency so that it is built from source and statically linked as part of the overall build.

Bazel integration¶

To add TensorStore as a dependency to an existing Bazel workspace:

load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
load("@bazel_tools//tools/build_defs/repo:utils.bzl", "maybe")

maybe(
    http_archive,
    name = "com_google_tensorstore",
    strip_prefix = "tensorstore-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
    url = "https://github.com/google/tensorstore/archive/XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.tar.gz",
    sha256 = "YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY",
)

Additionally, TensorStore must be built in C++17 mode. You should add the compiler flags specified in the .bazelrc file in the TensorStore repository to your dependent project’s .bazelrc.

The supported C++ toolchains are listed below.

CMake integration¶

To add TensorStore as a dependency to an existing CMake project:

include(FetchContent)

FetchContent_Declare(
  tensorstore
  URL "https://github.com/google/tensorstore/archive/XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.tar.gz"
  URL_HASH SHA256=YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY
)

# Additional FetchContent_Declare calls as needed...

FetchContent_MakeAvailable(tensorstore)

# Define a target that depends on TensorStore...

target_link_libraries(
  my_target
  PRIVATE
    tensorstore::tensorstore tensorstore::all_drivers
)

TensorStore requires that the project is built in C++17 mode.

The supported C++ toolchains and additional system requirements are listed below.

Supported generators¶

The following CMake generators are supported:

• Ninja and Ninja Multi-Config

• Makefile generators

• Visual Studio generators

• Xcode (targetting arm64 only)

The Ninja generator is recommended because it provides the fastest builds.

Third-party dependencies¶

By default, TensorStore’s CMake build also pulls in all of its dependencies via FetchContent and statically links them. This behavior may be overridden on a per-package basis via TENSORSTORE_USE_SYSTEM_<PACKAGE> options, which may be set on the CMake command line with the syntax -DTENSORSTORE_USE_SYSTEM_<PACKAGE>=ON.

Build caching¶

When using CMake, it is often helpful to use a build caching tool like sccache or to speed up re-builds. To enable sccache, specify -DCMAKE_{C,CXX}_COMPILER_LAUNCHER=ccache when invoking CMake.

Development¶

For development of TensorStore, ensure that you have the required build dependencies.

Building the documentation¶

python3 bazelisk.py run //tools/docs:build_docs -- --output /tmp/tensorstore-docs

Running tests¶

python3 bazelisk.py test //...

Build dependencies¶

TensorStore is written in C++ and is compatible with the following C++ compilers:

• GCC 10 or later (Linux)

• Clang 8 or later (Linux)

• Microsoft Visual Studio 2019 version 16.10 (MSVC 14.29.30037) or later

• Clang-cl 9 or later (Windows)

• Mingw64 GCC 12 or later (Windows); CMake only, Bazel is not supported, and lld is recommended over ld for speed.

• Apple Xcode 11.3.1 or later (earlier versions of XCode 11 have a code generation bug related to stack alignment)

Bazel build¶

The Bazel build system is used automatically when building the Python API, and may also be used to build the C++ API and command-line tools. You don’t need to install Bazel manually; the included copy of bazelisk automatically downloads a suitable version for your operating system. Bazelisk requires Python 2.7 or later to run.

TensorStore depends on a number of third-party libraries. By default, these dependencies are fetched and built automatically as part of the TensorStore Bazel build, which requires no additional effort.

On Linux and macOS, however, it is possible to override this behavior for a subset of these libraries and instead link to a system-provided version. This reduces the binary size, and if your system packages are kept up to date, ensures TensorStore uses up-to-date versions of these dependencies.

TENSORSTORE_SYSTEM_LIBS¶

To use system-provided libraries, set the TENSORSTORE_SYSTEM_LIBS environment variable to a comma-separated list of the following identifiers prior to invoking Bazel:

For example, to run the tests using the system-provided curl, jpeg, and SSL libraries:

export TENSORSTORE_SYSTEM_LIBS=se_curl,jpeg,com_google_boringssl
python3 bazelisk.py test //...

CMake build¶

In addition to a supported C++ toolchain, the following system dependencies are also required for the CMake build:

• Python 3.8 or later

• CMake 3.24 or later

• Perl, for building libaom from source (default). Must be in PATH. Not required if -DTENSORSTORE_USE_SYSTEM_LIBAOM=ON is specified.

• NASM, for building libjpeg-turbo, libaom, and dav1d from source (default). Must be in PATH.Not required if -DTENSORSTORE_USE_SYSTEM_{JPEG,LIBAOM,DAV1D}=ON is specified.

• GNU Patch or equivalent. Must be in PATH.