Building and Installing¶

TensorStore provides both a Python API and a C++ API.

Python API from PyPI¶

The simplest and fastest way to start using TensorStore is to install a PyPI package and use the TensorStore Python API bindings. TensorStore requires Python 3.9 or later (Python 2 is not supported).

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

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 build requirements must already be installed.

Python API from Source¶

To make changes to the TensorStore source code, an installation from a local checkout of the git repository is necessary. The TensorStore build has some prerequisites.

The Bazel build system is used automatically when building the Python API, and has additional build options.

When using installing from source for the Python API, consider creating a python virtual environment with with the python dependencies:

python3 -m venv ts-venv
source ts-venv/bin/activate
python3 -m pip install --upgrade pip setuptools numpy

Local checkout installation¶

Install from a local checkout of the git repository:

git clone https://github.com/google/tensorstore
cd tensorstore
python3 -m pip install .

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

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

python3 -m pip install .

to rebuild the extension module. Rebuilds will be faster than the initial since most of the build is incremental.

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 or configuration settings to specify when building. The encoding is the same as for TENSORSTORE_BAZEL_STARTUP_OPTIONS.

This may be used to enable additional debugging in tensorstore; see bool_flag use in BUILD files for more details.

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

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

load("@tensorstore//:external.bzl", "tensorstore_dependencies")

tensorstore_dependencies()

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.

See the supported C++ toolchains listed above.

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 build requirements installed.

Building the documentation¶

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

Running tests¶

python3 bazelisk.py test //...

Build Requirements¶

TensorStore is written in C++ with python bindings 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)

In order to build from source, one of the above compilers is necessary along with some additional tools detailed in the sections below. The actual requirements vary depending on how TensorStore is built. Installing the following packages (debian) will satisfy the build requirements for the examples in this document:

sudo apt-get install build-essential git nasm python3 python3-dev python3-pip python3-venv

Bazel Build Requirements¶

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:

PYTHON_BIN_PATH¶

Path to Python binary to use when running Python executables/tests. When Bazel is invoked by the Python package build (setup.py), this is set automatically.

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 Requirements¶

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

• Python 3.9 or later

• CMake 3.24 or later

• 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.