load("@python_version_repo//:py_version.bzl", "REQUIREMENTS")
load("@rules_python//python:pip.bzl", "compile_pip_requirements")

# TODO(ybaturina): Remove once TF is migrated to CUDA 12.9.
genrule(
    name = "nvidia_constraints",
    srcs = ["nvidia-requirements.txt"],
    outs = ["nvidia-constraints.txt"],
    cmd = """sed -E "s/>=/==/" $(location nvidia-requirements.txt) > $@;""",
)

        selective op registration headers else execution would fail with
        unregistered kernels error.

*   Modular File System Migration:

    *   Support for S3 and HDFS file systems has been migrated to a modular file
        systems based approach and is now available in
        https://github.com/tensorflow/io. The `tensorflow-io` python package
        should be installed for S3 and HDFS support with tensorflow.

And with this, support for **Pydantic v1 is now deprecated** and will be **removed** from FastAPI in a future version soon.

**Note**: have in mind that the Pydantic team already stopped supporting Pydantic v1 for recent versions of Python, starting with Python 3.14.

    - deployment/concepts.md
    - deployment/cloud.md
    - deployment/server-workers.md
    - deployment/docker.md
  - How To - Recipes:
    - how-to/index.md
    - how-to/general.md
    - how-to/migrate-from-pydantic-v1-to-pydantic-v2.md
    - how-to/graphql.md
    - how-to/custom-request-and-route.md
    - how-to/conditional-openapi.md
    - how-to/extending-openapi.md
    - how-to/separate-openapi-schemas.md

# See https://github.com/bazelbuild/bazel/issues/7362 for information on what
# --incompatible_remove_legacy_whole_archive flag does.
# This flag is set to true in Bazel 1.0 and newer versions. We tried to migrate
# Tensorflow to the default, however test coverage wasn't enough to catch the
# errors.
# There is ongoing work on Bazel team's side to provide support for transitive