# Requirements for NumPy 1.x
numpy ~= 1.26.0
wheel ~= 0.41.2
h5py >= 3.11.0, < 3.15.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.1, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version

  PROFILE_JSON_PATH="$PROFILE_JSON_PATH/profile.json.gz"
else
  PROFILE_JSON_PATH="$TFCI_OUTPUT_DIR/profile.json.gz"
fi

# TODO(b/361369076) Remove the following block after TF NumPy 1 is dropped
# Move hermetic requirement lock files for NumPy 1 to the root
if [[ "$TFCI_WHL_NUMPY_VERSION" == 1 ]]; then
  cp ./ci/official/requirements_updater/numpy1_requirements/*.txt .
fi

if [[ $TFCI_PYCPP_SWAP_TO_BUILD_ENABLE == 1 ]]; then

numpy >= 2.0.0, < 2.2.0 ; python_version <= "3.13"
numpy>=2.2.6 ; python_version == "3.14"
wheel ~= 0.41.2
h5py >= 3.11.0, < 3.15.0 ; python_version <= "3.13"
h5py ~= 3.15.1 ; python_version == "3.14"
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.4, < 0.6.0
auditwheel >= 6.1.0

fi

# TODO(b/366266944) Remove the check after tf docker image upgrade for NumPy 2
# and numpy 1 support is dropped b/361369076.
if [[ "$TFCI_WHL_NUMPY_VERSION" == 1 ]]; then
  if [[ "$TFCI_PYTHON_VERSION" == "3.13" ]]; then
    "$python" -m pip install numpy==1.26.4
  else
    "$python" -m pip install numpy==1.26.0
  fi
fi
if [[ "$TFCI_BAZEL_COMMON_ARGS" =~ gpu|cuda ]]; then

fi

# TODO(b/361369076) Remove the following block after TF NumPy 1 is dropped
# Move hermetic requirement lock files for NumPy 1 to the root
if [[ "$TFCI_WHL_NUMPY_VERSION" == 1 ]]; then
  cp ./ci/official/requirements_updater/numpy1_requirements/*.txt .
fi

[pip.parse(
    extra_hub_aliases = {
        "numpy": ["numpy_headers"],
    },
    hub_name = "tf_pypi",
    python_version = python_version,
    requirements_lock = "//:requirements_lock_{}.txt".format(python_version.replace(".", "_")),
    whl_modifications = {
        "@pypi_mods//:numpy.json": "numpy",  # @pypi_mods is defined in XLA's MODULE.bazel
    },
) for python_version in [

      with:
        python-version: "3.9"
    - name: Install Python dependencies
      run: |
        python -m pip install --upgrade pip
        pip install pylint==2.13.9 numpy wheel
    - name: Run PyLint on changed files
      run: |

  }
  std::string value_string;
  if (!SummarizeValue(value_string).ok()) {
    value_string = "<error computing value>";
  }
  if (value_string.length() > 100) {
    // The default NumPy-style output can be distractingly long in error
    // messages.
    value_string = absl::StrCat(value_string.substr(0, 100), " [...]");
  }
  absl::Status s;
  const char* device_name = DeviceName(&s);
  if (!s.ok()) {

#### *Try your first TensorFlow program*

```shell
$ python
```

```python
>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'
```

For more examples, see the
[TensorFlow Tutorials](https://www.tensorflow.org/tutorials/).

## Contribution guidelines

* TensorFlow now supports and is compiled with NumPy 2.0 by default. Please see the [NumPy 2 release notes](https://numpy.org/doc/stable/release/2.0.0-notes.html) and the [NumPy 2 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html#numpy-2-migration-guide).