termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.4.0, < 0.5.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.0.0.dev
tb-nightly ~= 2.19.0.a
# Test dependencies
grpcio >= 1.24.3, < 2.0
portpicker == 1.6.0

    # via
    #   -r ci/official/requirements_updater/requirements.in
    #   jax
    #   keras-nightly
namex==0.0.8 \
    --hash=sha256:32a50f6c565c0bb10aa76298c959507abdc0e850efe085dc38f3440fcb3aa90b \
    --hash=sha256:7ddb6c2bb0e753a311b7590f84f6da659dd0c05e65cb89d519d54c0a250c0487
    # via keras-nightly
numpy==2.1.1 \
    --hash=sha256:046356b19d7ad1890c751b99acad5e82dc4a02232013bd9a9a712fddf8eb60f5 \

    # via
    #   -r ci/official/requirements_updater/requirements.in
    #   jax
    #   keras-nightly
namex==0.0.8 \
    --hash=sha256:32a50f6c565c0bb10aa76298c959507abdc0e850efe085dc38f3440fcb3aa90b \
    --hash=sha256:7ddb6c2bb0e753a311b7590f84f6da659dd0c05e65cb89d519d54c0a250c0487
    # via keras-nightly
numpy==2.1.1 \
    --hash=sha256:046356b19d7ad1890c751b99acad5e82dc4a02232013bd9a9a712fddf8eb60f5 \

    # via
    #   -r ci/official/requirements_updater/requirements.in
    #   jax
    #   keras-nightly
namex==0.0.8 \
    --hash=sha256:32a50f6c565c0bb10aa76298c959507abdc0e850efe085dc38f3440fcb3aa90b \
    --hash=sha256:7ddb6c2bb0e753a311b7590f84f6da659dd0c05e65cb89d519d54c0a250c0487
    # via keras-nightly
numpy==2.0.2 \
    --hash=sha256:0123ffdaa88fa4ab64835dcbde75dcdf89c453c922f18dced6e27c90d1d0ec5a \

    # via
    #   -r ci/official/requirements_updater/requirements.in
    #   jax
    #   keras-nightly
namex==0.0.8 \
    --hash=sha256:32a50f6c565c0bb10aa76298c959507abdc0e850efe085dc38f3440fcb3aa90b \
    --hash=sha256:7ddb6c2bb0e753a311b7590f84f6da659dd0c05e65cb89d519d54c0a250c0487
    # via keras-nightly
numpy==1.26.4 \
    --hash=sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b \

title: 'Another Boilerplate to FastAPI: Azure Pipeline CI + Pytest' - author: Shane Soh author_link: https://medium.com/@shane.soh link: https://medium.com/analytics-vidhya/deploy-machine-learning-models-with-keras-fastapi-redis-and-docker-4940df614ece title: Deploy Machine Learning Models with Keras, FastAPI, Redis and Docker - author: Mandy Gu author_link: https://towardsdatascience.com/@mandygu link: https://towardsdatascience.com/deploying-iris-classifications-with-fastapi-and-docker-7c9b83fdec3a...

# TODO(b/173549186): Move Google-internal TF code out of learning/brain
package_group(
    name = "internal",
    packages = [
        "//devtools/python/indexer/...",
        "//learning/brain/keras/...",
        "//learning/brain/mlir/...",
        "//learning/brain/tfrt/...",
        "//learning/lib/ami/simple_ml/...",
        "//learning/pathways/...",

habiendo
habido
habida
habidos
habidas
soy
eres
es
somos
sois
son
sea
seas
seamos
seáis
sean
seré
serás
será
seremos
seréis
serán
sería
serías
seríamos
seríais
serían
era
eras
éramos
erais
eran
fui
fuiste
fue
fuimos
fuisteis
fueron
fuera
fueras
fuéramos
fuerais
fueran
fuese

Verás la respuesta en JSON:

```JSON
{"message": "Hello World"}
```

### Documentación interactiva de la API

Ahora dirígete a <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>.

Entonces, si corres este ejemplo y vas a <a href="http://127.0.0.1:8000/items/foo" class="external-link" target="_blank">http://127.0.0.1:8000/items/foo</a>, verás una respuesta de:

```JSON
{"item_id":"foo"}
```

## Parámetros de path con tipos

Puedes declarar el tipo de un parámetro de path en la función usando las anotaciones de tipos estándar de Python: