*   Keras 3.0 will be the default Keras version. You may need to update your
    script to use Keras 3.0.

*   Please refer to the new Keras documentation for Keras 3.0
    (https://keras.io/keras_3).

*   To continue using Keras 2.0, do the following.

*   1.  Install tf-keras via pip install tf-keras~=2.16

    1.  To switch tf.keras to use Keras 2 (tf-keras), set the environment

    # 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 \

    # 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 \

    # 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 \

    # 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==2.1.1 \
    --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...