[codex] Clarify TensorFlow compatibility docs

.github/workflows/ci.yml

@@ -9,16 +9,34 @@ on:
       - 'setup.cfg'
       - '.github/workflows/**'
   pull_request:
-    paths:
-      - 'deepctr/**'
-      - 'tests/**'
-      - 'setup.py'
-      - 'setup.cfg'
-      - '.github/workflows/**'
   workflow_dispatch:
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.ref_name }}
+  cancel-in-progress: true
+
 jobs:
+  changes:
+    runs-on: ubuntu-22.04
+    outputs:
+      code_related: ${{ steps.filter.outputs.code_related }}
+    steps:
+      - uses: actions/checkout@v5
+
+      - name: Detect code-related changes
+        id: filter
+        uses: dorny/paths-filter@v3
+        with:
+          filters: |
+            code_related:
+              - 'deepctr/**'
+              - 'tests/**'
+              - 'setup.py'
+              - 'setup.cfg'
+              - '.github/workflows/**'
+
   build:
+    needs: changes
     runs-on: ubuntu-22.04
     timeout-minutes: 240
     strategy:
@@ -29,14 +47,21 @@ jobs:
             tf-version: "1.15.5"
 
     steps:
+      - name: Docs-only PR fast-path
+        if: ${{ github.event_name == 'pull_request' && needs.changes.outputs.code_related != 'true' }}
+        run: echo "Docs-only PR detected; skipping heavy CI steps."
+
       - uses: actions/checkout@v5
+        if: ${{ github.event_name != 'pull_request' || needs.changes.outputs.code_related == 'true' }}
 
       - name: Setup python environment
+        if: ${{ github.event_name != 'pull_request' || needs.changes.outputs.code_related == 'true' }}
         uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
       - name: Install dependencies
+        if: ${{ github.event_name != 'pull_request' || needs.changes.outputs.code_related == 'true' }}
         run: |
           python -m pip install -q --upgrade pip setuptools wheel
           python -m pip install -q "numpy<2"
@@ -47,11 +72,13 @@ jobs:
           python -m pip check
 
       - name: Test with pytest
+        if: ${{ github.event_name != 'pull_request' || needs.changes.outputs.code_related == 'true' }}
         timeout-minutes: 240
         run: |
           pytest --cov=deepctr --cov-report=xml --cov-report=term-missing:skip-covered
 
       - name: Upload coverage to Codecov
+        if: ${{ github.event_name != 'pull_request' || needs.changes.outputs.code_related == 'true' }}
         uses: codecov/codecov-action@v6
         with:
           token: ${{ secrets.CODECOV_TOKEN }}

.github/workflows/ci2.yml

@@ -9,16 +9,34 @@ on:
       - 'setup.cfg'
       - '.github/workflows/**'
   pull_request:
-    paths:
-      - 'deepctr/**'
-      - 'tests/**'
-      - 'setup.py'
-      - 'setup.cfg'
-      - '.github/workflows/**'
   workflow_dispatch:
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.ref_name }}
+  cancel-in-progress: true
+
 jobs:
+  changes:
+    runs-on: ubuntu-22.04
+    outputs:
+      code_related: ${{ steps.filter.outputs.code_related }}
+    steps:
+      - uses: actions/checkout@v5
+
+      - name: Detect code-related changes
+        id: filter
+        uses: dorny/paths-filter@v3
+        with:
+          filters: |
+            code_related:
+              - 'deepctr/**'
+              - 'tests/**'
+              - 'setup.py'
+              - 'setup.cfg'
+              - '.github/workflows/**'
+
   build:
+    needs: changes
     runs-on: ubuntu-22.04
     timeout-minutes: 180
     strategy:
@@ -47,14 +65,21 @@ jobs:
       TF_USE_LEGACY_KERAS: ${{ matrix.use-legacy-keras }}
 
     steps:
+      - name: Docs-only PR fast-path
+        if: ${{ github.event_name == 'pull_request' && needs.changes.outputs.code_related != 'true' }}
+        run: echo "Docs-only PR detected; skipping heavy CI steps."
+
       - uses: actions/checkout@v5
+        if: ${{ github.event_name != 'pull_request' || needs.changes.outputs.code_related == 'true' }}
 
       - name: Setup python environment
+        if: ${{ github.event_name != 'pull_request' || needs.changes.outputs.code_related == 'true' }}
         uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
       - name: Install dependencies
+        if: ${{ github.event_name != 'pull_request' || needs.changes.outputs.code_related == 'true' }}
         run: |
           python -m pip install -q --upgrade pip setuptools wheel
           python -m pip install -q "numpy<2"
@@ -67,11 +92,13 @@ jobs:
           python -m pip check
 
       - name: Test with pytest
+        if: ${{ github.event_name != 'pull_request' || needs.changes.outputs.code_related == 'true' }}
         timeout-minutes: 180
         run: |
           pytest --cov=deepctr --cov-report=xml --cov-report=term-missing:skip-covered
 
       - name: Upload coverage to Codecov
+        if: ${{ github.event_name != 'pull_request' || needs.changes.outputs.code_related == 'true' }}
         uses: codecov/codecov-action@v6
         with:
           token: ${{ secrets.CODECOV_TOKEN }}

.readthedocs.yml

@@ -1,5 +1,13 @@
+version: 2
+
 build:
-    image: latest
+  os: ubuntu-22.04
+  tools:
+    python: "3.10"
+
+sphinx:
+  configuration: docs/source/conf.py
 
 python:
-    version: 3.6
+  install:
+    - requirements: docs/requirements.readthedocs.txt

README.md

@@ -25,7 +25,20 @@ core components layers which can be used to easily build custom models.You can u
 
 - Provide `tf.keras.Model` like interfaces for **quick experiment**. [example](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr)
 - Provide  `tensorflow estimator` interface for **large scale data** and **distributed training**. [example](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr-estimator-with-tfrecord)
-- It is compatible with both `tf 1.x`  and `tf 2.x`.
+- It is compatible with both `tf 1.15`  and `tf 2.x`.
+
+## Installation and compatibility
+
+DeepCTR does not pin or install TensorFlow for you. Install a TensorFlow build that matches your Python, NumPy, CPU/GPU, and operating system first, then install DeepCTR:
+
+```bash
+pip install tensorflow
+pip install deepctr
+```
+
+For Python `>=3.9`, DeepCTR allows modern `h5py` releases with `h5py>=3.7.0`. If TensorFlow reports a NumPy conflict, follow the TensorFlow requirement for your selected TensorFlow release, for example using `numpy<2` when required by TensorFlow.
+
+Use public `tensorflow.keras` APIs in your own code and examples. Avoid mixing `tensorflow.python.keras` with `tensorflow.keras`, because `tensorflow.python.*` is private TensorFlow API and can break model serialization or optimizer/metric loading across TensorFlow versions.
 
 Some related projects:
 

docs/requirements.readthedocs.txt

@@ -1,2 +1,12 @@
-tensorflow==2.6.2
-recommonmark==0.7.1
+numpy<2
+Jinja2<3.1
+docutils<0.18
+sphinx==4.5.0
+sphinx-rtd-theme==0.5.2
+sphinxcontrib-applehelp==1.0.2
+sphinxcontrib-devhelp==1.0.2
+sphinxcontrib-htmlhelp==2.0.0
+sphinxcontrib-qthelp==1.0.3
+sphinxcontrib-serializinghtml==1.1.5
+recommonmark==0.7.1
+tensorflow==2.15.0

docs/source/Examples.md

@@ -206,7 +206,7 @@ following codes.
 import numpy as np
 import pandas as pd
 from sklearn.preprocessing import LabelEncoder
-from tensorflow.python.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.sequence import pad_sequences
 
 from deepctr.models import DeepFM
 from deepctr.feature_column import SparseFeat, VarLenSparseFeat, get_feature_names
@@ -278,7 +278,7 @@ if __name__ == "__main__":
 ```python
 import numpy as np
 import pandas as pd
-from tensorflow.python.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.sequence import pad_sequences
 
 from deepctr.feature_column import SparseFeat, VarLenSparseFeat, get_feature_names
 from deepctr.models import DeepFM
@@ -334,7 +334,7 @@ from deepctr.models import DeepFM
 from deepctr.feature_column import SparseFeat, VarLenSparseFeat, get_feature_names
 import numpy as np
 import pandas as pd
-from tensorflow.python.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.sequence import pad_sequences
 
 try:
     import tensorflow.compat.v1 as tf
@@ -401,7 +401,6 @@ and run the following codes.
 ```python
 import tensorflow as tf
 
-from tensorflow.python.ops.parsing_ops import FixedLenFeature
 from deepctr.estimator import DeepFMEstimator
 from deepctr.estimator.inputs import input_fn_tfrecord
 
@@ -425,10 +424,10 @@ if __name__ == "__main__":
 
     # 2.generate input data for model
 
-    feature_description = {k: FixedLenFeature(dtype=tf.int64, shape=1) for k in sparse_features}
+    feature_description = {k: tf.io.FixedLenFeature(dtype=tf.int64, shape=1) for k in sparse_features}
     feature_description.update(
-        {k: FixedLenFeature(dtype=tf.float32, shape=1) for k in dense_features})
-    feature_description['label'] = FixedLenFeature(dtype=tf.float32, shape=1)
+        {k: tf.io.FixedLenFeature(dtype=tf.float32, shape=1) for k in dense_features})
+    feature_description['label'] = tf.io.FixedLenFeature(dtype=tf.float32, shape=1)
 
     train_model_input = input_fn_tfrecord('./criteo_sample.tr.tfrecords', feature_description, 'label', batch_size=256,
                                           num_epochs=1, shuffle_factor=10)
@@ -597,4 +596,4 @@ if __name__ == "__main__":
     print("test marital AUC", round(roc_auc_score(test['label_marital'], pred_ans[1]), 4))
 
 
-```
+```

docs/source/FAQ.md

@@ -13,7 +13,7 @@ model.load_weights('DeepFM_w.h5')
 To save/load models,just a little different.
 
 ```python
-from tensorflow.python.keras.models import  save_model,load_model
+from tensorflow.keras.models import save_model, load_model
 model = DeepFM()
 save_model(model, 'DeepFM.h5')# save_model, same as before
 
@@ -27,8 +27,8 @@ Here is a example of how to set learning rate and earlystopping:
 
 ```python
 import deepctr
-from tensorflow.python.keras.optimizers import Adam,Adagrad
-from tensorflow.python.keras.callbacks import EarlyStopping
+from tensorflow.keras.optimizers import Adam, Adagrad
+from tensorflow.keras.callbacks import EarlyStopping
 
 model = deepctr.models.DeepFM(linear_feature_columns,dnn_feature_columns)
 model.compile(Adagrad(0.1024),'binary_crossentropy',metrics=['binary_crossentropy'])
@@ -61,8 +61,8 @@ import itertools
 import deepctr
 from deepctr.models import AFM
 from deepctr.feature_column import get_feature_names
-from tensorflow.python.keras.models import Model
-from tensorflow.python.keras.layers import Lambda
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Lambda
 
 model = AFM(linear_feature_columns,dnn_feature_columns)
 model.fit(model_input,target)
@@ -145,10 +145,27 @@ model.fit(model_input,label)
 ```
 
 ## 7. How to run the demo with GPU ?
-just install deepctr with 
+Install the TensorFlow build recommended for your CUDA, cuDNN, and platform combination, then install `deepctr`.
+
+## 8. How to avoid TensorFlow, Keras, h5py, or NumPy compatibility errors?
+
+Install TensorFlow separately before installing DeepCTR. Pick the TensorFlow release according to your Python version, CPU/GPU environment, and platform.
+
 ```bash
-$ pip install deepctr[gpu]
+$ pip install tensorflow
+$ pip install deepctr
 ```
 
-## 8. How to run the demo with multiple GPUs
+For Python `>=3.9`, DeepCTR uses `h5py>=3.7.0`, so newer `h5py` releases such as `3.9+` and `3.12+` are allowed. If TensorFlow reports a NumPy conflict, follow the TensorFlow requirement for the TensorFlow release you installed, for example using `numpy<2` when required by TensorFlow.
+
+Use public `tensorflow.keras` imports in your own code:
+
+```python
+from tensorflow.keras.models import load_model
+from tensorflow.keras.optimizers import Adam
+```
+
+Avoid mixing `tensorflow.python.keras` with `tensorflow.keras`. `tensorflow.python.*` is private TensorFlow API and can break serialization, optimizer loading, or metric loading across TensorFlow versions.
+
+## 9. How to run the demo with multiple GPUs
 you can use multiple gpus with tensorflow version higher than ``1.4``,see [run_classification_criteo_multi_gpu.py](https://github.com/shenweichen/DeepCTR/blob/master/examples/run_classification_criteo_multi_gpu.py)

docs/source/Quick-Start.md

@@ -1,18 +1,27 @@
 # Quick-Start
 [![](https://pai-public-data.oss-cn-beijing.aliyuncs.com/EN-pai-dsw.svg)](https://dsw-dev.data.aliyun.com/#/?fileUrl=https://pai-public-data.oss-cn-beijing.aliyuncs.com/deep-ctr/Getting-started-4-steps-to-DeepCTR.ipynb&fileName=Getting-started-4-steps-to-DeepCTR.ipynb)
 ## Installation Guide
-Now `deepctr` is available for python `2.7 `and `3.5, 3.6, 3.7`.  
-`deepctr` depends on tensorflow, you can specify to install the cpu version or gpu version through `pip`.
+Now `deepctr` supports Python `>=3.7` and is tested with TensorFlow `1.15` and TensorFlow `2.x`.
 
-### CPU version
+DeepCTR does not pin or install TensorFlow for you. Install a TensorFlow build that matches your Python, NumPy, CPU/GPU, and operating system first, then install DeepCTR:
 
 ```bash
-$ pip install deepctr[cpu]
+$ pip install tensorflow
+$ pip install deepctr
 ```
-### GPU version
+
+For GPU environments, install the TensorFlow package recommended for your CUDA, cuDNN, and platform combination, then install `deepctr`.
+
+For Python `>=3.9`, DeepCTR allows modern `h5py` releases with `h5py>=3.7.0`. If TensorFlow reports a NumPy conflict, follow the TensorFlow requirement for your selected TensorFlow release, for example using `numpy<2` when required by TensorFlow.
+
+Use public `tensorflow.keras` APIs in your own code. Avoid mixing `tensorflow.python.keras` with `tensorflow.keras`, because `tensorflow.python.*` is private TensorFlow API and can break model serialization or optimizer/metric loading across TensorFlow versions.
+
+### Install from source
 
 ```bash
-$ pip install deepctr[gpu]
+$ git clone https://github.com/shenweichen/DeepCTR.git
+$ cd DeepCTR
+$ pip install .
 ```
 ## Getting started: 4 steps to DeepCTR
 
@@ -128,7 +137,6 @@ You also can run a distributed training job with the keras model on Kubernetes u
 ```python
 import tensorflow as tf
 
-from tensorflow.python.ops.parsing_ops import  FixedLenFeature
 from deepctr.estimator.inputs import input_fn_tfrecord
 from deepctr.estimator.models import DeepFMEstimator
 
@@ -155,10 +163,10 @@ for feat in dense_features:
 ### Step 3: Generate the training samples with TFRecord format
 
 ```python
-feature_description = {k: FixedLenFeature(dtype=tf.int64, shape=1) for k in sparse_features}
+feature_description = {k: tf.io.FixedLenFeature(dtype=tf.int64, shape=1) for k in sparse_features}
 feature_description.update(
-    {k: FixedLenFeature(dtype=tf.float32, shape=1) for k in dense_features})
-feature_description['label'] = FixedLenFeature(dtype=tf.float32, shape=1)
+    {k: tf.io.FixedLenFeature(dtype=tf.float32, shape=1) for k in dense_features})
+feature_description['label'] = tf.io.FixedLenFeature(dtype=tf.float32, shape=1)
 
 train_model_input = input_fn_tfrecord('./criteo_sample.tr.tfrecords', feature_description, 'label', batch_size=256,
                                       num_epochs=1, shuffle_factor=10)