Add pip install keras-rs to all examples that need it. (#2102)

This is needed for the colabs to work.

Author: hertschuh

examples/keras_rs/basic_retrieval.py

@@ -72,6 +72,10 @@
 the necessary libraries.
 """
 
+"""shell
+pip install -q keras-rs
+"""
+
 import os
 
 os.environ["KERAS_BACKEND"] = "jax"  # `"tensorflow"`/`"torch"`

examples/keras_rs/data_parallel_retrieval.py

@@ -29,6 +29,10 @@
    JAX as your backend!
 """
 
+"""shell
+pip install -q keras-rs
+"""
+
 import os
 
 os.environ["KERAS_BACKEND"] = "jax"

examples/keras_rs/dcn.py

@@ -76,6 +76,10 @@
 Let's set the backend to JAX, and get our imports sorted.
 """
 
+"""shell
+pip install -q keras-rs
+"""
+
 import os
 
 os.environ["KERAS_BACKEND"] = "jax"  # `"tensorflow"`/`"torch"`

examples/keras_rs/deep_recommender.py

@@ -76,6 +76,10 @@
 affects model performance.
 """
 
+"""shell
+pip install -q keras-rs
+"""
+
 import os
 
 os.environ["KERAS_BACKEND"] = "jax"  # `"tensorflow"`/`"torch"`

examples/keras_rs/ipynb/basic_retrieval.ipynb

@@ -84,6 +84,17 @@
     "the necessary libraries."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -q keras-rs"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 0,

examples/keras_rs/ipynb/data_parallel_retrieval.ipynb

@@ -41,6 +41,17 @@
     "   JAX as your backend!"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -q keras-rs"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 0,

examples/keras_rs/ipynb/dcn.ipynb

@@ -88,6 +88,17 @@
     "Let's set the backend to JAX, and get our imports sorted."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -q keras-rs"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 0,

examples/keras_rs/ipynb/deep_recommender.ipynb

@@ -88,6 +88,17 @@
     "affects model performance."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -q keras-rs"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 0,

examples/keras_rs/ipynb/listwise_ranking.ipynb

@@ -45,6 +45,17 @@
     "Let's begin by importing all the necessary libraries."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -q keras-rs"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 0,

examples/keras_rs/ipynb/multi_task.ipynb

@@ -49,6 +49,17 @@
     "Let's start by importing the necessary packages."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -q keras-rs"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 0,

examples/keras_rs/ipynb/sas_rec.ipynb

@@ -34,6 +34,17 @@
     "Let's begin by importing all the necessary libraries."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -q keras-rs"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 0,

examples/keras_rs/ipynb/scann.ipynb

@@ -64,17 +64,7 @@
    },
    "outputs": [],
    "source": [
-    "# ruff: noqa: E402"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
+    "!pip install -q keras-rs\n",
     "!pip install -q scann"
    ]
   },

examples/keras_rs/ipynb/sequential_retrieval.ipynb

@@ -33,6 +33,17 @@
     "the necessary libraries."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -q keras-rs"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 0,

examples/keras_rs/listwise_ranking.py

@@ -33,6 +33,10 @@
 Let's begin by importing all the necessary libraries.
 """
 
+"""shell
+pip install -q keras-rs
+"""
+
 import os
 
 os.environ["KERAS_BACKEND"] = "jax"  # `"tensorflow"`/`"torch"`

examples/keras_rs/md/basic_retrieval.md

@@ -75,6 +75,11 @@ Let's begin by choosing JAX as the backend we want to run on, and import all
 the necessary libraries.
 
 
+```python
+!pip install -q keras-rs
+```
+
+
 ```python
 import os
 

examples/keras_rs/md/data_parallel_retrieval.md

@@ -32,6 +32,11 @@ Before we begin, let's note down a few things:
    JAX as your backend!
 
 
+```python
+!pip install -q keras-rs
+```
+
+
 ```python
 import os
 

examples/keras_rs/md/dcn.md

@@ -79,6 +79,11 @@ learnt important feature crosses.
 Let's set the backend to JAX, and get our imports sorted.
 
 
+```python
+!pip install -q keras-rs
+```
+
+
 ```python
 import os
 

examples/keras_rs/md/deep_recommender.md

@@ -79,6 +79,11 @@ We'll do this by building progressively more complex models to see how this
 affects model performance.
 
 
+```python
+!pip install -q keras-rs
+```
+
+
 ```python
 import os
 

examples/keras_rs/md/listwise_ranking.md

@@ -36,6 +36,11 @@ goal is to minimize the number of incorrectly ordered pairs.
 Let's begin by importing all the necessary libraries.
 
 
+```python
+!pip install -q keras-rs
+```
+
+
 ```python
 import os
 

examples/keras_rs/md/multi_task.md

@@ -40,6 +40,11 @@ user to a movie.
 Let's start by importing the necessary packages.
 
 
+```python
+!pip install -q keras-rs
+```
+
+
 ```python
 import os
 

examples/keras_rs/md/sas_rec.md

@@ -25,6 +25,11 @@ for the same sequential recommendation task.
 Let's begin by importing all the necessary libraries.
 
 
+```python
+!pip install -q keras-rs
+```
+
+
 ```python
 import os
 

examples/keras_rs/md/scann.md

@@ -49,11 +49,7 @@ also set the backend to JAX.
 
 
 ```python
-# ruff: noqa: E402
-```
-
-
-```python
+!pip install -q keras-rs
 !pip install -q scann
 ```
 

examples/keras_rs/md/sequential_retrieval.md

@@ -24,6 +24,11 @@ Let's begin by choosing JAX as the backend we want to run on, and import all
 the necessary libraries.
 
 
+```python
+!pip install -q keras-rs
+```
+
+
 ```python
 import os
 

examples/keras_rs/multi_task.py

@@ -37,6 +37,10 @@
 Let's start by importing the necessary packages.
 """
 
+"""shell
+pip install -q keras-rs
+"""
+
 import os
 
 os.environ["KERAS_BACKEND"] = "jax"  # `"tensorflow"`/`"torch"`

examples/keras_rs/sas_rec.py

@@ -22,6 +22,10 @@
 Let's begin by importing all the necessary libraries.
 """
 
+"""shell
+pip install -q keras-rs
+"""
+
 import os
 
 os.environ["KERAS_BACKEND"] = "jax"  # `"tensorflow"`/`"torch"`

examples/keras_rs/scann.py

@@ -44,9 +44,8 @@
 also set the backend to JAX.
 """
 
-# ruff: noqa: E402
-
 """shell
+pip install -q keras-rs
 pip install -q scann
 """
 

examples/keras_rs/sequential_retrieval.py

@@ -21,6 +21,10 @@
 the necessary libraries.
 """
 
+"""shell
+pip install -q keras-rs
+"""
+
 import os
 
 os.environ["KERAS_BACKEND"] = "jax"  # `"tensorflow"`/`"torch"`