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Posted to github@beam.apache.org by "damccorm (via GitHub)" <gi...@apache.org> on 2023/04/28 15:31:50 UTC
[GitHub] [beam] damccorm commented on a diff in pull request #26472: [Python] Add saved_weights example to tf notebook
damccorm commented on code in PR #26472:
URL: https://github.com/apache/beam/pull/26472#discussion_r1180544965
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examples/notebooks/beam-ml/run_inference_tensorflow.ipynb:
##########
@@ -101,20 +110,44 @@
"To use RunInference with the built-in Tensorflow model handler, install Apache Beam version 2.46.0 or later."
],
"metadata": {
- "id": "gVCtGOKTHMm4"
+ "id": "YDHPlMjZRuY0"
}
},
{
"cell_type": "code",
"metadata": {
- "id": "jBakpNZnAhqk"
+ "id": "jBakpNZnAhqk",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "375cd47b-b837-4091-88e2-cdac12e3a4a1"
},
"source": [
"!pip install protobuf --quiet\n",
"!pip install apache_beam==2.46.0 --quiet"
],
- "execution_count": null,
- "outputs": []
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.2/14.2 MB\u001b[0m \u001b[31m24.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m152.0/152.0 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m135.6/135.6 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.7/2.7 MB\u001b[0m \u001b[31m51.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m516.2/516.2 kB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m53.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.7/89.7 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for crcmod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for dill (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
+ ]
+ }
+ ]
Review Comment:
```suggestion
"execution_count": null,
"outputs": []
```
Lets drop this output for the sake of a cleaner notebook
##########
examples/notebooks/beam-ml/run_inference_tensorflow.ipynb:
##########
@@ -313,14 +350,34 @@
"metadata": {
"id": "2JbE7WkGcAkK"
},
- "execution_count": 8,
+ "execution_count": 18,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Save the weights."
Review Comment:
Maybe something along the lines of: `Instead of saving the model, you can also save/load a model using just the model weights and class. This is a smaller, more efficient way to represent your model.`
Basically, I want something to justify to the user why we're having them save things 2 ways so they know that in reality they just have to pick one.
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