Referring to the official Vertex AI x TFX example:
The tutorial shows how to push the trained model to a GCS bucket as the last step. What if I wanted to also deploy it to Vertex AI with an endpoint enabled?
Any resources?
Bhack
July 23, 2021, 12:23pm
3
It’s out of scope for my query. I can easily run a CustomTrainingJob on Vertex AI and directly deploy that.
But my query involves that from a pipeline which is different than what you referenced.
@Sayak_Paul , That was by mistake. Not meant for your post.
Bhack
July 24, 2021, 3:29pm
9
Do you want to do this directly from a TFX pipeline?
After my model is pushed from the Pusher component of TFX, I want to be able to deploy it on Vertex AI.
Please follow the tutorial I mentioned in the initial post. You’ll see that the entire TFX Pipeline is orchestrated as a Vertex AI pipeline.
Ccing @Robert_Crowe if he has any suggestions.
Bhack
July 24, 2021, 4:24pm
11
Mhh I don’t think it is available off the shelf, but let’s wait for Robert.
Pobably a custom component is required, something like:
@component
def vertex_model_uploader(
project: Parameter[str],
region: Parameter[str],
model_display_name: Parameter[str],
pushed_model_location: Parameter[str],
serving_image_uri: Parameter[str],
model_blessing: InputArtifact[ModelBlessing],
uploaded_model: OutputArtifact[UploadedModel],
explanation_config: Parameter[str]="",
labels: Parameter[str]="",
):
vertex_ai.init(project=project, location=region)
blessing = artifact_utils.get_single_instance([model_blessing])
if not model_utils.is_model_blessed(blessing):
logging.info(f"Model is not uploaded to Vertex AI because it was not blessed by the evaluator.")
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I agree. This repository is gold, thanks for mentioning it here. It’s pretty recent as well.
Unfortunately the official Vertex Pusher component is still being developed, but if you push to a file system destination then you can script deployment to Vertex Prediction.
Thanks, @Robert_Crowe . Do you have a code example that I could refer to for supporting this?
Bhack
July 27, 2021, 9:09am
17
Isn’t it already in the mentioned repo (step 7)?
{
"cells": [
{
"cell_type": "markdown",
"id": "33b0bbba",
"metadata": {},
"source": [
"# 03 - TFX Interactive Training Pipeline Execution\n",
"\n",
"The purpose of this notebook is to interactively run the following `TFX` pipeline steps:\n",
"1. Receive hyperparameters using `hyperparam_gen` custom Python component.\n",
"2. Extract data from BigQuery using `BigQueryExampleGen` component.\n",
"3. Validate the raw data using `StatisticsGen` and `ExampleValidator` components.\n",
"4. Process the data using `Transform` component.\n",
"5. Train a custom model using `Trainer` component.\n",
"7. Evaluate and Validate the custom model using `ModelEvaluator` component.\n",
"7. Save the blessed to model registry location using `Pusher` component.\n",
"8. Upload the model to Vertex AI using `vertex_model_pusher` custom Python component\n",
"\n",
"The custom components are implemented in the [tfx_pipeline/components.py](tfx_pipeline/components) module."
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Step 9 I guess: “Upload the model to Vertex AI using vertex_model_pusher custom Python component”. I will try this one out and see.
Bhack
July 27, 2021, 10:26am
21
it was 7 but it was update some days ago now it is on point 8.
But: there’s a duplicate number.
Bhack
July 27, 2021, 10:58am
25
Yes but I don’t mean notebook cells number I meant 8 (it was 7) as 8. Model Upload to AI Platform
Bhack
July 27, 2021, 10:59am
26
P.s. 7. Model pushing is Robert:
but if you push to a file system destination …
Getting back to the solution that actually worked:
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Custom_Model_TFX",
"provenance": [],
"collapsed_sections": [],
"machine_shape": "hm",
"include_colab_link": true
},
"environment": {
"name": "tf2-gpu.2-4.mnightly-2021-02-02-debian-10-test",
"type": "gcloud",
"uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-4:mnightly-2021-02-02-debian-10-test"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
show original