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new ec312f00788 Copy editing ML notebooks for DevSite import (#30759)
ec312f00788 is described below
commit ec312f00788d8d7dc76deb191360e70e3a219b20
Author: Rebecca Szper <98840847+rsz...@users.noreply.github.com>
AuthorDate: Wed Mar 27 11:02:34 2024 -0700
Copy editing ML notebooks for DevSite import (#30759)
* Copy editing ML notebooks for DevSite import
* Update configuration parameter text
---
.../notebooks/beam-ml/run_inference_gemma.ipynb | 10 ++++----
.../vertex_ai_feature_store_enrichment.ipynb | 30 +++++++++-------------
2 files changed, 17 insertions(+), 23 deletions(-)
diff --git a/examples/notebooks/beam-ml/run_inference_gemma.ipynb
b/examples/notebooks/beam-ml/run_inference_gemma.ipynb
index 6af1bd07c14..489f01c4c9a 100644
--- a/examples/notebooks/beam-ml/run_inference_gemma.ipynb
+++ b/examples/notebooks/beam-ml/run_inference_gemma.ipynb
@@ -153,7 +153,7 @@
"id": "1FQdEMq8GEpl"
},
"source": [
- "The pipeline defined below automatically pulls the model weights from
Kaggle. Please go to https://www.kaggle.com/models/keras/gemma and accept the
terms of usage for Gemma models, then generate an API token using the
instructions at https://www.kaggle.com/docs/api and provide your username and
token here."
+ "The pipeline defined here automatically pulls the model weights from
Kaggle. First, accept the terms of use for Gemma models on the Keras
[Gemma](https://www.kaggle.com/models/keras/gemma) page. Next, generate an API
token by following the instructions in [How to use
Kaggle](https://www.kaggle.com/docs/api). Provide your username and token."
]
},
{
@@ -231,7 +231,7 @@
"## Import dependencies and provide a model preset\n",
"Use the following code to import dependencies.\n",
"\n",
- "Replace the `model_preset` variable with the name of the Gemma preset
to use. For example, if you want to use the default English weights, set the
preset to \"gemma_2b_en\". For this demo, we wil use the instruction-tuned
preset \"gemma_instruct_2b_en\". We also optionally use keras to run the model
at half-precision to reduce GPU memory usage."
+ "Replace the value for the `model_preset` variable with the name of
the Gemma preset to use. For example, to use the default English weights, use
the value `gemma_2b_en`. This example uses the instruction-tuned preset
`gemma_instruct_2b_en`. Optionally, to run the model at half-precision and
reduce GPU memory usage, use Keras."
]
},
{
@@ -269,8 +269,8 @@
"To run the pipeline, use a custom model handler.\n",
"\n",
"### Provide a custom model handler\n",
- "To simplify model loading, this notebook defines a custom model
handler that will load the model by pulling the model weights directly from
Kaggle presets. Implementing `load_model()`, `validate_inference_args()`, and
`share_model_across_processes()` allows us to customize the behavior of the
handler. The Keras implementation of the Gemma models has a `generate()`
method\n",
- "that generates text based on a prompt. Using this function in
`run_inference()` routes the prompts properly."
+ "To simplify model loading, this notebook defines a custom model
handler that loads the model by pulling the model weights directly from Kaggle
presets. To customize the behavior of the handler, implement `load_model`,
`validate_inference_args`, and `share_model_across_processes`. The Keras
implementation of the Gemma models has a `generate` method\n",
+ "that generates text based on a prompt. To route the prompts properly,
use this function in the `run_inference` method."
]
},
{
@@ -281,7 +281,7 @@
},
"outputs": [],
"source": [
- "# Define `GemmaModelHandler` to load the model and perform the
inference.\n",
+ "# To load the model and perform the inference, define
`GemmaModelHandler`.\n",
"\n",
"from apache_beam.ml.inference.base import ModelHandler\n",
"from apache_beam.ml.inference.base import PredictionResult\n",
diff --git
a/examples/notebooks/beam-ml/vertex_ai_feature_store_enrichment.ipynb
b/examples/notebooks/beam-ml/vertex_ai_feature_store_enrichment.ipynb
index c8ae558a1ba..ebfcca34b94 100644
--- a/examples/notebooks/beam-ml/vertex_ai_feature_store_enrichment.ipynb
+++ b/examples/notebooks/beam-ml/vertex_ai_feature_store_enrichment.ipynb
@@ -53,7 +53,7 @@
"id": "HrCtxslBGK8Z"
},
"source": [
- "This notebook shows how to enrich data by using the Apache Beam
[enrichment
transform](https://beam.apache.org/documentation/transforms/python/elementwise/enrichment/)
with [Vertex AI Feature Store](https://cloud.google.com/vertex-ai/docs). The
enrichment transform is a turnkey transform in Apache Beam that lets you enrich
data using a key-value lookup. This transform has the following features:\n",
+ "This notebook shows how to enrich data by using the Apache Beam
[enrichment
transform](https://beam.apache.org/documentation/transforms/python/elementwise/enrichment/)
with [Vertex AI Feature
Store](https://cloud.google.com/vertex-ai/docs/featurestore/latest/overview).
The enrichment transform is an Apache Beam turnkey transform that lets you
enrich data by using a key-value lookup. This transform has the following
features:\n",
"\n",
"- The transform has a built-in Apache Beam handler that interacts
with Vertex AI to get precomputed feature values.\n",
"- The transform uses client-side throttling to manage rate limiting
the requests.\n",
@@ -72,8 +72,8 @@
"\n",
"* Use a stream of online transactions from
[Pub/Sub](https://cloud.google.com/pubsub/docs/guides) that contains the
following fields: `product_id`, `user_id`, and `sale_price`.\n",
"* Deploy a pretrained model on Vertex AI based on the features
`product_id`, `user_id`, `sale_price`, `age`, `gender`, `state`, and
`country`.\n",
- "* Precompute the feature values for the pretrained model, and store
the values in the Vertex AI Feature Store.\n",
- "* Enrich the stream of transactions from Pub/Sub with feature values
from Vertex AI Feature Store by using the `Enrichment` transform.\n",
+ "* Precompute the feature values for the pretrained model, and store
the values in Vertex AI Feature Store.\n",
+ "* Enrich the stream of transactions from Pub/Sub with feature values
from Vertex AI Feature Store by using the enrichment transform.\n",
"* Send the enriched data to the Vertex AI model for online prediction
by using the `RunInference` transform, which predicts the product
recommendation for the user."
]
},
@@ -1139,7 +1139,7 @@
"source": [
"Deploy the model to the Vertex AI endpoint.\n",
"\n",
- "**Note:** This step is a Long Running Operation (LRO). Depending on
the size of the model, it might take more than five minutes to complete."
+ "**Note:** This step is a long running operation (LRO). Depending on
the size of the model, it might take more than five minutes to complete."
]
},
{
@@ -1186,7 +1186,7 @@
"id": "ouMQZ4sC4zuO"
},
"source": [
- "### Set up the Vertex AI Feature Store for online serving\n"
+ "### Set up Vertex AI Feature Store for online serving\n"
]
},
{
@@ -1544,7 +1544,7 @@
"id": "Mm-HCUaa3ROZ"
},
"source": [
- "Create a BigQuery dataset to use as the source for the Vertex AI
Feature Store."
+ "Create a BigQuery dataset to use as the source for Vertex AI Feature
Store."
]
},
{
@@ -2148,7 +2148,7 @@
"source": [
"## Use the Vertex AI Feature Store enrichment handler\n",
"\n",
- "The
[`VertexAIFeatureStoreEnrichmentHandler`](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.html#apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreEnrichmentHandler)
is a built-in handler included in the Apache Beam SDK versions 2.55.0 and
later."
+ "The
[`VertexAIFeatureStoreEnrichmentHandler`](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.html#apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreEnrichmentHandler)
is a built-in handler in the Apache Beam SDK versions 2.55.0 and later."
]
},
{
@@ -2157,16 +2157,16 @@
"id": "K41xhvmA5yQk"
},
"source": [
- "Configure the `VertexAIFeatureStoreEnrichmentHandler` with the
following required parameters:\n",
+ "Configure the `VertexAIFeatureStoreEnrichmentHandler` handler with
the following required parameters:\n",
"\n",
"* `project`: the Google Cloud project ID for the feature store\n",
"* `location`: the region of the feature store, for example
`us-central1`\n",
"* `api_endpoint`: the public endpoint of the feature store\n",
- "* `feature_store_name`: the name of the Vertex AI Feature Store\n",
- "* `feature_view_name`: the name of the feature view within the
Vertex AI Feature Store\n",
+ "* `feature_store_name`: the name of the Vertex AI feature store\n",
+ "* `feature_view_name`: the name of the feature view within the
Vertex AI feature store\n",
"* `row_key`: The field name in the input row containing the entity
ID for the feature store. This value is used to extract the entity ID from each
element. The entity ID is used to fetch feature values for that specific
element in the enrichment transform.\n",
"\n",
- "Optionally, to provide more configuration values to connect with the
Vertex AI client, the `VertexAIFeatureStoreEnrichmentHandler` accepts a keyword
argument (kwargs). For more information, see
[`FeatureOnlineStoreServiceClient`](https://cloud.google.com/php/docs/reference/cloud-ai-platform/latest/V1.FeatureOnlineStoreServiceClient).\n",
+ "Optionally, to provide more configuration values to connect with the
Vertex AI client, the `VertexAIFeatureStoreEnrichmentHandler` handler accepts a
keyword argument (kwargs). For more information, see
[`FeatureOnlineStoreServiceClient`](https://cloud.google.com/php/docs/reference/cloud-ai-platform/latest/V1.FeatureOnlineStoreServiceClient).\n",
"\n",
"**Note:** When exceptions occur, by default, the logging severity is
set to warning
([`ExceptionLevel.WARN`](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.enrichment_handlers.utils.html#apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel.WARN)).
To configure the severity to raise exceptions, set `exception_level` to
[`ExceptionLevel.RAISE`](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.enrichment_handlers.utils.html#apa
[...]
"\n",
@@ -2208,13 +2208,7 @@
"source": [
"## Use the enrichment transform\n",
"\n",
- "To use the [enrichment
transform](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.Enrichment),
the
[`EnrichmentHandler`](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.EnrichmentSourceHandler)
parameter is required. You can also use a configuration parameter to specify a
`lambda` for a join function, a timeout, a throttler, and a repeat [...]
- "\n",
- "\n",
- "* `join_fn`: A lambda function that takes dictionaries as input and
returns an enriched row (`Callable[[Dict[str, Any], Dict[str, Any]],
beam.Row]`). The enriched row specifies how to join the data fetched from the
API. Defaults to a
[cross-join](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.cross_join).\n",
- "* `timeout`: The number of seconds to wait for the request to be
completed by the API before timing out. Defaults to 30 seconds.\n",
- "* `throttler`: Specifies the throttling mechanism. The only
supported option is default client-side adaptive throttling.\n",
- "* `repeater`: Specifies the retry strategy when errors like
`TooManyRequests` and `TimeoutException` occur. Defaults to
[`ExponentialBackOffRepeater`](https://beam.apache.org/releases/pydoc/current/apache_beam.io.requestresponse.html#apache_beam.io.requestresponse.ExponentialBackOffRepeater).\n",
+ "To use the [enrichment
transform](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.Enrichment),
the
[`EnrichmentHandler`](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.EnrichmentSourceHandler)
parameter is required. You can also use configuration parameters to specify a
`lambda` for a join function, a timeout, a throttler, and a repeate [...]
"\n",
"\n",
"To use the Redis cache, apply the `with_redis_cache` hook to the
enrichment transform. The coders for encoding and decoding the input and output
for the cache are optional and are internally inferred."
