Use RunInference to perform custom inference in custom_inference notebook #29304
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Stopping reviewer notifications for this pull request: review requested by someone other than the bot, ceding control |
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The changes look good to me. Two suggestions:
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I would vote against this. There are other examples of using a DoFn (e.g. ), but the preferred way for people to do remote inference is with RunInference. |
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The current ParDo example is too simple and quite useless. I even think we should not just keep the old way but also use it to show why they should switch to |
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liferoad
approved these changes
Nov 7, 2023
liferoad
reviewed
Nov 7, 2023
| @@ -60,6 +60,7 @@ | |||
| "To reduce the number of steps that you need to take, RunInference supports features like batching. For more infomation about the RunInference API, review the [RunInference API](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.html#apache_beam.ml.inference.RunInference),\n", | |||
| "which demonstrates how to implement model inference in PyTorch, scikit-learn, and TensorFlow.\n", | |||
| "\n", | |||
| "There is [VertexAIModelHandlerJson](https://github.com/apache/beam/blob/1c24bc94c9ae5cf93dc129e657964858d4aafc67/sdks/python/apache_beam/ml/inference/vertex_ai_inference.py#L61) which is used to make remote inference calls to VertexAI. In this notebook, we will make custom `ModelHandler` to do remote inference calls using CloudVision API.\n", | |||
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do we use master or a commit hash?
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Modified it to master
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Fixes: #29297
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