TFX for Dummies TensorFlow Extended

_Disclaimer: Before you read this, I would strongly recommend reading the _TensorFlow Data for Dummies

TensorFlow is widely known for its scalability and efficiency. With TensorFlow 2.x automating the entire process of Graph nodes, creating Machine Learning Pipelines has been easier than ever.

But what if I told you there was a simpler approach. This approach is widely used in Google to develop their own sophisticated Machine Learning Models.

Before we jump into our first hands-on tutorial, a quick primer on the essential concepts you would need to thoroughly understand the working of Apache Beam.

What are PCollections? (Apache Beam Primer)

There are three main components in Beam: Pipeline, PCollection, and PTransform.

• Pipeline encapsulates the workflow of your entire data processing tasks from start to finish.

• PCollection is a distributed dataset abstraction that Beam uses to transfer data between PTransforms.

• PTransform is a process that operates with input data (input PCollection) and produces output data (output PCollection). Usually, the first and the last PTransformsrepresent a way to input/output data which can be bounded (batch processing) or unbounded (streaming processing).

To simplify things, we can consider Pipeline as DAG (directed acyclic graph) which represents your whole workflow, PTransforms as nodes (that transform the data) and PCollections as edges of this graph.

#tensorflow #data-science #artificial-intelligence #machine-learning #tensorflow-extended

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TFX for Dummies TensorFlow Extended

_Disclaimer: Before you read this, I would strongly recommend reading the _TensorFlow Data for Dummies

TensorFlow is widely known for its scalability and efficiency. With TensorFlow 2.x automating the entire process of Graph nodes, creating Machine Learning Pipelines has been easier than ever.

But what if I told you there was a simpler approach. This approach is widely used in Google to develop their own sophisticated Machine Learning Models.

Before we jump into our first hands-on tutorial, a quick primer on the essential concepts you would need to thoroughly understand the working of Apache Beam.

What are PCollections? (Apache Beam Primer)

There are three main components in Beam: Pipeline, PCollection, and PTransform.

• Pipeline encapsulates the workflow of your entire data processing tasks from start to finish.

• PCollection is a distributed dataset abstraction that Beam uses to transfer data between PTransforms.

• PTransform is a process that operates with input data (input PCollection) and produces output data (output PCollection). Usually, the first and the last PTransformsrepresent a way to input/output data which can be bounded (batch processing) or unbounded (streaming processing).

To simplify things, we can consider Pipeline as DAG (directed acyclic graph) which represents your whole workflow, PTransforms as nodes (that transform the data) and PCollections as edges of this graph.

#tensorflow #data-science #artificial-intelligence #machine-learning #tensorflow-extended

A comprehensive ML Metadata walkthrough for Tensorflow Extended

Why it exists and how it’s used in Beam Pipeline Components

ML Metadata (MLMD) is a library for recording and retrieving metadata associated with ML developer and data scientist workflows.

TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines

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The current version of ML Metadata by the time this article is being published is **v0.22 **(tfx is also v0.22). The API is mature enough to allow for mainstream usage and deployment on the public cloud. Tensorflow Extended uses this extensively for component — component communication, lineage tracking, and other tasks.

We are going to run a very simple pipeline that is just going to generate statistics and the schema for a sample csv of the famous Chicago Taxi Trips dataset. It’s a small ~10mb file and the pipeline can run locally.

PIPELINE_ROOT = '<your project root>/bucket' # pretend this is a storage bucket in the cloud
	METADATA_STORE = f'{PIPELINE_ROOT}/metadata_store.db'
	STAGING = 'staging'
	TEMP = 'temp'

	PROJECT_ID = ''
	JOB_NAME = ''

	DATASET_PATTERN = 'taxi_dataset.csv'

	BEAM_ARGS = [
	    '--runner=DirectRunner'
	]

	def create_pipeline():
	    no_eval_config = example_gen_pb2.Input(splits=[
	        example_gen_pb2.Input.Split(name='train', pattern=DATASET_PATTERN),
	    ])
	    example_gen = CsvExampleGen(input=external_input(
	        PIPELINE_ROOT), input_config=no_eval_config)
	    statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
	    schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'])

	    return pipeline.Pipeline(
	        pipeline_name=f'Pipeline {JOB_NAME}',
	        pipeline_root=PIPELINE_ROOT,
	        components=[example_gen, statistics_gen, schema_gen],
	        beam_pipeline_args=BEAM_ARGS,
	        metadata_connection_config=metadata.sqlite_metadata_connection_config(METADATA_STORE)
	    )

	if __name__ == '__main__':
	    BeamDagRunner().run(create_pipeline())
view raw
metadata_local.py hosted with ❤ by GitHub

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Generated Artifact List

Run it once and open up the metadata_store.db file for inspection.

#metadata #deep-learning #tensorflow #tensorflow-extended #machine-learning #deep learning

Tensorflow Extended, ML Metadata and Apache Beam on the Cloud

The fully end to end example that tensorflow extended provides by running tfx template copy taxi $target-dir produces 17 files scattered in 5 directories. If you are looking for a smaller, simpler and self contained example that actually runs on the cloud and not locally, this is what you are looking for. Cloud services setup is also mentioned here.

What’s going to be covered

We are going to generate statistics and a schema for the Chicago taxi trips csv dataset that you can find by running the tfx template copy taxi command under the data directory.

Generated artifacts such as data statistics or the schema are going to be viewed from a jupyter notebook, by connecting to the ML Metadata store or just by downloading artifacts from simple file/binary storage.

Full code sample at the bottom of the article

Services Used

• Dataflow as the Apache Beam Pipeline running service
• Storage Buckets as simple (but fast) binary and file storage service
• (Optional but comes with diminishing returns) Cloud SQL (MySQL) as the backing storage service for ML Metadata

The whole pipeline can run on your local machine (or on different cloud providers/your custom spark clusters as well). This is an example that can be scaled by using bigger datasets. If you wish to understand how this happens transparently

Execution Process

1. If running locally, code will not be serialised or sent to the cloud (of course). Otherwise, Beam is going to send everything to a staging location (typically bucket storage). Check out cloudpickle to get some intuition on how serialisation is done.
2. Your cloud running service of choice (ours is Dataflow) is going to check if all the mentioned resources exist and are accessible (for example, pipeline output, temporary file storage, etc)
3. Compute instances are going to be started and your pipeline is going to be executed in a distributed scenario, showing up in the job inspector while it is still running or finished.

It’s a good naming practise to use _/temp_ or _/tmp_ for temporary files and _/staging_ or _/binaries_ for the staging directory.

#apache-beam #tensorflow-extended #deep-learning #tensorflow #google-cloud-platform

5 Steps to Passing the TensorFlow Developer Certificate

Deep Learning is one of the most in demand skills on the market and TensorFlow is the most popular DL Framework. One of the best ways in my opinion to show that you are comfortable with DL fundaments is taking this TensorFlow Developer Certificate. I completed mine last week and now I am giving tips to those who want to validate your DL skills and I hope you love Memes!

1. Do the DeepLearning.AI TensorFlow Developer Professional Certificate Course on Coursera Laurence Moroney and by Andrew Ng.

2. Do the course questions in parallel in PyCharm.

…

#tensorflow #steps to passing the tensorflow developer certificate #tensorflow developer certificate #certificate #5 steps to passing the tensorflow developer certificate #passing

Transfer Learning on Images with Tensorflow 2 – Predictive Hacks

In this tutorial, we will provide you an example of how you can build a powerful neural network model to classify images of **cats **and dogs using transfer learning by considering as base model a pre-trained model trained on ImageNet and then we will train additional new layers for our cats and dogs classification model.

The Data

We will work with a sample of 600 images from the Dogs vs Cats dataset, which was used for a 2013 Kaggle competition.

#python #transfer learning #tensorflow #images #transfer learning on images with tensorflow #tensorflow 2