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End-to-End ML Pipelines
TFX + Kubeflow + Airflow + MLflow
Chris Fregly
Founder @ .
Founder @ PipelineAI
Continuous Machine Learning in Production
Former Databricks, Netflix
Apache Spark Contributor
O’Reilly Author
High Performance TensorFlow in Production
Meetup Organizer
Advanced Kubeflow Meetup
Who Am I? (@cfregly)
Advanced Kubeflow Meetup (Global+Bay Area, Monthly Events)
https://meetup.com/Advanced-Kubeflow-Meetup
Full-Day Workshop
https://pipeline.ai @cfregly @PipelineAI
1 OK with Command Line?
2 OK with Python?
3 OK with Linear Algebra?
Who are you?
4 OK with Docker?
6
5 OK with Jupyter Notebook?
Recent Poll (July 2019)
4,000 Stars = $6,000,000 Seed
$1,500 per GitHub Star?!
(Please star the repo ASAP!!)
Recent Comment from Popular VC Investor in Silicon Valley
Community Edition
https://community.pipeline.ai
After Pushing Your Model to Production, Your Model is…
1 Already Out of Date – Need to Re-Train
Biased – Need to Validate Before Pushing
Broken – Need to A/B Test in Production
Hacked – Need to Train With Data Privacy
Slow – Need to Quantize and Speed Up Predictions
2
3
4
5
1 Setup Environment with Kubernetes
TensorFlow Extended (TFX)
ML Pipelines with Airflow and KubeFlow
Agenda – Part 1 of 2
Hyper-Parameter Tuning with KubeFlow
Deploy Notebook with Kubernetes
2
3
4
5
Agenda – Part 2 of 2
6 MLflow
7
8
9
10
TensorFlow Privacy
Model Serving & A/B Tests
Model Optimization
Papermill
Note #1 of 11
IGNORE WARNINGS & ERRORS
(Everything will be OK!)
Note #2 of 11
THERE IS A LOT OF MATERIAL HERE
Many opportunities to explore on your own.
(Don’t upload sensitive data.)
Note #3 of 11
YOU HAVE YOUR OWN INSTANCE
16 CPU, 104 GB RAM, 200GB SSD
(Each with a full Kubernetes Cluster.)
Note #4 of 11
DATASETS
Chicago Taxi Dataset
(and various others.)
Note #5 of 11
SOME NOTEBOOKS TAKE MINUTES
Please be patient.
(We are using large datasets)
Note #6 of 11
QUESTIONS?
Post questions to Zoom chat or Q&A.
(Antje and I will answer soon)
Antje >
Note #7 of 11
KUBEFLOW IS NOT A SILVER BULLET
There are still gaps in the pipeline.
(But gaps are getting smaller)
Note #8 of 11
THIS IS NOT CLOUD DEPENDENT*
*Except for 2 small exceptions…
(Patches are underway.)
Note #9 of 11
PRIMARILY TENSORFLOW 1.x
TF 2.x is not fully supported by TFX
(Until Mid-2020.)
Note #10 of 11
SHUTDOWN EACH NOTEBOOK AFTER
We are using complex browser voo-doo.
(Javascript is a mystery.)
Note #11 of 11
Retrieve 1 Single IP Address Here…
<INSERT HERE>
(Do not click refresh.)
1 Setup Environment with Kubernetes
TensorFlow Extended (TFX)
ML Pipelines with Airflow and KubeFlow
Agenda – Part 1 of 2
Hyper-Parameter Tuning with KubeFlow
Deploy Notebook with Kubernetes
2
3
4
5
Agenda – Part 2 of 2
6 MLflow
7
8
9
10
TensorFlow Privacy
Model Serving & A/B Tests
Model Optimization
Papermill
Hands On
00_Explore_Environment
1.1 Kubernetes
TensorFlow Extended (TFX)
Airflow ML Pipelines
1.0 Environment Overview
KubeFlow ML Pipelines
6
Hyper-Parameter Tuning (Katib)
Prediction Traffic Router (Istio)
1.2
1.3
1.4
1.6
1.7
MLflow Pipelines1.5
1.1 Kubernetes
Kubernetes
NFS
Ceph
Cassandra
MySQL
Spark
Airflow
Tensorflow
Caffe
TF-Serving
Flask+Scikit
Operating system (Linux, Windows)
CPU Memory DiskSSD GPU FPGA ASIC NIC
Jupyter
GCP AWS Azure On-prem
Namespace
Quota Logging
Monitoring RBAC
Hands On
01_Explore_Kubernetes_Cluster
System 6
System 5System 4
Training
At Scale
System 3
System 1
Data
Ingestion
Data
Analysis
Data
Transform
Data
Validation
System 2
Build
Model
Model
Validation
Serving Logging
Monitoring
Roll-out
Data
Splitting
Ad-Hoc
Training
Why TFX and Why KubeFlow?
Improve Training/Serving
Consistency
Unify Disparate Systems
Manage Pipeline Complexity
Improve Portability
Wrangle Large Datasets
Improve Model Quality
Manage Versions
Composability
Distributed
Training
Configure
1.2 TensorFlow Extended (TFX)
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy
Reproduce
Training
Jenkins?
1.3 Airflow ML Pipelines
1.4 KubeFlow ML Pipelines
1.5 MLflow Experiment Tracking
1.6 Hyper-Parameter Tuning (Katib)
1.7 Prediction Traffic Routing (Istio)
1 Setup Environment with Kubernetes
TensorFlow Extended (TFX)
ML Pipelines with Airflow and KubeFlow
Agenda – Part 1 of 2
Hyper-Parameter Tuning with KubeFlow
Deploy Notebook with Kubernetes
2
3
4
5
2.1 TFX Internals
2.0 TFX Components
6
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
2.2 TFX Libraries
2.2 TFX Components
2.1 TFX Internals
Driver/Publisher
Moves data to/from Metadata Store
Executor
Runs the Actual Processing Code
Metadata Store
Artifact, execution, and lineage Info
Track inputs & outputs of all components
Stores training run including inputs & outputs
Analysis, validation, and versioning results
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
2.2 TFX Libraries
2.2.1
TFX Components Use These:
TensorFlow Data Validation (TFDV)
TensorFlow Transform (TFT)
TensorFlow Model Analysis (TFMA)
TensorFlow Metadata (TFMD) + ML Metadata (MLMD)
2.2.2
2.2.3
2.2.4
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
2.2.1 TFX Libraries - TFDV
TensorFlow Data Validation (TFDV)
Find Missing, Redundant & Important Features
Identify Features with Unusually-Large Scale
`infer_schema()` Generates Schema
Describe Feature Ranges
Detect Data Drift
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Uniformly
Distributed Data è
ç Non-Uniformly
Distributed Data
Hands On
02_TensorFlow_Data_Validation
(TFDV)
2.2.2 TFX Libraries - TFT
TensorFlow Transform (TFT)
Preprocess `tf.Example` data with TensorFlow
Useful for data that requires a full pass
Normalize all inputs by mean and std dev
Create vocabulary of strings è integers over all data
Bucketize features based on entire data distribution
Outputs a TensorFlow graph
Re-used across both training and serving
Uses Apache Beam (local mode) for Parallel Analysis
Can also use distributed mode
`preprocessing_fn(inputs)`: Primary Fn to Implement
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
import tensorflow as tf
import tensorflow_transform as tft
def preprocessing_fn(inputs):
x = inputs['x']
y = inputs['y']
s = inputs['s']
x_centered = x - tft.mean(x)
y_normalized = tft.scale_to_0_1(y)
s_integerized = tft.compute_and_apply_vocabulary(s)
x_centered_times_y_normalized = x_centered * y_normalized
return {
'x_centered': x_centered,
'y_normalized': y_normalized,
'x_centered_times_y_normalized':x_centered_times_y_normalized,
's_integerized': s_integerized
}
Hands On
03_TensorFlow_Transform
(TFT)
Hands On
03a_TensorFlow_Transform_Advanced
(TFT)
2.2.3 TFX Libraries - TFMA
TensorFlow Model Analysis (TFMA)
Analyze Model on Different Slices of Dataset
Track Metrics Over Time (“Next Day Eval”)
`EvalSavedModel` Contains Slicing Info
TFMA Pipeline: Read, Extract, Evaluate, Write
ie. Ensure Model Works Fairly Across All Users
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Hands On
04_TensorFlow_Model_Analysis
(TFMA)
2.2.4 TFX Libraries – Metadata
TensorFlow Metadata (TFMD)
ML Metadata (MLMD)
Record and Retrieve Experiment Metadata
Artifact, Execution, and Lineage Info
Track Inputs / Outputs of All TFX Components
Stores Training Run Info
Analysis and Validation Results
Model Versioning Info
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
2.3 TFX Components
ExampleGen
StatisticsGen
SchemaGen
ExampleValidator
Evaluator
Transform
ModelValidator
Trainer
Model Pusher2.3.92.3.1
2.3.2
2.3.3
2.3.4
2.3.5
2.3.6
2.3.7
2.3.8
Slack (!!)2.3.10
2.3.1 ExampleGen
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Load Training Data Into TFX Pipeline
Supports External Data Sources
Supports CSV and TFRecord Formats
Converts Data to tf.Example
Note: TFX Pipelines require tf.Example (?!)
Difficult to use non-TF models like XGBoost
from tfx.utils.dsl_utils import csv_input
from
tfx.components.example_gen.csv_example_gen.component
import CsvExampleGen
examples = csv_input(os.path.join(base_dir, 'data/simple'))
example_gen = CsvExampleGen(input_base=examples)
2.3.2 StatisticsGen
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Generates Statistics on Training Data
Global `mean` and `stddev` per input feature
Consumes tf.Example instances
from tfx import components
compute_eval_stats = components.StatisticsGen(
input_data=examples_gen.outputs.eval_examples,
name='compute-eval-stats'
)
2.3.3 SchemaGen
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Schema Needed by Some TFX Components
Data Types, Value Ranges, Optional, Required
Consumes Data from StatisticsGen
Schema used by TFDV, TFT, TFMA Libraries
Uses TFDV Library to infer schema
Best effort and basic
Human should verify
feature {
name: "age"
value_count {
min: 1
max: 1
}
type: FLOAT
presence {
min_fraction: 1
min_count: 1
}
}
from tfx import components
infer_schema = components.SchemaGen(
stats=compute_training_stats.outputs.output)
2.3.4 ExampleValidator
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Identifies Anomalies in Training Data
Used with serving data to detect drift / skew
Uses StatisticsGen and SchemaGen Outputs
Produces Validation Results
Uses TFDV Library for Input Validation
from tfx import components
infer_schema = components.SchemaGen(
stats=compute_training_stats.outputs.output
)
2.3.5 Transform
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Uses Data from ExampleGen & SchemaGen
Transformations Become Part of TF Graph (!!)
Helps Avoid Training/Serving Skew
Uses TFT Library for Transformations
Transformations Require Full Pass Thru Dataset
Global Reduction Across All Batches
Create Word Embeddings, Normalize, PCA
def preprocessing_fn(inputs):
# inputs: map from feature keys
# to raw not-yet-transformed features
# outputs: map from string feature key
# to transformed feature operations
2.3.6 Trainer
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Trains / Validates tf.Examples from Transform
Uses schema.proto from SchemaGen
Produces SavedModel and EvalSavedModel
Uses Core TensorFlow Python API
Works with TensorFlow 1.x Estimator API
TensorFlow 2.0 Keras Support Coming Soon
from tfx import components
trainer = components.Trainer(
module_file=taxi_pipeline_utils,
train_files=transform_training.outputs.output,
eval_files=transform_eval.outputs.output,
schema=infer_schema.outputs.output,
tf_transform_dir=transform_training.outputs.output,
train_steps=10000,
eval_steps=5000)
2.3.7 Evaluator
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Uses EvalSavedModel from Trainer
Writes Analysis Results to ML Metadata Store
Uses TFMA Library for Analysis
TFMA Uses Apache Beam to Scale Analysis
from tfx import components
import tensorflow_model_analysis as tfma
taxi_eval_spec = [
tfma.SingleSliceSpec(),
tfma.SingleSliceSpec(columns=['trip_start_hour'])
]
model_analyzer = components.Evaluator(
examples=examples_gen.outputs.eval_examples,
eval_spec=taxi_eval_spec,
model_exports=trainer.outputs.output)
2.3.8 ModelValidator
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Validate Models from Trainer
Uses Data from SchemaGen & StatisticsGen
Compares New Models to Baseline
Baseline == current model in production
New Model is Good if Meets/Exceeds Metrics
If Good, Notify Pusher to Deploy New Model
Simulate “Next Day Evaluation” On New Data
import tensorflow_model_analysis as tfma
taxi_mv_spec = [tfma.SingleSliceSpec()]
model_validator = components.ModelValidator(
examples=examples_gen.outputs.output,
model=trainer.outputs.output)
2.3.9 Model Pusher (Deployer)
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Push Good Model to Deployment Target
Uses Trained SavedModel
Writes Version Data to Metadata Store
Write to FileSystem or TensorFlow Hub
from tfx import components
pusher = components.Pusher(
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
serving_model_dir=serving_model_dir)
2.3.10 Slack Component (!!)
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Runs After ModelValidator
Adds Human-in-the-Loop Step to Pipeline
TFX Sends Message to Slack with Model URI
Asks Human to Review the New Model
Respond ‘LGTM’, ‘approve’, ‘decline’, ‘reject’
Requires Slack API Setup / Integration
export SLACK_BOT_TOKEN={your_token}
_channel_id = 'my-channel-id'
_slack_token = os.environ['SLACK_BOT_TOKEN’]
slack_validator = SlackComponent(
model_export=trainer.outputs.output,
model_blessing=model_validator.outputs.blessing,
slack_token=_slack_token, channel_id=_channel_id,
timeout_sec=3600, )
https://github.com/tensorflow/tfx/tree/master
/tfx/examples/custom_components/slack/slack_component
1 Setup Environment with Kubernetes
TensorFlow Extended (TFX)
ML Pipelines with Airflow and KubeFlow
Agenda – Part 1 of 2
Hyper-Parameter Tuning with KubeFlow
Deploy Notebook with Kubernetes
2
3
4
5
3.0 ML Pipelines with Airflow and KubeFlow
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy
3.1 Airflow
KubeFlow3.2
3.1 Airflow
6
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Most Widely-Used Workflow Orchestrator
Define Execution Graphs in Python
Decent UI
Good Community Support
Hands On
05_Airflow_ML_Pipelines
(Chicago Taxi Dataset)
Hands On
06_Airflow_Feature_Analysis
Hands On
07_Airflow_Model_Analysis
3.2 KubeFlow
6
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Pipelines
Based on Argo CI/CD Project from Intuit
TFJob & PyTorch Job
Supports Distributed Training
TensorFlow & PyTorch Jobs
KubeFlow Fairing Project (!!)
Run a notebook as a production job
Deploy training code with dependencies
Hands On
08_Simple_KubeFlow_ML_Pipeline
Hands On
09_Advanced_KubeFlow_ML_Pipeline
(Chicago Taxi Dataset)
Hands On
10_Distributed_TensorFlow_Job
Hands On
10a_Distributed_PyTorch_Job
1 Setup Environment with Kubernetes
TensorFlow Extended (TFX)
ML Pipelines with Airflow and KubeFlow
Agenda – Part 1 of 2
Hyper-Parameter Tuning with KubeFlow
Deploy Notebook with Kubernetes
2
3
4
5
4.0 Hyper-Parameter Tuning
6
Experiment
Single Optimization Run
Single Objective Function Across Runs
Contains Many Trials
Trial
List of Param Values
Suggestion
Optimization Algorithm
Job
Evaluates a Trial
Calculates Objective
Hands On
11_Hyper_Parameter_Tuning
1 Setup Environment with Kubernetes
TensorFlow Extended (TFX)
ML Pipelines with Airflow and KubeFlow
Agenda – Part 1 of 2
Hyper-Parameter Tuning with KubeFlow
Deploy Notebook with Kubernetes
2
3
4
5
5.0 Deploy Notebook as Job
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
6
5.1 Wrap Model in a Docker Image
Deploy Job to Kubernetes5.2
5.1 Create Docker Image
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
5.2 Deploy Notebook as Job
Feature
Load
Feature
Analyze Feature
Transform
Model
Train
Model
Evaluate
Model
Deploy Reproduce
Training
Hands On
12_Deploy_Notebook_Xgboost
Hands On
12a_Deploy_Notebook_TensorFlow
1 Setup Environment with Kubernetes
TensorFlow Extended (TFX)
ML Pipelines with TFX, Airflow, and KubeFlow
Agenda – Part 1 of 2
Hyper-Parameter Tuning with TFX and KubeFlow
Deploy Notebook with Kubernetes
2
3
4
5
Agenda – Part 2 of 2
6 MLflow
7
8
9
10
TensorFlow Privacy
Model Serving & A/B Tests
Model Optimization
Papermill
7.0 MLflow
7.1 Experiment Tracking
Hyper-Parameter Tuning
Kubernetes-based Jobs
7.2
7.3
Hands On
14_MLflow_Scikit_Learn
Hands On
14a_MLflow_Keras
Hands On
14b_MLflow_TensorFlow
Agenda – Part 2 of 2
6 MLflow
7
8
9
10
TensorFlow Privacy
Model Serving & A/B Tests
Model Optimization
Papermill
7.0 Papermill
Hands On
15_Papermill_Notebook_Job
Agenda – Part 2 of 2
6 MLflow
7
8
9
10
TensorFlow Privacy
Model Serving & A/B Tests
Model Optimization
Papermill
9.0 TensorFlow Privacy (Differential Privacy)
9.1 Differential Privacy + Stochastic Gradient Descent
9.1 Differential Privacy + Explainability + Drift Detection
Hands On
16_TF_Privacy
Agenda – Part 2 of 2
6 MLflow
7
8
9
10
TensorFlow Privacy
Model Serving & A/B Tests
Model Optimization
Papermill
10.0 Model Serving & A/B Tests
Hands On
17_Simple_Serving_REST
Hands On
17a_AB_Test_REST
Hands On
18_Metrics_and_Monitoring
10.0 Model Optimization
Agenda – Part 2 of 2
6 MLflow
7
8
9
10
TensorFlow Privacy
Model Serving & A/B Tests
Model Optimization
Papermill
Hands On
19_Optimize_Model
Agenda – Part 2 of 2
6 MLflow
7
8
9
10
TensorFlow Privacy
Model Serving & A/B Tests
Model Optimization
Papermill
1 Setup Environment with Kubernetes
TensorFlow Extended (TFX)
ML Pipelines with Airflow and KubeFlow
Agenda – Part 1 of 2
Hyper-Parameter Tuning with KubeFlow
Deploy Notebook with Kubernetes
2
3
4
5
After Pushing Your Model to Production, Your Model is…
1 Already Out of Date – Need to Re-Train
Biased – Need to Validate Before Pushing
Broken – Need to A/B Test in Production
Hacked – Need to Train With Data Privacy
Slow – Need to Quantize and Speed Up Predictions
2
3
4
5
Community Edition
https://community.pipeline.ai
Thank you!
https://pipeline.ai
@cfregly @PipelineAI

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KubeFlow + GPU + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTorch + XGBoost + Airflow + MLflow + Spark ML + Jupyter

  • 1. End-to-End ML Pipelines TFX + Kubeflow + Airflow + MLflow Chris Fregly Founder @ .
  • 2. Founder @ PipelineAI Continuous Machine Learning in Production Former Databricks, Netflix Apache Spark Contributor O’Reilly Author High Performance TensorFlow in Production Meetup Organizer Advanced Kubeflow Meetup Who Am I? (@cfregly)
  • 3. Advanced Kubeflow Meetup (Global+Bay Area, Monthly Events) https://meetup.com/Advanced-Kubeflow-Meetup
  • 5. 1 OK with Command Line? 2 OK with Python? 3 OK with Linear Algebra? Who are you? 4 OK with Docker? 6 5 OK with Jupyter Notebook?
  • 7. 4,000 Stars = $6,000,000 Seed $1,500 per GitHub Star?! (Please star the repo ASAP!!) Recent Comment from Popular VC Investor in Silicon Valley
  • 9. After Pushing Your Model to Production, Your Model is… 1 Already Out of Date – Need to Re-Train Biased – Need to Validate Before Pushing Broken – Need to A/B Test in Production Hacked – Need to Train With Data Privacy Slow – Need to Quantize and Speed Up Predictions 2 3 4 5
  • 10. 1 Setup Environment with Kubernetes TensorFlow Extended (TFX) ML Pipelines with Airflow and KubeFlow Agenda – Part 1 of 2 Hyper-Parameter Tuning with KubeFlow Deploy Notebook with Kubernetes 2 3 4 5
  • 11. Agenda – Part 2 of 2 6 MLflow 7 8 9 10 TensorFlow Privacy Model Serving & A/B Tests Model Optimization Papermill
  • 12. Note #1 of 11 IGNORE WARNINGS & ERRORS (Everything will be OK!)
  • 13. Note #2 of 11 THERE IS A LOT OF MATERIAL HERE Many opportunities to explore on your own. (Don’t upload sensitive data.)
  • 14. Note #3 of 11 YOU HAVE YOUR OWN INSTANCE 16 CPU, 104 GB RAM, 200GB SSD (Each with a full Kubernetes Cluster.)
  • 15. Note #4 of 11 DATASETS Chicago Taxi Dataset (and various others.)
  • 16. Note #5 of 11 SOME NOTEBOOKS TAKE MINUTES Please be patient. (We are using large datasets)
  • 17. Note #6 of 11 QUESTIONS? Post questions to Zoom chat or Q&A. (Antje and I will answer soon) Antje >
  • 18. Note #7 of 11 KUBEFLOW IS NOT A SILVER BULLET There are still gaps in the pipeline. (But gaps are getting smaller)
  • 19. Note #8 of 11 THIS IS NOT CLOUD DEPENDENT* *Except for 2 small exceptions… (Patches are underway.)
  • 20. Note #9 of 11 PRIMARILY TENSORFLOW 1.x TF 2.x is not fully supported by TFX (Until Mid-2020.)
  • 21. Note #10 of 11 SHUTDOWN EACH NOTEBOOK AFTER We are using complex browser voo-doo. (Javascript is a mystery.)
  • 22. Note #11 of 11 Retrieve 1 Single IP Address Here… <INSERT HERE> (Do not click refresh.)
  • 23. 1 Setup Environment with Kubernetes TensorFlow Extended (TFX) ML Pipelines with Airflow and KubeFlow Agenda – Part 1 of 2 Hyper-Parameter Tuning with KubeFlow Deploy Notebook with Kubernetes 2 3 4 5
  • 24. Agenda – Part 2 of 2 6 MLflow 7 8 9 10 TensorFlow Privacy Model Serving & A/B Tests Model Optimization Papermill
  • 26. 1.1 Kubernetes TensorFlow Extended (TFX) Airflow ML Pipelines 1.0 Environment Overview KubeFlow ML Pipelines 6 Hyper-Parameter Tuning (Katib) Prediction Traffic Router (Istio) 1.2 1.3 1.4 1.6 1.7 MLflow Pipelines1.5
  • 27. 1.1 Kubernetes Kubernetes NFS Ceph Cassandra MySQL Spark Airflow Tensorflow Caffe TF-Serving Flask+Scikit Operating system (Linux, Windows) CPU Memory DiskSSD GPU FPGA ASIC NIC Jupyter GCP AWS Azure On-prem Namespace Quota Logging Monitoring RBAC
  • 29. System 6 System 5System 4 Training At Scale System 3 System 1 Data Ingestion Data Analysis Data Transform Data Validation System 2 Build Model Model Validation Serving Logging Monitoring Roll-out Data Splitting Ad-Hoc Training Why TFX and Why KubeFlow? Improve Training/Serving Consistency Unify Disparate Systems Manage Pipeline Complexity Improve Portability Wrangle Large Datasets Improve Model Quality Manage Versions Composability Distributed Training Configure
  • 30. 1.2 TensorFlow Extended (TFX) Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Jenkins?
  • 31. 1.3 Airflow ML Pipelines
  • 32. 1.4 KubeFlow ML Pipelines
  • 35. 1.7 Prediction Traffic Routing (Istio)
  • 36. 1 Setup Environment with Kubernetes TensorFlow Extended (TFX) ML Pipelines with Airflow and KubeFlow Agenda – Part 1 of 2 Hyper-Parameter Tuning with KubeFlow Deploy Notebook with Kubernetes 2 3 4 5
  • 37. 2.1 TFX Internals 2.0 TFX Components 6 Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training 2.2 TFX Libraries 2.2 TFX Components
  • 38. 2.1 TFX Internals Driver/Publisher Moves data to/from Metadata Store Executor Runs the Actual Processing Code Metadata Store Artifact, execution, and lineage Info Track inputs & outputs of all components Stores training run including inputs & outputs Analysis, validation, and versioning results Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training
  • 39. 2.2 TFX Libraries 2.2.1 TFX Components Use These: TensorFlow Data Validation (TFDV) TensorFlow Transform (TFT) TensorFlow Model Analysis (TFMA) TensorFlow Metadata (TFMD) + ML Metadata (MLMD) 2.2.2 2.2.3 2.2.4 Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training
  • 40. 2.2.1 TFX Libraries - TFDV TensorFlow Data Validation (TFDV) Find Missing, Redundant & Important Features Identify Features with Unusually-Large Scale `infer_schema()` Generates Schema Describe Feature Ranges Detect Data Drift Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Uniformly Distributed Data è ç Non-Uniformly Distributed Data
  • 42. 2.2.2 TFX Libraries - TFT TensorFlow Transform (TFT) Preprocess `tf.Example` data with TensorFlow Useful for data that requires a full pass Normalize all inputs by mean and std dev Create vocabulary of strings è integers over all data Bucketize features based on entire data distribution Outputs a TensorFlow graph Re-used across both training and serving Uses Apache Beam (local mode) for Parallel Analysis Can also use distributed mode `preprocessing_fn(inputs)`: Primary Fn to Implement Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training import tensorflow as tf import tensorflow_transform as tft def preprocessing_fn(inputs): x = inputs['x'] y = inputs['y'] s = inputs['s'] x_centered = x - tft.mean(x) y_normalized = tft.scale_to_0_1(y) s_integerized = tft.compute_and_apply_vocabulary(s) x_centered_times_y_normalized = x_centered * y_normalized return { 'x_centered': x_centered, 'y_normalized': y_normalized, 'x_centered_times_y_normalized':x_centered_times_y_normalized, 's_integerized': s_integerized }
  • 45. 2.2.3 TFX Libraries - TFMA TensorFlow Model Analysis (TFMA) Analyze Model on Different Slices of Dataset Track Metrics Over Time (“Next Day Eval”) `EvalSavedModel` Contains Slicing Info TFMA Pipeline: Read, Extract, Evaluate, Write ie. Ensure Model Works Fairly Across All Users Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training
  • 47. 2.2.4 TFX Libraries – Metadata TensorFlow Metadata (TFMD) ML Metadata (MLMD) Record and Retrieve Experiment Metadata Artifact, Execution, and Lineage Info Track Inputs / Outputs of All TFX Components Stores Training Run Info Analysis and Validation Results Model Versioning Info Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training
  • 48. 2.3 TFX Components ExampleGen StatisticsGen SchemaGen ExampleValidator Evaluator Transform ModelValidator Trainer Model Pusher2.3.92.3.1 2.3.2 2.3.3 2.3.4 2.3.5 2.3.6 2.3.7 2.3.8 Slack (!!)2.3.10
  • 49. 2.3.1 ExampleGen Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Load Training Data Into TFX Pipeline Supports External Data Sources Supports CSV and TFRecord Formats Converts Data to tf.Example Note: TFX Pipelines require tf.Example (?!) Difficult to use non-TF models like XGBoost from tfx.utils.dsl_utils import csv_input from tfx.components.example_gen.csv_example_gen.component import CsvExampleGen examples = csv_input(os.path.join(base_dir, 'data/simple')) example_gen = CsvExampleGen(input_base=examples)
  • 50. 2.3.2 StatisticsGen Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Generates Statistics on Training Data Global `mean` and `stddev` per input feature Consumes tf.Example instances from tfx import components compute_eval_stats = components.StatisticsGen( input_data=examples_gen.outputs.eval_examples, name='compute-eval-stats' )
  • 51. 2.3.3 SchemaGen Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Schema Needed by Some TFX Components Data Types, Value Ranges, Optional, Required Consumes Data from StatisticsGen Schema used by TFDV, TFT, TFMA Libraries Uses TFDV Library to infer schema Best effort and basic Human should verify feature { name: "age" value_count { min: 1 max: 1 } type: FLOAT presence { min_fraction: 1 min_count: 1 } } from tfx import components infer_schema = components.SchemaGen( stats=compute_training_stats.outputs.output)
  • 52. 2.3.4 ExampleValidator Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Identifies Anomalies in Training Data Used with serving data to detect drift / skew Uses StatisticsGen and SchemaGen Outputs Produces Validation Results Uses TFDV Library for Input Validation from tfx import components infer_schema = components.SchemaGen( stats=compute_training_stats.outputs.output )
  • 53. 2.3.5 Transform Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Uses Data from ExampleGen & SchemaGen Transformations Become Part of TF Graph (!!) Helps Avoid Training/Serving Skew Uses TFT Library for Transformations Transformations Require Full Pass Thru Dataset Global Reduction Across All Batches Create Word Embeddings, Normalize, PCA def preprocessing_fn(inputs): # inputs: map from feature keys # to raw not-yet-transformed features # outputs: map from string feature key # to transformed feature operations
  • 54. 2.3.6 Trainer Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Trains / Validates tf.Examples from Transform Uses schema.proto from SchemaGen Produces SavedModel and EvalSavedModel Uses Core TensorFlow Python API Works with TensorFlow 1.x Estimator API TensorFlow 2.0 Keras Support Coming Soon from tfx import components trainer = components.Trainer( module_file=taxi_pipeline_utils, train_files=transform_training.outputs.output, eval_files=transform_eval.outputs.output, schema=infer_schema.outputs.output, tf_transform_dir=transform_training.outputs.output, train_steps=10000, eval_steps=5000)
  • 55. 2.3.7 Evaluator Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Uses EvalSavedModel from Trainer Writes Analysis Results to ML Metadata Store Uses TFMA Library for Analysis TFMA Uses Apache Beam to Scale Analysis from tfx import components import tensorflow_model_analysis as tfma taxi_eval_spec = [ tfma.SingleSliceSpec(), tfma.SingleSliceSpec(columns=['trip_start_hour']) ] model_analyzer = components.Evaluator( examples=examples_gen.outputs.eval_examples, eval_spec=taxi_eval_spec, model_exports=trainer.outputs.output)
  • 56. 2.3.8 ModelValidator Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Validate Models from Trainer Uses Data from SchemaGen & StatisticsGen Compares New Models to Baseline Baseline == current model in production New Model is Good if Meets/Exceeds Metrics If Good, Notify Pusher to Deploy New Model Simulate “Next Day Evaluation” On New Data import tensorflow_model_analysis as tfma taxi_mv_spec = [tfma.SingleSliceSpec()] model_validator = components.ModelValidator( examples=examples_gen.outputs.output, model=trainer.outputs.output)
  • 57. 2.3.9 Model Pusher (Deployer) Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Push Good Model to Deployment Target Uses Trained SavedModel Writes Version Data to Metadata Store Write to FileSystem or TensorFlow Hub from tfx import components pusher = components.Pusher( model_export=trainer.outputs.output, model_blessing=model_validator.outputs.blessing, serving_model_dir=serving_model_dir)
  • 58. 2.3.10 Slack Component (!!) Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Runs After ModelValidator Adds Human-in-the-Loop Step to Pipeline TFX Sends Message to Slack with Model URI Asks Human to Review the New Model Respond ‘LGTM’, ‘approve’, ‘decline’, ‘reject’ Requires Slack API Setup / Integration export SLACK_BOT_TOKEN={your_token} _channel_id = 'my-channel-id' _slack_token = os.environ['SLACK_BOT_TOKEN’] slack_validator = SlackComponent( model_export=trainer.outputs.output, model_blessing=model_validator.outputs.blessing, slack_token=_slack_token, channel_id=_channel_id, timeout_sec=3600, ) https://github.com/tensorflow/tfx/tree/master /tfx/examples/custom_components/slack/slack_component
  • 59. 1 Setup Environment with Kubernetes TensorFlow Extended (TFX) ML Pipelines with Airflow and KubeFlow Agenda – Part 1 of 2 Hyper-Parameter Tuning with KubeFlow Deploy Notebook with Kubernetes 2 3 4 5
  • 60. 3.0 ML Pipelines with Airflow and KubeFlow Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy 3.1 Airflow KubeFlow3.2
  • 61. 3.1 Airflow 6 Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Most Widely-Used Workflow Orchestrator Define Execution Graphs in Python Decent UI Good Community Support
  • 65. 3.2 KubeFlow 6 Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training Pipelines Based on Argo CI/CD Project from Intuit TFJob & PyTorch Job Supports Distributed Training TensorFlow & PyTorch Jobs KubeFlow Fairing Project (!!) Run a notebook as a production job Deploy training code with dependencies
  • 70. 1 Setup Environment with Kubernetes TensorFlow Extended (TFX) ML Pipelines with Airflow and KubeFlow Agenda – Part 1 of 2 Hyper-Parameter Tuning with KubeFlow Deploy Notebook with Kubernetes 2 3 4 5
  • 71. 4.0 Hyper-Parameter Tuning 6 Experiment Single Optimization Run Single Objective Function Across Runs Contains Many Trials Trial List of Param Values Suggestion Optimization Algorithm Job Evaluates a Trial Calculates Objective
  • 73. 1 Setup Environment with Kubernetes TensorFlow Extended (TFX) ML Pipelines with Airflow and KubeFlow Agenda – Part 1 of 2 Hyper-Parameter Tuning with KubeFlow Deploy Notebook with Kubernetes 2 3 4 5
  • 74. 5.0 Deploy Notebook as Job Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training 6 5.1 Wrap Model in a Docker Image Deploy Job to Kubernetes5.2
  • 75. 5.1 Create Docker Image Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training
  • 76. 5.2 Deploy Notebook as Job Feature Load Feature Analyze Feature Transform Model Train Model Evaluate Model Deploy Reproduce Training
  • 79. 1 Setup Environment with Kubernetes TensorFlow Extended (TFX) ML Pipelines with TFX, Airflow, and KubeFlow Agenda – Part 1 of 2 Hyper-Parameter Tuning with TFX and KubeFlow Deploy Notebook with Kubernetes 2 3 4 5
  • 80. Agenda – Part 2 of 2 6 MLflow 7 8 9 10 TensorFlow Privacy Model Serving & A/B Tests Model Optimization Papermill
  • 81. 7.0 MLflow 7.1 Experiment Tracking Hyper-Parameter Tuning Kubernetes-based Jobs 7.2 7.3
  • 85. Agenda – Part 2 of 2 6 MLflow 7 8 9 10 TensorFlow Privacy Model Serving & A/B Tests Model Optimization Papermill
  • 88. Agenda – Part 2 of 2 6 MLflow 7 8 9 10 TensorFlow Privacy Model Serving & A/B Tests Model Optimization Papermill
  • 89. 9.0 TensorFlow Privacy (Differential Privacy)
  • 90. 9.1 Differential Privacy + Stochastic Gradient Descent
  • 91. 9.1 Differential Privacy + Explainability + Drift Detection
  • 93. Agenda – Part 2 of 2 6 MLflow 7 8 9 10 TensorFlow Privacy Model Serving & A/B Tests Model Optimization Papermill
  • 94. 10.0 Model Serving & A/B Tests
  • 99. Agenda – Part 2 of 2 6 MLflow 7 8 9 10 TensorFlow Privacy Model Serving & A/B Tests Model Optimization Papermill
  • 101. Agenda – Part 2 of 2 6 MLflow 7 8 9 10 TensorFlow Privacy Model Serving & A/B Tests Model Optimization Papermill
  • 102. 1 Setup Environment with Kubernetes TensorFlow Extended (TFX) ML Pipelines with Airflow and KubeFlow Agenda – Part 1 of 2 Hyper-Parameter Tuning with KubeFlow Deploy Notebook with Kubernetes 2 3 4 5
  • 103. After Pushing Your Model to Production, Your Model is… 1 Already Out of Date – Need to Re-Train Biased – Need to Validate Before Pushing Broken – Need to A/B Test in Production Hacked – Need to Train With Data Privacy Slow – Need to Quantize and Speed Up Predictions 2 3 4 5