The document outlines a lecture on using Dask for accelerating NLP workflows on Saturn Cloud, particularly for extracting entities from the CORD-19 dataset, a key COVID-19 research initiative. It highlights the capabilities of Dask for parallel computing, compares it with Spark, and details the infrastructure and features of Saturn Cloud in supporting data science projects. Additionally, it discusses the methodology for automated entity recognition and the structure of deliverables, including data outputs in parquet format.

1. Saturn Cloud
Accelerating NLP with Dask on Saturn
Cloud
Elsevier Labs Online Lecture
November 2020
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2. Hi!
Speakers
Aaron Richter
Senior Data Scientist @ Saturn Cloud
aaron@saturncloud.io
@rikturr
Sujit Pal
Technology Research Director @ Elsevier
sujit.pal@elsevier.com
@palsujit
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3. Check out the
blog post!
🔗 How Elsevier Accelerated COVID-19 research using Dask on Saturn
Cloud
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4. Saturn Cloud
Data science with Python
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5. Data science with Python
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6. Saturn Cloud
What is Dask?
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7. Dask
● Parallel computing for Python people
● Anaconda, ~2015
● Built in Python; Python API
● Mature, scientific computing communities
● Low-level task library
● High-level libraries for DataFrames, arrays, ML
● Integrates with PyData ecosystem
● Runs on laptop, scales to clusters
https://dask.org/
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8. Dask
What does it do?
https://docs.dask.org/en/latest/user-interfaces.html
● Parallel machine learning (scikit)
● Parallel dataframes (pandas)
● Parallel arrays (numpy)
● Parallel anything else
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9. What does it do?
Arrays and Dataframes
https://docs.dask.org/en/latest/array.html https://docs.dask.org/en/latest/dataframe.html
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10. What does it do?
Anything else!
https://docs.dask.org/en/latest/delayed.html 10

11. What does it do?
Anything else!
https://dask.org/
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12. Getting up to
Speed with Dask
https://youtu.be/S_ncqocDcBA
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13. Spark vs. Dask
● Written in Scala with Python API
● All-in-one tool
○ Requires re-write to migrate
from PyData code
● Programming model not suited for
complex operations (multi-dim
arrays, machine learning)
● 100% Python
● Built to extend and interact with
PyData ecosystem
● High-level interfaces for
DataFrames, (multi-dim) Arrays,
and ML
● Native integration with RAPIDS for
GPU-acceleration

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15. How can I run Dask clusters?
● Manual setup
● SSH
● HPC: MPI, SLURM, SGE, TORQUE, LSF, DRMAA,
PBS
● Kubernetes (Docker, Helm)
● Hadoop/YARN
● Cloud provider: AWS or Azure
🔗 https://docs.dask.org/en/latest/setup.html
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16. Saturn Cloud
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17. ● Fast setup
● Enterprise secure
● Pythonic parallelism
● Rapidly scale
PyData
● Multi-GPU computing
● The future of HPC
● Workflow orchestration
● Flow insight and mgmt
Bringing together the fastest hardware + OSS
Saturn Cloud

18. ● Saturn manages all infrastructure
● Hosted: Run within our cloud
● Enterprise: Run within your AWS account
Saturn Cloud to the rescue!
Taking the DevOps out of Data Science
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19. ● Images
● Jupyter server
● Dask Cluster
● Deployments
Saturn Cloud
Core features
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20. 20

21. Saturn Cloud
Extracting Entities from
CORD-19
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22. Genesis
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23. Genesis
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● Based on one of the many
COVID-19 initiatives (COVID-
KG)
● Original intent: extract entities
from CORD-19 dataset for
relationship mining.

24. Genesis
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● Based on one of the many
COVID-19 initiatives (COVID-
KG)
● Original intent: extract entities
from CORD-19 dataset for
relationship mining.● CORD-19 dataset open sourced
by AllenAI.
● SciSpacy provided Language
models trained on Biomedical
text, and...
● Pre-trained Named Entity
Recognition (and linking) models.

25. Genesis
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● Based on one of the many
COVID-19 initiatives (COVID-
KG)
● Original intent: extract entities
from CORD-19 dataset for
relationship mining.● CORD-19 dataset open sourced
by AllenAI.
● SciSpacy provided Language
models trained on Biomedical
text, and...
● Pre-trained Named Entity
Recognition (and linking) models.
● Dask based distributed
computing platform
● Opportunity to evaluate.

26. Goals
● Create standoff entity annotations for CORD-19, modeled after CAT (Content
Analytics Toolbench) from Labs.
● Automated entity recognition using pre-trained SciSpaCy models, where each
model recognizes a different subset of entity classes, e.g. DNA, Gene,
Protein, Chemical, Organism, Disease, etc.
● Output is structured as Parquet files, consumable via Dask or Spark.
● Share output dataset with community.
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27. CORD-19 Dataset
● Started mid March 2020 with ~40k articles released weekly.
● By Sept/Oct 2020 ~200k articles released daily, growing everyday.
● Each release contains:
○ Metadata file (CSV)
○ Set of articles (JSON)
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28. SciSpaCy NER(L) models
● Medium English LM for sentence
splitting.
● 4 NER models
● 5 NERL models using LM’s
candidate entity generator and
trained entity linking models.
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29. Full Pipeline
● Read metadata.csv
● Parse each JSON file into
paragraphs.
● Split paragraphs into sentences.
● Extract entities from sentences
using a NER(L) model..
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30. Embarrassing Parallelism
● Pipeline is embarrassingly parallel.
● Parse files to paragraphs has no
dependencies (i.e., perfectly
parallel)
● ...
● ...
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31. Embarrassing Parallelism
● Pipeline is embarrassingly parallel
● ...
● Split paragraphs to sentences
needs sentence splitter model
assigned per partition.
● Load only models that you need.
● …
● ...
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32. Embarrassing Parallelism
● Pipeline is embarrassingly parallel.
● …
● ...
● Extract entities from sentence
needs NER model, assign lazily to
worker per partition.
● Use nlp.pipe and batching to exploit
multithreading.
● ...
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33. Embarrassing Parallelism
● Pipeline is embarrassingly parallel.
● …
● ...
● Extract and link entities from
sentence needs Language
Model, Entity Linker, etc. assign
eagerly per worker after cluster
creation.
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34. Parquet Dask / Spark interop
● Output of paragraph, sentence, and entities are in Parquet format.
● Things to keep in mind for Spark interoperability when writing from Dask.
○ Column data types must be declared explicitly on the Dask end.
○ Column names should be specified when saving (“hidden” columns visible in Spark).
○ Explicit re-partitioning may be necessary when saving on Dask.
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35. Incremental Pipeline
● Extracted Entities + new metadata
and JSON files.
● Compute diffs (additions +
deletions)
● Parse added articles to paragraphs,
paragraphs to sentences, and
sentences to entities.
● Remove paragraphs, sentences,
and entities for deleted articles.
● Merge diff and original.
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36. Deliverables
● Code
○ Set of Jupyter notebooks deployed on Saturn Cloud -- sujitpal/saturn-scispacy
● Data
○ Dataframes in Parquet format (approx 70 GB, 35 for Sep 2020, 35 for Oct 2020).
○ Publicly available on s3://els-saturn-scispacy/cord19-scispacy-entities (requester pays).
○ Available within ELS on Databricks under “/mnt/els/covid-19” -- “/mnt/els/covid-19/saturn-
scispacy/annotations-pq-full-20200928”
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37. Output formats
1 paragraph dataframe (3.4M paragraphs), 1 sentence dataframe (17.1M sentences),
and 9 entity dataframes (total 805.4M entities).
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38. Utility
● Envisioned usage similar to that for CAT annotations using AnnotationQuery,
but for biomedical entities on biomedical data.
● Query dataset using DataFrame API to create interesting micro-datasets
datasets potentially spanning across different NER(L) models. Example:
○ Human Phenotypes Annotations from HPO co-occurring in same sentence with Disease
Annotations from UMLS or BC5CDR.
○ Gene annotations co-occurring in same sentence with Cancer annotations (both from
BioNLP).
● Annotations can be features for Topic Modeling or Categorization.
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39. Additional Resources
● Labs Blog post -- How Elsevier Accelerated COVID-19 research using Dask
on Saturn Cloud
● Confluence page -- Dataset of Entity Annotations for CORD-19
● Book -- Data Science with Python and Dask by Jesse C. Daniel (also
available on Percipio)
● Dask online documentation
● Saturn Cloud documentations
● Cloud deployments using Dask
● Query interface example -- AnnotationQuery.
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