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Xiangrui Meng, Databricks
Updates from Project Hydrogen: Unifying
State-of-the-Art AI and Big Data in Apache Spark
#UnifiedAnalytics #SparkAISummit

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About me
● Software Engineer at Databricks
○ machine learning and data science/engineering
● Committer and PMC member of Apache Spark
○ MLlib, SparkR, PySpark, Spark Packages, etc

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Announced last June, Project Hydrogen is a major Spark initiative
to unify state-of-the-art AI and big data workloads.
About Project Hydrogen
Barrier
Execution
Mode
Optimized
Data
Exchange
Accelerator
Aware
Scheduling

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Why Spark + AI?

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Runtime
Delta
Spark Core Engine
Big Data Processing
ETL + SQL +Streaming
Machine Learning
MLlib + SparkR
Apache Spark:
The First Unified Analytics Engine
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and many more...
Internet of ThingsDigital Personalization
Huge disruptive innovations are affecting most enterprises on the planet
Healthcare and Genomics Fraud Prevention
AI is re-shaping the world
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Better AI needs more data
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When AI goes distributed ...
When datasets get bigger and bigger, we see more and more
distributed training scenarios and open-source offerings, e.g.,
distributed TensorFlow, Horovod, and distributed MXNet.
This is where Spark and AI cross.
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Why Project Hydrogen?

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Two simple stories
As a data scientist, I can:
● build a pipeline that fetches training events from a production
data warehouse and trains a DL model in parallel;
● apply a trained DL model to a distributed stream of events and
enrich it with predicted labels.
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Distributed training
data warehouse load fit model
Required: Be able to read from
Databricks Delta, Parquet,
MySQL, Hive, etc.
Answer: Apache Spark
Required: distributed GPU cluster
for fast training
Answer: Horovod, Distributed
Tensorflow, etc
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Two separate data and AI clusters?
load using a Spark
cluster
fit on a GPU
cluster
model
save data
required: glue code
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Streaming model inference
Kafka load predict model
required:
● save to stream sink
● GPU for fast inference
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A hybrid Spark and AI cluster?
load using a Spark
cluster w/ GPUs
fit a model
distributedly
on the same
cluster
model
load using a Spark
cluster w/ GPUs
predict w/ GPUs as
a Spark task
model
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Unfortunately, it doesn’t work out of the box.
See a previous demo.

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Project Hydrogen to fill the major gaps
Barrier
Execution
Mode
Optimized
Data
Exchange
Accelerator
Aware
Scheduling

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Updates from Project Hydrogen
As a Spark contributor, I want to present:
● what features from Project Hydrogen are available,
● what features are in development.
As a Databricks engineer, I want to share:
● how we utilized features from Project Hydrogen,
● lessons learned and best practices.

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Story #1:
Distributed training
load using a Spark
cluster w/ GPUs
fit a model
distributedly
on the same
cluster
model

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Project Hydrogen: barrier execution mode
Barrier
Execution
Mode
Optimized
Data
Exchange
Accelerator
Aware
Scheduling

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Different execution models
Task 1
Task 2
Task 3
Spark (MapReduce)
Tasks are independent of each other
Embarrassingly parallel & massively scalable
Distributed training
Complete coordination among tasks
Optimized for communication
Task 1
Task 2 Task 3

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Barrier execution mode
We introduced gang scheduling to Spark on top of MapReduce
execution model. So a distributed DL job can run as a Spark job.
● It starts all tasks together.
● It provides sufficient info and tooling to run a hybrid distributed job.
● It cancels and restarts all tasks in case of failures.
JIRA: SPARK-24374 (Spark 2.4)

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API: RDD.barrier()
RDD.barrier() tells Spark to launch the tasks together.
rdd.barrier().mapPartitions { iter =>
val context = BarrierTaskContext.get()
...
}

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API: context.barrier()
context.barrier() places a global barrier and waits until all tasks in
this stage hit this barrier.
val context = BarrierTaskContext.get()
… // preparation
context.barrier()

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API: context.getTaskInfos()
context.getTaskInfos() returns info about all tasks in this stage.
if (context.partitionId == 0) {
val addrs = context.getTaskInfos().map(_.address)
... // start a hybrid training job, e.g., via MPI
}
context.barrier() // wait until training finishes

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Barrier mode integration

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Horovod (an LF AI hosted project)
Horovod is a distributed training framework for TensorFlow,
Keras, PyTorch, and MXNet. It is originally developed at Uber, now
an LF AI hosted project at Linux Foundation.
● Little modification to single-node code.
● High-performance I/O via MPI and NCCL.
● Same convergence theory.
Some limitation:
● Before v0.16, user still needs to use mpirun to launch a job,
● … with a python training script: mpirun -np 16 -H server1:4,server2:4,server3:4,server4:4 -bind-to none
-map-by slot -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH -mca pml ob1 -mca btl ^openib python train.py

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Hydrogen integration with Horovod
Databricks released HorovodRunner w/ Runtime 5.0 ML built on
top of Horovod and Project Hydrogen.
● Runs Horovod under barrier execution mode.
● Hides cluster setup, scripts, MPI command line from users.
def train_hvd():
hvd.init()
… # train using Horovod
HorovodRunner(np=2).run(train_hvd)

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Implementation of HorovodRunner
Integrating Horovod with barrier mode is straightforward:
● Pickle and broadcast the train function.
○ Inspect code and warn users about potential issues.
● Launch a Spark job in barrier execution mode.
● In the first executor, use worker addresses to launch the Horovod MPI job.
● Terminate Horovod if the Spark job got cancelled.
○ Hint: PR_SET_PDEATHSIG
Limitation:
● Tailored for Databricks Runtime ML
○ Horovod built with TensorFlow/PyTorch, SSH, OpenMPI, NCCL, etc.
○ Spark 2.4, GPU cluster configuration, etc.

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horovod.spark
horovod.spark is a new feature in Horovod 0.16 release. Similar to
HorovodRunner, it runs Horovod as a Spark job and takes python
train functions. Its assumption is more general:
● no dependency on SSH,
● system-independent process termination,
● multiple Spark versions,
● and more … also check out horovodrun:)

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Collaboration on Horovod + Spark
Engineers at Uber and Databricks are collaborating on improving
the integration between Horovod and Spark. Goals:
● Merge design and code development into horovod.spark.
● HorovodRunner uses horovod.spark implementation with
extra Databricks-specific features.
● Support barrier execution mode and GPU-aware scheduling.
Stay tuned for the announcement from LF/Uber/Databricks!

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Project Hydrogen: GPU-aware scheduling
Barrier
Execution
Mode
Optimized
Data
Exchange
Accelerator
Aware
Scheduling

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Accelerator-aware scheduling
Accelerators (GPUs, FPGAs) are widely used for accelerating
specialized workloads like deep learning and signal processing.
To utilize accelerators in a Spark cluster, Spark needs to be aware
of the accelerators assigned to the driver and executors and
schedule them according to user requests.
JIRA: SPARK-24615 (ETA: Spark 3.0)

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● Mesos, YARN, and Kubernetes already support GPUs.
● However, even GPUs are allocated by a cluster manager for a Spark application,
Spark itself is not aware of the GPUs.
● Consider a simple case where one task needs one GPU:
Why Spark needs GPU awareness?
Executor 0
GPU:0
GPU:1
Task 0
Task 1
Executor 1
GPU:0
GPU:1
Task 2
Task 3
Task 4 ?

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Workarounds (a.k.a hacks)
● Limit Spark task slots per node to 1.
○ The running task can safely claim all GPUs on the node.
○ It might lead to resource waste if the workload doesn’t need all GPUs.
○ User also needs to write multithreading code to maximize data I/O.
● Let running tasks themselves to collaboratively decide which
GPUs to use, e.g., via shared locks.

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User Spark Cluster Manager
0. Auto-discover resources.
1. Submit an application with
resource requests.
2. Pass resource requests to
cluster manager.
4. Register executors.
3. Allocate executors with
resource isolation.
5. Submit a Spark job. 6. Schedule tasks on available
executors.
7. Dynamic allocation.
8. Retrieve assigned resources
and use them in tasks.
9. Monitor and recover failed
executors.
Proposed workflow

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Discover and request GPUs
Admin can specify a script to auto-discover GPUs (#24406)
● spark.driver.resource.gpu.discoveryScript
● spark.executor.resource.gpu.discoveryScript
● e.g., `nvidia-smi --query-gpu=index ...`
User can request GPUs at application level (#24374)
● spark.executor.resource.gpu.count
● spark.driver.resource.gpu.count
● spark.task.resource.gpu.count

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Retrieve assigned GPUs
User can retrieve assigned GPUs from task context (#24374)
context = TaskContext.get()
assigned_gpu = context.getResources()[“gpu”][0]
with tf.device(assigned_gpu):
# training code ...

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Cluster manager support
YARN
SPARK-27361
Kubernetes
SPARK-27362
Mesos
SPARK-27363
Standalone
SPARK-27361

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Jenkins support (SPARK-27365)
To support end-to-end integration test, we are adding GPU cards
to Spark Jenkins machines hosted by Berkeley RISELab.
Thanks NVIDIA for donating the latest Tesla T4 cards!

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Support other accelerators
We focus on GPU support but keep the interfaces general to
support other types of accelerators in the future, e.g., FPGA.
● “GPU” is not a hard-coded resource type.
● spark.executor.resource.{resourceType}.discoveryScript
● context.getResources() returns a map from resourceType to assigned addresses.

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Features beyond the current SPIP
● Resource request at task level.
● Fine-grained scheduling within one GPU.
● Affinity and anti-affinity.
● ...

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More on distributed training: data flow
We recommend the following data flow for training:
● Load and preprocess training data using Spark.
● Save preprocessed training data to a shared storage.
○ What format? TFRecords, Parquet + Petastorm.
○ Which shared storage? S3, Azure Blob Storage, HDFS, NFS, etc.
● Load training data in DL frameworks.
○ But DL frameworks do not work well with remote storage.
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Connect DL frameworks to remote storage
We recommend high-performance FUSE clients to mount remote
storage as local files so DL frameworks can load/save data easily.
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s3://bucket
wasb://container
file:/mnt/...
TensorFlow
+
Horovod
FUSE clients:
Goofys
blobfuse
worker

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Story #2:
Streaming model inference
load using a Spark
cluster w/ GPUs
predict w/ GPUs as
a Spark task
model

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Project Hydrogen: Optimized data exchange
Barrier
Execution
Mode
Optimized
Data
Exchange
Accelerator
Aware
Scheduling

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Optimized data exchange
None of the integrations are possible without exchanging data
between Spark and AI frameworks. And performance matters.
JIRA: SPARK-24579

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Pandas UDF
Pandas UDF was introduced in Spark 2.3, which uses Arrow for
data exchange and utilizes Pandas for vectorized computation.

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Pandas UDF for distributed inference
Pandas UDF makes it simple to apply a model to a data stream.
@pandas_udf(...)
def predict(features):
...
spark.readStream(...)
.withColumn(‘prediction’, predict(col(‘features’)))

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Return StructType from Pandas UDF
We improved scalar Pandas UDF to complex return types. So users
can return predicted labels and raw scores together.
JIRA: SPARK-23836 (Spark 3.0)
@pandas_udf(...)
def predict(features):
# ...
return pd.DataFrame({'labels': labels, 'scores': scores})

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Data pipelining
CPU GPU
t1 fetch batch #1
t2 fetch batch #2 process batch #1
t3 fetch batch #3 process batch #2
t4 process batch #3
CPU GPU
t1 fetch batch #1
t2 process batch #1
t3 fetch batch #2
t4 process batch #2
t5 fetch batch #3
t6 process batch #3 (pipelining)

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Pandas UDF prefetch
To improve the throughput, we prefetch Arrow record batches in
the queue while executing Pandas UDF on the current batch.
● Enabled by default on Databricks Runtime 5.2.
● Up to 2x for I/O and compute balanced workload.
● Observed 1.5x in real workload.
JIRA: SPARK-27569 (ETA: Spark 3.0)

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Per-batch initialization overhead
Loading model per batch introduces a constant overhead. We
propose a new Pandas UDF interface that takes an iterator of
batches so we only need to load the model once.
JIRA: SPARK-26412 (WIP)
@pandas_udf(...)
def predict(batches):
model = … # load model once
for batch in batches:
yield model.predict(batch)

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Standardize on the Arrow format
Many accelerated computing libraries now support Arrow. The
community is discussing whether we should expose the Arrow
format in a public interface.
● Simplify data exchange.
● Reduce data copy/conversion overhead.
● Allow pluggable vectorization code.
JIRA: SPARK-27396 (pending vote)

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Acknowledgement
● Many ideas in Project Hydrogen are based on previous
community work: TensorFrames, BigDL, Apache Arrow, Pandas
UDF, Spark GPU support, MPI, etc.
● We would like to thank many Spark committers and
contributors who helped the project proposal, design, and
implementation.

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Acknowledgement
● Xingbo Jiang
● Thomas Graves
● Andy Feng
● Alex Sergeev
● Shane Knapp
● Xiao Li
● Li Jin
● Bryan Cutler
● Takuya Ueshin
● Wenchen Fan
● Jason Lowe
● Hyukjin Kwon
● Madhukar Korupolu
● Robert Evans
● Yinan Li
● Felix Cheung
● Imran Rashid
● Saisai Shao
● Mark Hamstra
● Sean Owen
● Yu Jiang
● … and many more!

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Thank you!