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# IBM Deep Learning Service

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## Abstract and Figures

Deep learning, driven by large neural network models, is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision. At the same time, the “As-a-Service”-based business model for the cloud is fundamentally transforming the information technology industry. These two trends, deep learning and “As-a-Service,” are colliding to give rise to a new business model for cognitive application delivery: deep learning as a service in the cloud. In this paper, we discuss the details of the software architecture behind IBM's deep learning as a service (DLaaS). DLaaS provides developers the flexibility to use popular deep learning libraries—such as Caffe, Torch, and TensorFlow—in the cloud in a scalable and resilient manner with minimal effort. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes. A resource provisioning layer enables flexible job management on heterogeneous resources, such as graphics processing units and central processing units, in an infrastructure-as-a-service cloud.
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IBM Journal of Research and Development
IBM Deep Learning Service
B. Bhattacharjee, S. Boag, C. Doshi, P. Dube, B. Herta, V. Ishakian, K. R. Jayaram, R.
Khalaf, A. Krishna, Y. B. Li, V. Muthusamy, R. Puri, Y. Ren, F. Rosenberg, S. Seelam, Y.
Wang, J. M. Zhang, L. Zhang
Abstract
Deep learning driven by large neural network models is overtaking traditional machine learning
methods for understanding unstructured and perceptual data domains such as speech, text, and
vision. At the same time, the “as-a-Service”- based business model on the cloud is fundamentally
transforming the information technology industry. These two trends: deep learning, and “as-a-
service” are colliding to give rise to a new business model for cognitive application delivery:
deep learning as a service in the cloud. In this paper, we will discuss the details of the software
architecture behind IBM's deep learning as a service (DLaaS). DLaaS provides developers the
flexibility to use popular deep learning libraries such as Caffe, Torch and TensorFlow, in the
cloud in a scalable and resilient manner with minimal effort. The platform uses a distribution and
orchestration layer that facilitates learning from a large amount of data in a reasonable amount of
time across compute nodes. A resource provisioning layer enables flexible job management on
heterogeneous resources, such as graphics processing units (GPUs) and central processing units
(CPUs), in an infrastructure as a service (IaaS) cloud.
Introduction
The rise of deep learning [1,2] from its roots in neural networks to becoming the state-of-the-art
of AI has been fueled by three recent trends: the explosion in the amount of training data; the use
of accelerators such as graphics processing units (GPUs); and the advancement in the design of
models used for training. These three trends have made the task of training deep layer neural
networks with large amounts of data both tractable and useful.
Training deep neural networks, known as deep learning, is currently highly complex and
computationally intensive. While GPUs have helped accelerate training, the amount of data as
well as complexity of models have increased the computation need beyond the capability of a
single GPU. For example, training on 2.5 million images on a single GPU can take 6 days on a
simple model [3]. A typical user of deep learning, a data scientist, is also unnecessarily exposed
to the details of the underlying hardware and software infrastructure, including configuring
expensive GPU machines, installing deep learning libraries, and managing the jobs during
execution to handle failures and recovery. Despite the ease of obtaining hardware from
infrastructure as a service (IaaS) clouds and paying by the hour, the user still needs to manage
those machines, install required libraries, and ensure resiliency of the deep learning training jobs.
Furthermore, the user must implement highly complex techniques for scaling and resiliency on
their own, as well as keep pace with the updates to the deep learning frameworks in the open
source communities.
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Instead of being mired with infrastructure and cluster management problems, users would like to
focus on training a model in the easiest way possible that satisfies both their cost and
performance objectives. This is where the opportunity of deep learning as a service lies. It
combines the flexibility, ease-of-use, and economics of a cloud service with the power of deep
learning: It is easy to use using the REST APIs, one can train with different amounts of resources
per user requirements or budget, it is resilient (handles failures), and it frees users so that they
can spend time on deep learning and its applications. Users can choose from a set of supported
deep learning frameworks, a neural network model, training data, and cost constraints and then
the service takes care of the rest, providing them an interactive, iterative training
experience. The job gets scheduled and executed on a pool of heterogeneous infrastructure,
including GPUs and CPUs. A simple API (application programming interface) shields users
from the complexity of the infrastructure and the advanced mechanics of scaling through
distribution. Users can see the progress of their training job and terminate it or modify its
parameters based on how it is progressing. When it is done, the trained model is ready to be
deployed in the cloud to classify new data.
The value of DLaaS is not limited to data scientists, but extends to developers of new
applications and services that would like to add deep learning capabilities but are not able/do not
want to build their own software stacks and buy dedicated hardware, or handle scaling and
resiliency in-house. Some prominent examples of usage of deep learning within application and
services are: speech recognition [4], visual recognition [5], natural language understanding and
classification [6], and language translation [7].
IBM DLaaS makes it easy for a provider of such consumer facing cognitive services to provide
deep learning training to its users or use it to customize the models in order to provide better
outcomes for its customers.
In this paper, we will describe the architecture and experience of the IBM deep learning as a
service platform (DLaaS), running in the IBM Cloud. DLaaS was created from the start in close
collaboration with deep learning developers across speech, vision, and natural language
classification domains. These insights shaped our design, providing guidance into the
commonalities between these different workloads and enabled us to provide a cloud service
where the infrastructure is shared across these workloads while providing a common API-based
access. The rest of the paper describes the user experience of using the DLaaS platform and a
study of its usage by about 80 researchers at a workshop, followed by the architecture, and the
distribution model. DLaaS is built for extensibility so the paper concludes with a description of
how to bring a new framework into the platform. DLaaS is accessible from the IBM Bluemix
cloud catalog.
User Experience and Usage Study
There are four steps that users perform to use DLaaS: (1) prepare their deep learning model, (2)
upload the model and training data, (3) start the training job and monitor its progress, and (4)
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The DLaaS interface is designed to be simple and match a user's existing workflow, so step (1)
should require minimal effort. The subsequent steps require interacting with the DLaaS
Representational State Transfer Application Programming Interfaces (REST) API, either by
directly invoking the REST API endpoints, or by using the DLaaS command-line
interface (CLI). The CLI provides easy to use command interface over the REST API. The
subsequent sub-sections describe the four steps outlined above, with an additional sub-section
providing more details about monitoring a running job.
Prepare the model
Users can develop and train their models in their framework of choice (Caffe [8], Torch [9] or
TensorFlow [10]) on their local machines, perhaps using a small training dataset. To prepare
their models for DLaaS, users must perform three tasks: (1) configure a storage service
supported by DLaaS, such as Swift Object Storage, and obtain access credentials; (2) create a
manifest.yml file that among other things specifies the deep learning framework they are using
and the access tokens to fetch the training dataset from the storage service; and (3) possibly make
small changes to their model, such as adjustments to any absolute paths that point to the training
dataset. Listing 1 shows an example of a manifest.yml file for a Caffe model.
The manifest can include the resource requirements of the training job, such as the number of
learners, and GPUs and memory per learner. These properties can be overridden when a training
job is created. The data_stores section of the manifest must include a reference to the object
storage container that has the training dataset and credentials to access this container, and it may
optionally specify the container where the trained model and training logs should be uploaded
after the training job as completed. Finally, the framework section points to the main Caffe
solver file that defines the model hyperparameters and references the model definition file, as
well as any additional arguments to pass to the Caffe.
In the example in Listing 1, a “weights” argument is used to indicate that this model should be
incrementally trained by fine tuning the weights in a pre-trained model.
Once users have prepared the deep learning model, they must then upload their model to DLaaS
and their training dataset to storage service of choice. For the former, the user can either invoke
the appropriate DLaaS REST endpoint or use the DLaaS CLI. There are API endpoints to list,
create, update, and delete models. The result of deploying the model to DLaaS is a unique
generated model ID, which will be used in the next step where a training job is created.
Create and monitor a training job
Once the model has been uploaded, the user can start a training job to train the model. When
creating the training job, the user can specify the resource requirements such as the number of
learners and the number of GPUs. As with models, there are API endpoints to list, create, and
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delete training jobs. When a training job is created, a unique training ID is returned and can be
used to monitor the progress of the job as detailed in the section below.
Once the job has completed, the user can download the results, which includes the trained model
and a log file that captures the console output during the training job. The results are also
optionally stored in the user's storage service, so they can be retrieved directly from there.
Understanding Training Progress
As training jobs can take days or weeks to complete, it is important for a user to monitor its
progress to help debug and tune their models and hyperparameters.
Interviews with deep learning users in IBM indicated a number of useful progress indicators: (1)
Is the accuracy with expert parameter tuning better than random guessing? (2) Has accuracy hit
a plateau, i.e., it is not improving beyond that point? If so, the user would like to be notified and
may want to terminate the job. (3) Has a state of the model at a certain number of iterations been
persisted to checkpoint store? (4) Has a learning rate change after the number of iterations
reached a threshold? It is at this point the accuracy jumps, so it may be of interest. (5) Is the
accuracy stable for a long time? (6) How often does validation happen and how much time does
it take?
The interviews also revealed additional indicators that are relevant to the platform itself, such as
detecting idle nodes, and measuring the communication overhead among nodes. These are useful
in optimizing the DLaaS platform but are not exposed to the user.
A visualization of deep learning training metrics can be critical in helping unearth insights into
the performance of the model and network. A user can notice trends, patterns and anomalies at a
glance by visualizing the data, such as understanding when significant improvements or plateaus
in the model occurred, or if there are oscillations in the accuracy measure. Such insights, which
would be difficult at best to obtain by scrolling through a log file, let the user make quicker and
Figure 1 shows an example of ongoing feedback for training progress. This is based on data in
logs from the frameworks themselves (e.g. Caffe or Torch). Similar plots with data logs from
tools such as nvidia-smi (NVIDIA System Management Interface program) and sysstat/iostat are
useful in understanding the training progress and resource utilization. In DLaaS, users can
download these logs after the job has completed or stream them to monitor a running job as we
discuss later in the paper.
DLaaS Usage Study
We invited around 85 deep learning experts and novices from various universities to use DLaaS
in a hands-on colloquium in September 18, 2016 at the T. J. Watson Research Center. Around
75% of the users did not have any prior experience with any deep learning framework. The
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colloquium was organized in 2 sessions, each with 1.5 hour durations with two different groups
of researchers. This provided us with feedback on the usability of service, its scalability and how
it can help users to improve accuracy of their models.
During the colloquium, up to 45 users simultaneously started training jobs in DLaaS. Each user
submitted at least 1 job and many users submitted 10’s of jobs with different resource
requirements (e.g., 1, 2, 4 GPUs, different amounts of memory), optimization parameters, etc.
DLaaS handled over 200 hundred jobs in a span of three hours. Some jobs finished in a few
minutes while others ran for several hours. Outside of the colloquium we also have hundreds of
jobs, some that ran over 2 weeks from various researchers.
On the usability aspect, attendees were impressed with how quickly they were able to experiment
with deep learning. With infrastructure and cluster management taken care of by the DLaaS
system, users were able to begin training a model in minutes, submitting jobs from their own
laptops. Many users appreciated that DLaaS was "easy to use", and it was "not complicated to
change and redeploy" their jobs. Others liked that DLaaS offers "automatic distribution on
multiple nodes", and that the end user can focus on deep learning, and not spend time "having to
configure GPUs or handle failure". They liked that they can use custom training data and neural
network model defined in one of several supported deep learning frameworks, while DLaaS
takes care of hardware and software stack that matches their cost constraints, scalability, and
performance requirements.
With respect to the performance aspect, each of users ran using the sample workload (CIFAR10,
an established computer-vision dataset used for object recognition) that has a network model
with 3 convolutions and 2 fully connected layers. We provided them with hyperparameters that
produce about 71% accuracy, and we challenged them to improve the accuracy as much as
possible in 1 hour. They ran hundreds of jobs with many parameters changes and fine tuned
hyperparameters to achieve an average accuracy of 72.3% while some of them achieved over
77% accuracy. This exercise shows that the DLaaS platform can be used by experts as well as
novices to quickly develop and incrementally tune their models until they research the desired
accuracy.
We also learned of an interesting gap in our resource and job management. During the
colloquium, GPUs of one of the machines became unresponsive but our resource manager failed
to recognize this fact and kept scheduling jobs to this node. As a result, a few jobs failed to start
because GPUs on that node were not usable. Our resource management layer typically restarts
failed jobs but not when the job fails due to either an error in the code or due to a hardware
issues like a failed GPU. When the users restarted the failed jobs, they ran successfully.
DLaaS: Design Principles and Platform Architecture
DLaaS Design Principles
A key design principle of DLaaS is that it provides a large-scale deep-learning platform with
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multiple GPUs for a learning task by exploiting the economic and technical principles of the
cloud paradigm. Contemporary deep learning jobs use high performance computing (HPC)
environments with dedicated hardware and failure intolerant, highly customized software stacks.
To support deep learning in the cloud, DLaaS addresses the challenges of running on the Cloud
such as the dynamic nature of the Cloud, where appropriately handling failures is critical, and
exploits the elastic scalability features of Cloud. DLaaS uses a microservices architecture [11]
where services are built from the start with resilience in the face of expected failures in the
underlying infrastructure. Additionally, cloud services have been supporting mainly short-
running or stateless (e.g. Web apps) jobs while deep learning jobs can run for days or even
weeks; therefore, DLaaS addresses challenges around data persistence and development and
operational issues for non-transactional, data intensive services.
Another key principle of DLaaS is to provide users with the flexibility to use any of the popular
deep learning frameworks such as Caffe [8], Torch [9], and TensorFlow [10], combined with the
ability to select specific training job service level agreements.
DLaaS also aims to provide a simple and easy to use interface for the users irrespective of the
deep learning framework they need to use for the training. To this end, DLaaS provides
Application Programming Interfaces (APIs) to prepare the model, to upload the model and
training data, to start and get the progress of the training job, and to download the training model.
Following these principles, the next section describes the architecture of DLaaS.
DLaaS Platform Architecture
The DLaaS architecture consists of three major components, each deployed as a microservice, as
shown in Figure 2. The microservice-based approach [11] enables DLaaS to decompose the core
logic into discrete atomic units that can be individually deployed and scaled to handle the traffic.
The DLaaS API layer handles all the incoming API requests including load balancing, metering,
security and access management. DLaaS REST API service instances are dynamically registered
into a service registry that provides load balancing and fail-over support for incoming API
requests. Any jobs that fail are retried automatically a certain number of times before they are
marked as failed.
The DLaaS Core Services layer is responsible for handling training jobs from submission to
completion. This layer consists of five main microservices: (1) A model deployer service that
handles deploying the model created by the user and persists the model metadata and model
input configuration into respective databases. (2) A Trainer service that creates a training job out
of a given model, (3) A Lifecycle Manager (LCM) responsible for deploying training jobs via the
trainer service and ensures the progress and resilience of potentially long-running jobs (4) A
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Storage Manager provides reliable connectivity with internal and external storage systems to
load this training data and user models from a user-defined store (e.g., a Cloud Storage such as
ObjectStore or Network File System (NFS)) and to store the trained models, and (5) A Metrics
Service collects key metrics that are of interest to the user to understand the training progress and
the quality of the training.
The DLaaS Platform Services layer provides the key building blocks for executing and managing
long running training jobs. At the core is a GPU-enabled Container Service that is responsible
for executing a training job based on a predefined learner image from the Docker [12]
Registry. At the time of this writing, we have enhanced the open source Mesos resource manager
[13] and Marathon [14] job manager to support GPUs as first class resources. The training jobs
request a specified set of resources like number of CPUs, amount of physical memory, number
of GPUs and the Mesos/Marathon stack finds the nodes that satisfy these requirements and
provisions them for the duration of that job. Container managers such as Kubernetes [15] that
can provide the GPU and other resource allocation capability can be used in place of
Mesos/Marathon for these jobs. File Service and Object Store Service are the primary internal
data stores for the training execution to cache the training data, store checkpoints and the trained
model. Apache Zookeeper [16] (key-value) is used both by DLaaS microservices and by
executing training jobs to maintain (minimal) state. Zookeeper is replicated, and is highly
available enabling “recovered (after failure) microservice instances” and “training jobs” to
continue where their predecessors left off. The DLaaS training jobs as well as the DLaaS
platform components are deployed as Docker containers using Docker images available from
the container registry service. This section describes the architecture of the DLaaS platform
training jobs themselves may rely on other components for synchronization (e.g., the parameter
server) which will be described in the next section. A Logging and Monitoring Service, which is
based on the ELK (Elasticsearch, Logstash and Kibana) stack collects all the logs produced by
our services as well as the training jobs and enables users to view the logs of their training jobs
for debugging.
In addition to the above components, DLaaS has a real-time visualization component, to enable
ease of interaction with long running training jobs (an example shown in Figure 1). This
component involves four major aspects: (1) An API that efficiently streams raw logs over a
websocket connection. (2) An extensible log parsing API and service, which parses one or more
log streams into a common JSON list format. In many cases data points need to be correlated
across logs, such as the trainer log, and GPU utilization log from nvidia-smi. Extensibility here
allows for the installation of custom parsers to collect and correlate data. (3) The parsed logs are
sent to the visualization server, which is currently implemented as a Node.js application using
the Express framework. We are exploring a serverless architecture in the near future. (4) The
visualization is dynamically rendered on the client in a browser interface using Rickshaw, which
is a JavaScript toolkit for creating interactive time series graphs.
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Orchestration of Deep Learning Training Jobs
In this section, we will describe the orchestration mechanisms underlying distributed deep
learning training jobs within DLaaS. Requesting a training job results in a call to the DLaaS
Core Services, which in turn invoke the DLaaS Platform Services to fetch the necessary data to
start the job, monitor its progress until termination, take actions if the jobs exits or terminates
during training and store the trained models back to the user's data store. This section first
describes the architecture of a distributed training job (learners, parameter servers and how they
interact) before describing how DLaaS orchestrates and manages training jobs: that is, what
happens from the time the users request a training job through the DLaaS API until they are able
to retrieve their trained model. Figure 3 shows our job orchestration system.
Learners
The key component of distributed deep learning is the use of multiple learner tasks for data
parallelism. Each learner task is implemented in a (single-machine) deep learning framework
(e.g., Caffe, Torch, or TensorFlow) and containerized in a Docker image. Each learner is allotted
a configurable number of CPUs and GPUs.
Learner Coordination
DLaaS currently supports the data parallel strategy for workload sharing [17, 18] in contrast to
the model parallel strategy or the hybrid strategy [19, 10]. In the data parallelism strategy, each
learner has the entire copy of the model (i.e. the entire set of model weights). These model
weights are updated locally as a result of training on a new chunk of data. Periodically, learners
should synchronize with each other to update their local model by e.g., aggregating model
weights from other learners. To perform the model updates while allowing for fault tolerance of
the learners, DLaaS uses a parameter server [20] for learners to synchronize periodically and
aggregate their weights, as opposed to using broadcast algorithms. Periodically, each learner
pushes its weights to the parameter server and pulls updated weights from the parameter server.
Weight or parameter aggregation is performed by the parameter server. This leads to a
straightforward reduction in the number of messages. In the case of broadcast among all learners
(all to all broadcast), the total number of messages exchanged among L learners would be order
L2 (O(L2)). With the parameter server, the number of messages exchanged would be order L
(O(L)»2L), one message from the learner to the parameter with the new model weights and
another message from the parameter server to the learner with aggregated weights. Moreover,
each learner only needs to be aware of the parameter server (single entity) as opposed to all other
learners (L entities), thereby reducing coupling between the learners. The next section provides
details about the parameter server used in DLaaS
Global Cursor and Work Allocation
The learners train on data stored in one of the external storage services supported by DLaaS, and
collectively make passes over the data set. For each pass, each learner obtains a chunk of data to
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train on from the external store. Ideally, for each pass over the data set, to ensure data
parallelism, each learner operates on a mutually exclusive chunk of data with respect to the other
learners. Mutual exclusion is implemented through the use of a global cursor. Each learner
computes the size of the data partition that it wants to process, based on its available resources,
and assigns itself exclusive data chunks by incrementing the global cursor by the chunk size.
Global cursor is implemented through Apache Zookeeper (atomic access and increments to
Zookeeper data)
Parameter Server
We implemented a parameter server (PS) that is used by the learners to coordinate weights
among themselves. As models exhibit a diverse spectrum of training performance under different
hardware devices and optimization functions, the parameter server provides several optimization
solvers, including parallel stochastic gradient descent (PSGD), elastic averaging SGD, and model
averaging, to allow different models to select the most efficient parameter refinement
function. Though optimization solvers differ in implementation details, they commonly follow
standard iterative convergence algorithms, in which each learner computes local parameters
during the forward-pass and back-propagation phases. The forward pass is to assess the quality
of existing weights and the back propagation is to generate the gradients with respect to the
current weights used by the neural network. Periodically, the learner checks if the condition for
parameter push or pull has been satisfied (generally the condition is governed by a
communication frequency threshold, for example, a Caffe learner communicates with the PS
after 5 batch processing.) The pull function fetches global weights from the server to carry out
the next round(s) of iterations. The push function sends locally accumulated gradients or local
weights to the parameter server, which then uses a customized aggregation function to update the
global weights. Each training job deployed through DLaaS gets its own dedicated parameter
server deployment.
The DLaaS parameter server is made up of two key components: (i) a group of parameter
server shards that collectively store and aggregate the model parameters from a learning job, and
(ii) a PS client library that connects each learner with the parameter server cluster. The PS client
is integrated into the learner framework. During training, if the model doesn’t fit into the
memory (RAM) available on a single machine, the PS client adopts data partitioning to evenly
divide the entire model used by the learner based on the number of available servers, and sends
partitions to different servers according to the partition ID. As all the learners of the same
training job follow exactly the same model partitioning scheme, the same partitions from
different learners are gathered by the same server, which then computes a user-specified
aggregation function and returns the updated parameters back to the learners. Model partitioning
does not imply model-parallelism [19,10]. DLaaS currently only supports data parallelism [17,
18].
As a throughput-critical system, parameter server leverages lockless queues at both network and
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computation layers to achieve efficient resource utilization. Currently, it relies on TCP/IP
protocols to transport data, and uses multithreaded sender/receiver to improve the network
throughput. Upon detecting the arrival of a model partition, a receiving thread determines
whether it is necessary to invoke the aggregation based on the triggering condition defined by the
job. For example, Downpour SGD invokes the aggregation whenever a new partition is received
while BSP-based (Bulk Synchronous Parallel programming model) model averaging waits until
all partitions are gathered before triggering the aggregation computation. When a job is ready to
start the aggregation phase, the aggregation scheduler enqueues the computation into either the
CPU-based aggregation queue or the GPU-based queue by taking account of both the estimated
aggregation time and potential waiting time of each queue. Eventually, after new parameters are
generated, the response threads are invoked to send the new model partition back to all the
learners.
The PS client exposes two major synchronous interfaces, called push and pull, to let each learner
send out local parameters and retrieve updated models from the server. Additionally, the PS
client provides auxiliary functions to manage all the connections (join/leave) to the server. When
transferring the data, DLaaS does not use any parameter serialization or deserialization and
directly moves all the data in binary format.
Lifecycle Management
As the name suggests, the lifecycle manager (LCM) is the component of DLaaS that is
responsible for the entire lifecycle of the training job, from initial deployment to status updates,
failure handling and garbage collection of learners and parameter servers. A robust LCM is key
to any cloud-based distributed deep learning platform because (i) most public IaaS cloud
infrastructures shared between several tenants have a non-trivial rate of failures, network
congestions and partitions and because (ii) deep learning jobs are typically long running leaving
them susceptible to said failures. The LCM performs the following tasks: (1) Deploys a
submitted training job using the Mesos/Marathon cluster management system. (2) Checks
whether the parameter server and learners have successfully started. (3) Monitors the status of
the learners at runtime and reports their status to the other components. (4) Detects when learners
or parameter servers have failed and ensures that failed components are restarted by the cluster
management system, and that learning proceeds uninterrupted. (5) Determines when learning has
finished so that the training job can be safely terminated and resources allocated to it be
reclaimed.
The current implementation of LCM uses the Marathon cluster management system and Apache
Zookeeper. The LCM is a micro service that can be independently scaled as needed. It is
inherently stateless by itself and stores all state information in Zookeeper. Zookeeper is also used
to monitor the status of the learners and parameter server instances. Each container holding
either a learner or a parameter server shard is allocated a unique znode (Zookeeper path) by the
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LCM before deployment; a sidecar (auxiliary) process called the "watchdog" in the container
monitors the learner/parameter server and updates its status in the corresponding znode. Status
updates can then be read by LCM from Zookeeper. Through status monitoring, the LCM can
determine when all learners have finished training, decommission them and reclaim computing
resources allocated to them.
Upon job submission, the LCM first deploys the parameter server, and once it has started,
queries Marathon to determine the IP address and port on which the parameter server is listening
for learners. This information is essential for Learners to connect to the parameter server
instances and periodically update learned weights.
Single Learner Scenario
In the case of the training job that contains only one learner, there is no necessity to deploy a
parameter server. LCM then deploys the learner, monitors its progress and manages the learner
using Zookeeper as described earlier in this section.
Fault-Tolerance
DLaaS is a cloud service so it is expected to be available 24/7, 365 days a year and it is expected
to run jobs to completion under scheduled or unscheduled interruptions such as upgrades to the
underlying infrastructure, the software stack, failures in various components of the systems as
well connectivity issues with the dependent services. Failures in DLaaS can be caused due to
faults in DLaaS infrastructure and software stack or due to errors in user input.
Infrastructure faults include physical machine crashes and loss of network connectivity. Faults in
the software stack include (i) crashes of containers, (ii) failure of cluster manager (Mesos and
Marathon) components and (iii) failure of services on which DLaaS depends on including
ObjectStore and Zookeeper. If a node fails, the cluster manager automatically restarts the jobs on
that node on a different node. The cluster manager itself is deployed a HA service so unless a
majority of the nodes fail, the cluster manager operates without any interruption.
DLaaS microservices, except the storage service, are stateless so they can be upgraded with no
service.
In the case of faults caused by errors in user input, the learner terminates gracefully with
appropriate log messages (inside its container), which can be parsed by the “watchdog”. The
“watchdog”, in turn, sends an appropriate status message (JOB_FAILED) to ZooKeeper, which
is read by the LCM. The LCM, then updates all pertinent job records in DLaaS with this status,
and terminates the job.
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All cluster manager components (Mesos and Marathon) checkpoint their state in Zookeeper. If
any of these components crash, the recovered containers can continue by using the checkpointed
state in Zookeeper. Zookeeper itself is replicated (3-way), and updates to its state are Atomic,
strongly Consistent, Isolated and Durable (ACID) due to the use of Zookeeper atomic broadcast.
Each learner and parameter server container also creates an ephemeral znode at startup, enabling
the LCM to detect learner and parameter server container crashes. By counting the number of
active ephemeral znodes, and by reading the status of each active learner from Zookeeper, the
LCM can interpret whether the training job is making sufficient progress and whether training
can be continued even if a small fraction of learners have failed. After a failure, restarting of the
respective containers is handled by Marathon. The LCM also periodically directs learners and
parameter servers to checkpoint their state in Object Store. After a failure, recovered learners can
start the learning process from a checkpoint, instead of from the beginning. Checkpointing and
restart is currently supported for Caffe; we are currently implementing the same for the other
frameworks.
The use of Zookeeper decouples the training job (consisting of learners and parameter server
instances) from the LCM and other DLaaS microservices, enabling them to fail independently.
That is, learning can proceed if any microservice, e.g., the LCM crashed and the LCM can
continue accepting training jobs for deployment even if a fraction of currently deployed jobs
have failed (e.g., due to an error by the job submitter).
DLaaS microservices are developed to perform exponential backoffs and re-tries for failures
associated with network connectivity and access dependent services such as temporary failures in
As we discussed in the usage study section, our job manager cannot properly restart jobs that
gets scheduled to nodes with non-responsive GPU. We are working to periodically check the
GPU status and take the node offline in such cases.
Extensibility
In a field as dynamic and young as deep learning, there are a lot of diverse deep learning
framework used and where the training data is stored. DLaaS provides a pluggable approach to
both.
Integration of other Deep Learning frameworks
At the time of this writing, we have integrated three well known frameworks: Caffe, Torch, and
TensorFlow into DLaaS. DLaaS is designed for pluggability so adding a new DL framework
performing vision, speech or other analysis [5, 21] to DLaaS requires nothing more than creating
training data from a storage service, train the framework-specific model, and upload the trained
13
model to the storage service, respectively.
The framework-specific Docker image should be built on top of a base Docker image we provide
that consists of various platform extensions, and a standard set of common libraries for the
application like the GPU client libraries and common Operating System (OS) tools. The base
image also includes the load.sh and store.sh scripts that interact with different storage systems
like a clustered file system, OpenStack Object or block store, or even from other cloud providers.
The custom learner Docker image should include the framework-specific libraries and
executables, and an implementation of the train.sh script that will invoke the framework to train
a model.
Integration of Storage
An important requirement in deep learning jobs is the management of large training sets. Users
may store this data in any number of locations, ranging from local disks, to high performance
storage services that support large datasets. DLaaS abstracts access to the external storage
service through a pluggable storage component. Currently support for OpenStack Object Storage
has been implemented, but others can be easily added. Adding a new storage service to DLaaS
involves extending the Storage Manager microservice, updating the load.sh and train.sh scripts in
the base learner Docker image, and defining the schema in the model manifest.yml file for the
credentials to access the external storage service.
Related Deep Learning Offerings
Amazon offers Deep Learning Amazon Machine Images (AMI) with several deep learning
frameworks that can be launched on Amazon Web Services (AWS) cloud infrastructure. An
AMI can be launched on a multi-GPU machine, but the end-user is responsible for setting up
CUDA, cuDNN, and other libraries to make use of the GPUs, and the frameworks aren't
configured to take advantage of multiple machines. This offering makes it possible to run deep
learning workloads on AWS but it not a deep learning service like DLaaS, which abstracts of all
the complexity of setup, configuration, etc from the end user. Several other organizations
including Microsoft provide pre-configured machines to perform training and relieve some of the
setup burden, but these are not cloud-based services. Google offers a more complete distributed
deep learning service, but it only supports TensorFlow, not the other frameworks offered by
DLaaS.
Conclusions and Future Work
As deep learning continues to gain traction, the ability to train these complex models easily,
economically, and efficiently becomes paramount. This is the goal of DLaaS, the IBM deep
learning as a service platform described in this paper that allows a user to train deep learning
models in the cloud through REST APIs, without having to leave the comfort of the abstractions
and artifacts she is familiar with. We have detailed the architecture of the platform and described
the challenges of bringing deep learning to the cloud. We have shown how such a platform can
14
be built in a scalable and resilient manner with data parallelism using a parameter service
approach.
There are several open areas to consider from this point forward. We are integrating DLaaS with
a machine learning pipeline handling the full lifecycle including data ingestion, data cleaning,
inferencing, and so on. In addition, we are making the experience more interactive and
enhancing the visualization to deepen the understanding of the training behavior as it progresses
in real-time to enable users to react to relevant changes in a timely manner. While supporting end
users is important, DLaaS also aims to empower existing cognitive services by allowing them to
easily add deep learning capabilities. We are proving this out by integrating with such services.
In the future, DLaaS will provide hybrid parallelism and a hyperparameter tuning layer. Such a
layer tunes system configuration and training parameters with the goal of improving accuracy
while meeting the user’s cost and speed needs. Interestingly, DLaaS, as a cloud-based deep
learning service, affords the opportunity to learn from the performance and characteristics of
previously observed models and training parameters to optimize and offer suggestions to future
users.
References
[1] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436-
444, 2015.
[2] Y. Bengio, “Learning deep architectures for AI, Foundations and Trends in Machine
Learning, vol. 2, no. 1, pp. 1-127, 2009.
[3] B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, “Learning Deep Features for
Scene Recognition using Places Database,” in Proc. of Advances in Neural Information
Processing Systems (NIPS) 27, 2014, pp. 487-495.
[4] L. Deng, J. Li, J-Ting Huang, K. Yao, D. Yu, F. Seide, M. Seltzer, G. Zweig, X. He, J.
Williams, Y. Gong, and A. Acero, “Recent advances in deep learning for speech research at
Microsoft,” in Proc. of IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), 2013, pp. 8604-8608.
[5] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep
convolutional neural networks,” in Proc. of Advances in Neural Information Processing Systems
(NIPS), 2012, pp. 1097-1105.
[6] R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, “Natural
language processing (almost) from scratch,” Journal of Machine Learning Research, vol. 12, pp.
2493-2537, 2011.
[7] R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep
neural networks with multitask learning,” in Proc. of the 25th international conference on
Machine learning (ICML), 2008, pp. 160-167.
15
[8] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T.
Darrell, “Caffe: Convolutional Architecture for Fast Feature Embedding, in Proc. of the 22nd
ACM International Conference on Multimedia, 2014, pp. 675-678.
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[9] C. Ronan, K. Koray, F. Clement, “Torch7: A Matlab-like Environment for Machine
Learning,” Neural Information Processing Systems, 2011.
[10] M. Abadi et al., TensorFlow: Large-scale machine learning on heterogeneous systems,
2016. [Online]. Available at http://arxiv.org/abs/1603.04467.
[11] A. Balalaie, A. Heydarnoori, and P. Jamshidi, “Microservices Architecture Enables
DevOps: Migration to a Cloud-Native Architecture,” IEEE Software., vol. 33, no. 3, pp. 42–52,
2016.
[12] Docker (2016). Available at https://github.com/docker.
[13] B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. Joseph, R. Katz, S. Shenker, I.
Stoica, “Mesos: a platform for fine-grained resource sharing in the data center,” in Proc. of 8th
USENIX conference on Networked System Design and Implementation (NSDI), 2011, pp. 295-
308.
[14] “Marathon”. Available at https://mesosphere.github.io/marathon.
[15] B. Burns, B. Grant, D. Oppenheimer, E. Brewer, and J. Wilkes, “Borg, Omega, and
Kubernetes,” Communications of the ACM, vol. 59, issue 5, pp. 50-57, 2016.
[16] P. Hunt, M. Konar, F. P. Junqueira, and B. Reed, “ZooKeeper: Wait-free Coordination for
Internet-scale Systems,” in Proc. of the 2010 USENIX annual technical conference, 2010, pp.
11-11.
[17] E. P. Xing, Q. Ho, W. Dai, J. K. Kim, J. Wei, S. Lee, X. Zheng, P. Xie, A. Kumar, and Y.
Yu, “Petuum: A New Platform For Distributed Machine Learning on Big Data,” in Proc. of
IEEE Transactions on Big Data, pp. 49-67, 2015.
[18] P Moritz, R Nishihara, I Stoica, and M I. Jordan, “SparkNet: Training Deep Networks with
Spark,” in Proc. of 4th International Conference on Learning Representations (ICLR), 2016.
[19] T. Chilimbi, Y. Suzue, J. Apacible, and K. Kalyanaraman, “Project Adam: Building and
Efficient and Scalable Deep Learning Training System,” in Proc. of 11th USENIX Symposium on
Operating System Design and Implementation, 2014, Broomfield, CO, pp. 571-582.
[20] M. Li, D. G. Andersen, J. W. Park, A. J. Simola, A. Ahmed, V. Josifovski, J. Long, E. J.
Shekita, and B. Su, “Scaling Distributed Machine Learning with a Parameter Server,” in Proc. of
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16
[21] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V.
Vanhoucke, and A. Rabinovich, “Going Deeper with Convolutions, Computer Vision and
Pattern Recognition (CVPR), 2015.
17
name: my-mnist-model
version: "1.0"
description: Caffe MNIST (Mixed National Institute of Standards and
Technology database) model running on GPUs. The MNIST database (Mixed
National Institute of Standards and Technology database) is a large database
of handwritten digits that is commonly used for training various image
processing systems.
Learners: 2
gpus: 2
memory: 8000MiB
data_stores:
- id: softlayer-object-storage
type: softlayer_objectstore
training_data:
container: my_training_data
training_results:
container: my_training_results
connection:
auth_url: https://dal05.objectstorage.softlayer.net/auth/v1.0
user_name: my-user-name
framework:
name: caffe
version: "1"
job: lenet_solver.prototxt
arguments:
weights: pretrained.caffemodel
Listing 1: An example of a manifest.yml file for a Caffe model. The resource
requirements such as number of learners, number of GPUs, amount of memory can
be overridden when creating a training job.
18
Figures:
Figure 1: Example visualization provided in DLaaS service. This shows the accuracy loss as the
training progress in time. Developers can use this information to decided when to stop the job or
at what points to tune the model. In the example they may decide to understand why there is a
sudden drop in accuracy loss around 100000 iterations.
0
1
2
3
4
5
6
7
050000 100000 150000 200000 250000
Accuracy'Loss'(%)
Iteration'Number
19
Figure 2 - Overview of the DLaaS Architecture. Users interact with command line interface
(CLI) or Dashboard and the core system consists of APIs, a set of microservices for model and
training job life cycle management and a set of platform services that provide GPU enabled
infrastructure, data services to transfer the training data into the system and to transfer the trained
models and logs out of the system, and logging and monitoring services to get the details of the
training progress.
!
DLaaS Plat for m, Se rv ices
GPU$enabled+Container+Service (Marathon/Mesos) Hardware+and+Accelerators+(CPU,+GPU) DLaaS Cor e,S er vic es DashboardCLI Logging+&Monitoring+Service (ELK+Stack) Param eter+Server (PS) Lifecycle+ Manager Object+Service (Swift) Conta iner+ R egistry+Service Torch Caf fe Te nsorflow File+Service (NFS) Metrics Service Key$val ue+S ervi ce+
(Zookeeper)
Tra ine r
Service
Model+
Deployer
Storag e+
Manager
DLaaS APIs
REST+API+Service
API+Gateway
Service+Registry
Secur ity+ &+Access
20
Figure 3: DLaaS Distribution Model. The life cycle manager works with the cloud job manager
such as Mesos/Marathon to deploy a collection of parameter servers and learners of a multi-node
distributed job and it orchestrates the life cycle via the Zookeeper key value store.
Author Bios
Bishwaranjan Bhattacharjee IBM Research Division, Thomas J. Watson Research Center,
Yorktown Heights, New York 10598 (bhatta@us.ibm.com). Mr. Bhattacharjee is a Senior
Technical Staff Member and Master Inventor at the Thomas J. Watson Research Center. His
current research interests include scalable data management and deep learning.
Scott Boag IBM Research Division, Thomas J. Watson Research Center, Cambridge MA 02142
(scott_boag@us.ibm.com), Mr. Boag is a Senior Technical Staff Member at IBM Research. His
research interests are in compilers and data transformation.
21
Chandani Doshi IBM Research Division, Thomas J. Watson Research Center, Cambridge MA
02142 (cdoshi@mit.edu). Ms. Doshi is a Computer Science and Electrical Engineering student at
the Massachusetts Institute of Technology. She worked on DLaaS during a summer internship at
IBM Research.
Parijat Dube IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights,
NY 10598 (pdube@us.ibm.com). Dr. Dube is a Research Staff Member in Cloud and Cognitive
Platform department at the IBM T. J. Watson Research Center. He received his PhD (2002) in
computer science from INRIA, France. His research interests are in performance modeling,
analysis, and optimization of systems.
Ben Herta IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, New
York 10598 (bherta@us.ibm.com). Mr. Herta is a Senior Software Engineer interested in
ensuring programs run efficiently on large-scale systems, taking advantage of specialized
hardware such as Infiniband and GPUs especially for cognitive workloads.
Vatche Ishakian IBM Research Division, Thomas J. Watson Research Center, Cambridge MA
02142 (vishaki@us.ibm.com). Dr. Ishakian is a Research Staff Member and a Visiting Fellow at
Boston University. His interests are in cloud computing, resource management, application-level
scheduling, network optimization and economics, data placement, and network architecture.
K. R. Jayaram IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights,
New York 10598 (jayaramkr@us.ibm.com), Jayaram is a Research Staff Member at IBM
Research. His research interests include distributed systems, cloud infrastructure, and distributed
data analytics platforms.
Rania Khalaf IBM Research Division, Thomas J. Watson Research Center, Cambridge MA
02142 (rkhalaf@us.ibm.com). Dr. Khalaf is a Distinguished Research Staff Member and Senior
Manager at IBM Research. Her research is at the intersection of Cloud Computing, Service
Composition and Machine/Deep Learning.
Avesh Krishna IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights,
New York 10598. (ajk11@rice.edu) Mr. Krishna is a Computer Science and Political Science
student at Rice University. He worked on DLaaS during an internship at IBM Research.
Yu Bo Li IBM Research Division, China Research Laboratory, Haidian District, Beijing, China
100193 (liyubobj@cn.ibm.com). Dr. Li is a Research Staff Member at IBM Research - China.
His current research interests include GPU enablement and optimization on cloud, deep learning
framework, and container technology.
Vinod Muthusamy IBM Research Division, Thomas J. Watson Research Center, Yorktown
Heights, New York 10598 (vmuthus@us.ibm.com). Mr. Muthusamy is a Research Staff Member
at the Thomas J. Watson Research Center. His current research interests include cloud platforms
that support a variety of workloads, programming models and technologies to compose services,
and analytic tools to monitor and debug distributed applications.
22
Ruchir Puri IBM Watson, Yorktown Heights, New York 10598 (ruchir@us.ibm.com). Dr. Puri is
an IBM Fellow and Chief Architect of IBM Watson where he is responsible for architecture
across the range of Watson offerings. He led Deep Learning and Machine Learning Platform
Initiative at IBM Research and drove IBM’s strategy for differentiated cognitive computing
infrastructure.
Yufei Ren IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, New
York 10598 (yren@us.ibm.com). Dr. Ren is a Research Staff Member at IBM T. J. Watson
Research Center, working on system performance optimization and large-scale machine learning
system development. Ren's research interests span the areas of high performance networks,
storage systems, I/O performance optimization, and parallel and distributed computing.
Florian Rosenberg IBM Austria, Obere Donaustrasse 95, 1020 Vienna, Austria
(rosenberg@at.ibm.com). Dr. Rosenberg is a Senior Technical Staff Member at IBM Austria.
His current interests include cloud architectures and platforms, programming and deployment
models to deliver high-quality cloud services across different industry segments.
Seetharami R. Seelam IBM Research Division, Thomas J. Watson Research Center, Yorktown
Heights, New York 10598 (sseelam@us.ibm.com). Dr. Seelam is a Research Staff Member at the
T. J. Watson Research Center. His current research interests include developing technology to
deliver hardware, middleware, containers, and applications as-a-service on the cloud.
Yandong Wang IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 USA
(yandong@us.ibm.cim). Dr. Wang is a Research Staff Member in the Cognitive System Analysis
and Optimization Group. His current interests include building large-scale computing platform
for big data analytics and machine learning algorithms.
Jian Ming Zhang IBM Research Division, China Research Laboratory, Haidian District,
Beijing, China 100193 (zhangjm@cn.ibm.com). Mr. Zhang is a Research Staff Member in the
Cloud Services department at IBM Research - China. His current research interests include cloud
platform and DevOps, service management, and operational analytics.
Li Zhang IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 USA
(zhangli@us.ibm.com). Dr. Zhang is a Master inventor, Principal Research Staff Member, and
Manager of the Cognitive System Analysis and Optimization Group at the IBM T. J. Watson
Research Center. His current interests include large-scale Big Data and Machine learning
systems.
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Torch7 is a versatile numeric computing framework and machine learning library that extends Lua. Its goal is to provide a flexible environment to design and train learning machines. Flexibility is obtained via Lua, an extremely lightweight scripting language. High performance is obtained via efficient OpenMP/SSE and CUDA implementations of low-level numeric routines. Torch7 can easily be in- terfaced to third-party software thanks to Lua's light interface.
Technical Report
TensorFlow [1] is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.
Conference Paper
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implemen- tation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called dropout that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry
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Lessons learned from three container-management systems over a decade