TensorFlow-Serving:
Flexible, High-Performance ML Serving

Christopher Olston
Noah Fiedel
Kiril Gorovoy
olston@google.com
nfiedel@google.com
kgorovoy@google.com
Jeremiah Harmsen
Li LaoFangwei Li
jeremiah@google.com
llao@google.com
fangweil@google.com
Vinu Rajashekhar
Sukriti Ramesh
Jordan Soyke
vinuraja@google.com
sukritiramesh@google.com
jsoyke@google.com

Abstract
We describe TensorFlow-Serving, a system to serve machine learning models
inside Google which is also available in the cloud and via open-source. It isextremely flexible in terms of the types of ML platforms it supports, and ways
to integrate with systems that convey new models and updated versions fromtraining to serving. At the same time, the core code paths around model lookup andinference have been carefully optimized to avoid performance pitfalls observed innaive implementations. Google uses it in many production deployments, including
a multi-tenant model hosting service called TFS2 .

1Introduction

Machine learning (ML) lies at the heart of a growing number of important problems in many fields.
While there is vast literature and software devoted to training ML models, there has been little
systematic effort around deploying trained models in production.
Indeed, until recently within Google, ML serving infrastructure has consisted mainly of ad-hoc,
non-reusable solutions. Such solutions generally start off simple and seemingly don’t necessitate afancy or general-purpose solution, e.g. “just put the models in a BigTable, and write a simple serverthat loads from there and handles RPC requests to the models.”
One-off serving solutions quickly accumulate complexity as they add support for model versioning(for model updates with a rollback option) and multiple models (for experimentation via A/B testing),
which necessitate managing server RAM carefully while avoiding availability lapses during version
transitions. Then performance problems arise as model loading causes latency spikes for othermodels or versions concurrently serving, requiring careful thread management and other techniques
to keep tail latencies in check. Next, achieving high throughput via hardware acceleration (GPUs andTPUs) requires asynchronous batch scheduling with cross-model interleaving and more tail latency
protections. As the application matures and researchers do more experimentation with alternative andlarger models,1 the models may not all fit on a single server node, and a scheme must be devised tobin-pack models onto servers and route queries accordingly. And so on.
Over time, an application’s serving system becomes a complex piece of software. Yet applicationspecific APIs, assumptions and business logic baked in at the start prevent repurposing it. As ML
applications proliferate, having custom top-to-bottom serving code for each application becomes
untenable.
1
Model accuracy improvements are sometimes won at the cost of model bloat, e.g. larger embeddings, morelayers or more parameters per layer.

31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.

We set out to build a single, state-of-the-art software framework for most ML serving workloads at
Google, and in Google’s Cloud-ML offering. What we’ve built is called TensorFlow-Serving, forserving TensorFlow [19] and other types2 of ML models. It consists of three layers: (1) a C++ libraryconsisting of APIs and modules from which to construct an ML server, (2) an assemblage of thelibrary modules into a canonical server binary, and (3) a hosted service.
The hosted service is the easiest to use: just upload your model to it and it gets served. It also enables
us to codify best practices such as validating model quality before serving a new version, or logging
inferences to catch training/serving skew bugs [5]. Previously, these best practices were not widelyadopted, and much effort was undertaken to persuade each team using ML to adopt them.
Teams with specialized hardware or datacenter needs may elect to use the binary on their own servers.
Maximum customization is possible via our library, which splits a server’s functionality into distinct
modules connected via carefully designed APIs. We’ve seen many ML serving variations at Google,
e.g. transitioning to new model versions in-place versus taking a server offline, serving multiple
kinds of models (TensorFlow + SomeOtherFlow) in a given server, or receiving models to servefrom different kinds of storage or data-conveyance media. Variations like these can be realized byconfiguring and composing our modules in different ways, and/or creating custom implementationsof some modules. The default module implementations supplied in the library encapsulate subtle
performance optimizations based on hard lessons e.g. around inference tail latency, such as particular
data structures for looking up models, and being mindful of which thread performs a slow operation
like freeing a big chunk of memory.
The libraries, and an instance of the binary, are open-source.3 The hosted service is available to allteams within Google, as well as to the public via Google Cloud Platform.4
1.1

Related Work

There is a vast amount of literature and code devoted to machine learning. Some prominent opensource offerings include Hadoop Mahout [2], scikit-learn [18], Spark MLlib [3], TensorFlow [19] andWeka [21]. This paper focuses specifically on serving ML models in production, which has received
relatively little systematic attention outside of application-specific approaches.
The general-purpose approaches we are aware of are Clipper [8], LASER [1] and Velox [7]. Of
these, Clipper may be the closest effort to TensorFlow-Serving; the two systems were developed
concurrently. Clipper and TensorFlow-Serving share a focus on remaining largely agnostic to thespecific ML technology of the models being served, and have some similar components e.g. batching.
Whereas Clipper is a research system used as a vehicle to pursue speculative ideas, TensorFlowServing encapsulates the state of the art of production infrastructure as used inside Google and Google
Cloud Platform.
Lastly, there is of course a great deal of literature on web serving and other non-ML serving scenarios,
e.g. [14, 15, 17, 22], which has informed our work. With ML serving, the objects being served (theML models) are: (a) comprised of logic, not just data, giving rise to isolation and other challenges; (b)of greatly varying sizes, and in some cases hundreds of gigabytes such that two versions cannot residein RAM simultaneously; (c) at times highly dynamic, with new versions emitted from training every
few minutes5 ; (d) greatly helped by hardware acclerators (GPUs and TPUs [12]), which necessitate
carefully managed batching.

2

Library

The library has two parts: (1) lifecycle management modules that decide which models to load intomemory, sequence the loading/unloading of specific versions, and offer reference-counted access2
Despite the name, the core libraries are ML-platform-agnostic in that they treat models as a black boxes(via a safe void*-like construct), and the other layers contain very little TensorFlow-specific logic and would be
fairly easy to generalize.
3
https://github.com/tensorflow/serving
4
https://cloud.google.com/ml-engine
5
The extreme form of online learning, in which models are updated in-place while being served, is not
generally practiced at Google, in part due to challenges with debuggability and quality assurance.

2

Figure 1: Model lifecycle management: example topology.
to them; (2) modules that service RPC requests to carry out inference using loaded models, with
optional cross-request batching.
2.1

Model Lifecycle Management

As illustrated in Figure 1, the model lifecycle management aspect of a server consists of a chain ofindividual modules:• Sources, which monitor external storage systems (e.g. file systems) to discover models andtheir versions.
• Source Routers that split the stream of model versions to be loaded based on the kind ofmodel (e.g. TensorFlow versus BananaFlow models).
• Source Adapters, which transform metadata about the location of each model version into
Loaders that can load the version into memory.
• A Manager that sequences the loading and unloading of model versions, and provides
reference-counted access to them for inference.
Other than the TensorFlow and BananaFlow Source Adapters, these modules treat models as blackboxes called servables, which could be anything. Hence the mention of BananaFlow, a hypotheticalmachine learning framework unrelated to TensorFlow. In seriousness, Google uses TensorFlowServing for some proprietary non-TensorFlow machine learning frameworks as well as TensorFlow.
Servables do not need to be machine learning models at all, e.g. they could be lookup tables that
encode feature transformations.
The API used to connect these modules is called aspired versions. A call to this API passes thename of a servable, along with a list of versions (typically just one) that the source would like tobe memory-resident. (Implicitly, versions omitted from the list are ones it would like not to bememory-resident.) We chose this uni-directional, idempotent API to make it easy to build a Sourcethat periodically polls a storage system and emits servable versions that it aspires to reside in memory,
without needing to know which ones currently are in memory.
The aspired versions API is templated by the type of data T passed with each version. In our canonicalserving set-up, there is a Source with T = file path, which monitors a file system and emits paths toversions of servables it desires to reside in memory. A TensorFlow Source Adapter converts each file
path string to a TensorFlow model Loader. The Manager requires T to be Loader. Inside Google wehave production use-cases for chains of multiple Source Adapters, as well as Source Routers andcustom implementations of Sources and Source Adapters.
2.1.1

Canary and Rollback

Our canonical file-based Source is configured with a set of servable/directory pairs; in each directory it
looks for new versions of a given servable. By default the Source aspires the latest (largest numbered)version of each servable, which works well for casual deployments. For production deployments,
users can override that default to address the following critical use-cases:• Canary: When a new version arrives from training and the one currently serving traffic
becomes the second-newest version, the user can opt to aspire both of those versions
simultaneously (i.e. load the newest version without unloading the older one). They would
continue to send all prediction request traffic to the (now) second-newest version, while3also teeing a sample of the traffic to the newest version to enable a comparison of theirpredictions. Once there is enough confidence in the newest version, the user would thenswitch to aspiring only that version (i.e. unload the second-newest one). This approachrequires more peak resources, but can avoid exposing users to an important class of model
bugs.
• Rollback: If a flaw is detected with the current primary serving version (which was notcaught via canary), the user can request to switch to aspiring a specific older version (i.e.
cause the problematic version to be unloaded in favor of the older, presumably safe one; theorder of the unload and load is configurable—see Section 2.1.2). Later, when the problem
has been addressed and a newer, safe version has been written from training to the filesystem, the user can switch to aspiring that one, thus ending the rollback.
2.1.2

Aspired Versions Manager

Our flagship Manager implementation is called AspiredVersionsManager. It is parameterized bya version transition policy which is one of: (1) an availability-preserving policy that loads a newversion of a servable before unloading the old one; (2) a resource-preserving policy that does the
opposite. The resource-preserving policy is useful for extremely large models such that two versions
cannot fit in memory at the same time, and a lapse of availability is acceptable either because abroader system ensures there are other replicas not currently transitioning versions, or the clients arebatch jobs that can wait/retry. We use both policies at Google.
AspiredVersionsManager incorporates several subtle performance optimizations, some of which werediscussed in [4]:• Read-copy-update [16] data structure to ensure wait-free access to servables by inference
threads.
• Custom reference-counted servable handles that ensure the freeing of memory for no-longerwanted servables occurs in a manager thread, not an inference thread. This approach avoids
adding latency hiccups to inference requests.
• Releasing memory to the operating system upon servable unload.
• Isolated load and inference thread pools, to insulate inference requests from performanceissues due to concurrent loading of other servables or versions.
• One-time use of all threads to load the initial set of servable versions, to speed up server
start-up.
We have also made some low-level performance optimizations around the interaction between memory
allocation and CPU caches.
2.2

Inference

We offer several TensorFlow-specific RPC APIs for inference: a low-level tensor interface thatmirrors TensorFlow’s Session::Run() API, as well as higher-level interfaces for classification andregression. To go with the latter, as well as to integrate smoothly with training pipelines, we haveco-designed a canonical data format for examples called tf.Example. Inside Google many projects use
tf.Example with our classification or regression API, but we also have many that use the lower-leveltensor representation/API to gain more flexibility and control over performance.
We nevertheless do our best to optimize our standard example representation (e.g. compressing away
features common to a batch of examples), and advocate for its adoption where feasible. It offers
improved type safety, but more importantly it facilitates tools that provide other forms of safety e.g.
detecting outlier versions of a model (which can reveal training pipeline bugs) prior to serving, andflagging training/serving skew (another bug indicator). Such tools are part of our end-to-end ML
pipeline infrastructure (Section 3.2). To this end, we are working on additional high-level APIs e.g.
for sequence models. Our goal is to cover all but the most exotic use-cases with typed APIs.
Each API has an RPC handler that fetches a servable handler from the Manager, dereferences it
and invokes a method such as Session::Run(), and then discards it. The handlers are equipped withlogging capability, which is useful for debugging, detecting training/serving skew, and validating
model changes [23].
4Figure 2: TFS2 (hosted model serving) architecture.
2.2.1

Inter-Request Batching

As mentioned above, machine learning can leverage custom hardware e.g. GPUs and TPUs [12]. The
key is to combine many inference requests into a single merged request, e.g. by concatenating theunderlying input tensors. This strategy can boost throughput substantially, but it has to be managed
carefully to avoid unduly hurting latency.
TensorFlow-Serving comes with a core library of batching primitives that is templatized on thetype of request being batched (be it tensors or some other data). The core library supports multiple
batching queues, to batch requests for multiple servables or versions separately, and schedule themin a round-robin fashion onto a single shared device e.g. GPU. The set of queues can be dynamic,
added and removed as servable versions come and go.
The core batching library is wrapped in two ways for use with TensorFlow: (1) an implementation of
TensorFlow’s Session abstraction that batches multiple Run() calls together, concatenating their input
tensors, and then forwards to the wrapped Session’s Run(); (2) special Batch and Unbatch ops [20]that can be inserted into a TensorFlow graph around a set of regular ops, which pass batched data to
those ops. The latter is new and not yet fully vetted, but we are optimistic that it can supplant theformer because it is more flexible. In particular, it can be used to batch just the GPU/TPU portion ofa graph, batch the body of a sequence model’s while-loop, or independently batch multiple subgraphse.g. the encode and decode phases of a sequence-to-sequence model. It bears similarities to thebatching approach of [13].

3Canonical Binary and Hosted Service

While our library offers great flexibility, and we have use-cases that leverage that flexibility, manyuse-cases can make due with a “vanilla” set-up consisting of a file-system-monitoring Source, aTensorFlow Source Adapter and a Manager. We package this set-up as a binary so that most users do
not need to fuss with our lower-level library offering.
But our ultimate goal is to offer model serving as a hosted service, freeing users from even runningjobs. We want to raise the serving abstraction from “run these jobs, each of which serves a set ofmodels” to “serve these models,” with the jobs managed on their behalf. The hosted TensorFlowServing service is called “TFS2 .”
3.1

TFS2

In TFS2 , users issue high-level commands such as “add model,” “remove model,” and “add modelversion.” The TFS2 infrastructure, illustrated in Figure 2, takes care of the rest, including assigningeach model to one of a suite of serving jobs based on resource fit.
The Controller takes care of adding, removing and updating users’ models, as well as honoring canaryand rollback requests (see Section 2.1.1). It estimates the RAM required to serve a given modeland selects a serving job that has enough memory capacity. Compute capacity is provisioned via
user-supplied “hints” in advance of major production launches; experimental launches and gradualproduction traffic variations are handled automatically by a separate system that reactively auto-scales
each serving job (dynamically adding and removing job replicas as load fluctuates). The Controller
keeps all its state in Spanner [6], a globally-replicated database system, and manages it transactionally.
5

Once assigned a serving job by the Controller, models are disseminated to a Synchronizer job
in each data center configured to serve models. The Synchronizer instructs serving jobs whichmodels/versions to keep loaded at a given time, via a special RPC-based Source library component
that receives Synchronizer instructions, and reports back status. The Synchronizer informs a Routerjob which models are successfully loaded in which serving jobs, so it can forward inference RPC
requests appropriately. The Router uses hedged backup requests [9] to mitigate latency spikes fromtransient server issues or inter-request or -model interference.
We offer two TFS2 instances: (1) a Temp instance where employees taking machine learning courses
or experimenting with new types of models can try them out, and (2) a Prod instance for robust,
24/7 serving of production traffic. Within each instance there are several partitions which represent
specialization based on hardware (e.g. we offer partitions with TPUs) or geography (e.g. a partition
with jobs located in South America).
The serving jobs in TFS2 use the same binary6 we make available for users who wish to run their own
jobs. This approach reduces the maintenance burden, and also allows us to canary binary releases in
our Temp instance before rolling out the release more broadly to both non-hosted and hosted users.
3.2

End-to-End ML Pipelines

TensorFlow-Serving and TFS2 are part of Google’s overall machine learning infrastructure [4]. Other
key components include model training, quality validation (comparing inference results versus priortrained versions), robustness validation (ensuring a model does not induce a server to crash), anddetection of training/serving skew. Google users can set up pipelines consisting of these steps, which
inject successful model versions into either stand-alone serving jobs or TFS2 .

4

Project Status, Performance and Adoption

We started the project in the fall of 2015 and open sourced the library code in winter 2016. The binarycame after, and was incorporated into Google’s Cloud Machine Learning Engine offering in fall 2016.
We launched the Temp instance of TFS2 in fall 2016, and the Prod instance in winter 2017.
While always a work-in-progress, the current level of system performance is quite good: In termsof throughput, the main bottlenecks lie in the RPC and TensorFlow layers; we determined that
TensorFlow-Serving itself can handle about 100,000 requests per second per core,7 if those twolayers are factored out. Latency-wise, we have been able to rein in tail latency substantially whileother models or versions are loading, compared to our initial naive implementation; details andperformance results are reported in [4].
Each of the form-factors we offer (library, binary, service) is being used in production inside Google,
supporting various internal systems and user-facing products. The total number of Google projects
using TensorFlow-Serving is in the hundreds. Overall traffic from Google adoption is in the tensof millions of inferences per second. External adoption includes one-off usage e.g. [11], as well as
projects by Hortonworks [10], IBM and SAP to incorporate TensorFlow-serving into their generalpurpose machine learning and database platforms.

Acknowledgements
The Synchronizer system used in TFS2 as well as elsewhere in Google, was developed by the Ads
Serving team including Nels Beckman, Andrew Bernard, Scott Daley and Ahmed Shalaby.
TFS2 is made excellent by Google’s ML-SRE team: Travis Abbott, Chris Farrar, Scott Martin, Brian
McBarron, Daniel Papasian, Steven Ross and Sean Sechrist.
We are also grateful for the ideas, contributions and collaborations from the following individuals
and teams: Robson Araujo, Manasi Joshi, Andrey Khorlin, Alex Passos, Gautam Vasudevan, Jarek
Wilkiewicz, Google CloudML team (especially Bhupesh Chandra, Polina Dudnik and Robbie Haertel),TensorFlow team, TFX team, Berkeley RISELab (in particular Dan Crankshaw and Joseph Gonzalez),and the open-source GitHub community.
6

The Source to activate—RPC-based or file-system-based—is configurable; TFS2 uses the former while
standalone jobs use the latter.
7
Measurements taken on a 16 vCPU Intel Xeon E5 2.6 GHz machine.

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