So you just finished designing that great neural network architecture of yours.
It has a blazing number of 300 fully connected layers interleaved with 200
convolutional
layers
with 20 channels each, where the result is fed as the seed of a glorious
bidirectional
stacked
LSTM with a pinch
of
attention.
After training you get an accuracy of 99.99%, and you’re ready to ship it to
production.
But then you realize the production constraints won’t allow you to run inference
using this beast. You need the inference to be done in under 200 milliseconds.
In other words, you need to chop off half of the layers, give up on using
convolutions, and let’s not get started about the costly LSTM…
If only you could make that amazing model faster!
Here at Taboola we did it. Well, not exactly… Let me explain.
One of our models has to predict CTR (Click Through Rate) of an item, or in
other words — the probability the user will like an article recommendation and
click on it.
The model has multiple modalities as input, each goes through a different
transformation. Some of them are:
- categorical features: these are
embedded
into a dense representation - image: the pixels are passed through convolutional and fully connected layers
- text: after being tokenized, the text is passed through a LSTM which is followed
by self attention
These processed modalities are then passed through fully connected layers in
order to learn the interactions between the modalities, and finally, they are
passed through a
MDN
layer.
As you can imagine, this model is slow.
We decided to insist on the predictive power of the model, instead of trimming
components, and came up with an engineering solution.
Let’s focus on the image component. The output of this component is a learned
representation of the image. In other words, given an image, the image component
outputs an embedding.
The model is deterministic, so given the same image will result with the same
embedding. This is costly, so we can cache it. Let me elaborate on how we
implemented it.
- We used a Cassandra database as the cache which
maps an image URL to its embedding. - The service which queries Cassandra is called EmbArk (Embedding Archive,
misspelled of
course).
It’s a gRPC server which gets an image URL from a client and
retrieves the embedding from Cassandra. On cache miss EmbArk sends an async
request to embed that image. Why async? Because we need EmbArk to respond with
the result as fast as it can. Given it can’t wait for the image to be embedded,
it returns a special OOV (Out Of Vocabulary) embedding. - The async mechanism we chose to use is Kafka — a
streaming platform used as a message queue. - The next link is KFC (Kafka Frontend Client) — a Kafka consumer we implemented
to pass messages synchronously to the embedding service, and save the resulting
embeddings in Cassandra. - The embedding service is called Retina. It gets an image URL from KFC, downloads
it, preprocesses it, and evaluates the convolutional layers to get the final
embedding. - The load balancing of all the components is done using
Linkerd. - EmbArk, KFC, Retina and Linkerd run inside Docker,
and they are orchestrated by Nomad. This allows
us to easily scale each component as we see fit.
This architecture was initially used for images. After proving its worth, we
decided to use it for other components as well, such as text.
EmbArk proved to be a nice solution for transfer
learning too. Let’s say we believe the content
of the image has a good signal for predicting CTR. Thus, a model trained for
classifying the object in an image such as
Inception
would be valuable for our needs. We can load Inception into Retina, tell the
model we intend to train that we want to use Inception embedding, and that’s it.
Not only that the inference time was improved, but also the training process.
This is possible only when we don’t want to train end to end, since gradients
can’t backpropagate through EmbArk.
So whenever you use a model in production you should use EmbArk, right? Well,
not always…
There are three pretty strict assumptions here.
1. OOV embedding for new inputs is not a big deal
It doesn’t hurt us that the first time we see an image we won’t have its
embedding.
In our production system it’s ok, since CTR is evaluated multiple times for the
same item during a short period of time. We create lists of items we want to
recommend every few minutes, so even if an item won’t make it into the list
because of non optimal CTR prediction, it will in the next cycle.
2. The rate of new inputs is low
It’s true that in Taboola we get lots of new items all the time. But relative to
the number of inferences we need to perform for already known items are not that
much.
3. Embeddings don’t change frequently
Since the embeddings are cached, we count on the fact they don’t change over
time. If they do, we’ll need to perform cache invalidation, and recalculate the
embeddings using Retina. If this would happen a lot we would lose the advantage
of the architecture. For cases such as inception or language modeling, this
assumption holds, since semantics don’t change significantly over time.
Sometimes using state of the art models can be problematic due to their
computational demands. By caching intermediate results (embeddings) we were able
to overcome this challenge, and still enjoy state of the art results.
This solution isn’t right for everyone, but if the three aforementioned
assumptions hold for your application, you could consider using a similar
architecture.
By using a microservices paradigm, other teams in the company were able to use
EmbArk for needs other than CTR prediction. One team for instance used EmbArk to
get image and text embeddings for detecting duplicates across different items.
But I’ll leave that story for another post…
Originally published by me at
engineering.taboola.com.
Credit to
Source by [author_name]
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