Understanding BentoML adaptive micro batching¶
1. The overall architecture of BentoML’s micro-batching server¶
1.1 Why micro batching matters¶
While serving a TensorFlow model, batching individual model inference requests together can be important for performance. In particular, batching is necessary to unlock the high throughput promised by hardware accelerators such as GPUs.
Plus, under BentoML’s architecture, the HTTP handling and data preprocessing procedure will also benefit from micro-batching.
1.2 Architecture & Data Flow¶
1.3 Parameters & Concepts of micro batching¶
inbound requests: requests from user clients
outbound requests: requests to upstream model servers
mb_max_batch_sizeThe maximum size of any batch. This parameter governs the throughput/latency trade-off, and also avoids having batches that are so large they exceed some resource constraint (e.g. GPU memory to hold a batch’s data). Default: 1000.
mb_max_latencyThe latency goal of your service in milliseconds. Default: 10000.
outbound semaphore: The semaphore represents the degree of parallelism, i.e. the maximum number of batches processed concurrently. It is set automatically when launching the bento service as the same number of model server workers.
Estimated time: Estimated time for model server to execute a batch. Inferred from historical data and current batch size in queue.
1.4 Sequence & How it works¶
Take bento service with single API and —workers=1 as example
To achieve optimal efficiency, the CORK dispatcher performs a adaptive control to cork/release inbound requests. The releasing happens when:
meets one of the following conditions:
the waited time + estimated time exceeds
it is not worth to wait next inbound request *
AND the outbound semaphore is not locked
mb_max_latency didn’t represents that each request will be
responded in this latency. The algorithm will determine a adaptive wait
time between 0 and the
mb_max_latency. But when under excessive
request pressure, more response time will reach the
In each releasing, the count of released requests is decided by
algorithm, but less than
If the outbound semaphore is still locked, requests may be canceled once
1.5 The main design decisions and trade-offs¶
Throughput and latency are most concerned for API servers. BentoML will fine-tune batches automatically to(in the order priority):
Ensure the user defined constraint of
Maximum the Throughput
Minimum the average Latency
2. parameter tuning best practices & recommendations¶
Different from TensorFlow Serving, BentoML will automatically adjust the batch size and wait timeout, balancing the maximum throughput and latency. It will respond to the fluctuations of server loading.
class MovieReviewService(bentoml.BentoService): @bentoml.api(input=DataframeInput(), mb_max_latency=10000, mb_max_batch_size=1000, batch=True) def predict(self, inputs): pass
mb_max_batch_size is 1000 by default and
mb_max_latency is 10000
If the RAM of GPU only allowed input with 100 batch size, then you could set
If the clients using your API has the request timeout 200ms, then you could set
If you know the executing of your model is very slow (for example, the latency is more than 100ms), then enlarging the
mb_max_latencyto 10 * 100ms will help to achieve higher throughput.
3. How to implement batch mode for custom input adapters¶
TL;DR: Implement the method
following existent input adapters.
The batching service is HTTP request-wise now, which is mostly
transparent for developers. The only difference between
the input parameter is a list of request object
the return value should be a list of response object
To maximize the benefit of micro-batching, remember to use the batch
alternative of each operation from the beginning. For example, each
pd.read_csv/read_json take constantly 2ms, so code like this
def handle_batch_request(self, requests): dfs =  for req in requests: dfs.append(pd.read_csv(req.body)) # ...
will be O(N) in time complexity. Thus we implemented an nearly O(1)
function to concat DataFrame CSV strings, so that all DataFrames in
requests could be loaded by calling
4.1 TensorFlow Serving¶
Tensorflow Serving employed similar approach to batch individual
requests together. But the parameters of batching scheduling is static.
Assume your model had 1 ms latency. If you enabled batching and
configure it with
batch_timeout_micros = 300 * 1000, whether
necessary or not, the latency of every request now would be 300ms + 1ms.
You will need to fine-tune these parameters by experiments before deployment. Once deployed, it won’t change anymore.
The best values to use for the batch scheduling parameters depend on your model, system and environment, as well as your throughput and latency goals. Choosing good values is best done via experiments. Here are some guidelines that may be helpful in selecting values to experiment with.
Clipper applied a combination of TCP Nagle and AIMD algorithm. This approach is more similar with BentoML, the difference is scheduling algorithm and the goal of optimization.
To automatically find the optimal maximum batch size for each model container we employ an additive-increase-multiplicative-decrease (AIMD) scheme.
Clipper has parameter SLO(similar with mb_max_latency), the optimization goal of AIMD is to maximize the throughput under the bound of SLO.
Therefore, for most cases, Clipper have higher latency than BentoML, which also means it’s able to serve less users at same time.