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EXPERIMENTAL: gRPC support #2808

Merged
merged 8 commits into from Sep 16, 2022
Merged

EXPERIMENTAL: gRPC support #2808

merged 8 commits into from Sep 16, 2022

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aarnphm
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@aarnphm aarnphm commented Jul 26, 2022

BentoML 🀝 gRPC

Features Screenshot
Access Log/OpenTelemetry Screenshot 2022-09-04 at 15 20 00
Prometheus Screenshot 2022-09-04 at 15 20 59

⚠️ EXPERIMENTAL ⚠️

gRPC is currently an experimental features, and prone to bugs. We would love to hear more feedback from the community. Feel free to join our slack for supports as well as file a bug report if you encounter any πŸ˜ƒ

serve-grpc CLI entrypoint

To use gRPC use serve-grpc as an alternative bentoml serve:

Development Production
bentoml serve-grpc bentoml serve-grpc --production

By default, serve-grpc doesn't enable reflection. To use reflection and take advantages of tools such as https://github.com/fullstorydev/grpcui or https://github.com/fullstorydev/grpcurl, pass --enable-reflection:

bentoml serve-grpc --enable-reflection

start-grpc-server
One can start a standalone grpc-server:

bentoml start-grpc-server --remote-runner ...

Configuration

Under bentoml_configuration.yaml file, the given fields are introduced to api_server.grpc:

# bentoml_configuration.yaml
api_server:
  grpc:
    host: 0.0.0.0
    port: 50051
    max_concurrent_streams: 100
    maximum_concurrent_rpcs: ~
    max_message_length: -1
    reflection:
      enabled: false
    metrics:
      host: 0.0.0.0
      port: 50052

Some of notable configuration fields:

max_concurrent_streams

Maximum number of concurrent incoming streams to allow on a HTTP2 connection. Default to 100, See https://httpwg.org/specs/rfc7540.html#rfc.section.5.1.2 for more details.

A gRPC channel uses a single HTTP/2 connection, and concurrent calls are multiplexed on that connection. When the number of active calls reaches the connection stream limit, additional calls are queued in the client. Queued calls wait for active calls to complete before they are sent.

maximum_concurrent_rpcs

The maximum number of concurrent RPCs this server will service before returning RESOURCE_EXHAUSTED status, or None to indicate no limit.

Improvement

This PR also introduce a refactor of configuration. All HTTPs-only fields are now available under api_server.http. These options include cors, and max_request_size:

api_server:
  http:
    host: 0.0.0.0
    port: 3000
    max_request_size: 20971520
    cors:
      enabled: false
      access_control_allow_origin: ~
      access_control_allow_credentials: ~
      access_control_allow_methods: ~
      access_control_allow_headers: ~
      access_control_max_age: ~
      access_control_expose_headers: ~

Note that backlog is a shared options for both gRPC and HTTP. on gPRC, backlog is used for running Prometheus sidecar.

Prometheus will be running as a sidecar when using grpc. The default port is set to 50052, and host to 0.0.0.0. To change the port and host, customize bentoml_configugration.yaml:

api_server:
  grpc:
    metrics:
      port: 59281
      host: 10.23.1.8

Note that we set this port for prometheus instead of 9090 to avoid current collision with users' prometheus server.

CLI aliases

Given that now we have serve-grpc and serve, this PR introduce alias serve-http
that maps to serve:

bentoml serve-http ...

Help message:

Usage: bentoml [OPTIONS] COMMAND [ARGS]...

  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—β–‘β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—β–ˆβ–ˆβ–ˆβ•—β–‘β–‘β–ˆβ–ˆβ•—β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—β–‘β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—β–‘β–ˆβ–ˆβ–ˆβ•—β–‘β–‘β–‘β–ˆβ–ˆβ–ˆβ•—β–ˆβ–ˆβ•—β–‘β–‘β–‘β–‘β–‘
  β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•—β–ˆβ–ˆβ•”β•β•β•β•β•β–ˆβ–ˆβ–ˆβ–ˆβ•—β–‘β–ˆβ–ˆβ•‘β•šβ•β•β–ˆβ–ˆβ•”β•β•β•β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•—β–ˆβ–ˆβ–ˆβ–ˆβ•—β–‘β–ˆβ–ˆβ–ˆβ–ˆβ•‘β–ˆβ–ˆβ•‘β–‘β–‘β–‘β–‘β–‘
  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•¦β•β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—β–‘β–‘β–ˆβ–ˆβ•”β–ˆβ–ˆβ•—β–ˆβ–ˆβ•‘β–‘β–‘β–‘β–ˆβ–ˆβ•‘β–‘β–‘β–‘β–ˆβ–ˆβ•‘β–‘β–‘β–ˆβ–ˆβ•‘β–ˆβ–ˆβ•”β–ˆβ–ˆβ–ˆβ–ˆβ•”β–ˆβ–ˆβ•‘β–ˆβ–ˆβ•‘β–‘β–‘β–‘β–‘β–‘
  β–ˆβ–ˆβ•”β•β•β–ˆβ–ˆβ•—β–ˆβ–ˆβ•”β•β•β•β–‘β–‘β–ˆβ–ˆβ•‘β•šβ–ˆβ–ˆβ–ˆβ–ˆβ•‘β–‘β–‘β–‘β–ˆβ–ˆβ•‘β–‘β–‘β–‘β–ˆβ–ˆβ•‘β–‘β–‘β–ˆβ–ˆβ•‘β–ˆβ–ˆβ•‘β•šβ–ˆβ–ˆβ•”β•β–ˆβ–ˆβ•‘β–ˆβ–ˆβ•‘β–‘β–‘β–‘β–‘β–‘
  β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•¦β•β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—β–ˆβ–ˆβ•‘β–‘β•šβ–ˆβ–ˆβ–ˆβ•‘β–‘β–‘β–‘β–ˆβ–ˆβ•‘β–‘β–‘β–‘β•šβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•”β•β–ˆβ–ˆβ•‘β–‘β•šβ•β•β–‘β–ˆβ–ˆβ•‘β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ•—
  β•šβ•β•β•β•β•β•β–‘β•šβ•β•β•β•β•β•β•β•šβ•β•β–‘β–‘β•šβ•β•β•β–‘β–‘β–‘β•šβ•β•β–‘β–‘β–‘β–‘β•šβ•β•β•β•β•β–‘β•šβ•β•β–‘β–‘β–‘β–‘β–‘β•šβ•β•β•šβ•β•β•β•β•β•β•

Options:
  -v, --version  Show the version and exit.
  -h, --help     Show this message and exit.

Commands:
  build               Build a new Bento from current directory.
  containerize        Containerizes given Bento into a ready-to-use...
  delete              Delete Bento in local bento store.
  env                 Print environment info and exit
  export              Export a Bento to an external file archive
  get                 Print Bento details by providing the bento_tag.
  import              Import a previously exported Bento archive file
  list                List Bentos in local store
  models              Model Subcommands Groups
  pull                Pull Bento from a yatai server.
  push                Push Bento to a yatai server.
  serve (serve-http)  Start a HTTP BentoServer from a given 🍱
  serve-grpc          [EXPERIMENTAL] Start a gRPC BentoServer from a...
  yatai               Yatai Subcommands Groups

Custom gRPC server support

Mount custom gRPC server to bentoml.Service:

import route_guide_pb2
import route_guide_pb2_grpc
from servicer_impl import RouteGuideServicer

svc = bentoml.Service("iris_classifier", runners=[iris_clf_runner])

services_name = [
    v.full_name for v in route_guide_pb2.DESCRIPTOR.services_by_name.values()
]
svc.mount_grpc_servicer(
    RouteGuideServicer,
    add_servicer_fn=add_RouteGuideServicer_to_server,
    service_names=services_name,
)

Note that service_names is used for our health checking probe to ensure liveliness.

Custom gRPC interceptor

BentoML comes with some default interceptors to provide support for access log, OpenTelemetry, Prometheus.

Prometheus is enabled by default. To disable it, set api_server.metrics.enabled to false.

Note that the order of the interceptor is import here. The following graph demonstrate how interceptor flow get added to bentoml's gRPC server:

flowchart TD
    B[OpenTelemetryInterceptor]
    B --> C(Enable metrics?)
	C -- Yes --> D[PrometheusInterceptor]
	D --> E[AccessLogInterceptor]
	C -- No --> E
	E --> F(Custom interceptor?)
    F -- Yes --> G[User interceptors]
    F -- No --> E

To add your interceptor, simply use svc.add_grpc_interceptor:

Note that users interceptors should be read-only interceptors.

svc.add_grpc_interceptor(MyInterceptor)

If your interceptor requires additional arguments, you can do the following:

svc.add_grpc_interceptor(MultipleArgumentInterceptor, arg="foo", check=False, ...)

# support partial
svc.add_grpc_interceptor(functools.partial(MultipleArgumentInterceptor, arg="foo", check=False))

For grpc.ServerInterceptor (NOT STABLE YET)

All BentoML Interceptor are async interceptor and inherit from grpc.aio.ServerInterceptor.

If your interceptor are sync interceptor (grpc.ServerInterceptor), you can do something as follow:

# async_interceptor.py
from __future__ import annotations

from typing import Callable
from typing import Awaitable
from typing import TYPE_CHECKING
from functools import partial

import grpc
from grpc.aio import ServicerContext
from grpc.aio import ServerInterceptor
from bentoml.grpc.utils import wrap_rpc_handler
from bentoml.grpc.utils import parse_method_name
from my_sync_interceptor import SyncInterceptor as _SyncInterceptor

if TYPE_CHECKING:
    from grpc import RpcMethodHandler
    from grpc import HandlerCallDetails
    from bentoml.grpc.utils import MethodName


class AsyncInterceptor(ServerInterceptor):
    def __init__(*args, **kwargs):
        self._sync_interceptor = _SyncInterceptor(*args, **kwargs)

    async def intercept_service(
        self,
        continuation: Callable[[HandlerCallDetails], Awaitable[RpcMethodHandler]],
        handler_call_details: HandlerCallDetails,
    ) -> RpcMethodHandler:
        handler = await continuation(handler_call_details)
        method_name = handler_call_details.method

        wrapper = partial(self._func_wrapper, parse_method_name(method_name)[0])

        return wrap_rpc_handler(wrapper, handler)

    def _func_wrapper(
        self,
        method_name: MethodName,
        old_handler: RpcMethodHandler,
        request_streaming: bool,
        response_streaming: bool,
    ):
		# your intercept_service for sync interceptor here.
        ...

Then add it to BentoService:

from async_interceptor import AsyncInterceptor

svc.add_grpc_interceptor(AsyncInterceptor, foo="bar", debug=True)

Currently, only unary RPC are supported, as client and server streaming are not yet SUPPORTED.

BentoService Protobuf representation

// a gRPC BentoServer.
service BentoService {
  // Call handles methodcaller of given API entrypoint.
  rpc Call(Request) returns (Response) {}
}
Message Implementation
Request
// Request message for incoming Call.
message Request {
  // api_name defines the API entrypoint to call.
  // api_name is the name of the function defined in bentoml.Service.
  // Example:
  //
  //     @svc.api(input=NumpyNdarray(), output=File())
  //     def predict(input: NDArray[float]) -> bytes:
  //         ...
  //
  //     api_name is "predict" in this case.
  string api_name = 1;

  // NDArray represents a n-dimensional array of arbitrary type.
  NDArray ndarray = 3;

  // Tensor is similiar to ndarray but with a name
  // We are reserving it for now for future use.
  // repeated Tensor tensors = 4;
  reserved 4, 11 to 15;

  // DataFrame represents any tabular data type. We are using
  // DataFrame as a trivial representation for tabular type.
  DataFrame dataframe = 5;

  // Series portrays a series of values. This can be used for
  // representing Series types in tabular data.
  Series series = 6;

  // File represents for any arbitrary file type. This can be
  // plaintext, image, video, audio, etc.
  File file = 7;

  // Text represents a string inputs.
  google.protobuf.StringValue text = 8;

  // JSON is represented by using google.protobuf.Value.
  // see https://github.com/protocolbuffers/protobuf/blob/main/src/google/protobuf/struct.proto
  google.protobuf.Value json = 9;

  // Multipart represents a multipart message.
  // It comprises of a mapping from given type name to a subset of aforementioned types.
  map<string, Part> multipart = 10;

  // This field is reserved and INTERNAL to BentoML, and users are DISCOURAGED to use this.
  optional bytes serialized_bytes = 2;
}
Response
// Request message for incoming Call.
message Response {
  // NDArray represents a n-dimensional array of arbitrary type.
  NDArray ndarray = 1;

  // Tensor is similiar to ndarray but with a name
  // We are reserving it for now for future use.
  // repeated Tensor tensors = 4;
  reserved 4, 10 to 15;

  // DataFrame represents any tabular data type. We are using
  // DataFrame as a trivial representation for tabular type.
  DataFrame dataframe = 3;

  // Series portrays a series of values. This can be used for
  // representing Series types in tabular data.
  Series series = 5;

  // File represents for any arbitrary file type. This can be
  // plaintext, image, video, audio, etc.
  File file = 6;

  // Text represents a string inputs.
  google.protobuf.StringValue text = 7;

  // JSON is represented by using google.protobuf.Value.
  // see https://github.com/protocolbuffers/protobuf/blob/main/src/google/protobuf/struct.proto
  google.protobuf.Value json = 8;

  // Multipart represents a multipart message.
  // It comprises of a mapping from given type name to a subset of aforementioned types.
  map<string, Part> multipart = 9;

  // This field is reserved and INTERNAL to BentoML, and users are DISCOURAGED to use this.
  optional bytes serialized_bytes = 2;
}

Our goal is to optimize client writing experience as much as possible.

An example of gRPCurl request
OS Message
MacOS/Windows
docker run -i --rm fullstorydev/grpcurl -d @ -plaintext host.docker.internal:50051 bentoml.grpc.v1alpha1.BentoService/Call <<EOM
{
  "apiName": "aclassify",
  "ndarray": {
    "shape":[1,4], 
	"floatValues": [ 1.0 ,2.0 ,3.0 ,4.0 ]
  }
}
EOM
Linux
docker run -i --rm --network=host fullstorydev/grpcurl -d @ -plaintext 0.0.0.0:50051 bentoml.grpc.v1alpha1.BentoService/Call <<EOM
{
  "apiName": "aclassify",
  "ndarray": {
    "shape":[1,4], 
    "floatValues": [ 1.0 ,2.0 ,3.0 ,4.0 ]
  }
}
EOM
A toy client implementation in go
import (
    pb "github.com/bentoml/bentoml/grpc/v1/service"
    status "google.golang.org/grpc/status"
)

var opts []grpc.DialOption

conn, err := grpc.Dial(*serverAddr, opts...)
if err != nil {
    ...
}
defer conn.Close()

service_def = grpc.reflect(serverAddr)
service_def.get_endpoint_by_name("my_endpoint")
...
client = service_def.client()
client := pb.NewBentoServiceClient(conn)

resp, err := client.Call(context.Background(), &pb.Request{api_name="my_endpoint", ndarray=&pb.NDArray{dtype=pb.NDArray.DTYPE_FLOAT, shape=[1,4], float_values=[3.5, 2.4, 7.8]}})
if err != nil {
    errStatus, _ := status.FromError(err)
    // use errStatus.Message() and errStatus.Code() as needed
}

fmt.Print(resp.float_values)

Containerize your gRPC BentoService

To add bentoml additional components, such as gRPC, Tracing (zipkin, jaeger, etc.), a YAML dictionary field python.components is available to customize in your bentofile.yaml:

python:
  components:
    grpc: true
    tracing.jaeger: true

This fields are currently following BentoML's extras_require. This is can be one of [grpc, tracing, tracing.zipkin, tracing.jaeger, tracing.otlp].

Note that tracing is just for backward compatibility

To run your docker container with gRPC, provide either environment variable BENTOML_USE_GRPC=true to docker or use serve-grpc directly to the container:

docker run -it --rm -p 3001:3001 -p 3000:3000 iris_classifier:<tag> serve-grpc --production --enable-reflection

Note that if use serve-grpc direct entrypoint, --production is required to run the container in production mode.

--enable-<components> flag

This PR also introduces the ability to containerize previously built Bento with additional components via bentoml containerize:

bentoml containerize iris_classifier:latest [--enable-grpc|--enable-tracing|--enable-jaeger|--enable-otlp|--enable-zipkin]

This options map 1-to-1 with the python.components field in bentofile.yaml.a

Known limitation

SO_REUSEPORT

gRPC supports multiple workers out-of-the-box. However, they depends on the socket implementation of SO_REUSEPORT. This is known to have different implementation behaviour on different system (because of its hashing algorithm), and thus we can only ensure gRPC to be functional on Linux-based system.

This wouldn't affect a Bento container since it is running as a Linux container.
However, if users try to run bentoml serve --production locally on MacOS or any BSD system, the behaviour would not be the same.

Windows support

We have enabled Windows support while in development mode. Since the limitation lies at SO_REUSEPORT in production settings, Windows will not be supported with bentoml serve --production --grpc (as grpc themselves doesn't have a good support for Windows).

Therefore, we advise our Windows users to use WSL instead. This will give Windows users access to Linux where BentoML gRPC's integration will be fully supported.

Mischelaneous

  • This PR introduces a @experimental decorator for given functions that are not yet stable.
@experimental
def start_grpc_server(...):
    ...
  • Update Codespaces and devcontainers setup to be able to run grpc.

bentoml.testing

from bentoml.testing.server import host_bento

with host_bento("my_bento:latest", deployment_mode = "standalone", use_grpc=True) as host:
   ....

Currently there are three deployment_mode: [standalone, docker, distributed]. Note that on GitHub CI, BentoML currently run the following matrix:

OS Mode
Ubuntu [standalone, distributed, docker]
MacOS [standalone, distributed]
Windows [standalone]

Note that for MacOS and Windows running locally, docker will also be included. docker is disabled on CI due to licensing restriction.

To run each of the server separately, one can use run_bento_server_<deployment_mode>, for example:

if TYPE_CHECKING:
    from numpy.typing import NDArray
    from bentoml.grpc.v1alpha1 import service_pb2 as pb


async def test_predict(test_payload: NDArray[float]):
    bento = bentoml.get("iris_classifier")
    with run_bento_server_distributed(bento.tag, use_grpc=True) as host:
        async with create_channel(host) as channel:
            resp: pb.Response = await async_client_call(
                "predict",
                channel=channel,
                data={"ndarray": make_pb_ndarray(test_payload)},
            )


if __name__ == "__main__":
    import asyncio

    asyncio.run(test_predict)

TODOs:

  • follow up with a tests PR cherry-picked from this branch
  • Fully documents and address some API enhance correctly.
  • Test client from bentoml.testing.grpc are currently not thread-safe, hence forking is disabled. (FIXME)

Course of actions


Kudos

Many thanks to our MLH Intern Sadab Hafiz for contributing to this feature. πŸŽ‰

@codecov
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codecov bot commented Jul 26, 2022

Codecov Report

Merging #2808 (e291039) into main (e291039) will not change coverage.
The diff coverage is n/a.

❗ Current head e291039 differs from pull request most recent head f0ae7b3. Consider uploading reports for the commit f0ae7b3 to get more accurate results

Impacted file tree graph

@@           Coverage Diff           @@
##             main    #2808   +/-   ##
=======================================
  Coverage   69.00%   69.00%           
=======================================
  Files         122      122           
  Lines       10162    10162           
=======================================
  Hits         7012     7012           
  Misses       3150     3150           

@aarnphm aarnphm changed the title feat(wip): grpc [EXPERIMENTAL] [EXPERIMENTAL] feat(wip): grpc Jul 26, 2022
@aarnphm aarnphm changed the title [EXPERIMENTAL] feat(wip): grpc feat(EXPERIMENTAL): grpc Jul 27, 2022
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Few minor things we can fix in PRs to gRPC.

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@pep8speaks
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pep8speaks commented Jul 27, 2022

Hello @aarnphm, Thanks for updating this PR.

There are currently no PEP 8 issues detected in this PR. Cheers! 🍻

Comment last updated at 2022-09-13 06:38:30 UTC

@aarnphm aarnphm force-pushed the grpc branch 4 times, most recently from 3d0fffe to c0b5009 Compare August 3, 2022 11:39
@aarnphm aarnphm force-pushed the grpc branch 8 times, most recently from bd4ac4c to 7a9f276 Compare August 14, 2022 00:58
@aarnphm aarnphm marked this pull request as ready for review August 14, 2022 00:59
@aarnphm aarnphm requested review from ssheng, parano and a team as code owners August 14, 2022 00:59
@aarnphm aarnphm requested review from bojiang and removed request for a team August 14, 2022 00:59
sauyon
sauyon previously approved these changes Sep 16, 2022
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:)

Proto007 and others added 8 commits September 15, 2022 19:31
See #2634 and #2742.

Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>
We will generate gRPC stubs via a separate script instead of setuptools.

update codespaces and devcontainers configuration

ignore pyvenv

chore: ignore virtualenv

lock protobuf to 3.19.4

Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>
Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>
interceptor: access logs, prometheus, opentelemetry (#2825)

Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>
add options to assign alias for commands

Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>
include gRPC options and dependencies

enable alias to be parsed in docker container

Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>
Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>
Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com>
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:) x2

@aarnphm aarnphm changed the title EXPERIMENTAL[grpc]: gRPC support EXPERIMENTAL: gRPC support Sep 16, 2022
@aarnphm aarnphm merged commit fc39429 into main Sep 16, 2022
@aarnphm aarnphm deleted the grpc branch September 16, 2022 22:38
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6 participants