mylesagray / anpr-knative Goto Github PK
View Code? Open in Web Editor NEWA research project on running ML inferencing on KNative
A research project on running ML inferencing on KNative
TF Serving does not respond to SIGTERM, meaning when running in Docker/K8s it will timeout and be ended after the timeout by a SIGKILL:
Implement a wrapper script for the container entrypoint to take SIGTERM and automatically SIGKILL the child process as an interim step to having SIGTERM support added to TF Serving server upstream.
ref implementations:
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label_analyser/Dockerfile
python 3.7-slim
label_analyser/app/object_detection/dockerfiles/android/Dockerfile
label_analyser/app/object_detection/dockerfiles/tf1/Dockerfile
tensorflow/tensorflow 1.15.2-gpu-py3
label_analyser/app/object_detection/dockerfiles/tf2/Dockerfile
tensorflow/tensorflow 2.2.0-gpu
tensformation/Dockerfile
golang 1.15-buster
tensformation/go.mod
go 1.16
github.com/aws/aws-sdk-go v1.38.71
github.com/cloudevents/sdk-go/v2 v2.4.1
label_analyser/requirements.txt
absl-py ==0.13.0
astor ==0.8.1
cached-property ==1.5.2
cachetools ==4.2.2
certifi ==2021.5.30
chardet ==4.0.0
click ==8.0.1
cloudevents ==1.2.0
deprecation ==2.1.0
Flask ==2.0.1
gast ==0.2.2
google-api-core ==1.30.0
google-api-python-client ==2.11.0
google-auth ==1.32.0
google-auth-httplib2 ==0.1.0
google-auth-oauthlib ==0.4.4
google-pasta ==0.2.0
googleapis-common-protos ==1.53.0
grpcio ==1.38.1
h5py ==3.3.0
httplib2 ==0.19.1
idna ==2.10
importlib-metadata ==4.6.0
imutils ==0.5.4
itsdangerous ==2.0.1
Jinja2 ==3.0.1
Keras-Applications ==1.0.8
Keras-Preprocessing ==1.1.2
Markdown ==3.3.4
MarkupSafe ==2.0.1
mpmath ==1.2.1
numpy ==1.21.0
nupy ==0.1.1
oauthlib ==3.1.1
opencv-python ==4.5.2.54
opt-einsum ==3.3.0
packaging ==20.9
protobuf ==3.17.3
pyasn1 ==0.4.8
pyasn1-modules ==0.2.8
pyparsing ==2.4.7
pytz ==2021.1
requests ==2.25.1
requests-oauthlib ==1.3.0
rsa ==4.7.2
six ==1.16.0
sympy ==1.8
tensorboard ==1.15.0
tensorflow ==1.15.0
tensorflow-estimator ==1.15.1
termcolor ==1.1.0
typing-extensions ==3.10.0.0
uritemplate ==3.0.1
urllib3 ==1.26.6
Werkzeug ==2.0.1
wrapt ==1.12.1
zipp ==3.4.1
Currently we are using a set of numbers for batching in our tf-inference server that we have arrived at through trial and error.
There must be a more comprehensive way to look at these with a view to improving throughput and reliability.
refs:
I really do not like io.triggermesh.transformations.s3-tensorflow.response
I would like to see this replaced with io.triggermesh.transformations.tensformation.response
. However I want to wait for the demo to be recorded to avoid causing any havoc :)
Model warmup and pausing the container ready notification until the model has warmed up has the potential to increase performance by decreasing the latency per inference, and in particular on the first instance of the API's use per tf-inference container.
Need to generate model warmup data (which we can do with: https://github.com/mylesagray/tensorflow-anpr/tree/master/dataset_prep/artificial), include in the anpr-serving container image and configure TF Serving to load the warmup data on start.
ref:
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