This is an example project for a lightweight and ready-to-deploy machine learning application.
Install dependencies with Poetry:
$ poetry install
To enforce consistency, make sure you install the pre-commit hooks as well:
$ pre-commit install
Use DVC to check the status of the model:
$ dvc status
and re-train it, if necessary:
$ dvc repro
Start the server locally:
$ gunicorn application
Alternatively, you can also start it in a Docker container. Build it first:
$ docker build -t machine-learning-application .
and then run it:
docker run -p 8000:8000 machine-learning-application
You can POST requets to the /classification endpoint:
$ curl \
--request POST \
--data '{"text": "Die Sopranos ist eine US-amerikanische Fernsehserie"}' \
http://127.0.0.1:8000/classification
{"label": "show", "probability": 0.8808274865150452}
or check if the server is up and healthy:
$ curl \
--request GET \
http://127.0.0.1:8000/health
You can also profile the application:
$ python tools/profiling.py
and inspect the stats with SnakeViz:
$ snakeviz request.prof
You can inspect the model with onnx:
>>> import onnx
>>> onnx.load("logistic-regression.onnx")
ir_version: 8
producer_name: "skl2onnx"
producer_version: "1.10.0"
domain: "ai.onnx"
model_version: 0
doc_string: ""
graph {
node {
input: "input"
output: "output_label"
output: "probability_tensor"
name: "LinearClassifier"
op_type: "LinearClassifier"
attribute {
name: "classlabels_strings"
strings: "album"
strings: "painting"
strings: "show"
type: STRINGS
}
attribute {
name: "coefficients"
floats: -1.0915333032608032
floats: -0.3971465826034546
floats: -0.6551226377487183
floats: -1.1956942081451416
floats: -0.766581654548645
floats: -0.3559231162071228
floats: 2.1808230876922607
floats: 1.3179082870483398
floats: 1.132351279258728
floats: -0.9773756861686707
floats: -0.3483078181743622
floats: -0.5760289430618286
floats: 2.433957815170288
floats: 1.5667822360992432
floats: 0.7291973233222961
floats: -1.0122743844985962
floats: -0.5995703935623169
floats: -0.5131252408027649
floats: 2.068908929824829
floats: 0.7454543709754944
floats: 1.2311515808105469
floats: -1.238263487815857
floats: -0.8002005219459534
floats: -0.37327420711517334
floats: -1.168548822402954
floats: -0.7183379530906677
floats: -0.6192260384559631
type: FLOATS
}
attribute {
name: "intercepts"
floats: 0.050008490681648254
floats: -0.17995497584342957
floats: 0.1299464851617813
type: FLOATS
}
attribute {
name: "multi_class"
i: 1
type: INT
}
attribute {
name: "post_transform"
s: "SOFTMAX"
type: STRING
}
domain: "ai.onnx.ml"
}
node {
input: "probability_tensor"
output: "probabilities"
name: "Normalizer"
op_type: "Normalizer"
attribute {
name: "norm"
s: "L1"
type: STRING
}
domain: "ai.onnx.ml"
}
node {
input: "probabilities"
output: "output_probability"
name: "ZipMap"
op_type: "ZipMap"
attribute {
name: "classlabels_strings"
strings: "album"
strings: "painting"
strings: "show"
type: STRINGS
}
domain: "ai.onnx.ml"
}
name: "50c5cd6ccd014e1dbdfca8ce501e1bab"
input {
name: "input"
type {
tensor_type {
elem_type: 7
shape {
dim {
}
dim {
dim_value: 9
}
}
}
}
}
output {
name: "output_label"
type {
tensor_type {
elem_type: 8
shape {
dim {
}
}
}
}
}
output {
name: "output_probability"
type {
sequence_type {
elem_type {
map_type {
key_type: 8
value_type {
tensor_type {
elem_type: 1
}
}
}
}
}
}
}
}
opset_import {
domain: "ai.onnx.ml"
version: 1
}
opset_import {
domain: ""
version: 14
}
This package is licensed under the terms of the MIT license.
Made with ♥ at snapADDY