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Add catboost classifier support (#1403)
Co-authored-by: Theofilos Papapanagiotou <theofilos@gmail.com>
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# Serving CatBoost models\n", | ||
"\n", | ||
"Out of the box, `mlserver` supports the deployment and serving of `catboost` models.\n", | ||
"By default, it will assume that these models have been [serialised using the `save_model()` method](https://catboost.ai/en/docs/concepts/python-reference_catboost_save_model).\n", | ||
"\n", | ||
"In this example, we will cover how we can train and serialise a simple model, to then serve it using `mlserver`." | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Training\n", | ||
"\n", | ||
"To test the CatBoost Server, first we need to generate a simple CatBoost model using Python." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import numpy as np\n", | ||
"from catboost import CatBoostClassifier\n", | ||
"\n", | ||
"train_data = np.random.randint(0, 100, size=(100, 10))\n", | ||
"train_labels = np.random.randint(0, 2, size=(100))\n", | ||
"\n", | ||
"model = CatBoostClassifier(iterations=2,\n", | ||
" depth=2,\n", | ||
" learning_rate=1,\n", | ||
" loss_function='Logloss',\n", | ||
" verbose=True)\n", | ||
"model.fit(train_data, train_labels)\n", | ||
"model.save_model('model.cbm')" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"Our model will be persisted as a file named `model.cbm`." | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Serving\n", | ||
"\n", | ||
"Now that we have trained and saved our model, the next step will be to serve it using `mlserver`. \n", | ||
"For that, we will need to create 2 configuration files: \n", | ||
"\n", | ||
"- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.).\n", | ||
"- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.)." | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"### `settings.json`" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"%%writefile settings.json\n", | ||
"{\n", | ||
" \"debug\": \"true\"\n", | ||
"}" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"### `model-settings.json`" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"%%writefile model-settings.json\n", | ||
"{\n", | ||
" \"name\": \"catboost\",\n", | ||
" \"implementation\": \"mlserver_catboost.CatboostModel\",\n", | ||
" \"parameters\": {\n", | ||
" \"uri\": \"./model.cbm\",\n", | ||
" \"version\": \"v0.1.0\"\n", | ||
" }\n", | ||
"}" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"### Start serving our model\n", | ||
"\n", | ||
"Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", | ||
"\n", | ||
"```shell\n", | ||
"mlserver start .\n", | ||
"```\n", | ||
"\n", | ||
"Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"### Send test inference request\n", | ||
"\n", | ||
"We now have our model being served by `mlserver`.\n", | ||
"To make sure that everything is working as expected, let's send a request from our test set.\n", | ||
"\n", | ||
"For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": { | ||
"pycharm": { | ||
"name": "#%%\n" | ||
} | ||
}, | ||
"outputs": [], | ||
"source": [ | ||
"import requests\n", | ||
"import numpy as np\n", | ||
"\n", | ||
"test_data = np.random.randint(0, 100, size=(1, 10))\n", | ||
"\n", | ||
"x_0 = test_data[0:1]\n", | ||
"inference_request = {\n", | ||
" \"inputs\": [\n", | ||
" {\n", | ||
" \"name\": \"predict-prob\",\n", | ||
" \"shape\": x_0.shape,\n", | ||
" \"datatype\": \"FP32\",\n", | ||
" \"data\": x_0.tolist()\n", | ||
" }\n", | ||
" ]\n", | ||
"}\n", | ||
"\n", | ||
"endpoint = \"http://localhost:8080/v2/models/catboost/versions/v0.1.0/infer\"\n", | ||
"response = requests.post(endpoint, json=inference_request)\n", | ||
"\n", | ||
"print(response.json())" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.7.8" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 4 | ||
} |
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# Serving CatBoost models | ||
|
||
Out of the box, `mlserver` supports the deployment and serving of `catboost` models. | ||
By default, it will assume that these models have been [serialised using the `save_model()` method](https://catboost.ai/en/docs/concepts/python-reference_catboost_save_model). | ||
|
||
In this example, we will cover how we can train and serialise a simple model, to then serve it using `mlserver`. | ||
|
||
## Training | ||
|
||
To test the CatBoost Server, first we need to generate a simple CatBoost model using Python. | ||
|
||
|
||
```python | ||
import numpy as np | ||
from catboost import CatBoostClassifier | ||
|
||
train_data = np.random.randint(0, 100, size=(100, 10)) | ||
train_labels = np.random.randint(0, 2, size=(100)) | ||
|
||
model = CatBoostClassifier(iterations=2, | ||
depth=2, | ||
learning_rate=1, | ||
loss_function='Logloss', | ||
verbose=True) | ||
model.fit(train_data, train_labels) | ||
model.save_model('model.cbm') | ||
``` | ||
|
||
Our model will be persisted as a file named `model.cbm`. | ||
|
||
## Serving | ||
|
||
Now that we have trained and saved our model, the next step will be to serve it using `mlserver`. | ||
For that, we will need to create 2 configuration files: | ||
|
||
- `settings.json`: holds the configuration of our server (e.g. ports, log level, etc.). | ||
- `model-settings.json`: holds the configuration of our model (e.g. input type, runtime to use, etc.). | ||
|
||
### `settings.json` | ||
|
||
|
||
```python | ||
%%writefile settings.json | ||
{ | ||
"debug": "true" | ||
} | ||
``` | ||
|
||
### `model-settings.json` | ||
|
||
|
||
```python | ||
%%writefile model-settings.json | ||
{ | ||
"name": "catboost", | ||
"implementation": "mlserver_catboost.CatboostModel", | ||
"parameters": { | ||
"uri": "./model.cbm", | ||
"version": "v0.1.0" | ||
} | ||
} | ||
``` | ||
|
||
### Start serving our model | ||
|
||
Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are. | ||
|
||
```shell | ||
mlserver start . | ||
``` | ||
|
||
Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal. | ||
|
||
### Send test inference request | ||
|
||
We now have our model being served by `mlserver`. | ||
To make sure that everything is working as expected, let's send a request from our test set. | ||
|
||
For that, we can use the Python types that `mlserver` provides out of box, or we can build our request manually. | ||
|
||
|
||
```python | ||
import requests | ||
import numpy as np | ||
|
||
test_data = np.random.randint(0, 100, size=(1, 10)) | ||
|
||
x_0 = test_data[0:1] | ||
inference_request = { | ||
"inputs": [ | ||
{ | ||
"name": "predict-prob", | ||
"shape": x_0.shape, | ||
"datatype": "FP32", | ||
"data": x_0.tolist() | ||
} | ||
] | ||
} | ||
|
||
endpoint = "http://localhost:8080/v2/models/catboost/versions/v0.1.0/infer" | ||
response = requests.post(endpoint, json=inference_request) | ||
|
||
print(response.json()) | ||
``` |
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{ | ||
"name": "catboost", | ||
"implementation": "mlserver_catboost.CatboostModel", | ||
"parameters": { | ||
"uri": "./model.cbm", | ||
"version": "v0.1.0" | ||
} | ||
} |
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{ | ||
"debug": "true" | ||
} |
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```{include} ../../runtimes/catboost/README.md | ||
:relative-docs: ../../docs/ | ||
``` |
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