-
Notifications
You must be signed in to change notification settings - Fork 186
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Add catboost classifier support (#1403)
Co-authored-by: Theofilos Papapanagiotou <[email protected]>
- Loading branch information
1 parent
9b9580d
commit 90c3520
Showing
20 changed files
with
736 additions
and
11 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,190 @@ | ||
{ | ||
"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 | ||
} |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,104 @@ | ||
# 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()) | ||
``` |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,8 @@ | ||
{ | ||
"name": "catboost", | ||
"implementation": "mlserver_catboost.CatboostModel", | ||
"parameters": { | ||
"uri": "./model.cbm", | ||
"version": "v0.1.0" | ||
} | ||
} |
Binary file not shown.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,3 @@ | ||
{ | ||
"debug": "true" | ||
} |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,3 @@ | ||
```{include} ../../runtimes/catboost/README.md | ||
:relative-docs: ../../docs/ | ||
``` |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.