87 lines
No EOL
3.9 KiB
Markdown
87 lines
No EOL
3.9 KiB
Markdown
# Artificial Intelligence for IT operations (AIOps)
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Chronos provides a template(i.e., `ConfigGenerator`) as an easy-to-use builder for an AIOps decision system with the usage of `Trigger`.
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## How does it work
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AIOps application typically relies on a decision system with one or multiple AI models. Generally, this AI system needs to be trained with some training data with a self-defined checkpoint. When using the AI system, we first initialize it throught previously trained checkpoint and inform the AI system with current status to get the suggested configuration.
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Sometimes the AI system need to be informed some **timely** information (e.g., some events in log or some monitoring data every second). Chronos also defines some triggers for this kind of usage.
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## Define ConfigGenerator
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### Start from a trivial ConfigGenerator
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Chronos provides `bigdl.chronos.aiops.ConfigGenerator` as a template for users to define their own AIOps AI system. Following is a "hello-world" case.
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```python
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class MyConfigGenerator(ConfigGenerator):
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def __init__(self):
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super().__init__()
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self.best_config = [3.0, 1.6]
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def genConfig(self):
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return self.best_config
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```
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For this self-defined `MyConfigGenerator`, we keep generate a fixed best config with out considering current status. This could be a startpoint or smoke test configgenerator for your system. The whole system even do not need to be trained.
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### Add AI Model to ConfigGenerator
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Any model could be used in `ConfigGenerator`, to name a few, sklearn, pytorch or tensorflow models are all valid. Following is a normal flow you may want to add your model.
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```python
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class MyConfigGenerator(ConfigGenerator):
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def __init__(self, path):
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super().__init__()
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self.model = load_model_from_checkpoint(path)
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def genConfig(self, current_status):
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return self.model(current_status)
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@staticmethod
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def train(train_data, path):
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train_model_and_save_checkpoint(train_data, path)
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```
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- In `MyConfigGenerator.train`, users will define the way to train their model and save to a specific path.
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- In `MyConfigGenerator.__init__`, users will define the way to load the trained checkpoint.
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- In `MyConfigGenerator.genConfig`, users will define the way to use the loaded model to do the prediction and get the suggested config.
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Please refer to [ConfigGenerator API doc](../../PythonAPI/Chronos/aiops.html) for detailed information.
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#### Use Chronos Forecaster/Anomaly detector
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Chronos also provides some out-of-box forecasters and anomaly detectors for time series data for users to build their AIOps use-case easier.
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Please refer to [Forecaster User Guide](./forecasting.html) and [Anomaly Detector User Guide](./anomaly_detection.html) for detailed information.
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### Use trigger in ConfigGenerator
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Sometimes the AI system need to be informed some **timely** information (e.g., some events in log or some monitoring data every second). Chronos also defines some triggers for this kind of usage. Following is a trivial case to help users understand what a `Trigger` can do.
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```python
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class MyConfigGenerator(ConfigGenerator):
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def __init__(self):
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self.sweetpoint = 1
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super().__init__()
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def genConfig(self):
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return self.sweetpoint
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@triggerbyclock(2)
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def update_sweetpoint(self):
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self.sweetpoint += 1
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```
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In this case, once the `MyConfigGenerator` is initialized, `update_sweetpoint` will be called every 2 seconds, users will thus get an evolving ConfiguGenerator.
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```python
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mycg = MyConfigGenerator(1)
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time.sleep(2)
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assert mycg.genConfig() == 2
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time.sleep(2)
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assert mycg.genConfig() == 3
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```
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This trivial case may seem useless, but with a dedicated `update_sweetpoint`, such as get the CPU utils every second, users could bring useful information to their ConfigGenerator and make better decision with easy programming.
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Please refer to [Trigger API doc](../../PythonAPI/Chronos/aiops.html) for detailed information. |