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Chronos: add updated information about models and Nano to document (#4048)

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* add updated information about models and Nano

* fix false formate

* add quantization related information

* fix missing quantization api doc

* fix typos
This commit is contained in:
Junwei Deng 2022-02-17 11:10:12 +08:00 committed by GitHub
parent c9be2b979e
commit 1375438de3
3 changed files with 84 additions and 17 deletions
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23
docs/readthedocs/source/doc/Chronos/Overview/chronos.md
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@ -10,6 +10,8 @@ You can use _Chronos_ to do:
- **Anomaly Detection** (using [Anomaly Detectors](./anomaly_detection.html#anomaly-detection)) - **Anomaly Detection** (using [Anomaly Detectors](./anomaly_detection.html#anomaly-detection))
- **Synthetic Data Generation** (using [Simulators](./simulation.html#generate-synthetic-data)) - **Synthetic Data Generation** (using [Simulators](./simulation.html#generate-synthetic-data))
Furthermore, Chronos is adapted to integrate many optimized library and best known methods(BKMs) for accuracy and performance improvement.
--- ---
### **2. Install** ### **2. Install**
@ -26,6 +28,12 @@ pip install bigdl-chronos[all]
# nightly built version # nightly built version
pip install --pre --upgrade bigdl-chronos[all] pip install --pre --upgrade bigdl-chronos[all]
``` ```
```eval_rst
.. note::
**Supported OS**:
Chronos is thoroughly tested on Ubuntu (16.04/18.04/20.04). If you are a Windows user, the most convenient way to use Chronos on a windows laptop might be using WSL2, you may refer to https://docs.microsoft.com/en-us/windows/wsl/setup/environment or just install a ubuntu virtual machine.
```
--- ---
### **3. Run** ### **3. Run**
Various python programming environments are supported to run a _Chronos_ application. Various python programming environments are supported to run a _Chronos_ application.
@ -46,6 +54,18 @@ You can directly write _Chronos_ application in a python file (e.g. script.py) a
python script.py python script.py
``` ```
```eval_rst
.. note::
**Optimization on Intel® Hardware**:
Chronos integrated many optimized library and best known methods(BKMs), users can have best performance to add ``bigdl-nano-init`` before their scripts.
``bigdl-nano-init python script.py``
Currently, this function is under active development and we encourage our users to add ``bigdl-nano-init`` for forecaster's training.
```
--- ---
### **4. Get Started** ### **4. Get Started**
@ -61,7 +81,7 @@ Otherwise, there is no need to initialize an orca context.
View [Orca Context](../../Orca/Overview/orca-context.md) for more details. Note that argument `init_ray_on_spark` must be `True` for _Chronos_. View [Orca Context](../../Orca/Overview/orca-context.md) for more details. Note that argument `init_ray_on_spark` must be `True` for _Chronos_.
```python ```python
from bigdl.orca.common import init_orca_context, stop_orca_context from bigdl.orca import init_orca_context, stop_orca_context
# run in local mode # run in local mode
init_orca_context(cluster_mode="local", cores=4, init_ray_on_spark=True) init_orca_context(cluster_mode="local", cores=4, init_ray_on_spark=True)
@ -101,7 +121,6 @@ for tsdata in [tsdata_train, tsdata_val, tsdata_test]:
# AutoTSEstimator initalization # AutoTSEstimator initalization
autotsest = AutoTSEstimator(model="tcn", autotsest = AutoTSEstimator(model="tcn",
past_seq_len=hp.randint(50, 200),
future_seq_len=10) future_seq_len=10)
# AutoTSEstimator fitting # AutoTSEstimator fitting

52
docs/readthedocs/source/doc/Chronos/Overview/forecasting.md
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@ -7,21 +7,38 @@ There're three ways to do forecasting:
- Use [**auto forecasting models**](#use-auto-forecasting-model) with auto hyperparameter optimization. - Use [**auto forecasting models**](#use-auto-forecasting-model) with auto hyperparameter optimization.
- Use [**standalone forecasters**](#use-standalone-forecaster-pipeline). - Use [**standalone forecasters**](#use-standalone-forecaster-pipeline).
#### **0. Supported Time Series Forecasting Model**
- `Model`: Model name.
- `Style`: Forecasting model style. Detailed information will be stated in [this section](#time-series-forecasting-concepts).
- `Multi-Variate`: Predict more than one variable at the same time?
- `Multi-Step`: Predict more than one data point in the future?
- `Exogenous Variables`: Take other variables(you don't need to predict) into consideration?
- `Distributed`: Scale the model to a cluster and take data from distributed file system?
- `ONNX`: Export and use `OnnxRuntime` to do the inference.
- `Quantization`: Export and use quantized int8 model to do the inference.
- `Auto Models`: AutoModel API support.
- `AutoTS`: AutoTS API support.
- `Backend`: The DL framework we use to implement this model.
<span id="supported_forecasting_model"></span> <span id="supported_forecasting_model"></span>
| Model | Style | Multi-Variate | Multi-Step | Distributed\* | Auto Models | AutoTS | Backend | | Model | Style | Multi-Variate | Multi-Step | Exogenous Variables | Distributed | ONNX | Quantization | Auto Models | AutoTS | Backend |
| ----------------- | ----- | ------------- | ---------- | ----------- | ----------- | ----------- | ----------- | | ----------------- | ----- | ------------- | ---------- | ------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
| LSTM | RR | ✅ | ❌ | ✅ | ✅ | ✅ | pytorch | | LSTM | RR | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | pytorch |
| Seq2Seq | RR | ✅ | ✅ | ✅ | ✅ | ✅ | pytorch | | Seq2Seq | RR | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | pytorch |
| TCN | RR | ✅ | ✅ | ✅ | ✅ | ✅ | pytorch | | TCN | RR | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | pytorch |
| MTNet | RR | ✅ | ❌ | ✅ | ❌ | ✳️\*\*\* | tensorflow | | NBeats | RR | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | pytorch |
| TCMF | TS | ✅ | ✅ | ✳️\*\* | ❌ | ❌ | pytorch | | MTNet | RR | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✳️\*\* | tensorflow |
| Prophet | TS | ❌ | ✅ | ❌ | ✅ | ❌ | prophet | | TCMF | TS | ✅ | ✅ | ✅ | ✳️\* | ❌ | ❌ | ❌ | ❌ | pytorch |
| ARIMA | TS | ❌ | ✅ | ❌ | ✅ | ❌ | pmdarima | | Prophet | TS | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | prophet |
| ARIMA | TS | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | pmdarima |
| Customized\*\*\* | RR | Customized | Customized | Customized | ❌ |✅|❌|❌|✅|pytorch
\* TCMF only partially supports distributed training.<br>
\*\* Auto tuning of MTNet is only supported in our deprecated AutoTS API.<br>
\*\*\* Customized model is only supported in `AutoTSEstimator` with pytorch as backend.
\* Distributed training/inferencing is only supported by standalone forecasters.<br>
\*\* TCMF only partially supports distributed training.<br>
\*\*\* Auto tuning of MTNet is only supported in our deprecated AutoTS API.<br>
#### **1. Time Series Forecasting Concepts** #### **1. Time Series Forecasting Concepts**
@ -203,14 +220,19 @@ ProphetForecaster wraps the Prophet model ([site](https://github.com/facebook/pr
View Stock Prediction [notebook](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb) and [ProphetForecaster API Doc](../../PythonAPI/Chronos/forecasters.html#prophetforecaster) for more details. View Stock Prediction [notebook](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb) and [ProphetForecaster API Doc](../../PythonAPI/Chronos/forecasters.html#prophetforecaster) for more details.
<span id="NBeatsForecaster"></span>
###### **3.8 NBeatsForecaster**
Neural basis expansion analysis for interpretable time series forecasting ([N-BEATS](https://arxiv.org/abs/1905.10437)) is a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. Nbeats can solve univariate time series point forecasting problems, being interpretable, and fast to train.
#### **4. Use Auto forecasting model** #### **4. Use Auto forecasting model**
Auto forecasting models are designed to be used exactly the same as Forecasters. The only difference is that you can set hp search function to the hyperparameters and the `.fit()` method will search the best hyperparameter setting. Auto forecasting models are designed to be used exactly the same as Forecasters. The only difference is that you can set hp search function to the hyperparameters and the `.fit()` method will search the best hyperparameter setting.
```python ```python
# set hyperparameters in hp search function, loss, metric... # set hyperparameters in hp search function, loss, metric...
f = Forecaster(...) auto_model = AutoModel(...)
# input data, batch size, epoch... # input data, batch size, epoch...
f.fit(...) auto_model.fit(...)
# input test data x, batch size... # input test data x, batch size...
f.predict(...) auto_model.predict(...)
``` ```
The input data can be easily get from `TSDataset`. Users can refer to detailed [API doc](../../PythonAPI/Chronos/automodels.html). The input data can be easily get from `TSDataset`. Users can refer to detailed [API doc](../../PythonAPI/Chronos/automodels.html).

26
docs/readthedocs/source/doc/Chronos/Overview/useful_functionalities.md
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@ -90,3 +90,29 @@ f = Forecaster(..., distributed=True)
f.fit(tsdata_xshards, ...) f.fit(tsdata_xshards, ...)
f.predict(test_tsdata_xshards, ...) f.predict(test_tsdata_xshards, ...)
``` ```
#### **5. Quantization**
Quantization refers to processes that enable lower precision inference. In Chronos, post-training quantization is supported relied on [Intel® Neural Compressor](https://intel.github.io/neural-compressor/README.html).
```python
# init
f = Forecaster(...)
# train the forecaster
f.fit(train_data, ...)
# quantize the forecaster
f.quantize(train_data, ...)
# predict with int8 model with better inference throughput
f.predict(test_data, quantize=True)
# predict with fp32
f.predict(test_data, quantize=False)
# save
f.save(checkpoint_file="fp32.model"
quantize_checkpoint_file="int8.model")
# load
f.load(checkpoint_file="fp32.model"
quantize_checkpoint_file="int8.model")
```