Chronos: how to guide page cleaning (#5657)
* update * add nblink * windows guide fix * add some updated * update chronos * add update image * add new image * add updates * add update * add new image * fix typo * fix notebook location * add new index * update * fix some doc * update tree * fix typo
This commit is contained in:
Junwei Deng
GitHub
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- caption: Chronos
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entries:
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- file: doc/Chronos/Overview/chronos
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- file: doc/Chronos/Overview/windows_guide
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- file: doc/Chronos/Overview/quick-tour
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- file: doc/Chronos/Overview/deep_dive
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- file: doc/Chronos/Howto/index
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- file: doc/Chronos/QuickStart/index
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- file: doc/Chronos/Overview/deep_dive
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- file: doc/Chronos/Overview/chronos_known_issue
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- caption: PPML
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entries:
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- file: doc/PPML/Overview/ppml
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@ -0,0 +1,71 @@
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# Use Chronos in Container (docker)
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This dockerfile helps user to build a docker image where Chronos-nightly build version is deployed.
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## Build an image
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First clone the repo `BigDL` to the local.
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```bash
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git clone https://github.com/intel-analytics/BigDL.git
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```
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Then `cd` to the root directory of `BigDL`, and copy the Dockerfile to it.
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```bash
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cd BigDL
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cp docker/chronos-nightly/Dockerfile ./Dockerfile
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```
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Then build your docker image with Dockerfile:
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```bash
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sudo docker build -t chronos-nightly:b1 . # You may choose any NAME:TAG you want.
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```
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(Optional) Or build with a proxy:
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```bash
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# typically, you need a proxy for building since there will be some downloading.
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sudo docker build \
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--build-arg http_proxy=http://<your_proxy_ip>:<your_proxy_port> \ #optional
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--build-arg https_proxy=http://<your_proxy_ip>:<your_proxy_port> \ #optional
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-t chronos-nightly:b1 . # You may choose any NAME:TAG you want.
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```
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According to your network status, this building will cost **15-30 mins**.
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**Tips:** When errors happen like `E: Package 'apt-utils' has no installation candidate`, it's usually related to the bad network status. Please build with a proxy.
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## Run the image
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```bash
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sudo docker run -it --rm --net=host chronos-nightly:b1 bash
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```
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## Use Chronos
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A conda environment is created for you automatically. `bigdl-chronos` and all of its depenencies are installed inside this environment.
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```bash
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(chronos) root@cpx-3:/opt/work#
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```
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## Run unitest examples on Jupyter Notebook for a quick use
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You can run these on Jupyter Notebook on single node server if you pursue a quick use on Chronos.
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```bash
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(chronos) root@cpx-3:/opt/work# cd /opt/work/colab-notebook #Unitest examples are here.
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```
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```bash
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(chronos) root@cpx-3:/opt/work# jupyter notebook --notebook-dir=./ --ip=* --allow-root #Start the Jupyter Notebook services.
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```
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After the Jupyter Notebook service is successfully started, you can connect to the Jupyter Notebook service from a browser.
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1. Get the IP address of the container
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2. Launch a browser, and connect to the Jupyter Notebook service with the URL:
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</br>`https://container-ip-address:port-number/?token=your-token`
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</br>As a result, you will see the Jupyter Notebook opened.
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3. Open one of these `.ipynb` files, run through the example and learn how to use Chronos to predict time series.
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## Shut down docker container
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You should shut down the BigDL Docker container after using it.
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1. First, use `ctrl+p+q` to quit the container when you are still in it.
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2. Then, you can list all the active Docker containers by command line:
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```bash
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sudo docker ps
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```
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You will see your docker containers:
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```bash
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CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
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40de2cdad025 chronos-nightly:b1 "/opt/work/" 3 hours ago Up 3 hours upbeat_al
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```
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3. Shut down the corresponding docker container by its ID:
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```bash
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sudo docker rm -f 40de2cdad025
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```
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{
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"path": "../../../../../../python/chronos/colab-notebook/howto/how-to-create-forecaster.ipynb"
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}
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{
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"path": "../../../../../../python/chronos/colab-notebook/howto/how_to_speedup_inference_of_forecaster_through_ONNXRuntime.ipynb"
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}
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{
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"path": "../../../../../../python/chronos/colab-notebook/howto/how_to_speedup_inference_of_forecaster_through_OpenVINO.ipynb"
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}
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{
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"path": "../../../../../../python/chronos/colab-notebook/howto/how_to_train_forecaster_on_one_node.ipynb"
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}
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{
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"path": "../../../../../../python/chronos/colab-notebook/howto/how_to_tune_forecaster_model.ipynb"
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}
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@ -2,34 +2,31 @@ Chronos How-to Guides
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=========================
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How-to guides are bite-sized, executable examples where users could check when meeting with some specific topic during the usage.
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Forecasting
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Install
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-------------------------
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* `Create a forecaster <how-to-create-forecaster.html>`__
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In this guidance, we demonstrate **how to create a Forecaster**. Including two ways of creating a forecaster and an explanation of some important parameters.
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* `Train forecaster on single node <how_to_train_forecaster_on_one_node.html>`__
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In this guidance, **we demonstrate how to train forecasters on one node**. In the training process, forecaster will learn the pattern (like the period, scale...) in history data. Although Chronos supports training on a cluster, it's highly recommeneded to try one node first before allocating a cluster to make life easier.
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* `Tune forecaster on single node <how_to_tune_forecaster_model.html>`__
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In this guidance, we demonstrate **how to tune forecaster on single node**. In tuning process, forecaster will find the best hyperparameter combination among user-defined search space, which is a common process if users pursue a forecaster with higher accuracy.
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* `Speed up inference of forecaster through ONNXRuntime <how_to_speedup_inference_of_forecaster_through_ONNXRuntime.html>`__
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In this guidance, **we demonstrate how to speed up inference of forecaster through ONNXRuntime**. In inferencing process, Chronos supports ONNXRuntime to accelerate inferencing which is helpful to users.
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* `Speed up inference of forecaster through OpenVINO <how_to_speedup_inference_of_forecaster_through_OpenVINO.html>`__
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In this guidance, **we demonstrate how to speed up inference of forecaster through OpenVINO**. In inferencing process, Chronos supports OpenVINO to accelerate inferencing which is helpful to users.
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* `Install Chronos on Windows <windows_guide.html>`__
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* `Use Chronos in container(docker) <docker_guide_single_node.html>`__
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.. toctree::
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:maxdepth: 1
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:hidden:
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how-to-create-forecaster
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windows_guide
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docker_guide_single_node
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Forecasting
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-------------------------
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* `Create a forecaster <how_to_create_forecaster.html>`__
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* `Train forecaster on single node <how_to_train_forecaster_on_one_node.html>`__
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* `Tune forecaster on single node <how_to_tune_forecaster_model.html>`__
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* `Speed up inference of forecaster through ONNXRuntime <how_to_speedup_inference_of_forecaster_through_ONNXRuntime.html>`__
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* `Speed up inference of forecaster through OpenVINO <how_to_speedup_inference_of_forecaster_through_OpenVINO.html>`__
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.. toctree::
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:maxdepth: 1
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:hidden:
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how_to_create_forecaster
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how_to_train_forecaster_on_one_node
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how_to_tune_forecaster_model
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how_to_speedup_inference_of_forecaster_through_ONNXRuntime
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@ -1,4 +1,4 @@
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# Chronos Windows User Guide
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# Install Chronos on Windows
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## Step 1: Install WSL2
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@ -26,21 +26,5 @@ chmod +x Miniconda3-4.5.4-Linux-x86_64.sh
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./Miniconda3-4.5.4-Linux-x86_64.sh
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```
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## Step 3: Create a BigDL-Chronos env
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Use conda to create a new environment. For example, use `bigdl-chronos` as the new environemnt name:
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```bash
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conda create -n bigdl-chronos python=3.7 setuptools=58.0.4
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conda activate bigdl-chronos
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```
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## Step 4: Install BigDL Chronos from Pypi
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You can install BigDL chronos from Pypi with `pip`. Please run:
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```
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pip install bigdl-chronos
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```
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For more installation options, please refer to [Chronos User Guide.](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/chronos.html#install)
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## Step 3: Install Chronos
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For more installation options, please refer to [Chronos User Guide](https://bigdl.readthedocs.io/en/latest/doc/Chronos/Overview/chronos.html#install).
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<svg width="551" height="416" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" overflow="hidden"><defs><clipPath id="clip0"><rect x="681" y="729" width="551" height="416"/></clipPath></defs><g clip-path="url(#clip0)" transform="translate(-681 -729)"><path d="M692 996 813.747 1124.16" stroke="#4472C4" stroke-width="10.3125" stroke-miterlimit="8" fill="none" fill-rule="evenodd"/><path d="M0 0 76.8928 246.699" stroke="#4472C4" stroke-width="10.3125" stroke-miterlimit="8" fill="none" fill-rule="evenodd" transform="matrix(1 0 0 -1 814 1124.7)"/><path d="M891 873 1012.75 1001.16" stroke="#4472C4" stroke-width="10.3125" stroke-miterlimit="8" fill="none" fill-rule="evenodd"/><path d="M0 0 76.8928 246.699" stroke="#4472C4" stroke-width="10.3125" stroke-miterlimit="8" stroke-dasharray="41.25 30.9375" fill="none" fill-rule="evenodd" transform="matrix(1 0 0 -1 1012 1001.7)"/><path d="M1089 750 1210.75 878.155" stroke="#4472C4" stroke-width="10.3125" stroke-miterlimit="8" stroke-dasharray="41.25 30.9375" fill="none" fill-rule="evenodd"/><path d="M681 1007.5C681 996.178 690.178 987 701.5 987 712.822 987 722 996.178 722 1007.5 722 1018.82 712.822 1028 701.5 1028 690.178 1028 681 1018.82 681 1007.5Z" fill="#FFC000" fill-rule="evenodd"/><path d="M793 1124.5C793 1113.18 802.402 1104 814 1104 825.598 1104 835 1113.18 835 1124.5 835 1135.82 825.598 1145 814 1145 802.402 1145 793 1135.82 793 1124.5Z" fill="#FFC000" fill-rule="evenodd"/><path d="M870 875.5C870 864.178 879.402 855 891 855 902.598 855 912 864.178 912 875.5 912 886.822 902.598 896 891 896 879.402 896 870 886.822 870 875.5Z" fill="#FFC000" fill-rule="evenodd"/><path d="M992 996.5C992 985.178 1001.18 976 1012.5 976 1023.82 976 1033 985.178 1033 996.5 1033 1007.82 1023.82 1017 1012.5 1017 1001.18 1017 992 1007.82 992 996.5Z" fill="#FFC000" fill-rule="evenodd"/><path d="M1068 750C1068 738.402 1077.4 729 1089 729 1100.6 729 1110 738.402 1110 750 1110 761.598 1100.6 771 1089 771 1077.4 771 1068 761.598 1068 750Z" fill="#FFC000" fill-rule="evenodd"/><path d="M1191 873C1191 861.402 1200.18 852 1211.5 852 1222.82 852 1232 861.402 1232 873 1232 884.598 1222.82 894 1211.5 894 1200.18 894 1191 884.598 1191 873Z" fill="#FFC000" fill-rule="evenodd"/></g></svg>
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### **1. Overview**
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_BigDL-Chronos_ (_Chronos_ for short) is an application framework for building a fast, accurate and scalable time series analysis application.
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You can use _Chronos_ to do:
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You can use _Chronos_ to:
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- **Data pre/post-processing and feature generation** (using [TSDataset](./data_processing_feature_engineering.html))
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- **Time Series Forecasting** (using [Standalone Forecasters](./forecasting.html#use-standalone-forecaster-pipeline), [Auto Models](./forecasting.html#use-auto-forecasting-model) (with HPO) or [AutoTS](./forecasting.html#use-autots-pipeline) (full AutoML enabled pipelines))
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- **Anomaly Detection** (using [Anomaly Detectors](./anomaly_detection.html#anomaly-detection))
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- **Synthetic Data Generation** (using [Simulators](./simulation.html#generate-synthetic-data))
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- **Speed up or tune your customized time-series model** (using TSTrainer and [AutoTS](./forecasting.html#use-autots-pipeline))
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```eval_rst
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.. grid:: 3
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:gutter: 1
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.. grid-item-card::
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:class-footer: sd-bg-light
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**Forecasting**
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^^^
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.. image:: ../Image/forecasting.svg
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:width: 200
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:alt: Alternative text
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+++
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Predict future using history data.
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.. grid-item-card::
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:class-footer: sd-bg-light
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**Anomaly Detection**
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^^^
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.. image:: ../Image/anomaly_detection.svg
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:width: 200
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:alt: Alternative text
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+++
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Discover unexpected items in data.
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.. grid-item-card::
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:class-footer: sd-bg-light
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**Simulation**
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^^^
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.. image:: ../Image/simulation.svg
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:width: 200
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:alt: Alternative text
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+++
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Generate similar data as history data.
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```
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---
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### **2. Install**
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@ -100,15 +141,17 @@ When you install `bigdl-chronos` from PyPI. We recommend to install with a conda
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```bash
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conda create -n my_env python=3.7 setuptools=58.0.4
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conda activate my_env
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# click the installation panel above to find which installation option to use
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pip install --pre --upgrade bigdl-chronos[pytorch] # or other options you may want to use
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source bigdl-nano-init
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source bigdl-nano-init # accelerate the conda env
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```
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#### **2.2 Tensorflow backend**
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Tensorflow is one of the supported backend of Chronos in nightly release version, while it can not work alone without pytorch in Chronos for now. We will fix it soon. If you want to use tensorflow backend, please
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```bash
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pip install --pre --upgrade bigdl-nano[tensorflow]
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```
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after you install the pytorch backend chronos.
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**after you install the pytorch backend chronos.**
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#### **2.3 OS and Python version requirement**
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@ -155,7 +198,7 @@ after you install the pytorch backend chronos.
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+++
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`Data process <./data_processing_feature_engineering.html>`_ /
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`Data <./data_processing_feature_engineering.html>`_ /
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`Forecast <./forecasting.html>`_ /
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`Detect <./anomaly_detection.html>`_ /
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`Simulate <./simulation.html>`_
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@ -166,14 +209,15 @@ after you install the pytorch backend chronos.
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.. grid-item-card::
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:class-footer: sd-bg-light
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**How-to-Guide**
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**How-to-Guide** / **Example**
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^^^
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If you are meeting with some specific problems during the usage, how-to guides are good place to be checked.
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Examples provides short, high quality use case that users can emulated in their own works.
|
||||
|
||||
+++
|
||||
|
||||
Work In Progress
|
||||
`How-to-Guide <../Howto/index.html>`_ / `Example <../QuickStart/index.html>`_
|
||||
|
||||
.. grid-item-card::
|
||||
:class-footer: sd-bg-light
|
||||
|
|
@ -188,51 +232,3 @@ after you install the pytorch backend chronos.
|
|||
`API Document <../../PythonAPI/Chronos/index.html>`_
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### **4. Examples and Demos**
|
||||
- Quickstarts
|
||||
- [Use AutoTSEstimator for Time-Series Forecasting](../QuickStart/chronos-autotsest-quickstart.html)
|
||||
- [Use TSDataset and Forecaster for Time-Series Forecasting](../QuickStart/chronos-tsdataset-forecaster-quickstart.html)
|
||||
- [Use Anomaly Detector for Unsupervised Anomaly Detection](../QuickStart/chronos-anomaly-detector.html)
|
||||
- Examples
|
||||
- [Use AutoLSTM on nyc taxi dataset][autolstm_nyc_taxi]
|
||||
- [Use AutoProphet on nyc taxi dataset][autoprophet_nyc_taxi]
|
||||
- [High dimension time series forecasting with Chronos TCMFForecaster][run_electricity]
|
||||
- [Use distributed training with Chronos Seq2SeqForecaster][distributed_training_network_traffic]
|
||||
- [Use ONNXRuntime to accelerate the inference of AutoTSEstimator][onnx_autotsestimator_nyc_taxi]
|
||||
- [Use ONNXRuntime to accelerate the inference of Seq2SeqForecaster][onnx_forecaster_network_traffic]
|
||||
- [Generate synthetic data with DPGANSimulator in a data-driven fashion][simulator]
|
||||
- [Quantizate your forecaster to speed up inference][quantization]
|
||||
- Use cases
|
||||
- [Unsupervised Anomaly Detection][AIOps_anomaly_detect_unsupervised]
|
||||
- [Unsupervised Anomaly Detection based on Forecasts][AIOps_anomaly_detect_unsupervised_forecast_based]
|
||||
- [Stock Price Prediction with LSTM][stock_prediction]
|
||||
- [Stock Price Prediction with ProphetForecaster and AutoProphet][stock_prediction_prophet]
|
||||
- [Network Traffic Forecasting with AutoTSEstimator][network_traffic_autots_forecasting]
|
||||
- [Network Traffic Forecasting (using multivariate time series data)][network_traffic_model_forecasting]
|
||||
- [Network Traffic Forecasting (using multistep time series data)][network_traffic_multivariate_multistep_tcnforecaster]
|
||||
- [Network Traffic Forecasting with Customized Model][network_traffic_autots_customized_model]
|
||||
- [Help pytorch-forecasting improve the training speed of DeepAR model][pytorch_forecasting_deepar]
|
||||
- [Help pytorch-forecasting improve the training speed of TFT model][pytorch_forecasting_tft]
|
||||
|
||||
<!--Reference links in article-->
|
||||
[autolstm_nyc_taxi]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/auto_model/autolstm_nyc_taxi.py>
|
||||
[autoprophet_nyc_taxi]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/auto_model/autoprophet_nyc_taxi.py>
|
||||
[run_electricity]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/tcmf/run_electricity.py>
|
||||
[distributed_training_network_traffic]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/distributed/distributed_training_network_traffic.py>
|
||||
[onnx_autotsestimator_nyc_taxi]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/onnx/onnx_autotsestimator_nyc_taxi.py>
|
||||
[onnx_forecaster_network_traffic]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/onnx/onnx_forecaster_network_traffic.py>
|
||||
[simulator]: <https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/simulator>
|
||||
[AIOps_anomaly_detect_unsupervised]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb>
|
||||
[AIOps_anomaly_detect_unsupervised_forecast_based]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb>
|
||||
[stock_prediction]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction.ipynb>
|
||||
[stock_prediction_prophet]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb>
|
||||
[network_traffic_autots_forecasting]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_forecasting.ipynb>
|
||||
[network_traffic_model_forecasting]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb>
|
||||
[network_traffic_multivariate_multistep_tcnforecaster]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb>
|
||||
[network_traffic_autots_customized_model]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_customized_model.ipynb>
|
||||
[quantization]: <https://github.com/intel-analytics/BigDL/blob/main/python/chronos/example/quantization/quantization_tcnforecaster_nyc_taxi.py>
|
||||
[pytorch_forecasting_deepar]: <https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/DeepAR>
|
||||
[pytorch_forecasting_tft]: <https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/TFT>
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# Chronos Tutorial
|
||||
# Chronos Examples
|
||||
|
||||
</br>
|
||||
|
||||
|
|
@ -9,7 +9,7 @@
|
|||
|
||||
<div id="tutorial">
|
||||
<h3 style="text-align:left">Filter:</h3>
|
||||
<p>Please <span style="font-weight:bold;">check</span> the checkboxes or <span style="font-weight:bold;">click</span> tag buttons to show the related tutorials. Reclick or uncheck will hide corresponding tutorials. If nothing is checked or clicked, all the tutorials will be displayed. </p>
|
||||
<p>Please <span style="font-weight:bold;">check</span> the checkboxes or <span style="font-weight:bold;">click</span> tag buttons to show the related examples. Reclick or uncheck will hide corresponding examples. If nothing is checked or clicked, all the examples will be displayed. </p>
|
||||
<div class="border">
|
||||
<div class="choiceline">
|
||||
<div class="choicebox"><input type="checkbox" class="checkboxes" name="choice" value="forecast" id="forecast">forecast </div>
|
||||
|
|
|
|||
Loading…
Reference in a new issue