ayo/ipex-llm
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 1

[PPML]Upgrade PPML image version to 2.1.0-SNAPSHOT in readthedoc (#4253)

Browse source 
* Upgrade PPML image version to 2.1.0-SNAPSHOT in readthedoc
This commit is contained in:
Qiyuan Gong 2022-03-16 16:02:12 +08:00 committed by GitHub
parent d473177599
commit 93ea53e1e4
4 changed files with 12 additions and 12 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

8
docs/readthedocs/source/doc/PPML/Overview/ppml.md
View file

@ -87,7 +87,7 @@ cd BigDL/ppml/
Pull Docker image from Dockerhub
```bash
docker pull intelanalytics/bigdl-ppml-trusted-big-data-ml-scala-graphene:0.14.0-SNAPSHOT
docker pull intelanalytics/bigdl-ppml-trusted-big-data-ml-scala-graphene:2.1.0-SNAPSHOT
```
Alternatively, you can build Docker image from Dockerfile (this will take some time):
@ -263,7 +263,7 @@ Then stop the service:
Pull Docker image from Dockerhub
```bash
docker pull intelanalytics/bigdl-ppml-trusted-big-data-ml-python-graphene:0.14-SNAPSHOT
docker pull intelanalytics/bigdl-ppml-trusted-big-data-ml-python-graphene:2.1.0-SNAPSHOT
```
Alternatively, you can build Docker image from Dockerfile (this will take some time):
@ -697,12 +697,12 @@ Pull Docker image from Dockerhub
```bash
# For Graphene
docker pull intelanalytics/bigdl-ppml-trusted-realtime-ml-scala-graphene:0.14.0-SNAPSHOT
docker pull intelanalytics/bigdl-ppml-trusted-realtime-ml-scala-graphene:2.1.0-SNAPSHOT
```
```bash
# For Occlum
docker pull intelanalytics/bigdl-ppml-trusted-realtime-ml-scala-occlum:0.14.0-SNAPSHOT
docker pull intelanalytics/bigdl-ppml-trusted-realtime-ml-scala-occlum:2.1.0-SNAPSHOT
```
Also, you can build Docker image from Dockerfile (this will take some time).

4
docs/readthedocs/source/doc/PPML/Overview/trusted_big_bata_analytics_and_ml.md
View file

@ -7,11 +7,11 @@ BigDL helps to build PPML applications (including big data analytics, machine le
1. Big Data analytics and ML/DL (supporting [Apache Spark](https://spark.apache.org/) and [BigDL](https://github.com/intel-analytics/BigDL))
2. Realtime compute and ML/DL (supporting [Apache Flink](https://flink.apache.org/) and BigDL [Cluster Serving](https://www.usenix.org/conference/opml20/presentation/song))
## [1. Trusted Big Data ML](https://github.com/intel-analytics/BigDL/tree/branch-2.0/ppml/trusted-big-data-ml)
## [1. Trusted Big Data ML](https://github.com/intel-analytics/BigDL/tree/main/ppml/trusted-big-data-ml)
With the trusted Big Data analytics and ML/DL support, users can run standard Spark data analysis (such as Spark SQL, Dataframe, MLlib, etc.) and distributed deep learning (using BigDL) in a secure and trusted fashion.
## [2. Trusted Real Time ML](https://github.com/intel-analytics/BigDL/tree/branch-2.0/ppml/trusted-realtime-ml/scala)
## [2. Trusted Real Time ML](https://github.com/intel-analytics/BigDL/tree/main/ppml/trusted-realtime-ml/scala)
With the trusted realtime compute and ML/DL support, users can run standard Flink stream processing and distributed DL model inference (using Cluster Serving) in a secure and trusted fashion.

4
docs/readthedocs/source/doc/PPML/QuickStart/tpc-h_with_sparksql_on_k8s.md
View file

@ -2,7 +2,7 @@
### Prerequisites ###
- Hardware that supports SGX
- A fully configured Kubernetes cluster
- A fully configured Kubernetes cluster
- Intel SGX Device Plugin to use SGX in K8S cluster (install following instructions [here](https://bigdl.readthedocs.io/en/latest/doc/PPML/QuickStart/deploy_intel_sgx_device_plugin_for_kubernetes.html "here"))
### Prepare TPC-H kit and data ###
@ -136,7 +136,7 @@ export OUTPUT_DIR=hdfs://$HDFS_HOST:$HDFS_PORT/tpc-h/output \
--executor-cores 8 \
--total-executor-cores 192 \
--executor-memory 16G \
--properties-file /ppml/trusted-big-data-ml/work/bigdl-0.14.0-SNAPSHOT/conf/spark-bigdl.conf \
--properties-file /ppml/trusted-big-data-ml/work/bigdl-2.1.0-SNAPSHOT/conf/spark-bigdl.conf \
--conf spark.kubernetes.authenticate.serviceAccountName=spark \
--conf spark.kubernetes.container.image=$RUNTIME_K8S_SPARK_IMAGE \
--conf spark.kubernetes.executor.podTemplateFile=/ppml/trusted-big-data-ml/spark-executor-template.yaml \

8
docs/readthedocs/source/doc/PPML/QuickStart/trusted-serving-on-k8s-guide.md
View file

@ -3,21 +3,21 @@
## Prerequisites ##
Prior to deploying PPML Cluster Serving, please make sure the following is setup
- Hardware that supports SGX
- A fully configured Kubernetes cluster
- Intel SGX Device Plugin to use SGX in K8S cluster (install following instructions [here](https://github.com/intel-analytics/BigDL/tree/branch-2.0/ppml/trusted-realtime-ml/scala/docker-graphene/kubernetes#deploy-the-intel-sgx-device-plugin-for-kubernetes "here"))
- A fully configured Kubernetes cluster
- Intel SGX Device Plugin to use SGX in K8S cluster (install following instructions [here](https://github.com/intel-analytics/BigDL/tree/main/ppml/trusted-realtime-ml/scala/docker-graphene/kubernetes#deploy-the-intel-sgx-device-plugin-for-kubernetes "here"))
- Java
## Deploy Trusted Realtime ML for Kubernetes ##
1. Pull docker image from dockerhub
```
$ docker pull intelanalytics/bigdl-ppml-trusted-realtime-ml-scala-graphene:0.14.0-SNAPSHOT
$ docker pull intelanalytics/bigdl-ppml-trusted-realtime-ml-scala-graphene:2.1.0-SNAPSHOT
```
2. Pull the source code of BigDL and enter PPML graphene k8s directory
```
$ git clone https://github.com/intel-analytics/BigDL.git
$ cd BigDL/ppml/trusted-realtime-ml/scala/docker-graphene/kubernetes
```
3. Generate secure keys and passwords, and deploy as secrets (Refer [here](https://github.com/intel-analytics/BigDL/tree/branch-2.0/ppml/trusted-realtime-ml/scala/docker-graphene/kubernetes#secure-keys-and-password) for details)
3. Generate secure keys and passwords, and deploy as secrets (Refer [here](https://github.com/intel-analytics/BigDL/tree/main/ppml/trusted-realtime-ml/scala/docker-graphene/kubernetes#secure-keys-and-password) for details)
1. Generate keys and passwords
Note: Make sure to add `${JAVA_HOME}/bin` to `$PATH` to avoid `keytool: command not found` error.