diff --git a/docs/readthedocs/source/doc/PPML/Overview/ppml.md b/docs/readthedocs/source/doc/PPML/Overview/ppml.md index 6a265e3a..0db48d04 100644 --- a/docs/readthedocs/source/doc/PPML/Overview/ppml.md +++ b/docs/readthedocs/source/doc/PPML/Overview/ppml.md @@ -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). diff --git a/docs/readthedocs/source/doc/PPML/Overview/trusted_big_bata_analytics_and_ml.md b/docs/readthedocs/source/doc/PPML/Overview/trusted_big_bata_analytics_and_ml.md index 3ff8be50..37e3883c 100644 --- a/docs/readthedocs/source/doc/PPML/Overview/trusted_big_bata_analytics_and_ml.md +++ b/docs/readthedocs/source/doc/PPML/Overview/trusted_big_bata_analytics_and_ml.md @@ -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. diff --git a/docs/readthedocs/source/doc/PPML/QuickStart/tpc-h_with_sparksql_on_k8s.md b/docs/readthedocs/source/doc/PPML/QuickStart/tpc-h_with_sparksql_on_k8s.md index 51bc3e0d..6fed6303 100644 --- a/docs/readthedocs/source/doc/PPML/QuickStart/tpc-h_with_sparksql_on_k8s.md +++ b/docs/readthedocs/source/doc/PPML/QuickStart/tpc-h_with_sparksql_on_k8s.md @@ -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 \ diff --git a/docs/readthedocs/source/doc/PPML/QuickStart/trusted-serving-on-k8s-guide.md b/docs/readthedocs/source/doc/PPML/QuickStart/trusted-serving-on-k8s-guide.md index 3fe3f935..aec83359 100644 --- a/docs/readthedocs/source/doc/PPML/QuickStart/trusted-serving-on-k8s-guide.md +++ b/docs/readthedocs/source/doc/PPML/QuickStart/trusted-serving-on-k8s-guide.md @@ -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.