From 1ca1424b8589ade0b0c4ac8a132319957fa26724 Mon Sep 17 00:00:00 2001 From: jalencato Date: Tue, 30 Jul 2024 11:48:01 -0700 Subject: [PATCH] [Doc] Graph Construction PR Refactor - PR2: GPartition Doc (#918) *Issue #, if available:* *Description of changes:* Preview version: https://jalencato-graphstorm-doc.readthedocs.io/en/gsprocessing-gpartition-doc/graph-construction/index.html By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: Theodore Vasiloudis Co-authored-by: Jian Zhang (James) <6593865@qq.com> Co-authored-by: xiang song(charlie.song) --- .../gs-processing/example.rst | 111 +++++++++++---- .../gspartition/ec2-clusters.rst | 119 ++++++++++++++++ .../gs-processing/gspartition/index.rst | 28 ++++ .../gs-processing/gspartition/sagemaker.rst | 134 ++++++++++++++++++ .../gs-processing/index.rst | 17 ++- .../gs-processing/prerequisites/index.rst | 2 + tutorial/distributed_construction.png | Bin 0 -> 141499 bytes 7 files changed, 383 insertions(+), 28 deletions(-) create mode 100644 docs/source/graph-construction/gs-processing/gspartition/ec2-clusters.rst create mode 100644 docs/source/graph-construction/gs-processing/gspartition/index.rst create mode 100644 docs/source/graph-construction/gs-processing/gspartition/sagemaker.rst create mode 100644 tutorial/distributed_construction.png diff --git a/docs/source/graph-construction/gs-processing/example.rst b/docs/source/graph-construction/gs-processing/example.rst index 23afa10ddd..14d2d0e984 100644 --- a/docs/source/graph-construction/gs-processing/example.rst +++ b/docs/source/graph-construction/gs-processing/example.rst @@ -1,41 +1,54 @@ .. _distributed_construction_example: -GraphStorm Processing Example -============================= +A GraphStorm Distributed Graph Construction Example +=================================================== -To demonstrate how to use the library locally we will +GraphStorm's distributed graph construction is involved with multiple steps. +To help users better understand these steps, we provide an example of distributed graph construction, +which can run locally in one instance. + +To demonstrate how to use distributed graph construction locally we will use the same example data as we use in our unit tests, which you can find in the project's repository, under ``graphstorm/graphstorm-processing/tests/resources/small_heterogeneous_graph``. -Install example dependencies ----------------------------- +Install dependencies +-------------------- -To run the local example you will need to install the GSProcessing +To run the local example you will need to install the GSProcessing and GraphStorm library to your Python environment, and you'll need to clone the -GraphStorm repository to get access to the data. +GraphStorm repository to get access to the data, and DGL tool for GSPartition. Follow the :ref:`gsp-installation-ref` guide to install the GSProcessing library. -You can clone the repository using +To run GSPartition job, you can install the dependencies as following: .. code-block:: bash + pip install graphstorm + pip install pydantic + pip install torch==2.1.0 --index-url https://download.pytorch.org/whl/cpu + pip install dgl==1.1.3 -f https://data.dgl.ai/wheels-internal/repo.html git clone https://github.com/awslabs/graphstorm.git + cd graphstorm + git clone --branch v1.1.3 https://github.com/dmlc/dgl.git You can then navigate to the ``graphstorm-processing/`` directory that contains the relevant data: .. code-block:: bash - cd ./graphstorm/graphstorm-processing/ + cd ./graphstorm-processing/ Expected file inputs and configuration -------------------------------------- +The example will include GSProcessing as the first step and GSPartition as the second step. + GSProcessing expects the input files to be in a specific format that will allow us to perform the processing and prepare the data for partitioning and training. +GSPartition then takes the output of GSProcessing to produce graph data in DistDGLGraph format for training or inference.. The data files are expected to be: @@ -202,8 +215,8 @@ For more details on the re-partitioning step see .. _gsp-examining-output: -Examining the job output ------------------------- +Examining the job output of GSProcessing +------------------------------------------ Once the processing and re-partitioning jobs are done, we can examine the outputs they created. The output will be @@ -281,28 +294,74 @@ in an ``edge_data`` directory. for node id 1 etc. -At this point you can use the DGL distributed partitioning pipeline -to partition your data, as described in the -`DGL documentation `_ -. +Run a GSPartition job locally +------------------------------ +While :ref:`GSPartition` is designed to run on a multi-machine cluster, +you can run GSPartition job locally for the example. Once you have completed the installation +and the GSProcessing example described in the previous section, you can proceed to run the GSPartition step. + +Assuming your working directory is ``graphstorm``, +you can use the following command to run the partition job locally: + +.. code:: bash + + echo 127.0.0.1 > ip_list.txt + python3 -m graphstorm.gpartition.dist_partition_graph \ + --input-path /tmp/gsprocessing-example/ \ + --metadata-filename updated_row_counts_metadata.json \ + --output-path /tmp/gspartition-example/ \ + --num-parts 2 \ + --dgl-tool-path ./dgl/tools \ + --partition-algorithm random \ + --ip-config ip_list.txt -To simplify the process of partitioning and training, without the need -to manage your own infrastructure, we recommend using GraphStorm's -`SageMaker wrappers `_ -that do all the hard work for you and allow -you to focus on model development. In particular you can follow the GraphStorm documentation to run -`distributed partitioning on SageMaker `_. +The command above will first do graph partitioning to determine the ownership for each partition and save the results. +Then it will do data dispatching to physically assign the partitions to graph data and dispatch them to each machine. +Finally it will generate the graph data ready for training/inference. +Examining the job output of GSPartition +--------------------------------------- -To run GSProcessing jobs on Amazon SageMaker we'll need to follow -:ref:`GSProcessing distributed setup` to set up our environment -and :ref:`Running GSProcessing on SageMaker` to execute the job. +Once the partition job is done, you can examine the outputs. + +.. code-block:: bash + $ cd /tmp/gspartition-example + $ ls -ltR + + dist_graph/ + metadata.json + |- part0/ + edge_feat.dgl + graph.dgl + node_feat.dgl + orig_eids.dgl + orig_nids.dgl + partition_assignment/ + director.txt + genre.txt + movie.txt + partition_meta.json + user.txt + +The ``dist_graph`` folder contains partitioned graph ready for training and inference. + +* ``part0``: As we only specify 1 partition in the previous command, we have one part folder here. +There are five files for the partition + * ``edge_feat.dgl``: The edge features for part 0 stored in binary format. + * ``graph.dgl``: The graph structure data for part 0 stored in binary format. + * ``node_feat.dgl``: The node features data for part 0 stored in binary format. + * ``orig_eids.dgl``: The mapping for edges between raw edge IDs and the partitioned graph edge IDs. + * ``orig_nids.dgl``: The mapping for nodes between raw node IDs and the partitioned graph node IDs. + +* ``metadata.json``: This file contains metadata about the distributed DGL graph. + +The ``partition_assignment`` directory contains different partition results for different node types, +which can reused for the `dgl dispatch pipeline `_ .. rubric:: Footnotes .. [#f1] Note that this is just a hint to the Spark engine, and it's not guaranteed that the number of output partitions will always match - the requested value. -.. [#f2] This doc will be future extended to include a partition example. \ No newline at end of file + the requested value. \ No newline at end of file diff --git a/docs/source/graph-construction/gs-processing/gspartition/ec2-clusters.rst b/docs/source/graph-construction/gs-processing/gspartition/ec2-clusters.rst new file mode 100644 index 0000000000..e12253cbcb --- /dev/null +++ b/docs/source/graph-construction/gs-processing/gspartition/ec2-clusters.rst @@ -0,0 +1,119 @@ +====================================== +Running partition jobs on EC2 Clusters +====================================== + +Once the :ref:`distributed processing` is completed, +users can start the partition jobs. This tutorial will provide instructions on how to setup an EC2 cluster and +start GSPartition jobs on it. + +Create a GraphStorm Cluster +---------------------------- + +Setup instances of a cluster +............................. +A cluster contains several instances, each of which runs a GraphStorm Docker container. Before creating a cluster, we recommend to +follow the :ref:`Environment Setup `. The guide shows how to build GraphStorm Docker images, and use a Docker container registry, +e.g. `AWS ECR `_ , to upload the GraphStorm image to an ECR repository, pull it on the instances in the cluster, +and finally start the image as a container. + +.. note:: + + If you are planning to use **parmetis** algorithm, please prepare your docker image using the following instructions: + + .. code-block:: bash + + git clone https://github.com/awslabs/graphstorm.git + + cd /path-to-graphstorm/docker/ + + bash /path-to-graphstorm/docker/build_docker_parmetis.sh /path-to-graphstorm/ image-name image-tag + + There are three positional arguments for ``build_docker_parmetis.sh``: + + 1. **path-to-graphstorm** (**required**), is the absolute path of the "graphstorm" folder, where you cloned the GraphStorm source code. For example, the path could be ``/code/graphstorm``. + 2. **image-name** (optional), is the assigned name of the Docker image to be built . Default is ``graphstorm``. + 3. **image-tag** (optional), is the assigned tag prefix of the Docker image. Default is ``local``. + +Setup a shared file system for the cluster +........................................... +A cluster requires a shared file system, such as NFS or `EFS `_, mounted to each instance in the cluster, in which all GraphStorm containers can share data files, save model artifacts and prediction results. + +`Here `_ is the instruction of setting up an NFS for a cluster. As the steps of setting an NFS could be various on different systems, we suggest users to look for additional information about NFS setting. Here are some available resources: `NFS tutorial `_ by DigitalOcean, `NFS document `_ for Ubuntu. + +For an AWS EC2 cluster, users can also use EFS as the shared file system. Please follow 1) `the instruction of creating EFS `_; 2) `the instruction of installing an EFS client `_; and 3) `the instructions of mounting the EFS filesystem `_ to set up EFS. + +After setting up a shared file system, we can keep all graph data in a shared folder. Then mount the data folder to the ``/path_to_data/`` of each instances in the cluster so that all GraphStorm containers can access the data. + +Run a GraphStorm container +........................... +In each instance, use the following command to start a GraphStorm Docker container and run it as a backend daemon on cpu. + +.. code-block:: shell + + docker run -v /path_to_data/:/data \ + -v /dev/shm:/dev/shm \ + --network=host \ + -d --name test graphstorm:local-cpu service ssh restart + +This command mounts the shared ``/path_to_data/`` folder to a container's ``/data/`` folder by which GraphStorm codes can access graph data and save the partition result. + +Setup the IP Address File and Check Port Status +---------------------------------------------------------- + +Collect the IP address list +........................... +The GraphStorm Docker containers use SSH on port ``2222`` to communicate with each other. Users need to collect all IP addresses of all the instances and put them into a text file, e.g., ``/data/ip_list.txt``, which is like: + +.. figure:: ../../../../../tutorial/distributed_ips.png + :align: center + +.. note:: We recommend to use **private IP addresses** on AWS EC2 cluster to avoid any possible port constraints. + +Put the IP list file into container's ``/data/`` folder. + +Check port +................ +The GraphStorm Docker container uses port ``2222`` to **ssh** to containers running on other machines without password. Please make sure the port is not used by other processes. + +Users also need to make sure the port ``2222`` is open for **ssh** commands. + +Pick one instance and run the following command to connect to the GraphStorm Docker container. + +.. code-block:: bash + + docker container exec -it test /bin/bash + +Users need to exchange the ssh key from each of GraphStorm Docker container to +the rest containers in the cluster: copy the keys from the ``/root/.ssh/id_rsa.pub`` from one container to ``/root/.ssh/authorized_keys`` in containers on all other containers. +In the container environment, users can check the connectivity with the command ``ssh -o StrictHostKeyChecking=no -p 2222``. Please replace the ```` with the real IP address from the ``ip_list.txt`` file above, e.g., + +.. code-block:: bash + + ssh 172.38.12.143 -o StrictHostKeyChecking=no -p 2222 + +If successful, you should login to the container with ip 172.38.12.143. + +If not, please make sure there is no restriction of exposing port 2222. + + +Launch GSPartition Jobs +----------------------- + +Now we can ssh into the **leader node** of the EC2 cluster, and start GSPartition process with the following command: + +.. code:: bash + + python3 -m graphstorm.gpartition.dist_partition_graph + --input-path ${LOCAL_INPUT_DATAPATH} \ + --metadata-filename ${METADATA_FILE} \ + --output-path ${LOCAL_OUTPUT_DATAPATH} \ + --num-parts ${NUM_PARTITIONS} \ + --partition-algorithm ${ALGORITHM} \ + --ip-config ${IP_CONFIG} + +.. warning:: + 1. Please make sure the both ``LOCAL_INPUT_DATAPATH`` and ``LOCAL_OUTPUT_DATAPATH`` are located on the shared filesystem. + 2. The number of instances in the cluster should be equal to ``NUM_PARTITIONS``. + 3. For users who only want to generate partition assignments instead of the partitioned DGL graph, please add ``--partition-assignment-only`` flag. + +Currently we support both ``random`` and ``parmetis`` as the partitioning algorithm for EC2 clusters. diff --git a/docs/source/graph-construction/gs-processing/gspartition/index.rst b/docs/source/graph-construction/gs-processing/gspartition/index.rst new file mode 100644 index 0000000000..1e4032175c --- /dev/null +++ b/docs/source/graph-construction/gs-processing/gspartition/index.rst @@ -0,0 +1,28 @@ +.. _gspartition_index: + +=================================== +Running partition jobs on AWS Infra +=================================== + +GraphStorm Distributed Graph Partition (GSPartition) allows users to do distributed partition on preprocessed graph data +prepared by :ref:`GSProcessing`. To enable distributed training, the preprocessed input data must be converted to a partitioned graph representation. +GSPartition allows user to handle massive graph data in distributed clusters. GSPartition is built on top of the +dgl `distributed graph partitioning pipeline `_. + +GSPartition consists of two steps: Graph Partitioning and Data Dispatching. Graph Partitioning step will assign each node to one partition +and save the results as a set of files called partition assignment. Data Dispatching step will physically partition the +graph data and dispatch them according to the partition assignment. It will generate the graph data in DGL format, ready for distributed training and inference. + +Tutorials for GSPartition are specifically prepared based on AWS infrastructure, +i.e., `Amazon SageMaker `_ and `Amazon EC2 clusters `_. +But, users can create your own clusters easily by following the GSPartition tutorial on Amazon EC2. + +The first section includes instructions on how to run GSPartition on `Amazon SageMaker `_. +The second section includes instructions on how to run GSPartition on `Amazon EC2 clusters `_. + +.. toctree:: + :maxdepth: 1 + :glob: + + sagemaker.rst + ec2-clusters.rst diff --git a/docs/source/graph-construction/gs-processing/gspartition/sagemaker.rst b/docs/source/graph-construction/gs-processing/gspartition/sagemaker.rst new file mode 100644 index 0000000000..946de73ea4 --- /dev/null +++ b/docs/source/graph-construction/gs-processing/gspartition/sagemaker.rst @@ -0,0 +1,134 @@ +========================================== +Running partition jobs on Amazon SageMaker +========================================== + +Once the :ref:`distributed processing` is complete, +you can use Amazon SageMaker launch scripts to launch distributed processing jobs with AWS resources. + +Build the Docker Image for GSPartition Jobs on Amazon SageMaker +--------------------------------------------------------------- +GSPartition job on Amazon SageMaker uses its SageMaker's **BYOC** (Bring Your Own Container) mode. + +Step 1: Build an Amazon SageMaker-compatible Docker image +.......................................................... + +.. note:: + * Please make sure your account has access key (AK) and security access key (SK) configured to authenticate accesses to AWS services, users can refer to `example policy `_. + * For more details of Amazon ECR operation via CLI, users can refer to the `Using Amazon ECR with the AWS CLI document `_. + +First, in a Linux machine, configure a Docker environment by following the `Docker documentation `_ suggestions. + +In order to use the Amazon SageMaker base Docker image, users need to refer the `DLC image command `_ +to find the specific Docker image commands. For example, below is the command for user authentication to access the Amazon SageMaker base Docker image. + +.. code-block:: bash + + aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 763104351884.dkr.ecr.us-east-1.amazonaws.com + +.. note:: + For region other than ``us-east-1``, please refer to `available region `_ + +Secondly, clone GraphStorm source code, and build a GraphStorm SageMaker compatible Docker image from source with commands: + +.. code-block:: bash + + git clone https://github.com/awslabs/graphstorm.git + + cd /path-to-graphstorm/docker/ + + bash build_docker_sagemaker.sh ../ + +The ``build_docker_sagemaker.sh`` script takes four arguments: + +1. **path-to-graphstorm** (**required**), is the path of the ``graphstorm`` folder, where you cloned the GraphStorm source code. For example, the path could be ``/code/graphstorm``. +2. **DEVICE_TYPE**, is the intended device type of the to-be built Docker image. Please specify ``cpu`` for building CPU-compatible images for partition job. +3. **IMAGE_NAME** (optional), is the assigned name of the to-be built Docker image. Default is ``graphstorm``. + +.. warning:: + In order to upload the GraphStorm SageMaker Docker image to Amazon ECR, users need to define the to include the ECR URI string, **.dkr.ecr..amazonaws.com/**, e.g., ``888888888888.dkr.ecr.us-east-1.amazonaws.com/graphstorm``. + +4. **IMAGE_TAG** (optional), is the assigned tag name of the to-be built Docker image. Default is ``sm-``, + that is, ``sm-cpu`` for CPU images. + +Once the ``build_docker_sagemaker.sh`` command completes successfully, there will be a Docker image, named ``:``, +such as ``888888888888.dkr.ecr.us-east-1.amazonaws.com/graphstorm:sm-cpu``, in the local repository, which could be listed by running: + +.. code-block:: bash + + docker images graphstorm + +.. _upload_sagemaker_docker: + +Step 2: Upload Docker images to Amazon ECR repository +....................................................... +Because Amazon SageMaker relies on Amazon ECR to access customers' own Docker images, users need to upload the Docker images built in the Step 1 to their own ECR repository. + +The following command will authenticate the user account to access to user's ECR repository via AWS CLI. + +.. code-block:: bash + + aws ecr get-login-password --region | docker login --username AWS --password-stdin .dkr.ecr..amazonaws.com + +Please replace the `` and `` with your own account information and be consistent with the values used in the **Step 1**. + +In addition, users need to create an ECR repository at the specified `` with the name as `` **WITHOUT** the ECR URI string, e.g., ``graphstorm``. + +And then use the following command to push the built GraphStorm Docker image to users' own ECR repository. + +.. code-block:: bash + + docker push : + +Please replace the `` and `` with the actual Docker image name and tag, e.g., ``888888888888.dkr.ecr.us-east-1.amazonaws.com/graphstorm:sm-gpu``. + +Launch the GSPartition Job on Amazon SageMaker +----------------------------------------------- +For this example, we'll use an Amazon SageMaker cluster with 2 ``ml.t3.xlarge`` instances. +We assume the data is already on an AWS S3 bucket. +For large graphs, users can choose larger instances or more instances. + +Install dependencies +..................... +To run GraphStorm with the Amazon SageMaker service, users should install the Amazon SageMaker library and copy GraphStorm's SageMaker tools. + +1. Use the below command to install Amazon SageMaker. + +.. code-block:: bash + + pip install sagemaker + +2. Copy GraphStorm SageMaker tools. Users can clone the GraphStorm repository using the following command or copy the `sagemaker folder `_ to the instance. + +.. code-block:: bash + + git clone https://github.com/awslabs/graphstorm.git + +Launch GSPartition task +........................ +Users can use the following command to launch partition jobs. + +.. code:: bash + + python launch/launch_partition.py \ + --graph-data-s3 ${DATASET_S3_PATH} \ + --num-parts ${NUM_PARTITIONS} \ + --instance-count ${NUM_INSTANCES} \ + --output-data-s3 ${OUTPUT_PATH} \ + --instance-type ${INSTANCE_TYPE} \ + --image-url ${IMAGE_URI} \ + --region ${REGION} \ + --role ${ROLE} \ + --entry-point "run/partition_entry.py" \ + --metadata-filename ${METADATA_FILE} \ + --log-level INFO \ + --partition-algorithm ${ALGORITHM} + +.. warning:: + The ``NUM_INSTANCES`` should be equal to the ``NUM_PARTITIONS`` here. + +Running the above will take the dataset after GSProcessing +from ``${DATASET_S3_PATH}`` as input and create a DistDGL graph with +``${NUM_PARTITIONS}`` under the output path, ``${OUTPUT_PATH}``. +Currently we only support ``random`` as the partitioning algorithm for sagemaker. + + diff --git a/docs/source/graph-construction/gs-processing/index.rst b/docs/source/graph-construction/gs-processing/index.rst index ca239f9bc7..8635b00ffc 100644 --- a/docs/source/graph-construction/gs-processing/index.rst +++ b/docs/source/graph-construction/gs-processing/index.rst @@ -4,13 +4,25 @@ Distributed Graph Construction Beyond single-machine graph construction, distributed graph construction offers enhanced scalability and efficiency. This process involves two main steps: GraphStorm Distributed Data Processing (GSProcessing) -and GraphStorm Distributed Data Partitioning (GPartition). The documentations of GPartition will be released soon. +and GraphStorm Distributed Graph Partitioning (GSPartition). GSProcessing will preprocess the raw data into structured +datasets, and GSPartition will process these structured datasets to create multiple partitions in +`DGL format `_. + +Here is an overview of the workflow for distributed graph construction: + +* **Prepare input data**: GraphStorm Distributed Construction accepts tabular files in parquet/CSV format. +* **Run GSProcessing**: Use GSProcessing to process the input data. This step prepares the data for partitioning including edge and node data, transformation details, and node id mappings. +* **Run GSPartition**: Use GSPartition to partition the processed data into graph files suitable for distributed training. + +.. figure:: ../../../../tutorial/distributed_construction.png + :align: center The following sections provide guidance on doing distributed graph construction. The first section details the execution environment setup for GSProcessing. The second section offers examples on drafting a configuration file for a GSProcessing job. The third section explains how to deploy your GSProcessing job with AWS infrastructure. -The final section shows an example to quick start GSProcessing. +The fourth section includes how to do GSPartition with the GSProcessing output. +The final section shows an example to quickly start GSProcessing and GSPartition. .. toctree:: :maxdepth: 1 @@ -19,4 +31,5 @@ The final section shows an example to quick start GSProcessing. prerequisites/index.rst input-configuration.rst aws-infra/index.rst + gspartition/index.rst example.rst \ No newline at end of file diff --git a/docs/source/graph-construction/gs-processing/prerequisites/index.rst b/docs/source/graph-construction/gs-processing/prerequisites/index.rst index 1e1cf534f7..7951c26994 100644 --- a/docs/source/graph-construction/gs-processing/prerequisites/index.rst +++ b/docs/source/graph-construction/gs-processing/prerequisites/index.rst @@ -1,3 +1,5 @@ +.. _gsprocessing_prerequisites_index: + =============================================== 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