7.4 Tools for launching distributed training/inference

(中文版)

DGL provides two scripts to assist in distributed training:

  • tools/copy_files.py for copying graph partitions to a graph,

  • tools/launch.py for launching a distributed training job in a cluster of machines.

copy_files.py copies partitioned data and related files (e.g., training script) in a machine (where the graph is partitioned) to a cluster of machines (where the distributed training occurs). The script copies a partition to a machine where the distributed training job will require the partition. The script contains four arguments:

  • --part_config specifies the partition configuration file that contains the information of the partitioned data in the local machine.

  • --ip_config specifies the IP configuration file of the cluster.

  • --workspace specifies the directory in the training machines where all data related to distributed training are stored.

  • --rel_data_path specifies the relative path under the workspace directory where the partitioned data will be stored.

  • --script_folder specifies the relative path under the workspace directory where user’s training scripts are stored.

Note: copy_files.py finds the right machine to store a partition based on the IP configuration file. Therefore, the same IP configuration file should be used by copy_files.py and launch.py.

DGL provides tools/launch.py to launch a distributed training job in a cluster. This script makes the following assumptions:

  • The partitioned data and the training script have been copied to the cluster or a global storage (e.g., NFS) accessible to all machines in the cluster.

  • The master machine (where the launch script is executed) has passwordless ssh access to all other machines.

Note: The launch script has to be invoked on one of the machines in the cluster.

Below shows an example of launching a distributed training job in a cluster.

python3 tools/launch.py \
--workspace ~graphsage/ \
--num_trainers 2 \
--num_samplers 4 \
--num_servers 1 \
--part_config data/ogb-product.json \
--ip_config ip_config.txt \
"python3 code/train_dist.py --graph-name ogb-product --ip_config ip_config.txt --num-epochs 5 --batch-size 1000 --lr 0.1"

The configuration file ip_config.txt contains the IP addresses of the machines in a cluster. A typical example of ip_config.txt is as follows:

172.31.19.1
172.31.23.205
172.31.29.175
172.31.16.98

Each row is an IP address of a machine. Optionally, the IP address can be followed by a port that specifies the port used by network communication between trainers. When the port is not provided, a default one is 30050.

The workspace specified in the launch script is the working directory in the machines, which contains the training script, the IP configuration file, the partition configuration file as well as the graph partitions. All paths of the files should be specified as relative paths to the workspace.

The launch script creates a specified number of training jobs (--num_trainers) on each machine. In addition, a user needs to specify the number of sampler processes for each trainer (--num_samplers). The number of sampler processes has to match with the number of worker processes specified in initialize().

It is common that users may want to try different models or training configurations against the same graph data. To avoid repetitively loading the same graph data, DGL allows users to launch a persistent graph server to be shared across multiple training jobs. A persistent graph server will stay alive even all training workers have finished and exited. Below shows an example of launching a persistent graph server:

We first launch the graph server together with the first group of training workers.

python3 tools/launch.py \
--workspace ~graphsage/ \
--num_trainers 2 \
--num_samplers 4 \
--num_servers 1 \
--part_config data/ogb-product.json \
--ip_config ip_config.txt \
--keep_alive \
--server_name long_live \
"python3 code/train_dist.py --graph-name ogb-product --ip_config ip_config.txt --num-epochs 5 --batch-size 1000 --lr 0.1"

Pay attention to the --keep_alive option, which indicates the server should stay alive after workers have finished. --server_name is the given name of the server which will be referred when launching new training jobs.

Launch another group of distributed training job and connect to the existing persistent server.

python3 tools/launch.py \
--workspace ~graphsage/ \
--num_trainers 2 \
--num_samplers 4 \
--num_servers 1 \
--part_config data/ogb-product.json \
--ip_config ip_config.txt \
--server_name long_live \
"python3 code/train_dist.py --graph-name ogb-product --ip_config ip_config.txt --num-epochs 5 --batch-size 1000 --lr 0.1"

Note

All the arguments for launch.py should be kept same as previous launch. And below arguments for specific training script should be kept same as well: --graph-name, --ip_config. The rest arguments such as --num-epochs, --batch-size and so on are free to change.