wehub-resource-sync
222 lines
7.8 KiB
Markdown
222 lines
7.8 KiB
Markdown
## Distributed training
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This is an example of training GraphSage in a distributed fashion. Before training, please install some python libs by pip:
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```
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pip3 install ogb
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```
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**Requires PyTorch 1.12.0+ to work.**
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To train GraphSage, it has five steps:
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### Step 0: Setup a Distributed File System
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* You may skip this step if your cluster already has folder(s) synchronized across machines.
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To perform distributed training, files and codes need to be accessed across multiple machines. A distributed file system would perfectly handle the job (i.e., NFS, Ceph).
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#### Server side setup
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Here is an example of how to setup NFS. First, install essential libs on the storage server
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```
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sudo apt-get install nfs-kernel-server
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```
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Below we assume the user account is `ubuntu` and we create a directory of `workspace` in the home directory.
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```
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mkdir -p /home/ubuntu/workspace
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```
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We assume that the all servers are under a subnet with ip range `192.168.0.0` to `192.168.255.255`. The exports configuration needs to be modifed to
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```
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sudo vim /etc/exports
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# add the following line
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/home/ubuntu/workspace 192.168.0.0/16(rw,sync,no_subtree_check)
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```
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The server's internal ip can be checked via `ifconfig` or `ip`. If the ip does not begin with `192.168`, then you may use
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```
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/home/ubuntu/workspace 10.0.0.0/8(rw,sync,no_subtree_check)
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/home/ubuntu/workspace 172.16.0.0/12(rw,sync,no_subtree_check)
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```
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Then restart NFS, the setup on server side is finished.
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```
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sudo systemctl restart nfs-kernel-server
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```
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For configraution details, please refer to [NFS ArchWiki](https://wiki.archlinux.org/index.php/NFS).
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#### Client side setup
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To use NFS, clients also require to install essential packages
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```
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sudo apt-get install nfs-common
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```
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You can either mount the NFS manually
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```
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mkdir -p /home/ubuntu/workspace
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sudo mount -t nfs <nfs-server-ip>:/home/ubuntu/workspace /home/ubuntu/workspace
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```
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or edit the fstab so the folder will be mounted automatically
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```
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# vim /etc/fstab
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## append the following line to the file
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<nfs-server-ip>:/home/ubuntu/workspace /home/ubuntu/workspace nfs defaults 0 0
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```
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Then run `mount -a`.
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Now go to `/home/ubuntu/workspace` and clone the DGL Github repository.
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### Step 1: set IP configuration file.
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User need to set their own IP configuration file `ip_config.txt` before training. For example, if we have four machines in current cluster, the IP configuration
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could like this:
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```
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172.31.19.1
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172.31.23.205
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172.31.29.175
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172.31.16.98
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```
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Users need to make sure that the master node (node-0) has right permission to ssh to all the other nodes without password authentication.
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[This link](https://linuxize.com/post/how-to-setup-passwordless-ssh-login/) provides instructions of setting passwordless SSH login.
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### Step 2: partition the graph.
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The example provides a script to partition some builtin graphs such as Reddit and OGB product graph.
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If we want to train GraphSage on 4 machines, we need to partition the graph into 4 parts.
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In this example, we partition the ogbn-products graph into 4 parts with Metis on node-0. The partitions are balanced with respect to
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the number of nodes, the number of edges and the number of labelled nodes.
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```
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python3 partition_graph.py --dataset ogbn-products --num_parts 4 --balance_train --balance_edges
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```
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This script generates partitioned graphs and store them in the directory called `data`.
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### Step 3: Launch distributed jobs
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DGL provides a script to launch the training job in the cluster. `part_config` and `ip_config`
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specify relative paths to the path of the workspace.
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The command below launches one process per machine for both sampling and training.
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```
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python3 ~/workspace/dgl/tools/launch.py \
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--workspace ~/workspace/dgl/examples/distributed/graphsage/ \
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--num_trainers 1 \
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--num_samplers 0 \
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--num_servers 1 \
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--part_config data/ogbn-products.json \
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--ip_config ip_config.txt \
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"python3 node_classification.py --graph_name ogbn-products --ip_config ip_config.txt --num_epochs 30 --batch_size 1000"
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```
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By default, this code will run on CPU. If you have GPU support, you can just add a `--num_gpus` argument in user command:
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```
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python3 ~/workspace/dgl/tools/launch.py \
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--workspace ~/workspace/dgl/examples/distributed/graphsage/ \
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--num_trainers 4 \
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--num_samplers 0 \
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--num_servers 1 \
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--part_config data/ogbn-products.json \
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--ip_config ip_config.txt \
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"python3 node_classification.py --graph_name ogbn-products --ip_config ip_config.txt --num_epochs 30 --batch_size 1000 --num_gpus 4"
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```
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Unsupervised training(train with link prediction dataloader).
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```
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python3 ~/workspace/dgl/tools/launch.py \
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--workspace ~/workspace/dgl/examples/distributed/graphsage/ \
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--num_trainers 1 \
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--num_samplers 0 \
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--num_servers 1 \
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--part_config data/ogbn-products.json \
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--ip_config ip_config.txt \
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"python3 node_classification_unsupervised.py --graph_name ogbn-products --ip_config ip_config.txt --num_epochs 30 --batch_size 1000 --remove_edge"
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```
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### Running with GraphBolt
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In order to run with `GraphBolt`, we need to partition graph into `GraphBolt` data formats.Please note that both `DGL` and `GraphBolt` partitions are saved together.
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If we have already partitioned into `DGL` format, just convert them directly like below:
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```
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python3 -c "import dgl; dgl.distributed.dgl_partition_to_graphbolt('ogbn-products.json')"
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```
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Or partition from scratch like this:
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```
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python3 partition_graph.py --dataset ogbn-products --num_parts 2 --balance_train --balance_edges --use_graphbolt
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```
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#### Partition sizes compared to DGL
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Compared to `DGL`, `GraphBolt` partitions are much smaller(reduced to **16%** and **19%** for `ogbn-products` and `ogbn-papers100M` respectively).
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`ogbn-products`
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| Data Formats | File Name | Part 0 | Part 1 |
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| ------------ | ---------------------------- | ------ | ------ |
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| DGL | graph.dgl | 1.5GB | 1.6GB |
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| GraphBolt | fused_csc_sampling_graph.pt | 255MB | 265MB |
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`ogbn-papers100M`
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| Data Formats | File Name | Part 0 | Part 1 |
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| ------------ | ---------------------------- | ------ | ------ |
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| DGL | graph.dgl | 23GB | 22GB |
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| GraphBolt | fused_csc_sampling_graph.pt | 4.4GB | 4.1GB |
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Then run example with `--use_graphbolt`.
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```
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python3 ~/workspace/dgl/tools/launch.py \
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--workspace ~/workspace/dgl/examples/distributed/graphsage/ \
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--num_trainers 4 \
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--num_samplers 0 \
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--num_servers 2 \
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--part_config data/ogbn-products.json \
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--ip_config ip_config.txt \
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"python3 node_classification.py --graph_name ogbn-products --ip_config ip_config.txt --num_epochs 10 --use_graphbolt"
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```
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#### Performance compared to `DGL`
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Compared to `DGL`, `GraphBolt`'s sampler works faster(reduced to **80%** and **77%** for `ogbn-products` and `ogbn-papers100M` respectively). `Min` and `Max` are statistics of all trainers on all nodes(machines).
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As for RAM usage, the shared memory(measured by **shared** field of `free` command) usage is decreased due to smaller graph partitions in `GraphBolt` though the peak memory used by processes(measured by **used** field of `free` command) does not decrease.
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`ogbn-products`
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| Data Formats | Sample Time Per Epoch (CPU) | Test Accuracy (10 epochs) | shared | used (peak) |
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| ------------ | --------------------------- | -------------------------------- | ----- | ---- |
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| DGL | Min: 1.2884s, Max: 1.4159s | Min: 64.38%, Max: 70.42% | 2.4GB | 7.8GB|
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| GraphBolt | Min: 1.0589s, Max: 1.1400s | Min: 61.68%, Max: 71.23% | 1.1GB | 7.8GB|
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`ogbn-papers100M`
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| Data Formats | Sample Time Per Epoch (CPU) | Test Accuracy (10 epochs) | shared | used (peak) |
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| ------------ | --------------------------- | -------------------------------- | ----- | ---- |
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| DGL | Min: 5.5570s, Max: 6.1900s | Min: 29.12%, Max: 34.33% | 84GB | 43GB |
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| GraphBolt | Min: 4.5046s, Max: 4.7718s | Min: 29.11%, Max: 33.49% | 67GB | 43GB |
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