/examples/pytorch/graphsage/dist/README.md

## Distributed training

This is an example of training GraphSage in a distributed fashion. Before training, please install some python libs by pip:

```bash
sudo pip3 install ogb
```

**Requires PyTorch 1.10.0+ to work.**

To train GraphSage, it has five steps:

### Step 0: Setup a Distributed File System
* You may skip this step if your cluster already has folder(s) synchronized across machines.

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).

#### Server side setup
Here is an example of how to setup NFS. First, install essential libs on the storage server
```bash
sudo apt-get install nfs-kernel-server
```

Below we assume the user account is `ubuntu` and we create a directory of `workspace` in the home directory.
```bash
mkdir -p /home/ubuntu/workspace
```

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

```bash
sudo vim /etc/exports
# add the following line
/home/ubuntu/workspace  192.168.0.0/16(rw,sync,no_subtree_check)
```

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
```bash
/home/ubuntu/workspace  10.0.0.0/8(rw,sync,no_subtree_check)
/home/ubuntu/workspace  172.16.0.0/12(rw,sync,no_subtree_check)
```

Then restart NFS, the setup on server side is finished.

```
sudo systemctl restart nfs-kernel-server
```

For configraution details, please refer to [NFS ArchWiki](https://wiki.archlinux.org/index.php/NFS).

#### Client side setup

To use NFS, clients also require to install essential packages

```
sudo apt-get install nfs-common
```

You can either mount the NFS manually

```
mkdir -p /home/ubuntu/workspace
sudo mount -t nfs <nfs-server-ip>:/home/ubuntu/workspace /home/ubuntu/workspace
```

or edit the fstab so the folder will be mounted automatically

```
# vim /etc/fstab
## append the following line to the file
<nfs-server-ip>:/home/ubuntu/workspace   /home/ubuntu/workspace   nfs   defaults	0 0
```

Then run `mount -a`.

Now go to `/home/ubuntu/workspace` and clone the DGL Github repository.

### Step 1: set IP configuration file.

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
could like this:

```bash
172.31.19.1
172.31.23.205
172.31.29.175
172.31.16.98
```

Users need to make sure that the master node (node-0) has right permission to ssh to all the other nodes without password authentication.
[This link](https://linuxize.com/post/how-to-setup-passwordless-ssh-login/) provides instructions of setting passwordless SSH login.

### Step 2: partition the graph.

The example provides a script to partition some builtin graphs such as Reddit and OGB product graph.
If we want to train GraphSage on 4 machines, we need to partition the graph into 4 parts.

We need to load some function from the parent directory.
```bash
export PYTHONPATH=$PYTHONPATH:..
```

In this example, we partition the OGB product graph into 4 parts with Metis on node-0. The partitions are balanced with respect to
the number of nodes, the number of edges and the number of labelled nodes.
```bash
python3 partition_graph.py --dataset ogb-product --num_parts 4 --balance_train --balance_edges
```

This script generates partitioned graphs and store them in the directory called `data`.


### Step 3: Launch distributed jobs

DGL provides a script to launch the training job in the cluster. `part_config` and `ip_config`
specify relative paths to the path of the workspace.

The command below launches one process per machine for both sampling and training.

```bash
python3 ~/workspace/dgl/tools/launch.py \
--workspace ~/workspace/dgl/examples/pytorch/graphsage/dist/ \
--num_trainers 1 \
--num_samplers 0 \
--num_servers 1 \
--part_config data/ogb-product.json \
--ip_config ip_config.txt \
"python3 train_dist.py --graph_name ogb-product --ip_config ip_config.txt --num_epochs 30 --batch_size 1000"
```

To run unsupervised training:

```bash
python3 ~/workspace/dgl/tools/launch.py \
--workspace ~/workspace/dgl/examples/pytorch/graphsage/dist/ \
--num_trainers 1 \
--num_samplers 0 \
--num_servers 1 \
--part_config data/ogb-product.json \
--ip_config ip_config.txt \
"python3 train_dist_unsupervised.py --graph_name ogb-product --ip_config ip_config.txt --num_epochs 3 --batch_size 1000"
```

By default, this code will run on CPU. If you have GPU support, you can just add a `--num_gpus` argument in user command:

```bash
python3 ~/workspace/dgl/tools/launch.py \
--workspace ~/workspace/dgl/examples/pytorch/graphsage/dist/ \
--num_trainers 4 \
--num_samplers 0 \
--num_servers 1 \
--part_config data/ogb-product.json \
--ip_config ip_config.txt \
"python3 train_dist.py --graph_name ogb-product --ip_config ip_config.txt --num_epochs 30 --batch_size 1000 --num_gpus 4"
```

To run supervised with transductive setting (nodes are initialized with node embedding)
```bash
python3 ~/workspace/dgl/tools/launch.py --workspace ~/workspace/dgl/examples/pytorch/graphsage/dist/ \
--num_trainers 4 \
--num_servers 1 \
--num_samplers 0 \
--part_config data/ogb-product.json \
--ip_config ip_config.txt \
"python3 train_dist_transductive.py --graph_name ogb-product --ip_config ip_config.txt --batch_size 1000 --num_gpus 4 --eval_every 5"
```

To run supervised with transductive setting using dgl distributed DistEmbedding
```bash
python3 ~/workspace/dgl/tools/launch.py --workspace ~/workspace/dgl/examples/pytorch/graphsage/dist/ \
--num_trainers 4 \
--num_servers 1 \
--num_samplers 0 \
--part_config data/ogb-product.json \
--ip_config ip_config.txt \
"python3 train_dist_transductive.py --graph_name ogb-product --ip_config ip_config.txt --batch_size 1000 --num_gpus 4 --eval_every 5  --dgl_sparse"
```

To run unsupervised with transductive setting (nodes are initialized with node embedding)
```bash
python3 ~/workspace/dgl/tools/launch.py --workspace ~/workspace/dgl/examples/pytorch/graphsage/dist/ \
--num_trainers 4 \
--num_samplers 0 \
--num_servers 1 \
--part_config data/ogb-product.json \
--ip_config ip_config.txt \
"python3 train_dist_unsupervised_transductive.py --graph_name ogb-product --ip_config ip_config.txt --num_epochs 3 --batch_size 1000 --num_gpus 4"
```

To run unsupervised with transductive setting using dgl distributed DistEmbedding
```bash
python3 ~/workspace/dgl/tools/launch.py --workspace ~/workspace/dgl/examples/pytorch/graphsage/dist/ \
--num_trainers 4 \
--num_samplers 0 \
--num_servers 1 \
--part_config data/ogb-product.json \
--ip_config ip_config.txt \
"python3 train_dist_unsupervised_transductive.py --graph_name ogb-product --ip_config ip_config.txt --num_epochs 3 --batch_size 1000 --num_gpus 4 --dgl_sparse"
```

**Note:** if you are using conda or other virtual environments on the remote machines, you need to replace `python3` in the command string (i.e. the last argument) with the path to the Python interpreter in that environment.

## Distributed code runs in the standalone mode

The standalone mode is mainly used for development and testing. The procedure to run the code is much simpler.

### Step 1: graph construction.

When testing the standalone mode of the training script, we should construct a graph with one partition.
```bash
python3 partition_graph.py --dataset ogb-product --num_parts 1
```

### Step 2: run the training script

To run supervised training:

```bash
python3 train_dist.py --graph_name ogb-product --ip_config ip_config.txt --num_epochs 3 --batch_size 1000 --part_config data/ogb-product.json --standalone
```

To run unsupervised training:

```bash
python3 train_dist_unsupervised.py --graph_name ogb-product --ip_config ip_config.txt --num_epochs 3 --batch_size 1000 --part_config data/ogb-product.json --standalone
```

Note: please ensure that all environment variables shown above are unset if they were set for testing distributed training.