Update multi-node.qmd

docs/multi-node.qmd

@@ -1,12 +1,13 @@
 ---
-title: Multi Node
+title: Distributed Finetuning For Multi-Node with Axolotl and FSDP
 description: How to use Axolotl on multiple machines
 ---
+# Distributed Finetuning For Multi-Node with Axolotl and FSDP
+You will need to create a configuration for accelerate, either by using `accelerate config` and follow the instructions or you can use 
+one of the preset below:
 
-You will need to create a configuration for accelerate, either by using `accelerate config` and follow the instructions or you can use one of the preset below:
-
-~/.cache/huggingface/accelerate/default_config.yaml
 ```yaml
+~/.cache/huggingface/accelerate/default_config.yaml
 compute_environment: LOCAL_MACHINE
 debug: false
 distributed_type: FSDP
@@ -27,6 +28,7 @@ use_cpu: false
 ```
 
 Configure your model to use FSDP with for example:
+
 ```yaml
 fsdp:
   - full_shard
@@ -38,11 +40,114 @@ fsdp_config:
 ```
 
 ## Machine configuration
-
 On each machine you need a copy of Axolotl, we suggest using the same commit to ensure compatibility.
-
 You will also need to have the same configuration file for your model on each machine.
+On the main machine only, make sure the port you set as main_process_port is open in TCP and reachable by other machines.
+All you have to do now is launch using accelerate as you would usually do on each machine and voila, the processes will start once you have launched accelerate on every machine.
 
-On the main machine only, make sure the port you set as `main_process_port` is open in TCP and reachable by other machines.
+--------------------------------------------------------------------------------------------------------------------------------------
 
-All you have to do now is launch using accelerate as you would usually do on each machine and voila, the processes will start once you have launched accelerate on every machine.
+# Distributed Finetuning For Multi-Node with Axolotl and Deepspeed On AWS EC2 Instances
+This guide explains how to set up a distributed finetuning environment using Axolotl, a framework for finetuning large language models. The setup involves configuring SSH for passwordless access, generating public keys, and configuring Axolotl and Accelerate for multi-node training.
+
+## Prerequisites
+- Multiple nodes (servers or machines) with GPUs
+- Ubuntu or any other Linux distribution
+
+## Step 1: Configure SSH for Passwordless Access
+1. Open the `sshd_config` file on all nodes and the server:
+
+```bash
+sudo nano ~/etc/ssh/sshd_config
+```
+
+2. Uncomment the `PubkeyAuthentication` line to enable public key authentication.
+3. Save the changes and restart the SSH service:
+
+```bash
+sudo systemctl restart sshd
+```
+
+## Step 2: Generate Public Key
+1. Generate a public key using `ssh-keygen`:
+
+```bash
+ssh-keygen -t rsa
+```
+
+2. Copy the public key to the clipboard:
+
+```bash
+cat ~/.ssh/id_rsa.pub
+```
+
+3. Add the public key to the `authorized_keys` file on all nodes and the server:
+
+```bash
+nano ~/.ssh/authorized_keys
+```
+
+4. Repeat steps 1-3 on all other nodes.
+5. Exchange public keys between nodes:
+   - Paste the public key of Node 1 into the `authorized_keys` file of Node 2, and vice versa.
+   - Repeat this process for all node pairs.
+6. Test passwordless SSH access:
+
+```bash
+ssh <ip-of-other-node>
+```
+
+## Step 3: Configure Axolotl
+1. Configure Axolotl on each node with the same `.yml` files and settings.
+2. Create a `deepspeed_hostfile` inside the Axolotl folder:
+
+```bash
+nano deepspeed_hostfile
+```
+
+3. Add the IP addresses and GPU slots for each node:
+
+```
+<ip-node-1> slots=<num_gpu_in_node1>
+<ip-node-2> slots=<num_gpu_in_node2>
+```
+
+## Step 4: Configure Accelerate
+Follow these steps on each node to configure Accelerate:
+
+```bash
+accelerate config
+```
+
+### Node 1 (Server) Configuration
+1. Select `This machine` for the compute environment.
+2. Select `multi_gpu` for the compute type.
+3. Enter the number of machines (e.g., `2` for two nodes).
+4. Enter the machine rank `0` for the first node (server).
+5. Enter the server IP address.
+6. Enter the main process port (e.g., `5000`).
+7. Select `no` for setting up custom environment variables.
+8. Select `static` for the rendezvous backend.
+9. Select `yes` for running on the same network.
+10. Select `no` for using a cluster.
+11. Select `yes` for using Deepspeed.
+12. Select `yes` for using Deepspeed configs.
+13. Enter the Deepspeed config file (e.g., `deepspeed_configs/zero2.json`).
+14. Select `no` for using Zero 3.
+15. Enter `pdsh` for the Deepspeed multinode launcher.
+16. Enter `deepspeed_hostfile` for the Deepspeed hostfile.
+17. Select `no` for using custom launch utility options.
+18. Select `no` for using a TPU.
+19. Enter the number of processes (e.g., `8` for 8 GPUs).
+
+### Node 2 Configuration
+Repeat the above steps for Node 2, but change the machine rank to `1`.
+
+## Step 5: Finetuning
+On Node 1 (server), run the finetuning process using Accelerate:
+
+```bash
+accelerate launch -m axolotl.cli.train examples/llama-2/qlora.yml
+```
+
+This will start the finetuning process across all nodes. You can check the different IP addresses before each step to verify that the training is running on every node.