Example for Slurm and various fixes (#3038) [skip ci]

* slurm example and make preprocess play nicely

* start slurm if it init file exists

* remove incorrect comment

* feat: add slurm docs

---------

Co-authored-by: NanoCode012 <nano@axolotl.ai>

examples/slurm/README.md

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+# SLURM Multi-Node Training
+
+This directory contains an example SLURM script for running Axolotl training jobs across multiple nodes in a SLURM cluster.
+
+## Prerequisites
+
+- Access to a SLURM cluster with GPU nodes
+- Axolotl installed on all nodes (see [installation docs](https://docs.axolotl.ai/docs/installation.html))
+
+## Usage
+
+### Standard SLURM Clusters
+
+1. Copy [`axolotl.slurm`](./axolotl.slurm) to your working directory.
+2. Place your Axolotl config file (`train.yaml`) in the same directory.
+3. Set the appropriate environment variables for the job:
+    ```bash
+    export HF_TOKEN="your-huggingface-token"
+
+    # metric tracking
+    # export WANDB_API_KEY="your-wandb-api-key"
+    # ...
+    ```
+4. Submit the job:
+   ```bash
+   sbatch --export=ALL,NUM_NODES=2,NUM_TRAINERS=8,PRIMARY_ADDR=<master-node>,PRIMARY_PORT=29400 axolotl.slurm
+   ```
+
+   Where:
+   - `NUM_NODES`: Number of nodes to use
+   - `NUM_TRAINERS`: GPUs per node (typically 8)
+   - `PRIMARY_ADDR`: Hostname/IP of the master node
+   - `PRIMARY_PORT`: Port for distributed training (default: 29400)
+
+5. (Optional) Run other slurm commands:
+    ```bash
+    # check job info
+    scontrol show job axolotl-cli
+
+    # check job queue
+    squeue
+
+    # check cluster status
+    sinfo
+    ```
+
+### RunPod Instant Clusters
+
+Axolotl works with RunPod Instant Clusters. This feature provides managed SLURM clusters with zero configuration.
+
+1. **Deploy a SLURM Cluster**:
+   - Go to [RunPod Instant Clusters](https://console.runpod.io/cluster)
+   - Click "Create a Cluster"
+   - Choose your GPU type, node count, and region
+   - Choose an [Axolotl cloud docker image](https://docs.axolotl.ai/docs/docker.html#cloud)
+   - Deploy the cluster
+
+2. **Connect to the Controller Node**: Find the controller node in the RunPod console and connect via SSH
+
+3. **Follow the instructions in [Standard SLURM Clusters](#standard-slurm-clusters)**
+
+## Additional Resources
+
+- [Axolotl Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+- [SLURM Documentation](https://slurm.schedmd.com/documentation.html)
+- [RunPod SLURM Clusters Guide](https://docs.runpod.io/instant-clusters/slurm-clusters)

examples/slurm/axolotl.slurm

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+#!/bin/bash
+# Prior to running this script, export your HF_TOKEN and WANDB_API_KEY to your environment; i.e.
+# export HF_TOKEN="..."
+# export WANDB_API_KEY="..."
+#
+
+# ---------- SBATCH commands ---------- #
+#SBATCH --job-name=axolotl-slurm-multinode
+#SBATCH --ntasks-per-node=1
+#SBATCH --nodes=$NUM_NODES
+#SBATCH --gpus-per-task=8
+#SBATCH --cpus-per-task=128
+
+export TORCH_DIST_INIT_BARRIER=0
+
+srun axolotl preprocess train.yaml
+
+srun axolotl train train.yaml --launcher torchrun -- \
+    --nproc_per_node=$NUM_TRAINERS --nnodes=$NUM_NODES \
+    --rdzv_id axolotl-cli --rdzv_backend c10d --rdzv_endpoint "${PRIMARY_ADDR}:${PRIMARY_PORT}" --rdzv-conf="join_timeout=1800"