docs: apply tech pubs launcher copyedits

Signed-off-by: Zeel <desaizeel2128@gmail.com>

Author: zeel2104

docs/launcher/overview.md

@@ -8,20 +8,20 @@ NeMo AutoModel provides several ways to launch training. The right choice depend
 |---|---|---|---|
 | **Local Workstation** | Getting started, debugging, single-node training | 1-8 on one machine | [Local Workstation](./local-workstation.md) |
 | **NeMo-Run** | Managed execution on Slurm, Kubernetes, Docker, local | 1+ | [NeMo-Run](./nemo-run.md) |
-| **SkyPilot** | Cloud training (AWS, GCP, Azure) with spot pricing | Any | [SkyPilot](./skypilot.md) |
+| **SkyPilot** | Cloud training or Kubernetes clusters | Any | [SkyPilot](./skypilot.md) |
 | **Slurm** | Multi-node batch jobs on HPC clusters | 8+ across nodes | [Slurm](./slurm.md) |
 
 ### I Have 1–2 GPUs on My Workstation
 
-Use the **interactive** launcher. No scheduler or cluster software needed:
+Use the **interactive** launcher. No scheduler or cluster software is needed:
 
 ```bash
 automodel examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml
 ```
 
 See the [Local Workstation](./local-workstation.md) guide.
 
-### I have access to a Slurm cluster
+### I Have Access to a Slurm Cluster
 
 Add a `slurm:` section to your YAML config and submit with the same `automodel` command. The CLI generates the `torchrun` invocation and calls `sbatch` for you:
 
@@ -31,7 +31,7 @@ automodel config_with_slurm.yaml
 
 See the [Slurm](./slurm.md) guide.
 
-### I want managed job submission (Slurm, Kubernetes, Docker)
+### I Want Managed Job Submission (Slurm, Kubernetes, Docker)
 
 Add a `nemo_run:` section to your YAML config. NeMo-Run loads a pre-configured executor for your compute target and submits the job:
 
@@ -41,7 +41,7 @@ automodel config_with_nemo_run.yaml
 
 See the [NeMo-Run](./nemo-run.md) guide.
 
-### I want to train on the cloud
+### I Want to Train on the Cloud
 
 Add a `skypilot:` section to your YAML config. SkyPilot provisions VMs on any major cloud and handles spot-instance preemption automatically:
 
@@ -51,7 +51,7 @@ automodel config_with_skypilot.yaml
 
 See the [SkyPilot](./skypilot.md) guide.
 
-### I want to train on Kubernetes with SkyPilot
+### I Want to Train on Kubernetes with SkyPilot
 
 Use the same `skypilot:` launcher, but set `cloud: kubernetes`. This is a good fit when your team already has a GPU-backed Kubernetes cluster and you want SkyPilot to handle job submission and multi-node orchestration:
 

docs/launcher/skypilot.md

@@ -106,7 +106,7 @@ model:
 
 ## Cloud Examples
 
-### AWS - On-demand A10G
+### AWS — On-Demand A10G
 
 ```yaml
 skypilot:
@@ -118,7 +118,7 @@ skypilot:
   hf_token: ${HF_TOKEN}
 ```
 
-### GCP - Spot V100, 8 GPUs (single node)
+### GCP — Spot V100, 8 GPUs (Single Node)
 
 ```yaml
 skypilot:
@@ -130,7 +130,7 @@ skypilot:
   hf_token: ${HF_TOKEN}
 ```
 
-### Multi-node distributed training (2 x 8 x A100)
+### Multi-Node Distributed Training (2 x 8 x A100)
 
 ```yaml
 skypilot:
@@ -142,7 +142,7 @@ skypilot:
   hf_token: ${HF_TOKEN}
 ```
 
-For multi-node jobs the launcher automatically adds the SkyPilot rendezvous environment variables (`$SKYPILOT_NODE_RANK`, `$SKYPILOT_NUM_NODES`, `$SKYPILOT_NODE_IPS`) to the `torchrun` command.
+For multi-node jobs, the launcher automatically adds the SkyPilot rendezvous environment variables (`$SKYPILOT_NODE_RANK`, `$SKYPILOT_NUM_NODES`, `$SKYPILOT_NODE_IPS`) to the `torchrun` command.
 
 ## Monitor and Manage Jobs
 
@@ -176,7 +176,7 @@ automodel config_with_skypilot.yaml \
   --model.pretrained_model_name_or_path meta-llama/Llama-3.2-3B
 ```
 
-## Kubernetes users
+## Kubernetes Users
 
 If you want to run on a Kubernetes cluster, use `cloud: kubernetes` and follow the dedicated [SkyPilot + Kubernetes tutorial](./skypilot-kubernetes.md). That guide includes: