chore: remove Kubernetes deployment path (Helm chart, trainer metrics server)

Dockerfile.cuda

@@ -112,7 +112,6 @@ COPY packages/ /app/packages/
 COPY deps/ /app/deps/
 COPY configs /app/configs
 COPY examples /app/examples
-COPY k8s /app/k8s
 COPY benchmarks/scripts /app/benchmarks/scripts
 COPY scripts/docker-editable-pretend-versions.sh /app/scripts/docker-editable-pretend-versions.sh
 
@@ -304,6 +303,6 @@ ENV LD_LIBRARY_PATH="/opt/ucx/lib:/opt/ucx/lib/ucx${LD_LIBRARY_PATH:+:${LD_LIBRA
 ENV HF_HUB_ETAG_TIMEOUT=500
 ENV HF_HUB_DOWNLOAD_TIMEOUT=300
 
-# Use entrypoint for setup (ulimit, etc) but default to sleep infinity for K8s
+# Use entrypoint for setup (ulimit, etc) but default to sleep infinity
 ENTRYPOINT ["/app/docker-entrypoint.sh"]
 CMD ["sleep", "infinity"]

README.md

@@ -35,7 +35,7 @@ prime-rl is a framework for large-scale reinforcement learning. It is designed t
 2. Performant: built to train 1T+ MoE models on 1000+ GPUs with [FSDP2](https://docs.pytorch.org/tutorials/intermediate/FSDP_tutorial.html) for training and [vLLM](https://github.com/vllm-project/vllm) for inference, with FP8 inference, PD disaggregation, EP and CP parallelism, and more.
 3. Native integration with [`verifiers`](https://github.com/PrimeIntellect-ai/verifiers) environments through the [Environments Hub](https://app.primeintellect.ai/dashboard/environments?ex_sort=most_stars), including built-in support for SWE and agentic environments.
 4. End-to-end post-training: SFT, RL training, and evals.
-5. Multi-node deployment with Slurm and Kubernetes support.
+5. Multi-node deployment with Slurm support.
 6. Multimodal support for VLMs such as Qwen3.5.
 7. Hackable, modular, and extensible by design.
 8. One-line SLURM deployment for frontier models — e.g. [`GLM-5` FP8 with P/D disaggregation, the `llm-d` router, and Mooncake KV offload](examples/advanced/glm-5.3/).
@@ -181,7 +181,7 @@ Follow this guide to learn the basics of prime-rl. You can train your own models
 ### Advanced Training: 32 - 2048 GPUs:
 
 Follow this guide to train large models on hard reasoning and agentic tasks.
-These guides are designed to be run from a Slurm cluster but can also be adapted to k8s deployments.
+These guides are designed to be run from a Slurm cluster.
 
 1. [**Qwen3-30B-A3B**](examples/advanced/qwen3-30b-a3b/): Train `Qwen3-30B-A3B` on math, SWE, and agentic tool use.
 2. [**GLM-4.5-Air**](examples/advanced/glm-4.5-air/): Train `GLM-4.5-Air` on search, SWE, and terminal tasks.

docs/training.md

@@ -37,7 +37,7 @@ This page covers everything you need to launch, observe, checkpoint, and recover
 | `uv run inference` | vLLM server. | Always use this entrypoint over `vllm serve` — it adds `/update_weights`, `/load_lora_adapter`, and `/init_broadcaster`. |
 | `uv run orchestrator` | Standalone orchestrator process. | Pair with a separately-launched inference server and one `env-server` per source. |
 | `uv run eval` | Multi-env evals against a live inference server. | One epoch per source, pinned (or adaptive) concurrency, cursor checkpoints + `--resume`, dashboard + optional platform upload; see [Eval](eval.md). |
-| `uv run env-server` | Standalone env server for one environment. | The `rl` launcher starts these automatically (one per train/eval source; each binds an OS-assigned loopback port and publishes it to `configs/attempt_N/resolved/envs/<split>/<name>.address` for the orchestrator); only needed when running the orchestrator standalone, or for sources with an explicit `serve.address` — those are externally managed (e.g. their own k8s pod) and the launcher expects the server to already run there. |
+| `uv run env-server` | Standalone env server for one environment. | The `rl` launcher starts these automatically (one per train/eval source; each binds an OS-assigned loopback port and publishes it to `configs/attempt_N/resolved/envs/<split>/<name>.address` for the orchestrator); only needed when running the orchestrator standalone, or for sources with an explicit `serve.address` — those are externally managed (e.g. on their own host) and the launcher expects the server to already run there. |
 
 ## RL Trainer
 

examples/basic/reverse-text/README.md

@@ -98,123 +98,3 @@ uv run eval @ examples/basic/reverse-text/eval.toml -m PrimeIntellect/Qwen3-0.6B
 ```
 
 Way better! Now we get an **average reward of ~0.8**.
-
-## Kubernetes Deployment
-
-If you're running on Kubernetes, you can deploy this example using the provided Helm chart. The Helm chart automatically configures all components with proper networking and shared storage.
-
-### Step 1: Deploy for SFT Training
-
-The reverse-text example is configured with `autoStart: true` for RL training, but we need to run SFT first. Deploy with autoStart disabled:
-
-```bash
-cd k8s
-helm install my-exp ./prime-rl -f ./prime-rl/examples/reverse-text.yaml \
-  --set orchestrator.autoStart=false \
-  --set inference.autoStart=false \
-  --set trainer.autoStart=false
-
-# Check pods are ready
-kubectl get pods -l app=prime-rl,example=reverse-text
-# All pods should show 1/1 Running
-```
-
-### Step 2: Run SFT Training
-
-Exec into the trainer pod and run SFT:
-
-```bash
-kubectl exec -it my-exp-trainer-0 -- bash
-uv run sft @ /app/examples/basic/reverse-text/sft.toml --output-dir /data/outputs --run.name sft
-# This will save DCP checkpoints to /data/outputs/sft/checkpoints/step_100
-```
-
-### Step 3: Deploy RL Training
-
-Now upgrade the deployment to enable autoStart (uses the default `autoStart: true` from reverse-text.yaml):
-
-```bash
-# Exit the pod first (Ctrl+D)
-cd k8s
-helm upgrade my-exp ./prime-rl -f ./prime-rl/examples/reverse-text.yaml
-
-# Check pods have restarted
-kubectl get pods -l app=prime-rl,example=reverse-text
-```
-
-The RL components will automatically start. Monitor the logs:
-
-```bash
-# View inference server logs
-kubectl logs -f my-exp-inference-0
-
-# View orchestrator logs
-kubectl logs -f my-exp-orchestrator-0
-
-# View trainer logs
-kubectl logs -f my-exp-trainer-0
-```
-
-### Alternative: Manual RL Training
-
-If you prefer to run RL components manually, keep `autoStart: false` and exec into each pod:
-
-```bash
-# Terminal 1 - Inference
-kubectl exec -it my-exp-inference-0 -- bash
-uv run inference @ /app/k8s/prime-rl/examples/reverse-text/infer.toml
-
-# Terminal 2 - Orchestrator
-kubectl exec -it my-exp-orchestrator-0 -- bash
-uv run orchestrator @ /app/k8s/prime-rl/examples/reverse-text/orch.toml --output-dir /data/outputs --client.base-url '["'$INFERENCE_URL'"]'
-
-# Terminal 3 - Trainer
-kubectl exec -it my-exp-trainer-0 -- bash
-uv run trainer @ /app/k8s/prime-rl/examples/reverse-text/train.toml --output-dir /data/outputs
-```
-
-### Access Outputs and Checkpoints
-
-All outputs are written to `/data/outputs` on the shared NFS storage:
-
-```bash
-# Exec into any pod to access outputs
-kubectl exec -it my-exp-trainer-0 -- bash
-ls -lh /data/outputs/weights/
-ls -lh /data/outputs/rollouts/
-```
-
-### Step 4: Run Evaluation
-
-To evaluate the trained RL model:
-
-```bash
-# Exec into trainer pod
-kubectl exec -it my-exp-trainer-0 -- bash
-
-# If inference server isn't running with the RL model, start it in another terminal:
-kubectl exec -it my-exp-inference-0 -- bash
-uv run inference --vllm.model /data/outputs/weights_hf/step_20
-
-# Back in trainer pod, run evaluation
-uv run eval @ examples/basic/reverse-text/eval.toml \
-  -m /data/outputs/weights_hf/step_20 \
-  --client.base-url $INFERENCE_URL
-```
-
-### Clean Up
-
-```bash
-helm uninstall my-exp
-# Optionally delete shared data. The PVC is named <release-name>-shared-data:
-kubectl delete pvc my-exp-shared-data
-```
-
-**What the Helm chart provides:**
-- Predictably named pods: `<release-name>-trainer-0`, `<release-name>-inference-0`, `<release-name>-orchestrator-0`
-- Shared NFS storage at `/data` for model checkpoints and outputs
-- GPU resources (1 GPU per inference/trainer pod)
-- Kubernetes services for component communication
-- Auto-start capability with proper networking configured
-
-See the [K8s deployment guide](../../../k8s/README.md) for more details on scaling, distributed training, and advanced configurations.