# GPU Resource Optimization Implementation Plan > **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task. **Goal:** Route RL training (DQN, PPO) to cheaper L4 GPUs and supervised models to L40S, with tuned CPU/memory per workload. **Architecture:** Add a `.train-rl-base` CI template (no node selector override → runner default ci-compile/L4). Update `.train-validate-base` CPU down for GPU-bound supervised models. Switch 3 RL jobs to new template. Reduce RL hyperopt epochs 15→8. **Tech Stack:** GitLab CI YAML, Kubernetes node selectors via runner config overwrite variables --- ### Task 1: Add `.train-rl-base` CI template **Files:** - Modify: `.gitlab-ci.yml:413` (insert new template BEFORE existing `.train-validate-base`) **Step 1: Insert `.train-rl-base` template above `.train-validate-base`** Find the comment block starting at line 410 and insert the new template before it. The new template goes right before the `# Runs the training binary...` comment block: ```yaml # -------------------------------------------------------------------------- # RL training base: DQN + PPO → ci-compile pool (L4) # RL models are CPU-bound (environment simulation), use <1GB VRAM. # No KUBERNETES_NODE_SELECTOR_POOL → runner default = ci-compile (L4-1-24G) # -------------------------------------------------------------------------- .train-rl-base: stage: train image: ${REGISTRY}/training:${CI_COMMIT_SHA} tags: - kapsule - gpu variables: # RL is CPU-bound — give it most of the L4's 7800m allocatable CPU. # 3000m request allows 2 concurrent RL jobs on one L4 node. KUBERNETES_CPU_REQUEST: "3000m" KUBERNETES_CPU_LIMIT: "3800m" KUBERNETES_MEMORY_REQUEST: "8Gi" KUBERNETES_MEMORY_LIMIT: "16Gi" rules: - if: $CI_COMMIT_BRANCH == "main" && $CI_PIPELINE_SOURCE == "push" when: manual allow_failure: true - if: $CI_PIPELINE_SOURCE == "schedule" && $TRAIN_VALIDATE == "true" - if: $CI_PIPELINE_SOURCE == "web" || $CI_PIPELINE_SOURCE == "api" when: manual allow_failure: true before_script: - export LD_LIBRARY_PATH=$(echo "$LD_LIBRARY_PATH" | tr ':' '\n' | grep -v stubs | tr '\n' ':' | sed 's/:$//') - nvidia-smi - mkdir -p ${CI_PROJECT_DIR}/output ``` Insert this BEFORE the existing line 410 (`# Runs the training binary for a few steps...`). **Step 2: Update `.train-validate-base` comment and CPU** In the existing `.train-validate-base` (now shifted down), update: - Comment: change to clarify it's for supervised models on L40S - CPU request: `"2000m"` → `"1000m"` - CPU limit: `"3500m"` → `"2000m"` The updated `.train-validate-base` should read: ```yaml # -------------------------------------------------------------------------- # Supervised training base: TFT, Mamba2, TGGN, TLOB, Liquid, KAN, xLSTM, Diffusion → L40S # Supervised models are GPU-bound — low CPU, high VRAM. Routes to ci-training (L40S-1-48G). # -------------------------------------------------------------------------- .train-validate-base: stage: train image: ${REGISTRY}/training:${CI_COMMIT_SHA} tags: - kapsule - gpu variables: # Route to L40S pool (48GB VRAM for large supervised models) KUBERNETES_NODE_SELECTOR_POOL: "k8s.scaleway.com/pool-name=ci-training" # Supervised is GPU-bound — minimal CPU. Low request allows 3-4 concurrent jobs. KUBERNETES_CPU_REQUEST: "1000m" KUBERNETES_CPU_LIMIT: "2000m" KUBERNETES_MEMORY_REQUEST: "16Gi" KUBERNETES_MEMORY_LIMIT: "40Gi" ``` **Step 3: Commit** ```bash git add .gitlab-ci.yml git commit -m "feat(ci): add .train-rl-base template for L4 routing, tune supervised CPU" ``` --- ### Task 2: Switch RL jobs to `.train-rl-base` **Files:** - Modify: `.gitlab-ci.yml` — 3 jobs: `train-validate-rl`, `hyperopt-ppo`, `hyperopt-dqn` **Step 1: Change `train-validate-rl` extends** Find `train-validate-rl:` and change: ```yaml # BEFORE: train-validate-rl: extends: .train-validate-base # AFTER: train-validate-rl: extends: .train-rl-base ``` **Step 2: Change `hyperopt-ppo` extends** Find `hyperopt-ppo:` and change: ```yaml # BEFORE: hyperopt-ppo: extends: .train-validate-base # AFTER: hyperopt-ppo: extends: .train-rl-base ``` **Step 3: Change `hyperopt-dqn` extends** Find `hyperopt-dqn:` and change: ```yaml # BEFORE: hyperopt-dqn: extends: .train-validate-base # AFTER: hyperopt-dqn: extends: .train-rl-base ``` **Step 4: Commit** ```bash git add .gitlab-ci.yml git commit -m "feat(ci): route RL jobs (DQN, PPO) to L4 via .train-rl-base" ``` --- ### Task 3: Reduce RL hyperopt epochs 15 → 8 **Files:** - Modify: `.gitlab-ci.yml` — `hyperopt-ppo` and `hyperopt-dqn` scripts **Step 1: Change `hyperopt-ppo` epochs** In the `hyperopt-ppo` script section, find `--epochs 15` and change to `--epochs 8`. **Step 2: Change `hyperopt-dqn` epochs** In the `hyperopt-dqn` script section, find `--epochs 15` and change to `--epochs 8`. **Step 3: Commit** ```bash git add .gitlab-ci.yml git commit -m "feat(ci): reduce RL hyperopt epochs 15→8 for faster iteration" ``` --- ### Task 4: Validate pipeline and push **Step 1: Lint the YAML** ```bash python3 -c "import yaml; yaml.safe_load(open('.gitlab-ci.yml'))" && echo "YAML valid" ``` Expected: `YAML valid` **Step 2: Verify job routing by checking extends** ```bash grep -E '(train-validate-rl|hyperopt-ppo|hyperopt-dqn):' -A1 .gitlab-ci.yml | grep extends ``` Expected: All 3 show `extends: .train-rl-base` ```bash grep -E '(train-validate-tft|train-validate-mamba2|hyperopt-tft|hyperopt-mamba2):' -A1 .gitlab-ci.yml | grep extends ``` Expected: All show `extends: .train-validate-base` **Step 3: Verify epochs** ```bash grep -B5 'epochs 8' .gitlab-ci.yml | grep -E '(hyperopt-ppo|hyperopt-dqn)' ``` Expected: Both RL hyperopt jobs show epochs 8. ```bash grep -B5 'epochs 15' .gitlab-ci.yml | head -20 ``` Expected: Only supervised hyperopt jobs (tft, mamba2, liquid, tggn, tlob, kan, xlstm, diffusion) still have `--epochs 15`. **Step 4: Push and verify** ```bash git push origin main ``` Trigger a pipeline via Rails console and verify: - RL jobs (train-validate-rl, hyperopt-ppo, hyperopt-dqn) schedule on ci-compile (L4) node - Supervised jobs schedule on ci-training (L40S) node - RL hyperopt shows `--epochs 8` in log output