GPU-SHARE Phase 3: train submit + cluster-status CLI#401
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GPU-SHARE Phase 3: train submit + cluster-status CLI#401
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APR CPU was 23x slower than llama.cpp because it used the F32 AprTransformer instead of the fused Q4K engine. Now routes through OwnedQuantizedModel (same path as GGUF/SafeTensors), achieving parity with GGUF CPU (~18 tok/s). Wire --trace flag through to AppState.inference_trace for all serve paths. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add `apr gpu` command: displays GPU UUID, VRAM capacity, active reservations, and available budget from the entrenar VRAM ledger - Add `apr gpu --json` for machine-readable output - Add `--wait-gpu <SECS>` flag to `apr finetune`: polls VRAM ledger until sufficient budget is available (GPU-SHARE-003) - Wire wait_gpu parameter through dispatch → finetune::run() Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
#206) Wire --adapters DATA:CHECKPOINT pairs through finetune command to MultiAdapterPipeline. Parses adapter specs, loads independent corpora, creates round-robin adapter slots on shared frozen base model. Also fixes serde_yaml_ng → serde_yaml migration in distill and serve_plan. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
… (Refs #206) Call save_adapter_checkpoint() at the end of each epoch for every adapter slot. Each adapter saves metadata.json + model.safetensors to its own checkpoint_dir/epoch-N/ independently. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implements two new train subcommands for GPU-SHARE Phase 3: - `apr train submit --cluster cluster.yaml --model model.apr --adapter ...` Places adapter jobs across cluster nodes using greedy placement, shows launch commands (local + SSH). Supports --dry-run and --json. - `apr train cluster-status --cluster cluster.yaml` Displays cluster node info, GPUs, VRAM, and adapter capacity. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
GPU-SHARE Phase 3 CLI: Two new
apr trainsubcommands for cluster management.apr train submit: Places adapter jobs across cluster nodes using greedy placement algorithm, generates local and SSH launch commands. Supports--dry-runand--jsonoutput.apr train cluster-status: Displays cluster configuration including nodes, GPUs, VRAM, and adapter capacity.Files changed
train_commands.rs— AddedSubmitandClusterStatusvariantsdispatch_analysis.rs— Added match arms indispatch_train_command()commands/train.rs—run_submit()andrun_cluster_status()implementationsUsage
Test plan
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