Plugins

Couche d'exécution consciente du GPU pour DeepSeek Harness : outils gpu_status / gpu_exec / gpu_run_bg, injection de contexte GPU par étape, sélection automatique de carte via CUDA_VISIBLE_DEVICES.

Outils d'informations d'appareil Windows en lecture seule : un outil agent par catégorie d'appareil Win32 (heure, système, cpu, mémoire, disque, gpu, réseau, batterie, processus, usb, audio, imprimantes) via WMI/CIM et des collecteurs Node os.

Compact macOS strip above the composer showing CPU, GPU, RAM, swap, memory pressure and local Ollama model ON/OFF status. Tested on Harness 0.2.1-rc.5.

DeepSeek Harness plugin: control your LAN AMD inference server over SSH — GPU power limit, Radiance service (docker compose), image updates, shutdown/reboot, plus a live sidebar widget (decode/prefill t/s, CPU/GPU). 通过 SSH 远程控制局域网 AMD 推理服务器，全程仅一条常驻连接。

One-step local model onboarding plus a light harness for DSH. On load it probes loopback and private-network inference endpoints (llama.cpp, Ollama, vLLM, LM Studio), reads each model's context window and output budget from the backend, and registers the provider through settings, so a local model becomes selectable without hand-writing config. It then trims only that provider: masks the tool list through tools.restrict, drops named system-prompt sections and memory contexts, swaps the injected instruction block for a short one while preserving its first 80 and last 96 characters, and truncates tool-schema descriptions without touching structure or required fields. Cloud providers are returned as the same assembly object, untouched. The injected block is re-read from disk on every assembly, so editing the file takes effect on the next request with no reload. The panel in the plugin detail page is a live operations view: it shows whether the takeover is active and by which hook, how many characters the last assembly was trimmed from and to, and which providers are online (probed concurrently, with a real one-shot completion reporting latency and reply text per provider). If a local backend is not running, the panel offers a one-click launch: it probes the port first (never starting a duplicate), then starts the matching server (ollama serve, or a custom command you supply) detached and polls until the endpoint answers, clamping the wait to 3-600 seconds, reaping the child it just spawned if it never answers, and naming the paths it searched when it cannot find the binary. A stop button kills backends this plugin started (SIGTERM, then SIGKILL) and refuses to touch a server somebody else launched. When a model has just been downloaded and no backend exists yet, the panel can also scan the machine for .gguf files with hard caps (300 models, 20000 directory entries, Downloads only one level deep), start llama-server on the chosen one (skipping the .mmproj. vision projection and reusing a server that already answers), register the provider under the model id the backend reports, and run one real completion to prove it works. Before spawning anything it does a fit check that counts only the part of the file the GPU cannot hold: hostBytes = fileSize - VRAM, compared against max(total RAM - 2GB, free RAM). A file far larger than RAM is refused outright; a heavy host-side remainder is refused unless you confirm; the same remainder under mmap is only a warning, because the weights land in reclaimable page cache. A load-mode selector (auto / mmap / none / mlock / mmap+mlock) maps to llama.cpp --load-mode, and the default is whatever the engine picks. From 0.4.2 everything above is derived from the platform instead of assumed: home and data directories come from os.homedir() and the OS conventions (LOCALAPPDATA on Windows, ~/Library/Application Support on macOS, XDG_DATA_HOME elsewhere); backend binaries are looked up in the official install locations of each OS and can be pinned with OLLAMA_BIN, LLAMA_SERVER_BIN, VLLM_BIN or LMSTUDIO_BIN; model discovery walks DSH_LOCAL_MODELS_DIR, ~/models, ~/llama.cpp/models, ~/.cache/llama.cpp and the LM Studio data directory, with the WorkBuddy layout demoted to one candidate among them. VRAM is asked from nvidia-smi, then rocm-smi, then sysctl hw.memsize on Apple Silicon (flagged unified, so the same bytes are not charged twice), and any flip to a unified-memory budget is handled explicitly. Before building a command line the plugin runs llama-server --help once and keeps only the flags that build actually has, so older engines get --n-gpu-layers 99 and no KV quantization while newer ones get auto plus q8_0; anything it cannot probe is passed through rather than dropped. LM Studio is started with lms server start --port N rather than being lumped in with the engines that need a model argument, and vLLM gets its own --model example instead of llama.cpp syntax. Windows, macOS and Linux are all exercised in CI, including a windows-latest and a macos-latest job, because a cross-platform claim that has only ever run on one OS is not a claim. Two of those suites exist specifically to catch this machine's blind spots: one replays the whole suite with process.platform rewritten to each OS, and one deletes every advantage this development box has — no nvidia-smi, no rocm-smi, no sysctl, an empty PATH, a home directory with no models — and requires graceful degradation rather than a crash. That last one caught a real class of bug: several assertions hardcoded 'lms.exe', which cannot pass where the executable has no suffix. Measured on the reference machine: a 16.9GB 27.2B MoE with 14.5GB of VRAM holds a 285-691MB working set and sustains 76 tok/s after a 1000-2000 token warm-up, so file size alone is not a fit signal. From 0.4.3 the scanned models are shown as a table rather than a dropdown (one row per gguf, with its size and any fit warning), so you can see what is on disk before choosing. From 0.4.4 the delete button on the provider table removes the provider entry, not the weights file on disk. That distinction matters: what people actually want to clear out is the dead entry - one pointing at a port that never answered, one registered twice for the same port, one whose model file was deleted long ago - not a 15.8GB gguf. Deleting a config is undone by importing again; deleting a gguf is not. The removal goes through settings.mutate with an unset op on \['providers', id], because update merges layers: writing null there leaves a null provider key that the loader reads as a broken config rather than an absent entry, and only mutate/unset is implemented as Reflect.deleteProperty. The result is read back afterwards, because unset on a key that comes from a lower layer (the profile cordis.patch.yml or a home patch) does not delete it - it restores the inherited value, so the call succeeds while the entry stays. When the entry is still there the panel says so and points at the file to edit instead of reporting success, and a DSH whose settings service predates mutate() is told plainly rather than silently ignored. Deleting takes two clicks and clearing the confirmation is as easy as clicking another row; on success the online and baseURL caches are cleared and the tool surface is recomputed, since the provider list changed. The gguf itself is never touched by this plugin - the scanned-model table is display and selection only.VRAM reporting is driver-dependent. From 0.4.5 model discovery also reads Ollama's own storage: its blobs are named sha256-… with no extension, so extension matching could never list them. Weights are now accepted by extension OR by GGUF magic header (only dot-free files of at least 8MB, capped at 600 header reads per scan; obviously-not-weights extensions like .json/.md/.safetensors/.part are never opened). Ollama models are resolved from their manifests: the model layer is picked by mediaType application/vnd.ollama.image.model (never adapter/mmproj layers), the human-readable name is restored from the manifest path (…/qwen3/8b becomes qwen3:8b, …/latest stays bare), tags sharing one blob merge into a single row, and the metadata kv supplies name, quantization and context length. Adopting an Ollama model does not start a duplicate llama-server over the same weights: it confirms ollama serve answers on 11434 (launching it if not) and registers the provider under the tag the endpoint itself reports — the blob's sha256 name would never match anything, since 11434 lists qwen3:14b, not sha256-a8cc…. Two real-machine-only traps fixed on the way: existsSync('<file>/') returns true on Windows, so manifest-vs-directory branching now goes by parseable content instead of trailing-slash semantics; and the quantization label table had been copied from a stale ggml enum generation, labelling ggml_type 15 as Q5_K_S while ollama show reports Q4_K_M. Same-blob dedup is by (dev,ino), keeping whichever entry has a name. Verified on the reference machine: the scan goes from 1 row to 4 — qwen3:14b (8.64G) and qwen3:8b (4.87G) now appear with Q4_K_M badges — in 4ms; npm run verify exits 0 (launcher 57, portable 35, panel-host green including the linux/darwin simulated-platform runs); the new panel-host suite builds a real miniature Ollama store in tmpdir and points OLLAMA_MODELS at it, so results no longer depend on whether the test machine happens to have Ollama installed. From 0.4.6 the panel shows each model its real name instead of its file name, because some download tooling stores every weight under one generic file name, leaving the real name only inside the GGUF header. The scan now reads each file GGUF KV section - not a fixed-size head buffer, because tokenizer.ggml.tokens (hundreds of thousands of strings on a large MoE model) and the chat template push general.file_type past any window; and GGUF string arrays are interleaved (u64 length plus payload per element), so treating them as a length table walks the cursor off the rails and produces astronomically large reads. Reading and skipping are accounted separately: key names and types are always read, string values over 256KB and string arrays are only walked past without copying, non-string arrays over 64 elements are skipped outright, and a cursor past the file size stops the read, because an out-of-range position means the cursor is wrong rather than that a limit should be raised. The displayed name goes Ollama tag, then general.basename plus size_label, then general.name, then the file name, with the full string kept for the hover text since it usually carries internal build numbers; mmproj files are now identified by general.type as well as by name. A short read (truncated file) no longer feeds half a value into the parser. On the reference machine, reading one file header parses 44 keys in about 16ms, and a whole scan takes about 31ms. From 0.4.7 the smoke test no longer fails the first time and passes the second. The old code gave both request attempts one shared AbortController and one shared 30-second timer, so the total budget covered both rounds: a cold load of a large model burned it in round one, the abort left the signal permanently aborted, and the second round (the Ollama-style reasoning_effort: none retry) could not even be sent - which is exactly why the first click returned This operation was aborted and the second succeeded. Now each attempt gets its own controller and timer, a timeout retries once automatically (retryOnTimeout, on by default) because a first-request timeout almost always means the weights are still being read from disk, and non-timeout errors (refused connection, HTTP error) are not retried at all. Timeouts report themselves: the message says the wait exceeded smokeTimeout, whether a retry already happened, and points at the new smokeTimeout setting (seconds, default 60, clamped to 5-600) - port responding is not the same as tokens coming out, so it is a separate knob from launchTimeout. Unrelated: an example naming one specific model was dropped from the panel copy, since not every machine has that model downloaded, and the explanation now describes the mechanism instead.

Affiche dans l’en-tête le nombre de GPU disponibles sur l’hôte dsh-remote et, dans le panneau latéral droit, l’utilisation, la mémoire, la température et l’utilisateur propriétaire de chaque GPU. Ajoute l’outil `remote_gpu`, qui renvoie le même instantané.

Ajoute au sélecteur les modèles de votre propre Space Hugging Face avec GPU, démarre le Space à l’envoi d’un message, affiche sa progression puis le met en pause lorsqu’il est inactif ou atteint la limite de temps. Nécessite un compte Hugging Face, un Space GPU exposant une API compatible OpenAI, un token stocké dans le vault DSH sous HF_TOKEN et une formule payante pour le matériel GPU. Hugging Face facture le temps GPU.

WispOps (dsh-wisp-ops) : plugin d’opérations multi-hôtes pour DeepSeek Harness — inspection du parc, analyse des disques/fichiers récents avec conseils de nettoyage omics, triage de sécurité, rapports planifiés par serveur et sudo approuvé par une personne.

Exécute Python sur le GPU NVIDIA CUDA local depuis DeepSeek Harness : état de l’appareil, tâches foreground/detached, journaux persistants et pic de VRAM mesuré par exécution.

HUD de performance transparent et déplaçable pour l’interface Web DeepSeek Harness : CPU, GPU et mémoire en trois lignes compactes, superposées sans fond de carte. S’ancre au coin de fenêtre le plus proche et garde sa place après redimensionnement ou maximisation. Température CPU via helper facultatif avec privilèges élevés (LibreHardwareMonitor + PawnIO). Le plugin utilise une fraction de 1 % CPU. Windows uniquement.

Boucle d’auto-amélioration à trois rôles : Data-RSI nettoie quotidiennement (winsorize→standardisation cross-sectionnelle→median fill) et extrait l’expérience ; Harness-RSI fait évoluer prompt/mémoire/skills ; Model-RSI fait évoluer les recettes alpha par walk-forward natif, gap de 2 jours et multi-seed. Hors ligne en une commande, détection GPU, un skill.

Affiche une carte Spark Scope en bas de la barre latérale dsh : débits decode/prefill du serveur de modèles avec courbes, requêtes en cours et en attente, cache KV, TTFT et TPOT p95, ainsi que température, puissance, charge et mémoire GPU de chaque nœud DGX Spark. Lecture seule ; nécessite un serveur Spark Scope actif. Compatible avec dsh web et l’application de bureau.

Plugin de prédiction et de conception de structures protéiques : conception de séquences (ProteinMPNN/SolubleMPNN/LigandMPNN), prédiction de structure (ESMFold), sélection d’interfaces de complexes, scores physico-chimiques et regroupement par diversité. Aucune initialisation manuelle ; adaptation automatique au CPU/GPU local. Utilisé par dsh-bio-genie.

Moniteur de performances de l’hôte dans la barre latérale droite : jauges CPU, mémoire et swap, fréquence GPU et barre VRAM, températures CPU/GPU/carte mère/disques, tableau des processus triable par CPU, mémoire ou nom.

Orchestre plusieurs instances ComfyUI : sonde le GPU, la VRAM libre et la profondeur de file de chaque endpoint configuré, puis envoie la génération à l’instance éligible la moins occupée. Trois outils : comfyui_farm_status, comfyui_farm_pick, comfyui_farm_run. Une instance doit être joignable, disposer d’au moins la VRAM demandée et rester sous le plafond de file configuré. Classe selon VRAM libre moins profondeur de file × poids, favorisant une file courte et inactive. Aucune dépendance d’exécution. Nouvelle tentative facultative après échec de sonde ; un seul timeout via tunnel SSH ne prouve pas l’arrêt.

AlphaRSI : boucle d’auto-amélioration à trois rôles qui fait évoluer la recette d’entraînement des modèles de séquences alpha. Operator exécute le walk-forward, Evaluator note avec OOS RankIC et Refiner propose des hyperparamètres par recherche bayésienne TPE ; seules les recettes strictement meilleures sont conservées. Exécution hors ligne en une commande, bundle de données vérifié par sha1, détection GPU automatique. Une skill incluse.

Reconnaissance de grandes images pour les modèles vision-exp : traitement sans perte par tuiles de 800×800 (smart/pipeline/full), OCR local avec prétraitement et orientation de l’écriture manuscrite. GPU multi-fournisseur (DirectML/CUDA/OpenVINO) facultatif, avec repli automatique sur CPU.

Ajoute les autorisations de lecture/écriture de /dev/dxg et /proc au sandbox Landlock DSH sur WSL2, afin que CUDA soit disponible dans une session bash confinée de l’Agent.

Thème trou noir WebGPU activable pour DeepSeek Harness Web UI : conserve la palette Dark intégrée et n’anime que l’arrière-plan des nouvelles sessions.

Garde le contexte du modèle local à une taille adaptée au GPU : mesure la vitesse de prefill à chaque requête et, avant chaque étape, avertit ou compacte en avance selon une limite stricte de tokens, un budget observé de time-to-first-token ou un budget cold-prefill estimé. `/context-budget` affiche les chiffres et le coût d’un compactage immédiat.

Panneau de contrôle des serveurs d’inférence LLM locaux dans la vue Web DSH : registre de services avec contrôles de santé/arrêt, profils nommés llama.cpp/SGLang/vLLM et validation KV/VRAM avant sauvegarde, détection GPU et benchmarks tok/s.

Plugin DSH (dsh-desktop-launcher) qui lance le shell Electron Windows depuis la conversation : un double-clic ouvre DSH Web dans une fenêtre autonome. Inclut une capture de région avec sélection accélérée par GPU et collage automatique dans le chat (compatible avec les bridges de vision), une zone de notification, un gestionnaire de sessions, la configuration automatique de l’environnement, un port configurable et un verrou mono-instance.