mirror of
https://github.com/mudler/LocalAI.git
synced 2026-06-22 15:49:12 -04:00
* feat(watchdog): add size-aware LRU eviction mode When the model count hits the LRU limit or the memory reclaimer fires, evict the largest model by on-disk file size first rather than the least-recently-used one. For GGUF models the file size is a reliable proxy for GPU/RAM footprint, so evicting the largest candidate maximises freed memory per eviction round while keeping small utility models (embeddings, classifiers, rerankers) resident. Changes: - `pkg/model/watchdog.go`: add `sizeAwareEviction` flag and `modelSizes map[string]int64` to `WatchDog`; sort candidates by `sizeBytes` desc (LRU time as tiebreaker) when the flag is set; add `RegisterModelSize`, `SetSizeAwareEviction`, `GetSizeAwareEviction` - `pkg/model/watchdog_options.go`: add `WithSizeAwareEviction` option - `pkg/model/initializers.go`: stat model file after load and call `RegisterModelSize` so size data is available before the first eviction - `core/config/application_config.go`, `runtime_settings.go`: add `SizeAwareEviction` field and `WithSizeAwareEviction` app option; expose via `ToRuntimeSettings` / `ApplyRuntimeSettings` for the `POST /api/settings` live-reload path - `core/cli/run.go`: add `--size-aware-eviction` flag / `LOCALAI_SIZE_AWARE_EVICTION` env var - `core/application/startup.go`, `watchdog.go`: wire the new option through to `NewWatchDog` - `pkg/model/watchdog_test.go`: 5 new specs — option enable, dynamic toggle, largest-first ordering, equal-size LRU tiebreaker, no-size fallback to LRU, and size-map cleanup on eviction Closes #9375 Signed-off-by: supermario_leo <leo.stack@outlook.com> * refactor(watchdog): use vram estimation scaffolding for model size Replace the brittle os.Stat(modelFile) approach with a proper call to pkg/vram, which handles multi-file models (DownloadFiles, MMProj) and all weight file types, not just single GGUF files. - Add estimateModelSizeBytes() in core/backend/options.go that collects all weight file URIs from the model config, resolves them to file:// URIs, and calls vram.Estimate() with the shared DefaultCachedSizeResolver (15-min TTL cache avoids redundant stat calls on repeated loads) - Thread the result through via a new WithModelSizeBytes() loader option - In initializers.go, consume the pre-computed size instead of calling os.Stat; if no size was supplied (e.g. for external/router-dispatched models) the registration is simply skipped Signed-off-by: supermario_leo <leo.stack@outlook.com> * refactor(watchdog): use EstimateModel with HF fallback for size estimation Switch estimateModelSizeBytes from calling vram.Estimate directly to the unified vram.EstimateModel entry point, which adds automatic fallbacks: file-based GGUF metadata → HF API → size string. Also extract the HuggingFace repo ID from model URIs (huggingface://, hf://, https://huggingface.co/ and org/model short-form) and pass it as ModelEstimateInput.HFRepo, so models not yet downloaded locally can still get a size estimate via the HF API. Addresses @mudler's review feedback: "better to rely on EstimateModel and pass by the HF URL of the model extracted from the URI". Signed-off-by: supermario_leo <leo.stack@outlook.com> * feat(webui): add Size-Aware Eviction toggle to settings page The size-aware eviction setting was wired through the CLI flag and the RuntimeSettings live-reload path (POST /api/settings) but had no handle on the React settings page, so it could not be toggled from the UI. Add a Size-Aware Eviction toggle to the Watchdog section, next to the existing Force Eviction When Busy / LRU eviction handles. The settings page loads and saves the whole RuntimeSettings object, so the new size_aware_eviction key is picked up with no extra plumbing. Addresses @mudler's review feedback: the application config setting should land on the same UI settings page as the other handles. Signed-off-by: supermario_leo <leo.stack@outlook.com> --------- Signed-off-by: supermario_leo <leo.stack@outlook.com>
403 lines
13 KiB
Go
403 lines
13 KiB
Go
package model
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import (
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"context"
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"errors"
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"fmt"
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"os"
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"strings"
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"time"
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grpc "github.com/mudler/LocalAI/pkg/grpc"
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"github.com/mudler/xlog"
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"github.com/phayes/freeport"
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)
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const (
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LLamaCPP = "llama-cpp"
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IKLLamaCPP = "ik-llama-cpp"
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)
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var Aliases = map[string]string{
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"go-llama": LLamaCPP,
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"llama": LLamaCPP,
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"ik_llama": IKLLamaCPP,
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"ik-llama": IKLLamaCPP,
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"embedded-store": LocalStoreBackend,
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"huggingface-embeddings": TransformersBackend,
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"transformers-musicgen": TransformersBackend,
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"sentencetransformers": TransformersBackend,
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"mamba": TransformersBackend,
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"stablediffusion": StableDiffusionGGMLBackend,
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}
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var TypeAlias = map[string]string{
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"sentencetransformers": "SentenceTransformer",
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"huggingface-embeddings": "SentenceTransformer",
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"mamba": "Mamba",
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"transformers-musicgen": "MusicgenForConditionalGeneration",
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}
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const (
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WhisperBackend = "whisper"
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StableDiffusionGGMLBackend = "stablediffusion-ggml"
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TransformersBackend = "transformers"
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LocalStoreBackend = "local-store"
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)
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// starts the grpcModelProcess for the backend, and returns a grpc client
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// It also loads the model
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func (ml *ModelLoader) grpcModel(backend string, o *Options) func(string, string, string) (*Model, error) {
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return func(modelID, modelName, modelFile string) (*Model, error) {
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xlog.Debug("Loading Model with gRPC", "modelID", modelID, "file", modelFile, "backend", backend, "options", *o)
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// Distributed mode: delegate to the model router if set
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ml.mu.Lock()
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router := ml.modelRouter
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ml.mu.Unlock()
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if router != nil {
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xlog.Info("Routing model to remote node via ModelRouter", "modelID", modelID, "backend", backend)
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return router(o.context, backend, modelID, modelName, modelFile, o.gRPCOptions, o.parallelRequests)
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}
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var client *Model
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getFreeAddress := func() (string, error) {
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port, err := freeport.GetFreePort()
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if err != nil {
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return "", fmt.Errorf("failed allocating free ports: %s", err.Error())
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}
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return fmt.Sprintf("127.0.0.1:%d", port), nil
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}
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// If no specific model path is set for transformers/HF, set it to the model path
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for _, env := range []string{"HF_HOME", "TRANSFORMERS_CACHE", "HUGGINGFACE_HUB_CACHE"} {
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if os.Getenv(env) == "" {
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err := os.Setenv(env, ml.ModelPath)
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if err != nil {
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xlog.Error("unable to set environment variable to modelPath", "error", err, "name", env, "modelPath", ml.ModelPath)
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}
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}
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}
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// Check if the backend is provided as external
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if uri, ok := ml.GetAllExternalBackends(o)[backend]; ok {
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xlog.Debug("Loading external backend", "uri", uri)
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// check if uri is a file or an address
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if fi, err := os.Stat(uri); err == nil {
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xlog.Debug("external backend is file", "file", fi)
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serverAddress, err := getFreeAddress()
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if err != nil {
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return nil, fmt.Errorf("failed allocating free ports: %s", err.Error())
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}
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// Make sure the process is executable
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process, err := ml.startProcess(uri, modelID, serverAddress)
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if err != nil {
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xlog.Error("failed to launch", "error", err, "path", uri)
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return nil, err
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}
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xlog.Debug("GRPC Service Started")
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client = NewModel(modelID, serverAddress, process)
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} else {
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xlog.Debug("external backend is a uri")
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// address
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client = NewModel(modelID, uri, nil)
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}
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} else {
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xlog.Error("Backend not found", "backend", backend)
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return nil, fmt.Errorf("backend not found: %s", backend)
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}
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xlog.Debug("Wait for the service to start up")
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xlog.Debug("Options", "options", o.gRPCOptions)
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// Wait for the service to start up
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ready := false
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for i := range o.grpcAttempts {
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alive, err := client.GRPC(o.parallelRequests, ml.wd).HealthCheck(context.Background())
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if alive {
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xlog.Debug("GRPC Service Ready")
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ready = true
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break
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}
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if err != nil && i == o.grpcAttempts-1 {
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xlog.Error("failed starting/connecting to the gRPC service", "error", err)
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}
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time.Sleep(time.Duration(o.grpcAttemptsDelay) * time.Second)
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}
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if !ready {
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xlog.Debug("GRPC Service NOT ready")
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if process := client.Process(); process != nil {
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process.Stop()
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}
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return nil, fmt.Errorf("grpc service not ready")
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}
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options := *o.gRPCOptions
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options.Model = modelName
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options.ModelFile = modelFile
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options.ModelPath = ml.ModelPath
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xlog.Debug("GRPC: Loading model with options", "options", options)
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res, err := client.GRPC(o.parallelRequests, ml.wd).LoadModel(o.context, &options)
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if err != nil {
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if process := client.Process(); process != nil {
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process.Stop()
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}
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return nil, fmt.Errorf("could not load model: %w", err)
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}
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if !res.Success {
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if process := client.Process(); process != nil {
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process.Stop()
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}
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return nil, fmt.Errorf("could not load model (no success): %s", res.Message)
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}
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// Register size for size-aware eviction using the caller-supplied estimate
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// (computed via pkg/vram, which handles multi-file and non-GGUF models).
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if ml.wd != nil && o.modelSizeBytes > 0 {
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ml.wd.RegisterModelSize(modelID, o.modelSizeBytes)
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}
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return client, nil
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}
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}
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func (ml *ModelLoader) backendLoader(opts ...Option) (client grpc.Backend, err error) {
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o := NewOptions(opts...)
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xlog.Info("BackendLoader starting", "modelID", o.modelID, "backend", o.backendString, "model", o.model)
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backend := strings.ToLower(o.backendString)
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if realBackend, exists := Aliases[backend]; exists {
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typeAlias, exists := TypeAlias[backend]
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if exists {
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xlog.Debug("alias is a type alias", "alias", backend, "realBackend", realBackend, "type", typeAlias)
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o.gRPCOptions.Type = typeAlias
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} else {
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xlog.Debug("alias", "alias", backend, "realBackend", realBackend)
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}
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backend = realBackend
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}
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model, err := ml.LoadModel(o.modelID, o.model, ml.grpcModel(backend, o))
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if err != nil {
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// Defensive cleanup: the model usually wasn't registered yet (LoadModel
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// failed before that), so StopGRPC reporting "model not found" is the
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// expected case, not an error. The outer Failed-to-load log below
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// carries the real reason.
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if stopErr := ml.StopGRPC(only(o.modelID)); stopErr != nil {
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xlog.Debug("cleanup stop after failed load", "error", stopErr, "model", o.modelID)
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}
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xlog.Error("Failed to load model", "modelID", o.modelID, "error", err, "backend", o.backendString)
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return nil, err
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}
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return model.GRPC(o.parallelRequests, ml.wd), nil
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}
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// retryEnforce repeatedly invokes fn until it returns NeedMore=false or the
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// retry budget is exhausted. It sleeps `retryInterval` between attempts and
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// logs progress under `label`. Used by both LRU and group-exclusivity
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// enforcement so the busy-model wait behaviour is identical.
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func retryEnforce(fn func() EnforceLRULimitResult, maxRetries int, retryInterval time.Duration, label string) {
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for attempt := range maxRetries {
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result := fn()
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if !result.NeedMore {
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if result.EvictedCount > 0 {
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xlog.Info("[ModelLoader] "+label+" enforcement complete", "evicted", result.EvictedCount)
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}
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return
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}
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if attempt < maxRetries-1 {
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xlog.Info("[ModelLoader] Waiting for busy models to become idle before eviction",
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"label", label,
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"evicted", result.EvictedCount,
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"attempt", attempt+1,
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"maxRetries", maxRetries,
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"retryIn", retryInterval)
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time.Sleep(retryInterval)
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} else {
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xlog.Warn("[ModelLoader] "+label+" enforcement incomplete after max retries",
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"evicted", result.EvictedCount,
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"reason", "conflicts are still busy or pinned")
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}
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}
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}
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// enforceLRULimit enforces the LRU limit before loading a new model.
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// This is called before loading a model to ensure we don't exceed the limit.
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// It accounts for models that are currently being loaded by other goroutines.
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// If models are busy and can't be evicted, it will wait and retry until space is available.
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func (ml *ModelLoader) enforceLRULimit() {
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if ml.wd == nil {
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return
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}
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pendingLoads := ml.GetLoadingCount()
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ml.mu.Lock()
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maxRetries := ml.lruEvictionMaxRetries
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retryInterval := ml.lruEvictionRetryInterval
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ml.mu.Unlock()
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retryEnforce(func() EnforceLRULimitResult {
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return ml.wd.EnforceLRULimit(pendingLoads)
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}, maxRetries, retryInterval, "LRU")
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}
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// enforceGroupExclusivity evicts every loaded model that shares a concurrency
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// group with modelID before loading proceeds. Reuses the LRU retry settings so
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// busy conflicts wait for the same window as a busy LRU eviction.
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func (ml *ModelLoader) enforceGroupExclusivity(modelID string) {
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if ml.wd == nil {
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return
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}
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ml.mu.Lock()
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maxRetries := ml.lruEvictionMaxRetries
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retryInterval := ml.lruEvictionRetryInterval
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ml.mu.Unlock()
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retryEnforce(func() EnforceLRULimitResult {
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return ml.wd.EnforceGroupExclusivity(modelID)
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}, maxRetries, retryInterval, "group-exclusivity")
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}
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// updateModelLastUsed updates the last used time for a model (for LRU tracking)
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func (ml *ModelLoader) updateModelLastUsed(m *Model) {
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if ml.wd == nil || m == nil {
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return
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}
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ml.wd.UpdateLastUsed(m.address)
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}
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func (ml *ModelLoader) Load(opts ...Option) (grpc.Backend, error) {
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o := NewOptions(opts...)
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ml.mu.Lock()
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distributed := ml.modelRouter != nil
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ml.mu.Unlock()
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// In distributed mode, SmartRouter must run per inference request so
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// PickBestReplica (core/services/nodes/replicapicker.go) picks the
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// least-loaded replica each time. Bypass the local cache and the local
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// LRU / concurrency-group watchdog enforcement: both are scoped to the
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// in-process Model store, which in distributed mode only holds stubs for
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// remote replicas. SmartRouter handles cluster-wide eviction
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// (evictLRUAndFreeNode) and concurrency-group anti-affinity
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// (narrowByGroupAntiAffinity) at the scheduler layer.
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//
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// TODO(distributed-cache): see LoadModel for the rotating-replica-cache
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// integration point that would let hot paths skip the per-request DB
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// round-trip without giving up the shared PickBestReplica policy.
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if distributed {
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client, err := ml.backendLoader(opts...)
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if err != nil {
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return nil, err
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}
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if m := ml.CheckIsLoaded(o.modelID); m != nil && m.Process() == nil {
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client = newConnectionEvictingClient(client, o.modelID, func() {
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if err := ml.ShutdownModel(o.modelID); err != nil {
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xlog.Warn("Failed to shut down remote model after connection error", "model", o.modelID, "error", err)
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}
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})
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}
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return client, nil
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}
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// Return earlier if we have a model already loaded
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// (avoid looping through all the backends)
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if m := ml.CheckIsLoaded(o.modelID); m != nil {
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xlog.Debug("Model already loaded", "model", o.modelID)
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// Update last used time for LRU tracking
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ml.updateModelLastUsed(m)
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client := m.GRPC(o.parallelRequests, ml.wd)
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// Wrap remote models so connection errors during inference trigger eviction
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if m.Process() == nil {
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client = newConnectionEvictingClient(client, o.modelID, func() {
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ml.ShutdownModel(o.modelID)
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})
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}
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return client, nil
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}
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// Evict any loaded model that shares a concurrency group with the
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// requested one before applying the global LRU cap — group eviction may
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// already make room, and otherwise LRU might evict an unrelated model
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// only for the group check to immediately evict another.
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ml.enforceGroupExclusivity(o.modelID)
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// Enforce LRU limit before loading a new model
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ml.enforceLRULimit()
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// if a backend is defined, return the loader directly
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if o.backendString != "" {
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client, err := ml.backendLoader(opts...)
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if err != nil {
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return nil, err
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}
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// Wrap remote models so connection errors during inference trigger eviction
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if m := ml.CheckIsLoaded(o.modelID); m != nil && m.Process() == nil {
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client = newConnectionEvictingClient(client, o.modelID, func() {
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ml.ShutdownModel(o.modelID)
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})
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}
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return client, nil
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}
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// Otherwise scan for backends in the asset directory
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var err error
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// get backends embedded in the binary
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autoLoadBackends := []string{}
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// append externalBackends supplied by the user via the CLI
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for b := range ml.GetAllExternalBackends(o) {
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autoLoadBackends = append(autoLoadBackends, b)
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}
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if len(autoLoadBackends) == 0 {
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xlog.Error("No backends found")
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return nil, fmt.Errorf("no backends found")
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}
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xlog.Debug("Loading from the following backends (in order)", "backends", autoLoadBackends)
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xlog.Info("Trying to load the model", "modelID", o.modelID, "backends", autoLoadBackends)
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for _, key := range autoLoadBackends {
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xlog.Info("Attempting to load", "backend", key)
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options := append(opts, []Option{
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WithBackendString(key),
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}...)
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model, modelerr := ml.backendLoader(options...)
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if modelerr == nil && model != nil {
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xlog.Info("Loads OK", "backend", key)
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// Wrap remote models so connection errors during inference trigger eviction
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if m := ml.CheckIsLoaded(o.modelID); m != nil && m.Process() == nil {
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model = newConnectionEvictingClient(model, o.modelID, func() {
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ml.ShutdownModel(o.modelID)
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})
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}
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return model, nil
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} else if modelerr != nil {
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err = errors.Join(err, fmt.Errorf("[%s]: %w", key, modelerr))
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xlog.Info("Fails", "backend", key, "error", modelerr.Error())
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} else if model == nil {
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err = errors.Join(err, fmt.Errorf("backend %s returned no usable model", key))
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xlog.Info("Fails", "backend", key, "error", "backend returned no usable model")
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}
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}
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return nil, fmt.Errorf("could not load model - all backends returned error: %s", err.Error())
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}
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