mirror of
https://github.com/1Panel-dev/1Panel.git
synced 2025-12-17 21:08:25 +08:00
feat: support tensorrt LLM speedup (#10905)
This commit is contained in:
CityFun
GitHub
parent
da5668ac27
commit
3b73de32ad
18 changed files with 149 additions and 19 deletions
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@ -14,6 +14,8 @@ type TensorRTLLMCreate struct {
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ModelDir string `json:"modelDir" validate:"required"`
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Image string `json:"image" validate:"required"`
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Command string `json:"command" validate:"required"`
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ModelType string `json:"modelType"`
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ModelSpeedup bool `json:"modelSpeedup"`
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DockerConfig
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}
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@ -6,8 +6,9 @@ type TensorRTLLM struct {
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DockerCompose string `json:"dockerCompose"`
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ContainerName string `json:"containerName"`
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Message string `json:"message"`
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//Port int `json:"port"`
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Status string `json:"status"`
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Env string `json:"env"`
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TaskID string `json:"taskID"`
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ModelType string `json:"modelType"`
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ModelSpeedup bool `json:"modelSpeedup"`
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}
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@ -1,6 +1,7 @@
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package service
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import (
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"errors"
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"fmt"
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"github.com/1Panel-dev/1Panel/agent/app/dto/request"
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"github.com/1Panel-dev/1Panel/agent/app/dto/response"
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@ -15,7 +16,9 @@ import (
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"github.com/1Panel-dev/1Panel/agent/utils/files"
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"github.com/subosito/gotenv"
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"gopkg.in/yaml.v3"
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"os"
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"path"
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"path/filepath"
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"regexp"
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"strconv"
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"strings"
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@ -52,7 +55,7 @@ func (t TensorRTLLMService) Page(req request.TensorRTLLMSearch) response.TensorR
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serverDTO.ModelDir = envs["MODEL_PATH"]
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serverDTO.Dir = path.Join(global.Dir.TensorRTLLMDir, item.Name)
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serverDTO.Image = envs["IMAGE"]
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serverDTO.Command = envs["COMMAND"]
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serverDTO.Command = getCommand(item.Env)
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for k, v := range envs {
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if strings.Contains(k, "CONTAINER_PORT") || strings.Contains(k, "HOST_PORT") {
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@ -94,7 +97,7 @@ func (t TensorRTLLMService) Page(req request.TensorRTLLMSearch) response.TensorR
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}
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var defaultVolumes = map[string]string{
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"${MODEL_PATH}": "/models",
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"${MODEL_PATH}": "${MODEL_PATH}",
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}
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for _, volume := range volumes {
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exist := false
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@ -227,14 +230,21 @@ func (t TensorRTLLMService) Create(create request.TensorRTLLMCreate) error {
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}
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tensorrtLLMDir := path.Join(global.Dir.TensorRTLLMDir, create.Name)
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filesOP := files.NewFileOp()
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if !filesOP.Stat(tensorrtLLMDir) {
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_ = filesOP.CreateDir(tensorrtLLMDir, 0644)
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filesOp := files.NewFileOp()
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if !filesOp.Stat(tensorrtLLMDir) {
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_ = filesOp.CreateDir(tensorrtLLMDir, 0644)
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}
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if create.ModelSpeedup {
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if err := handleModelArchive(create.ModelType, create.ModelDir); err != nil {
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return err
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}
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}
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tensorrtLLM := &model.TensorRTLLM{
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Name: create.Name,
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ContainerName: create.ContainerName,
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Status: constant.StatusStarting,
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ModelType: create.ModelType,
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ModelSpeedup: create.ModelSpeedup,
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}
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if err := handleLLMParams(tensorrtLLM, create); err != nil {
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@ -247,7 +257,7 @@ func (t TensorRTLLMService) Create(create request.TensorRTLLMCreate) error {
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return err
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}
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dockerComposePath := path.Join(llmDir, "docker-compose.yml")
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if err := filesOP.SaveFile(dockerComposePath, tensorrtLLM.DockerCompose, 0644); err != nil {
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if err := filesOp.SaveFile(dockerComposePath, tensorrtLLM.DockerCompose, 0644); err != nil {
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return err
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}
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tensorrtLLM.Status = constant.StatusStarting
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@ -269,8 +279,10 @@ func (t TensorRTLLMService) Update(req request.TensorRTLLMUpdate) error {
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return err
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}
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}
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tensorrtLLM.ModelType = req.ModelType
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tensorrtLLM.ModelSpeedup = req.ModelSpeedup
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tensorrtLLM.ContainerName = req.ContainerName
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if err := handleLLMParams(tensorrtLLM, req.TensorRTLLMCreate); err != nil {
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return err
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}
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@ -381,3 +393,51 @@ func syncTensorRTLLMContainerStatus(tensorrtLLM *model.TensorRTLLM) error {
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}
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return tensorrtLLMRepo.Save(tensorrtLLM)
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}
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func findModelArchive(modelType string) (string, error) {
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const baseDir = "/home/models"
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prefix := fmt.Sprintf("FusionXplay_%s_Accelerator", modelType)
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entries, err := os.ReadDir(baseDir)
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if err != nil {
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return "", fmt.Errorf("failed to read %s: %w", baseDir, err)
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}
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for _, entry := range entries {
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if entry.IsDir() {
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continue
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}
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name := entry.Name()
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if strings.HasPrefix(name, prefix) && strings.HasSuffix(name, ".tar.gz") {
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return filepath.Join(baseDir, name), nil
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}
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}
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return "", errors.New(fmt.Sprintf("no FusionXplay_%s_Accelerator*.tar.gz found in /home/models", modelType))
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}
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func handleModelArchive(modelType string, modelDir string) error {
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filePath, err := findModelArchive(modelType)
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if err != nil {
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return err
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}
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fileOp := files.NewFileOp()
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if err = fileOp.TarGzExtractPro(filePath, modelDir, ""); err != nil {
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return err
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}
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if err = fileOp.ChmodR(path.Join(modelDir, "fusionxpark_accelerator"), 0755, false); err != nil {
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return err
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}
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return nil
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}
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func getCommand(envStr string) string {
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lines := strings.Split(envStr, "\n")
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for _, line := range lines {
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line = strings.TrimSpace(line)
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if strings.HasPrefix(line, "COMMAND=") {
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return strings.TrimPrefix(line, "COMMAND=")
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}
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}
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return ""
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}
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@ -14,7 +14,7 @@ services:
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networks:
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- 1panel-network
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volumes:
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- ${MODEL_PATH}:/models
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- ${MODEL_PATH}:${MODEL_PATH}
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ipc: host
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ulimits:
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memlock:
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@ -50,6 +50,7 @@ func InitAgentDB() {
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migrations.UpdateMonitorInterval,
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migrations.AddMonitorProcess,
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migrations.UpdateCronJob,
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migrations.UpdateTensorrtLLM,
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})
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if err := m.Migrate(); err != nil {
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global.LOG.Error(err)
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@ -673,3 +673,10 @@ var UpdateCronJob = &gormigrate.Migration{
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return tx.AutoMigrate(&model.Cronjob{})
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},
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}
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var UpdateTensorrtLLM = &gormigrate.Migration{
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ID: "20251110-update-tensorrt-llm",
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Migrate: func(tx *gorm.DB) error {
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return tx.AutoMigrate(&model.TensorRTLLM{})
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},
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}
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@ -746,6 +746,8 @@ const message = {
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'After /models in the startup command, the model name needs to be completed; if external access is required, set the port in the command to be the same as the application port',
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imageAlert:
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'Due to the large image size, it is recommended to manually download the image to the server before installation',
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modelSpeedup: 'Enable model acceleration',
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modelType: 'Model type',
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},
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},
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container: {
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@ -747,6 +747,8 @@ const message = {
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'Después de /models en el comando de inicio, se debe completar el nombre del modelo; si se requiere acceso externo, configure el puerto en el comando para que sea el mismo que el puerto de la aplicación',
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imageAlert:
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'Debido al gran tamaño de la imagen, se recomienda descargar manualmente la imagen al servidor antes de la instalación',
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modelSpeedup: 'Habilitar aceleración de modelo',
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modelType: 'Tipo de modelo',
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},
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},
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container: {
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@ -734,6 +734,8 @@ const message = {
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'起動コマンドの /models の後にはモデル名を補完する必要があります；外部アクセスが必要な場合は、コマンド内のポートをアプリケーションポートと同じに設定してください',
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imageAlert:
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'イメージサイズが大きいため、インストール前にサーバーにイメージを手動でダウンロードすることをお勧めします',
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modelSpeedup: 'モデル加速を有効化',
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modelType: 'モデルタイプ',
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},
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},
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container: {
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@ -729,6 +729,8 @@ const message = {
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commandHelper:
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'시작 명령의 /models 뒤에는 모델 이름을 완성해야 합니다; 외부 액세스가 필요한 경우 명령의 포트를 애플리케이션 포트와 동일하게 설정하세요',
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imageAlert: '이미지 크기가 크므로 설치 전에 서버에 이미지를 수동으로 다운로드하는 것이 좋습니다',
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modelSpeedup: '모델 가속 활성화',
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modelType: '모델 유형',
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},
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},
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container: {
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@ -747,6 +747,8 @@ const message = {
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'Selepas /models dalam arahan permulaan, nama model perlu dilengkapkan; jika akses luar diperlukan, tetapkan port dalam arahan sama dengan port aplikasi',
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imageAlert:
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'Disebabkan saiz imej yang besar, disyorkan untuk memuat turun imej secara manual ke pelayan sebelum pemasangan',
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modelSpeedup: 'Dayakan pecutan model',
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modelType: 'Jenis model',
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},
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},
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container: {
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@ -743,6 +743,8 @@ const message = {
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'Após /models no comando de inicialização, o nome do modelo precisa ser completado; se for necessário acesso externo, defina a porta no comando para ser a mesma que a porta do aplicativo',
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imageAlert:
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'Devido ao grande tamanho da imagem, recomenda-se baixar manualmente a imagem para o servidor antes da instalação',
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modelSpeedup: 'Ativar aceleração de modelo',
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modelType: 'Tipo de modelo',
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},
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},
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container: {
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@ -741,6 +741,8 @@ const message = {
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'После /models в команде запуска необходимо указать имя модели; если требуется внешний доступ, установите порт в команде таким же, как порт приложения',
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imageAlert:
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'Из-за большого размера образа рекомендуется вручную загрузить образ на сервер перед установкой',
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modelSpeedup: 'Включить ускорение модели',
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modelType: 'Тип модели',
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},
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},
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container: {
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@ -755,6 +755,8 @@ const message = {
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'Başlatma komutundaki /models sonrasında model adı tamamlanmalıdır; harici erişim gerekiyorsa, komuttaki bağlantı noktasını uygulama bağlantı noktasıyla aynı olacak şekilde ayarlayın',
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imageAlert:
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'Görüntü boyutu büyük olduğundan, kurulumdan önce görüntüyü sunucuya manuel olarak indirmeniz önerilir',
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modelSpeedup: 'Model hızlandırmayı etkinleştir',
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modelType: 'Model türü',
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},
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},
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container: {
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@ -716,6 +716,8 @@ const message = {
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modelDir: '模型目錄',
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commandHelper: '啟動指令中的 /models 後需補全模型名稱；若需外部訪問，請將指令中的埠設定為與應用埠相同',
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imageAlert: '由於鏡像較大，建議先手動將鏡像下載到伺服器後再進行安裝',
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modelSpeedup: '啟用模型加速',
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modelType: '模型類型',
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},
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},
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container: {
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@ -714,8 +714,10 @@ const message = {
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tensorRT: {
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llm: 'TensorRT LLM',
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modelDir: '模型目录',
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commandHelper: '启动命令中的 /models 后需补全模型名称；若需外部访问，请将命令中的端口设置为与应用端口相同',
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commandHelper: '若需外部访问，请将命令中的端口设置为与应用端口相同',
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imageAlert: '由于镜像较大，建议先手动将镜像下载到服务器后再进行安装',
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modelSpeedup: '启用模型加速',
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modelType: '模型类型',
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},
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},
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container: {
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@ -14,6 +14,20 @@
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<el-form-item :label="$t('app.version')" prop="version">
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<el-input v-model.trim="tensorRTLLM.version" />
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</el-form-item>
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<div v-if="isFxplay">
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<el-form-item :label="$t('aiTools.tensorRT.modelSpeedup')" prop="modelSpeedup">
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<el-switch v-model="tensorRTLLM.modelSpeedup" @change="changeModelSpeedup"></el-switch>
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</el-form-item>
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<el-form-item
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:label="$t('aiTools.tensorRT.modelType')"
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prop="modelType"
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v-if="tensorRTLLM.modelSpeedup"
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>
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<el-select v-model="tensorRTLLM.modelType">
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<el-option label="Qwen3" value="Qwen3" />
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</el-select>
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</el-form-item>
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</div>
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<el-form-item :label="$t('aiTools.tensorRT.modelDir')" prop="modelDir">
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<el-input v-model="tensorRTLLM.modelDir">
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<template #prepend>
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@ -52,6 +66,8 @@ import i18n from '@/lang';
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import { ElForm, FormInstance } from 'element-plus';
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import { createTensorRTLLM, updateTensorRTLLM } from '@/api/modules/ai';
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import { MsgSuccess } from '@/utils/message';
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import { useGlobalStore } from '@/composables/useGlobalStore';
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const { isFxplay } = useGlobalStore();
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const loading = ref(false);
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const mode = ref('create');
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@ -63,11 +79,13 @@ const newTensorRTLLM = () => {
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version: '1.2.0rc0',
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modelDir: '',
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image: 'nvcr.io/nvidia/tensorrt-llm/release',
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command: 'bash -c "trtllm-serve /models/ --host 0.0.0.0 --port 8000"',
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command: 'bash -c "trtllm-serve ${MODEL_PATH} --host 0.0.0.0 --port 8000"',
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exposedPorts: [],
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environments: [],
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extraHosts: [],
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volumes: [],
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modelSpeedup: false,
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modelType: 'Qwen3',
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};
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};
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const modelDirRef = ref();
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@ -106,6 +124,15 @@ const getModelDir = (path: string) => {
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tensorRTLLM.value.modelDir = path;
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};
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const changeModelSpeedup = () => {
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if (tensorRTLLM.value.modelSpeedup) {
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tensorRTLLM.value.command =
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'bash -c "${MODEL_PATH}/fusionxpark_accelerator --model_path ${MODEL_PATH} --host 0.0.0.0 --port 8000"';
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} else {
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tensorRTLLM.value.command = 'bash -c "trtllm-serve ${MODEL_PATH} --host 0.0.0.0 --port 8000"';
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}
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};
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const rules = reactive({
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name: [Rules.requiredInput],
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version: [Rules.requiredInput],
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@ -113,6 +140,7 @@ const rules = reactive({
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containerName: [Rules.requiredInput],
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image: [Rules.requiredInput],
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command: [Rules.requiredInput],
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modelType: [Rules.requiredSelect],
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});
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const formRef = ref<FormInstance>();
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@ -6,7 +6,9 @@
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<el-tab-pane :label="$t('runtime.environment')">
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<Environment :environments="runtime.environments" />
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</el-tab-pane>
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<el-tab-pane :label="$t('container.mount')"><Volumes :volumes="runtime.volumes" /></el-tab-pane>
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<el-tab-pane :label="$t('container.mount')">
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<Volumes :volumes="runtime.volumes" />
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</el-tab-pane>
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<el-tab-pane :label="$t('runtime.extraHosts')">
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<ExtraHosts :extraHosts="runtime.extraHosts" />
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</el-tab-pane>
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@ -18,17 +20,26 @@ import PortConfig from '@/views/website/runtime/components/port/index.vue';
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import Environment from '@/views/website/runtime/components/environment/index.vue';
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import Volumes from '@/views/website/runtime/components/volume/index.vue';
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import ExtraHosts from '@/views/website/runtime/components/extra_hosts/index.vue';
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import { Runtime } from '@/api/interface/runtime';
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import { useVModel } from '@vueuse/core';
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import type { PropType } from 'vue';
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interface RuntimeConfigFields {
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exposedPorts: any[];
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environments: any[];
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volumes: any[];
|
||||
extraHosts: any[];
|
||||
}
|
||||
|
||||
const props = defineProps({
|
||||
modelValue: {
|
||||
type: Object as PropType<Runtime.Runtime>,
|
||||
type: Object as PropType<RuntimeConfigFields>,
|
||||
required: true,
|
||||
},
|
||||
});
|
||||
|
||||
const emit = defineEmits(['update:modelValue']);
|
||||
const emit = defineEmits<{
|
||||
'update:modelValue': [value: RuntimeConfigFields];
|
||||
}>();
|
||||
|
||||
const runtime = useVModel(props, 'modelValue', emit);
|
||||
</script>
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue