Improved classification performance
This commit is contained in:
parent
516d1d7634
commit
652542d261
@ -31,7 +31,10 @@ ADD go.mod .
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ADD go.sum .
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ADD main.go .
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ADD logic logic
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ADD entrypoint.sh .
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RUN go install || true
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CMD ["go", "run", "."]
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RUN go build .
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CMD ["./entrypoint.sh"]
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@ -12,7 +12,7 @@ USER = "service"
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[Worker]
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PULLING_TIME = "500ms"
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NUMBER_OF_WORKERS = 1
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NUMBER_OF_WORKERS = 16
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[DB]
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MAX_CONNECTIONS = 600
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@ -23,6 +23,13 @@ services:
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- db
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volumes:
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- "./config.toml:/app/config.toml"
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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proxy-server:
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image: andre-fyp-proxy
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networks:
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4
entrypoint.sh
Executable file
4
entrypoint.sh
Executable file
@ -0,0 +1,4 @@
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#/bin/bash
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while true; do
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./fyp
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done
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@ -16,7 +16,6 @@ import (
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)
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func loadBaseImage(c *Context, id string) {
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// TODO handle more types than png
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infile, err := os.Open(path.Join("savedData", id, "baseimage.png"))
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if err != nil {
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c.Logger.Errorf("Failed to read image for model with id %s\n", id)
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@ -4,6 +4,7 @@ import (
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"errors"
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"os"
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"path"
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"runtime/debug"
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. "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
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. "git.andr3h3nriqu3s.com/andr3/fyp/logic/tasks/utils"
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@ -37,11 +38,19 @@ func ReadJPG(scope *op.Scope, imagePath string, channels int64) *image.Image {
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return image.Scale(0, 255)
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}
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func runModelNormal(model *BaseModel, def_id string, inputImage *tf.Tensor) (order int, confidence float32, err error) {
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func runModelNormal(model *BaseModel, def_id string, inputImage *tf.Tensor, data *RunnerModelData) (order int, confidence float32, err error) {
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order = 0
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err = nil
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tf_model := tg.LoadModel(path.Join("savedData", model.Id, "defs", def_id, "model"), []string{"serve"}, nil)
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var tf_model *tg.Model = nil
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if data.Id != nil && *data.Id == def_id {
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tf_model = data.Model
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} else {
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tf_model = tg.LoadModel(path.Join("savedData", model.Id, "defs", def_id, "model"), []string{"serve"}, nil)
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data.Model = tf_model
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data.Id = &def_id
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}
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results := tf_model.Exec([]tf.Output{
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tf_model.Op("StatefulPartitionedCall", 0),
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@ -125,10 +134,15 @@ func runModelExp(base BasePack, model *BaseModel, def_id string, inputImage *tf.
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return
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}
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func ClassifyTask(base BasePack, task Task) (err error) {
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type RunnerModelData struct {
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Id *string
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Model *tg.Model
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}
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func ClassifyTask(base BasePack, task Task, data *RunnerModelData) (err error) {
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defer func() {
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if r := recover(); r != nil {
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base.GetLogger().Error("Task failed due to", "error", r)
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base.GetLogger().Error("Task failed due to", "error", r, "stack", string(debug.Stack()))
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task.UpdateStatusLog(base, TASK_FAILED_RUNNING, "Task failed running")
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}
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}()
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@ -186,6 +200,8 @@ func ClassifyTask(base BasePack, task Task) (err error) {
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if model.ModelType == 2 {
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base.GetLogger().Info("Running model normal", "model", model.Id, "def", def_id)
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data.Model = nil
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data.Id = nil
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vi, confidence, err = runModelExp(base, model, def_id, inputImage)
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if err != nil {
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task.UpdateStatusLog(base, TASK_FAILED_RUNNING, "Failed to run model")
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@ -193,7 +209,7 @@ func ClassifyTask(base BasePack, task Task) (err error) {
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}
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} else {
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base.GetLogger().Info("Running model normal", "model", model.Id, "def", def_id)
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vi, confidence, err = runModelNormal(model, def_id, inputImage)
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vi, confidence, err = runModelNormal(model, def_id, inputImage, data)
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if err != nil {
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task.UpdateStatusLog(base, TASK_FAILED_RUNNING, "Failed to run model")
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return
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@ -1191,7 +1191,7 @@ func generateDefinition(c BasePack, model *BaseModel, target_accuracy int, numbe
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}
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order++
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loop := max(1, int((math.Log(float64(model.Width)) / math.Log(float64(10)))))
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loop := max(1, int(math.Ceil((math.Log(float64(model.Width))/math.Log(float64(10)))))+1)
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for i := 0; i < loop; i++ {
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_, err = def.MakeLayer(db, order, LAYER_SIMPLE_BLOCK, "")
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order++
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@ -1299,7 +1299,7 @@ func generateExpandableDefinition(c BasePack, model *BaseModel, target_accuracy
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order++
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// Create the blocks
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loop := int((math.Log(float64(model.Width)) / math.Log(float64(10))))
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loop := int(math.Ceil((math.Log(float64(model.Width)) / math.Log(float64(10))))) + 1
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/*if model.Width < 50 && model.Height < 50 {
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loop = 0
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@ -68,7 +68,7 @@ func handleTasksStats(handle *Handle) {
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} else if task.Status < 2 {
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total.Classfication_pre += 1
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hours[hour].Classfication_pre += 1
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} else if task.Status < 4 {
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} else if task.Status < 4 || task.Status == 5 {
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total.Classfication_running += 1
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hours[hour].Classfication_running += 1
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}
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@ -19,6 +19,8 @@ import (
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. "git.andr3h3nriqu3s.com/andr3/fyp/logic/utils"
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)
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var QUEUE_SIZE = 10
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/**
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* Actually runs the code
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*/
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@ -47,17 +49,28 @@ func runner(config Config, db db.Db, task_channel chan Task, index int, back_cha
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Host: config.Hostname,
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}
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loaded_model := RunnerModelData{
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Id: nil,
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Model: nil,
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}
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count := 0
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for task := range task_channel {
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logger.Info("Got task", "task", task)
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task.UpdateStatusLog(base, TASK_PICKED_UP, "Runner picked up task")
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if task.TaskType == int(TASK_TYPE_CLASSIFICATION) {
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logger.Info("Classification Task")
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if err = ClassifyTask(base, task); err != nil {
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if err = ClassifyTask(base, task, &loaded_model); err != nil {
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logger.Error("Classification task failed", "error", err)
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}
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back_channel <- index
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if count == QUEUE_SIZE {
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back_channel <- index
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count = 0
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} else {
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count += 1
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}
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continue
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} else if task.TaskType == int(TASK_TYPE_TRAINING) {
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logger.Info("Training Task")
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@ -65,7 +78,12 @@ func runner(config Config, db db.Db, task_channel chan Task, index int, back_cha
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logger.Error("Failed to tain the model", "error", err)
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}
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back_channel <- index
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if count == QUEUE_SIZE {
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back_channel <- index
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count = 0
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} else {
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count += 1
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}
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continue
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} else if task.TaskType == int(TASK_TYPE_RETRAINING) {
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logger.Info("Retraining Task")
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@ -73,7 +91,12 @@ func runner(config Config, db db.Db, task_channel chan Task, index int, back_cha
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logger.Error("Failed to tain the model", "error", err)
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}
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back_channel <- index
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if count == QUEUE_SIZE {
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back_channel <- index
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count = 0
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} else {
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count += 1
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}
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continue
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} else if task.TaskType == int(TASK_TYPE_DELETE_USER) {
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logger.Warn("User deleting Task")
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@ -81,13 +104,23 @@ func runner(config Config, db db.Db, task_channel chan Task, index int, back_cha
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logger.Error("Failed to tain the model", "error", err)
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}
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back_channel <- index
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if count == QUEUE_SIZE {
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back_channel <- index
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count = 0
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} else {
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count += 1
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}
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continue
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}
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logger.Error("Do not know how to route task", "task", task)
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task.UpdateStatusLog(base, TASK_FAILED_RUNNING, "Do not know how to route task")
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back_channel <- index
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if count == QUEUE_SIZE {
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back_channel <- index
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count = 0
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} else {
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count += 1
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}
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}
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}
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@ -145,7 +178,7 @@ func handleRemoteTask(handler *Handle, base BasePack, runner_id string, task Tas
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/**
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* Tells the orcchestator to look at the task list from time to time
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*/
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func attentionSeeker(config Config, back_channel chan int) {
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func attentionSeeker(config Config, db db.Db, back_channel chan int) {
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logger := log.NewWithOptions(os.Stdout, log.Options{
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ReportCaller: true,
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ReportTimestamp: true,
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@ -170,6 +203,20 @@ func attentionSeeker(config Config, back_channel chan int) {
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for true {
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back_channel <- 0
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for {
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var s struct {
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Count int `json:"count(*)"`
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}
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err := GetDBOnce(db, &s, "tasks where stauts = 5 or status = 3")
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if err != nil {
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break
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}
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if s.Count == 0 {
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break
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}
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time.Sleep(t)
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}
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time.Sleep(t)
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}
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}
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@ -194,11 +241,16 @@ func RunnerOrchestrator(db db.Db, config Config, handler *Handle) {
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}
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gpu_workers := config.GpuWorker.NumberOfWorkers
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def_wait, err := time.ParseDuration(config.GpuWorker.Pulling)
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if err != nil {
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logger.Error("Failed to load", "error", err)
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return
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}
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logger.Info("Starting runners")
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task_runners := make([]chan Task, gpu_workers)
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task_runners_used := make([]bool, gpu_workers)
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task_runners_used := make([]int, gpu_workers)
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// One more to accomudate the Attention Seeker channel
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back_channel := make(chan int, gpu_workers+1)
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@ -213,12 +265,12 @@ func RunnerOrchestrator(db db.Db, config Config, handler *Handle) {
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}
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}()
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go attentionSeeker(config, back_channel)
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// go attentionSeeker(config, db, back_channel)
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// Start the runners
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for i := 0; i < gpu_workers; i++ {
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task_runners[i] = make(chan Task, 10)
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task_runners_used[i] = false
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task_runners[i] = make(chan Task, QUEUE_SIZE)
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task_runners_used[i] = 0
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AddLocalRunner(handler, LocalRunner{
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RunnerNum: i + 1,
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Task: nil,
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@ -226,82 +278,107 @@ func RunnerOrchestrator(db db.Db, config Config, handler *Handle) {
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go runner(config, db, task_runners[i], i+1, back_channel)
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}
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var task_to_dispatch *Task = nil
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for i := range back_channel {
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if i != 0 {
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if i > 0 {
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logger.Info("Runner freed", "runner", i)
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task_runners_used[i-1] = false
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} else if i < 0 {
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logger.Error("Runner died! Restarting!", "runner", i)
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i = int(math.Abs(float64(i)) - 1)
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task_runners_used[i] = false
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go runner(config, db, task_runners[i], i+1, back_channel)
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used := 0
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wait := time.Nanosecond * 100
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for {
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out := true
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for out {
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select {
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case i := <-back_channel:
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if i != 0 {
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if i > 0 {
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logger.Info("Runner freed", "runner", i)
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task_runners_used[i-1] = 0
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used = 0
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} else if i < 0 {
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logger.Error("Runner died! Restarting!", "runner", i)
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i = int(math.Abs(float64(i)) - 1)
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task_runners_used[i] = 0
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used = 0
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go runner(config, db, task_runners[i], i+1, back_channel)
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}
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AddLocalTask(handler, int(math.Abs(float64(i))), nil)
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} else if used == len(task_runners_used) {
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continue
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}
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case <-time.After(wait):
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if wait == time.Nanosecond*100 {
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wait = def_wait
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}
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out = false
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}
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AddLocalTask(handler, int(math.Abs(float64(i))), nil)
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}
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if task_to_dispatch == nil {
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var task TaskT
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err := GetDBOnce(db, &task, "tasks as t "+
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for {
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tasks, err := GetDbMultitple[TaskT](db, "tasks as t "+
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// Get depenencies
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"left join tasks_dependencies as td on t.id=td.main_id "+
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// Get the task that the depencey resolves to
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"left join tasks as t2 on t2.id=td.dependent_id "+
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"where t.status=1 "+
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"group by t.id having count(td.id) filter (where t2.status in (0,1,2,3)) = 0;")
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"group by t.id having count(td.id) filter (where t2.status in (0,1,2,3)) = 0 limit 20;")
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if err != NotFoundError && err != nil {
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log.Error("Failed to get tasks from db", "err", err)
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continue
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}
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if err == NotFoundError {
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task_to_dispatch = nil
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} else {
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temp := Task(task)
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task_to_dispatch = &temp
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}
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}
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if task_to_dispatch != nil && task_to_dispatch.TaskType != int(TASK_TYPE_DELETE_USER) {
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// TODO split tasks into cpu tasks and GPU tasks
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mutex := handler.DataMap["runners_mutex"].(*sync.Mutex)
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mutex.Lock()
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remote_runners := handler.DataMap["runners"].(map[string]interface{})
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for k, v := range remote_runners {
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runner_data := v.(map[string]interface{})
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runner_info := runner_data["runner_info"].(*Runner)
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if runner_data["task"] != nil {
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continue
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}
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if runner_info.UserId != task_to_dispatch.UserId {
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continue
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}
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go handleRemoteTask(handler, base, k, *task_to_dispatch)
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task_to_dispatch = nil
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if err == NotFoundError || len(tasks) == 0 {
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break
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}
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mutex.Unlock()
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}
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for _, task_to_dispatch := range tasks {
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ttd := Task(*task_to_dispatch)
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if task_to_dispatch != nil && task_to_dispatch.TaskType != int(TASK_TYPE_DELETE_USER) {
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// TODO split tasks into cpu tasks and GPU tasks
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mutex := handler.DataMap["runners_mutex"].(*sync.Mutex)
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mutex.Lock()
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remote_runners := handler.DataMap["runners"].(map[string]interface{})
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if task_to_dispatch != nil {
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for i := 0; i < len(task_runners_used); i += 1 {
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if !task_runners_used[i] {
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task_runners[i] <- *task_to_dispatch
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task_runners_used[i] = true
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AddLocalTask(handler, i+1, task_to_dispatch)
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task_to_dispatch = nil
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for k, v := range remote_runners {
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runner_data := v.(map[string]interface{})
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runner_info := runner_data["runner_info"].(*Runner)
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if runner_data["task"] != nil {
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continue
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}
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if runner_info.UserId != task_to_dispatch.UserId {
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continue
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}
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go handleRemoteTask(handler, base, k, ttd)
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task_to_dispatch = nil
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break
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}
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mutex.Unlock()
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}
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used = 0
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if task_to_dispatch != nil {
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for i := 0; i < len(task_runners_used); i += 1 {
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if task_runners_used[i] <= QUEUE_SIZE {
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ttd.UpdateStatusLog(base, TASK_QUEUED, "Runner picked up task")
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task_runners[i] <- ttd
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task_runners_used[i] += 1
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AddLocalTask(handler, i+1, &ttd)
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task_to_dispatch = nil
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wait = time.Nanosecond * 100
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break
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} else {
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used += 1
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}
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}
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}
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if used == len(task_runners_used) {
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break
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}
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}
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}
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if used == len(task_runners_used) {
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break
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}
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}
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}
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}
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|
@ -50,6 +50,7 @@ const (
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TASK_PREPARING = 0
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TASK_TODO = 1
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TASK_PICKED_UP = 2
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TASK_QUEUED = 5
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TASK_RUNNING = 3
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TASK_DONE = 4
|
||||
)
|
||||
|
@ -102,7 +102,7 @@ func (c *Config) Cleanup(db db.Db) {
|
||||
failLog(err)
|
||||
_, err = db.Exec("update models set status=$1 where status=$2", FAILED_PREPARING, PREPARING)
|
||||
failLog(err)
|
||||
_, err = db.Exec("update tasks set status=$1 where status=$2", TASK_TODO, TASK_PICKED_UP)
|
||||
_, err = db.Exec("update tasks set status=$1 where status=$2 or status=$3", TASK_TODO, TASK_PICKED_UP, TASK_QUEUED)
|
||||
failLog(err)
|
||||
|
||||
tasks, err := GetDbMultitple[Task](db, "tasks where status=$1", TASK_RUNNING)
|
||||
|
@ -1,6 +1,6 @@
|
||||
|
||||
events {
|
||||
worker_connections 1024;
|
||||
worker_connections 2024;
|
||||
}
|
||||
|
||||
http {
|
||||
|
2
run.sh
2
run.sh
@ -1,2 +1,2 @@
|
||||
#!/bin/bash
|
||||
podman run --rm --network host --gpus all --name fyp-server -it -v $(pwd):/app -e "TERM=xterm-256color" fyp-server bash
|
||||
podman run --network host --gpus all --replace --name fyp-server --ulimit=nofile=100000:100000 -it -v $(pwd):/app -e "TERM=xterm-256color" --restart=always andre-fyp-server
|
||||
|
@ -59,7 +59,6 @@ create table if not exists model_definition (
|
||||
accuracy real default 0,
|
||||
target_accuracy integer not null,
|
||||
epoch integer default 0,
|
||||
-- TODO add max epoch
|
||||
-- 1: Pre Init
|
||||
-- 2: Init
|
||||
-- 3: Training
|
||||
@ -78,7 +77,7 @@ create table if not exists model_definition_layer (
|
||||
-- 1: input
|
||||
-- 2: dense
|
||||
-- 3: flatten
|
||||
-- TODO add conv
|
||||
-- 4: block
|
||||
layer_type integer not null,
|
||||
-- ei 28,28,1
|
||||
-- a 28x28 grayscale image
|
||||
@ -102,7 +101,6 @@ create table if not exists exp_model_head (
|
||||
|
||||
accuracy real default 0,
|
||||
|
||||
-- TODO add max epoch
|
||||
-- 1: Pre Init
|
||||
-- 2: Init
|
||||
-- 3: Training
|
||||
|
@ -143,6 +143,15 @@ def addBlock(
|
||||
model.add(layers.Dropout(0.4))
|
||||
return model
|
||||
|
||||
def resblock(x, kernelsize = 3, filters = 128):
|
||||
fx = layers.Conv2D(filters, kernelsize, activation='relu', padding='same')(x)
|
||||
fx = layers.BatchNormalization()(fx)
|
||||
fx = layers.Conv2D(filters, kernelsize, padding='same')(fx)
|
||||
out = layers.Add()([x,fx])
|
||||
out = layers.ReLU()(out)
|
||||
out = layers.BatchNormalization()(out)
|
||||
return out
|
||||
|
||||
|
||||
{{ if .LoadPrev }}
|
||||
model = tf.keras.saving.load_model('{{.LastModelRunPath}}')
|
||||
|
@ -42,6 +42,7 @@
|
||||
|
||||
import 'src/styles/forms.css';
|
||||
import { notificationStore } from 'src/lib/NotificationsStore.svelte';
|
||||
import Spinner from 'src/lib/Spinner.svelte';
|
||||
|
||||
let model: Promise<Model> = $state(new Promise(() => {}));
|
||||
let _model: Model | undefined = $state(undefined);
|
||||
@ -188,7 +189,6 @@
|
||||
<h1 class="text-center">
|
||||
{m.name}
|
||||
</h1>
|
||||
<!-- TODO improve message -->
|
||||
<h2 class="text-center">Failed to prepare model</h2>
|
||||
|
||||
<DeleteModel model={m} />
|
||||
@ -206,8 +206,7 @@
|
||||
{:else if m.status == 3}
|
||||
<BaseModelInfo model={m} />
|
||||
<div class="card">
|
||||
<!-- TODO improve this -->
|
||||
Processing zip file...
|
||||
Processing zip file... <Spinner />
|
||||
</div>
|
||||
{:else if m.status == -3 || m.status == -4}
|
||||
<BaseModelInfo model={m} />
|
||||
|
@ -70,16 +70,16 @@
|
||||
<button class="tab" class:selected={isActive('upload')} onclick={setActive('upload')}>
|
||||
Upload
|
||||
</button>
|
||||
<button
|
||||
<!--button
|
||||
class="tab"
|
||||
class:selected={isActive('create-class')}
|
||||
onclick={setActive('create-class')}
|
||||
>
|
||||
Create Class
|
||||
</button>
|
||||
<button class="tab" class:selected={isActive('api')} onclick={setActive('api')}>
|
||||
</button-->
|
||||
<!--button class="tab" class:selected={isActive('api')} onclick={setActive('api')}>
|
||||
Api
|
||||
</button>
|
||||
</button-->
|
||||
</div>
|
||||
<div class="content" class:selected={isActive('upload')}>
|
||||
<form onsubmit={preventDefault(uploadZip)}>
|
||||
@ -111,10 +111,10 @@
|
||||
{/if}
|
||||
</form>
|
||||
</div>
|
||||
<div class="content" class:selected={isActive('create-class')}>
|
||||
<!--div class="content" class:selected={isActive('create-class')}>
|
||||
<ModelTable {classes} {model} {onreload} />
|
||||
</div>
|
||||
<div class="content" class:selected={isActive('api')}>TODO</div>
|
||||
</div-->
|
||||
<!--div class="content" class:selected={isActive('api')}>TODO</div-->
|
||||
</Tabs>
|
||||
<div class="tabs"></div>
|
||||
{:else}
|
||||
@ -122,7 +122,7 @@
|
||||
{#if numberOfInvalidImages > 0}
|
||||
<p class="danger">
|
||||
There are images {numberOfInvalidImages} that were loaded that do not have the correct format.
|
||||
These images will be delete when the model trains.
|
||||
These images will be deleted when the model trains.
|
||||
</p>
|
||||
{/if}
|
||||
<ModelTable {classes} {model} {onreload} />
|
||||
|
@ -54,7 +54,7 @@
|
||||
{#if number_of_invalid_images > 0}
|
||||
<p class="danger">
|
||||
There are images {number_of_invalid_images} that were loaded that do not have the correct format.DeleteZip
|
||||
These images will be delete when the model trains.
|
||||
These images will be deleted when the model trains.
|
||||
</p>
|
||||
{/if}
|
||||
<!-- TODO expading mode -->
|
||||
@ -101,14 +101,14 @@
|
||||
<h2>To train the model please provide data to the model first</h2>
|
||||
{/if}
|
||||
</form>
|
||||
{:else}
|
||||
{:else if ![4, 6, 7].includes(model.status)}
|
||||
<form class:submitted onsubmit={submitRetrain}>
|
||||
{#if has_data}
|
||||
<h2>This model has new classes and can be expanded</h2>
|
||||
{#if number_of_invalid_images > 0}
|
||||
<p class="danger">
|
||||
There are images {number_of_invalid_images} that were loaded that do not have the correct format.DeleteZip
|
||||
These images will be delete when the model trains.
|
||||
These images will be deleted when the model trains.
|
||||
</p>
|
||||
{/if}
|
||||
<button> Retrain </button>
|
||||
|
Loading…
Reference in New Issue
Block a user