Tensorflow Serving supports three kinds of API: predict, classify, regress. In order to fully support possible API types, this PR adds code to split the entry point with separator ":" to get model name and the API type separately. And append the API type to proxy URL when receiving the requests.
$ fission fn create --name t1 --pkg <pkg name> \
--env tensorflow --entrypoint "half_plus_two:regress"
226 lines
6.0 KiB
Go
226 lines
6.0 KiB
Go
package main
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import (
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"encoding/json"
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"fmt"
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"io/ioutil"
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"log"
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"net"
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"net/http"
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"net/http/httputil"
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"os"
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"os/exec"
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"path/filepath"
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"strings"
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"time"
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"github.com/pkg/errors"
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"go.uber.org/zap"
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)
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const (
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PortgRPC = 8500
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PortRestAPI = 8501
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)
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var (
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specialized = false
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// for tensorflow serving to use
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MODEL_NAME = ""
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API_TYPE = ""
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)
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type (
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FunctionLoadRequest struct {
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// FilePath is an absolute filesystem path to the
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// function. What exactly is stored here is
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// env-specific. Optional.
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FilePath string `json:"filepath"`
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// FunctionName has an environment-specific meaning;
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// usually, it defines a function within a module
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// containing multiple functions. Optional; default is
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// environment-specific.
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FunctionName string `json:"functionName"`
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// URL to expose this function at. Optional; defaults
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// to "/".
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URL string `json:"url"`
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}
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)
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func specializeHandler(logger *zap.Logger) func(http.ResponseWriter, *http.Request) {
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return func(w http.ResponseWriter, r *http.Request) {
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logger.Error("v1 interface is not implemented")
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w.WriteHeader(http.StatusNotImplemented)
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}
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}
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func specializeHandlerV2(logger *zap.Logger) func(http.ResponseWriter, *http.Request) {
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return func(w http.ResponseWriter, r *http.Request) {
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if specialized {
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w.WriteHeader(http.StatusBadRequest)
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w.Write([]byte("Not a generic container"))
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return
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}
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body, err := ioutil.ReadAll(r.Body)
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if err != nil {
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logger.Error("error reading request body", zap.Error(err))
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w.WriteHeader(http.StatusInternalServerError)
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return
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}
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var loadreq FunctionLoadRequest
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err = json.Unmarshal(body, &loadreq)
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if err != nil {
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w.WriteHeader(http.StatusBadRequest)
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return
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}
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// Tensorflow-serving supports three types of API: classify, regress, predict
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// To get the API type, we need to split the entry point with separator ":"
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// POST http://host:port/v1/models/${MODEL_NAME}:(classify|regress:predict)
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entrypoint := strings.Split(loadreq.FunctionName, ":")
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modelDir, apiType := "", ""
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if len(entrypoint) == 0 {
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logger.Error("unable to load model due to empty entrypoint")
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w.WriteHeader(http.StatusBadRequest)
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return
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} else if len(entrypoint) == 1 {
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modelDir = entrypoint[0]
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apiType = "predict" // assign default API type
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} else {
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modelDir = entrypoint[0]
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apiType = entrypoint[1]
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}
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// To ensure we load model from the expected path
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basePath := fmt.Sprintf("%v/%v", loadreq.FilePath, modelDir)
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basePath, err = filepath.Abs(basePath)
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if err != nil {
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msg := "error getting absolute path of model"
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logger.Error(msg, zap.Error(err))
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w.WriteHeader(http.StatusInternalServerError)
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w.Write([]byte(err.Error()))
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return
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} else if !strings.HasPrefix(basePath, loadreq.FilePath) {
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msg := "incorrect model base path"
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logger.Error(msg, zap.String("model_base_path", basePath))
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w.WriteHeader(http.StatusInternalServerError)
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w.Write([]byte(msg))
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return
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}
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_, err = os.Stat(basePath)
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if err != nil {
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msg := "error checking model status"
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logger.Error(msg, zap.Error(err))
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w.WriteHeader(http.StatusInternalServerError)
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return
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}
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// get directory name that holds model
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MODEL_NAME = filepath.Base(basePath)
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API_TYPE = apiType
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argModelBasePath := fmt.Sprintf("--model_base_path=%v", basePath)
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argModelName := fmt.Sprintf("--model_name=%v", MODEL_NAME)
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argPortgRPC := fmt.Sprintf("--port=%v", PortgRPC)
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argPortREST := fmt.Sprintf("--rest_api_port=%v", PortRestAPI)
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logger.Info(fmt.Sprintf("specializing: %v %v", loadreq.FunctionName, loadreq.FilePath))
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// Future: could be improved by keeping subprocess open while environment is specialized
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cmd := exec.Command("tensorflow_model_server",
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argPortgRPC, argPortREST, argModelName, argModelBasePath)
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cmd.Stdout = os.Stdout
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cmd.Stderr = os.Stderr
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err = cmd.Start()
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if err != nil {
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msg := "error starting tensorflow serving"
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logger.Error(msg, zap.Error(err))
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err = errors.Wrap(err, msg)
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w.WriteHeader(http.StatusInternalServerError)
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w.Write([]byte(err.Error()))
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return
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}
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go func() {
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err = cmd.Wait()
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if err != nil {
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logger.Fatal("error running tensorflow serving", zap.Error(err))
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}
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}()
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t := time.Now()
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retryInterval := 50 * time.Millisecond
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// tensorflow serving takes some time to load model
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// into memory, keep retrying until it starts REST api server.
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for {
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if time.Since(t) > 30*time.Second {
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w.WriteHeader(http.StatusGatewayTimeout)
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return
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}
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conn, err := net.Dial("tcp", "localhost:8501")
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if err == nil {
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conn.Close()
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break
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} else {
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logger.Info(fmt.Sprintf("waiting for tensorflow serving to be ready: %v", err.Error()))
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time.Sleep(retryInterval)
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retryInterval = retryInterval * 2
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}
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}
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specialized = true
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logger.Info("done")
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}
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}
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func readinessProbeHandler(w http.ResponseWriter, r *http.Request) {
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w.WriteHeader(http.StatusOK)
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}
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func main() {
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logger, err := zap.NewProduction()
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if err != nil {
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log.Fatalf("can't initialize zap logger: %v", err)
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}
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defer logger.Sync()
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http.HandleFunc("/healthz", readinessProbeHandler)
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http.HandleFunc("/specialize", specializeHandler(logger.Named("specialize_handler")))
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http.HandleFunc("/v2/specialize", specializeHandlerV2(logger.Named("specialize_v2_handler")))
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// Generic route -- all http requests go to the user function.
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http.HandleFunc("/", func(w http.ResponseWriter, r *http.Request) {
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if !specialized {
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w.WriteHeader(http.StatusInternalServerError)
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w.Write([]byte("Generic container: no requests supported"))
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return
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}
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// TODO: replace it with gRPC (https://gist.github.com/mauri870/1f953a183ee6c186e70a0a72e78b088c)
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// set up proxy server director
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director := func(req *http.Request) {
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req.URL.Scheme = "http"
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req.URL.Host = "localhost:8501"
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req.URL.Path = fmt.Sprintf("/v1/models/%v:%v", MODEL_NAME, API_TYPE)
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}
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proxy := &httputil.ReverseProxy{
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Director: director,
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}
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proxy.ServeHTTP(w, r)
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})
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logger.Info("listening on 8888 ...")
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http.ListenAndServe(":8888", nil)
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}
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