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Add experimental environment: tensorflow-serving (#1212)
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| ARG GO_VERSION=1.9.2 | ||
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| FROM tensorflow/serving as serving | ||
| RUN apt update && apt install -y ca-certificates && rm -rf /var/lib/apt/lists/* | ||
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| FROM golang:${GO_VERSION} AS builder | ||
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| ENV GOPATH /usr | ||
| ENV APP ${GOPATH}/src/github.com/fission/fission/environments/tensorflow-serving | ||
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| WORKDIR ${APP} | ||
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| ADD server.go ${APP} | ||
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| RUN go get | ||
| RUN go build -a -o /server server.go | ||
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| FROM serving | ||
| WORKDIR / | ||
| COPY --from=builder /server / | ||
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| ENTRYPOINT ["/server"] | ||
| EXPOSE 8888 |
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| # Fission: Tensorflow Serving Environment | ||
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| This is the Tensorflow Serving environment for Fission. | ||
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| It's a Docker image containing a Go runtime, along with a tensorflow serving service. | ||
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| ## How it works | ||
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| Tensorflow Serving is an serving service that supports both RESTful API and gRPC endpoints. In current implementation, | ||
| Go server launches `tensorflow_model_server` to load in model during specialization. As long as the Go server receives | ||
| requests from router it creates a reverse proxy that connects to RESTful API endpoint exposed by tensorflow_model_server | ||
| and get response from the upstream server for user. | ||
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| ## Build this image | ||
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| ``` | ||
| docker build -t USER/tensorflow-serving . && docker push USER/tensorflow-serving | ||
| ``` | ||
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| ## Using the image in fission | ||
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| You can add this customized image to fission with "fission env create": | ||
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| ``` | ||
| fission env create --name tensorflow --image USER/tensorflow-serving --version 2 | ||
| ``` | ||
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| After this, fission functions that have the env parameter set to the | ||
| same environment name as this command will use this environment. |
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| package main | ||
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| import ( | ||
| "encoding/json" | ||
| "fmt" | ||
| "io/ioutil" | ||
| "log" | ||
| "net" | ||
| "net/http" | ||
| "net/http/httputil" | ||
| "os" | ||
| "os/exec" | ||
| "path/filepath" | ||
| "strings" | ||
| "time" | ||
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| "github.com/pkg/errors" | ||
| "go.uber.org/zap" | ||
| ) | ||
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| const ( | ||
| PortgRPC = 8500 | ||
| PortRestAPI = 8501 | ||
| ) | ||
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| var ( | ||
| specialized = false | ||
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| // for tensorflow serving to use | ||
| MODEL_NAME = "" | ||
| ) | ||
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| type ( | ||
| FunctionLoadRequest struct { | ||
| // FilePath is an absolute filesystem path to the | ||
| // function. What exactly is stored here is | ||
| // env-specific. Optional. | ||
| FilePath string `json:"filepath"` | ||
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| // FunctionName has an environment-specific meaning; | ||
| // usually, it defines a function within a module | ||
| // containing multiple functions. Optional; default is | ||
| // environment-specific. | ||
| FunctionName string `json:"functionName"` | ||
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| // URL to expose this function at. Optional; defaults | ||
| // to "/". | ||
| URL string `json:"url"` | ||
| } | ||
| ) | ||
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| func specializeHandler(logger *zap.Logger) func(http.ResponseWriter, *http.Request) { | ||
| return func(w http.ResponseWriter, r *http.Request) { | ||
| logger.Error("v1 interface is not implemented") | ||
| w.WriteHeader(http.StatusNotImplemented) | ||
| } | ||
| } | ||
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| func specializeHandlerV2(logger *zap.Logger) func(http.ResponseWriter, *http.Request) { | ||
| return func(w http.ResponseWriter, r *http.Request) { | ||
| if specialized { | ||
| w.WriteHeader(http.StatusBadRequest) | ||
| w.Write([]byte("Not a generic container")) | ||
| return | ||
| } | ||
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| body, err := ioutil.ReadAll(r.Body) | ||
| if err != nil { | ||
| logger.Error("error reading request body", zap.Error(err)) | ||
| w.WriteHeader(http.StatusInternalServerError) | ||
| return | ||
| } | ||
| var loadreq FunctionLoadRequest | ||
| err = json.Unmarshal(body, &loadreq) | ||
| if err != nil { | ||
| w.WriteHeader(http.StatusBadRequest) | ||
| return | ||
| } | ||
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| // To ensure we load model from the expected path | ||
| basePath := fmt.Sprintf("%v/%v", loadreq.FilePath, loadreq.FunctionName) | ||
| basePath, err = filepath.Abs(basePath) | ||
| if err != nil { | ||
| msg := "error getting absolute path of model" | ||
| logger.Error(msg, zap.Error(err)) | ||
| w.WriteHeader(http.StatusInternalServerError) | ||
| w.Write([]byte(err.Error())) | ||
| return | ||
| } else if !strings.HasPrefix(basePath, loadreq.FilePath) { | ||
| msg := "incorrect model base path" | ||
| logger.Error(msg, zap.String("model_base_path", basePath)) | ||
| w.WriteHeader(http.StatusInternalServerError) | ||
| w.Write([]byte(msg)) | ||
| return | ||
| } | ||
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| _, err = os.Stat(basePath) | ||
| if err != nil { | ||
| msg := "error checking model status" | ||
| logger.Error(msg, zap.Error(err)) | ||
| w.WriteHeader(http.StatusInternalServerError) | ||
| return | ||
| } | ||
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| // get directory name that holds model | ||
| MODEL_NAME = filepath.Base(basePath) | ||
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| argModelBasePath := fmt.Sprintf("--model_base_path=%v", basePath) | ||
| argModelName := fmt.Sprintf("--model_name=%v", MODEL_NAME) | ||
| argPortgRPC := fmt.Sprintf("--port=%v", PortgRPC) | ||
| 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 | ||
| cmd := exec.Command("tensorflow_model_server", | ||
| argPortgRPC, argPortREST, argModelName, argModelBasePath) | ||
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| cmd.Stdout = os.Stdout | ||
| cmd.Stderr = os.Stderr | ||
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| err = cmd.Start() | ||
| if err != nil { | ||
| msg := "error starting tensorflow serving" | ||
| logger.Error(msg, zap.Error(err)) | ||
| err = errors.Wrap(err, msg) | ||
| w.WriteHeader(http.StatusInternalServerError) | ||
| w.Write([]byte(err.Error())) | ||
| return | ||
| } | ||
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| go func() { | ||
| err = cmd.Wait() | ||
| if err != nil { | ||
| logger.Fatal("error running tensorflow serving", zap.Error(err)) | ||
| } | ||
| }() | ||
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| t := time.Now() | ||
| retryInterval := 50 * time.Millisecond | ||
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| // tensorflow serving takes some time to load model | ||
| // into memory, keep retrying until it starts REST api server. | ||
| for { | ||
| if time.Since(t) > 30*time.Second { | ||
| w.WriteHeader(http.StatusGatewayTimeout) | ||
| return | ||
| } | ||
| conn, err := net.Dial("tcp", "localhost:8501") | ||
| if err == nil { | ||
| conn.Close() | ||
| break | ||
| } else { | ||
| logger.Info(fmt.Sprintf("waiting for tensorflow serving to be ready: %v", err.Error())) | ||
| time.Sleep(retryInterval) | ||
| retryInterval = retryInterval * 2 | ||
| } | ||
| } | ||
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| specialized = true | ||
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| logger.Info("done") | ||
| } | ||
| } | ||
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| func readinessProbeHandler(w http.ResponseWriter, r *http.Request) { | ||
| w.WriteHeader(http.StatusOK) | ||
| } | ||
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| func main() { | ||
| logger, err := zap.NewProduction() | ||
| if err != nil { | ||
| log.Fatalf("can't initialize zap logger: %v", err) | ||
| } | ||
| defer logger.Sync() | ||
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| http.HandleFunc("/healthz", readinessProbeHandler) | ||
| http.HandleFunc("/specialize", specializeHandler(logger.Named("specialize_handler"))) | ||
| http.HandleFunc("/v2/specialize", specializeHandlerV2(logger.Named("specialize_v2_handler"))) | ||
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| // Generic route -- all http requests go to the user function. | ||
| http.HandleFunc("/", func(w http.ResponseWriter, r *http.Request) { | ||
| if !specialized { | ||
| w.WriteHeader(http.StatusInternalServerError) | ||
| w.Write([]byte("Generic container: no requests supported")) | ||
| return | ||
| } | ||
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| // TODO: replace it with gRPC (https://gist.github.com/mauri870/1f953a183ee6c186e70a0a72e78b088c) | ||
| // set up proxy server director | ||
| director := func(req *http.Request) { | ||
| req.URL.Scheme = "http" | ||
| req.URL.Host = "localhost:8501" | ||
| req.URL.Path = fmt.Sprintf("/v1/models/%v:predict", MODEL_NAME) | ||
| } | ||
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| proxy := &httputil.ReverseProxy{ | ||
| Director: director, | ||
| } | ||
| proxy.ServeHTTP(w, r) | ||
| }) | ||
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| logger.Info("listening on 8888 ...") | ||
| http.ListenAndServe(":8888", nil) | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,23 @@ | ||
| # Tensorflow Serving Environment Example | ||
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| ## Create Environment | ||
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| ```bash | ||
| $ fission env create --name tensorflow --image fission/tensorflow-serving --version 2 | ||
| ``` | ||
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| ## Create Package | ||
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| ```bash | ||
| $ zip -r half_plus_two.zip ./half_plus_two | ||
| $ fission pkg create --env tensorflow --deploy half_plus_two.zip | ||
| ``` | ||
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| ## Create Function | ||
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| Here, the `--entrypoint` represents the name of top directory contains trained model. | ||
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| ```bash | ||
| $ fission fn create --name t1 --pkg <pkg name> --env tensorflow --entrypoint "half_plus_two" | ||
| $ fission fn test --name t1 --body '{"instances": [1.0, 2.0, 0.0]}' --method POST | ||
| ``` |
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