/
train.go
134 lines (114 loc) · 3.58 KB
/
train.go
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/*
Copyright © 2020 NAME HERE <EMAIL ADDRESS>
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
package cmd
import (
"context"
"fmt"
"io"
"log"
"os"
"os/exec"
"runtime"
"strconv"
"github.com/docker/docker/api/types"
"github.com/docker/docker/api/types/container"
"github.com/docker/docker/client"
"github.com/docker/go-connections/nat"
"github.com/spf13/cobra"
)
var (
local bool
localPort int
localName string
Stg, Prod bool
)
// trainCmd represents the train command
var trainCmd = &cobra.Command{
Use: "train",
Short: "Train your ML model",
Long: `Train your ML model`,
Run: func(cmd *cobra.Command, args []string) {
ctx := context.Background()
cli, err := client.NewClientWithOpts(client.FromEnv, client.WithAPIVersionNegotiation())
if err != nil {
panic(err)
}
//imageName := "jupyter/datascience-notebook:latest"
imageName := "jupyter/datascience-notebook:r-3.6.2"
if local {
//pullJupyterImage(imageName, ctx, cli)
createJupyterInstance(imageName, localName, localPort, ctx, cli)
openbrowser("http://localhost:" + strconv.Itoa(localPort))
} else if Stg {
// create Jupyter notebook in stg
} else {
// create Jupyter notebook in prod
}
},
}
func init() {
modelCmd.AddCommand(trainCmd)
// for local
trainCmd.Flags().BoolVarP(&local, "local", "l", false, "Train ML models in your local Jupyter notebook")
trainCmd.Flags().IntVar(&localPort, "port", 8888, "Specify your local Jupyter instance port")
trainCmd.Flags().StringVar(&localName, "name", "local-", "Name your local Jupyter instance")
// for stg
trainCmd.Flags().BoolVarP(&Stg, "-stg", "s", false, "Train ML models in STG Jupyter notebooks")
// for prod
trainCmd.Flags().BoolVarP(&Prod, "-prod", "p", false, "Train ML models in PROD Jupyter notebooks")
}
// pull Jupyter image to local
func pullJupyterImage(imageName string, ctx context.Context, cli *client.Client) {
out, err := cli.ImagePull(ctx, imageName, types.ImagePullOptions{})
if err != nil {
panic(err)
}
io.Copy(os.Stdout, out)
}
func createJupyterInstance(imageName string, localName string, localPort int, ctx context.Context, cli *client.Client) {
hostBinding := nat.PortBinding{
HostIP: "0.0.0.0",
HostPort: strconv.Itoa(localPort),
}
containerPort, _ := nat.NewPort("tcp", "8888")
portBinding := nat.PortMap{containerPort: []nat.PortBinding{hostBinding}}
resp, err := cli.ContainerCreate(ctx, &container.Config{
Image: imageName,
Cmd: []string{"start-notebook.sh", "--NotebookApp.token=''", "--NotebookApp.password=''"},
}, &container.HostConfig{
PortBindings: portBinding,
}, nil, localName)
if err != nil {
panic(err)
}
if err := cli.ContainerStart(ctx, resp.ID, types.ContainerStartOptions{}); err != nil {
panic(err)
}
fmt.Println(resp.ID)
}
func openbrowser(url string) {
var err error
switch runtime.GOOS {
case "linux":
err = exec.Command("xdg-open", url).Start()
case "windows":
err = exec.Command("rundll32", "url.dll,FileProtocolHandler", url).Start()
case "darwin":
err = exec.Command("open", url).Start()
default:
err = fmt.Errorf("unsupported platform")
}
if err != nil {
log.Fatal(err)
}
}