Gs/doc/user tutorial

User_Guide.md

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+# User guide
+- You need a username and password on the database of the GraphL server, if you want one contact someone with an admin account.  
+- Create an account on https://wandb.ai/site and get an API key (in settings), it will be used to get the results of your jobs.
+- You need a docker image for your projects with the setps you want to execute on the workstation as entrypoint or cmd in the dockerfile.  
+Create your docker image and upload it to https://hub.docker.com/, it must NOT contain any secret.
+- In order to get back the results of your tasks, you need to add code executed by your container to save the results on your wandb account.  
+A minimal python script that saves the content of a directory called `models` to your wandb account:  
+```
+import wandb  
+wandb.init(project="project_name")  
+wandb.save("models/*", policy="now")
+```  
+You can also use more advanced saving options from the SDK of wandb : https://docs.wandb.ai/.  
+- Go to http://54.77.14.151:8080/playground.  
+- Follow the steps in [readme](https://github.com/42-AI/ws-backend#login) for login :  
+copy the following request to log in your user :
+```
+query login {
+  login (id: "your_user_id", pwd: "your-password") {
+    ... on Token {
+      token
+      userId
+      username
+      isAdmin
+    }
+    ... on Error {
+      code
+      message
+    }
+  }
+}
+```
+You should get a response similar to this :
+```
+{
+  "data": {
+    "login": {
+      "token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdXRob3JpemVkIjp0cnVlLCJleHAiOjE2MTczODMzNDksInVzZXJfaWQiOiJkZjljNDYzZC00ZmIwLTRmYzAtYTU5OC00YmQ3NzEzMzg2ZDAifQ.Xj_rUGIB7l90kiXD_U12ni2kf9U-afARaCZKbEao-oU",
+      "userId": "df9c463d-4fb0-4fc0-a598-4bd7713386d0",
+      "username": "your_user_id"
+    }
+  }
+}
+```
+Copy the `token` value.
+- Use the login token as header variable (cf readme [create a user](https://github.com/42-AI/ws-backend/tree/gs/doc/user-tutorial#create-a-user)) in `HTTP HEADERS` :
+```
+{
+  "auth": "your_token"
+}
+```
+and [create a task](https://github.com/42-AI/ws-backend/tree/gs/doc/user-tutorial#create-a-machine-learning-task) with your wandb API key as env variable :
+```
+mutation createTask {
+  create_task(input:{env:"WANDB_API_KEY=putYourKeyHere", docker_image:"jjauzion/wandb-test", dataset:"s3//"}) {
+    id
+    user_id
+        created_at
+        started_at
+        ended_at
+        status
+    job { dataset, docker_image, env }
+  }
+}
+```
+- You should get the results of your job on your wandb account.