ZenML Pro web login implementation (#3141)

Author: stefannica

docs/book/getting-started/deploying-zenml/README.md

@@ -62,8 +62,8 @@ This default setup allows you to get started and try out the core features but
 you won't be able to use cloud-based components like serverless orchestrators
 and so on.
 
-Users can run the `zenml up` command to spin up a local ZenML OSS server to serve the
-dashboard. For the local OSS server option, the `zenml up` command implicitly
+Users can run the `zenml login --local` command to spin up a local ZenML OSS server to serve the
+dashboard. For the local OSS server option, the `zenml login --local` command implicitly
 connects the client to the server. The diagram for this looks as follows:
 
 ![ZenML with a local ZenML OSS Server](../../.gitbook/assets/Scenario2.png)
@@ -72,7 +72,7 @@ In order to move into production, the ZenML server needs to be deployed somewher
 
 ![ZenML centrally deployed for multiple users](../../.gitbook/assets/Scenario3.2.png)
 
-You connect to your deployed ZenML server using the `zenml connect` command and
+You connect to your deployed ZenML server using the `zenml login` command and
 then you have the full benefits and power of ZenML. You can use all the
 cloud-based components, your metadata will be stored and synchronized across all
 the users of the server and you can leverage features like centralized logs

docs/book/getting-started/deploying-zenml/deploy-using-huggingface-spaces.md

@@ -43,7 +43,7 @@ Your Space's URL will only be available and usable for connecting from your loca
 You can use the 'Direct URL' to connect to your ZenML server from your local machine with the following CLI command (after installing ZenML, and using your custom URL instead of the placeholder):
 
 ```shell
-zenml connect --url '<YOUR_HF_SPACES_DIRECT_URL>'
+zenml login '<YOUR_HF_SPACES_DIRECT_URL>'
 ```
 
 You can also use the Direct URL in your browser to use the ZenML dashboard as a fullscreen application (i.e. without the HuggingFace Spaces wrapper around it).

docs/book/getting-started/deploying-zenml/deploy-with-docker.md

@@ -11,10 +11,10 @@ The ZenML server container image is available at [`zenmldocker/zenml-server`](ht
 If you're just looking for a quick way to deploy the ZenML server using a container, without going through the hassle of interacting with a container management tool like Docker and manually configuring your container, you can use the ZenML CLI to do so. You only need to have Docker installed and running on your machine:
 
 ```bash
-zenml up --docker
+zenml login --local --docker
 ```
 
-This command deploys a ZenML server locally in a Docker container, then connects your client to it. Similar to running plain `zenml up`, the server and the local ZenML client share the same SQLite database.
+This command deploys a ZenML server locally in a Docker container, then connects your client to it. Similar to running plain `zenml login --local`, the server and the local ZenML client share the same SQLite database.
 
 The rest of this guide is addressed to advanced users who are looking to manually deploy and manage a containerized ZenML server.
 
@@ -284,7 +284,7 @@ The above command will start a containerized ZenML server running on your machin
 You need to visit the ZenML dashboard at `http://localhost:8080` and activate the server by creating an initial admin user account. You can then connect your client to the server with the web login flow:
 
 ```shell
-$ zenml connect --url http://localhost:8080
+$ zenml login http://localhost:8080
 Connecting to: 'http://localhost:8080'...
 If your browser did not open automatically, please open the following URL into your browser to proceed with the authentication:
 
@@ -364,7 +364,7 @@ docker run -it -d -p 8080:8080 --name zenml \
 You need to visit the ZenML dashboard at `http://localhost:8080` and activate the server by creating an initial admin user account. You can then connect your client to the server with the web login flow:
 
 ```shell
-zenml connect --url http://localhost:8080
+zenml login http://localhost:8080
 ```
 
 ### Direct MySQL database connection
@@ -377,10 +377,10 @@ As previously covered, the containerized MySQL database service can be started w
 docker run --name mysql -d -p 3306:3306 -e MYSQL_ROOT_PASSWORD=password mysql:8.0
 ```
 
-The ZenML client on the host machine can then be configured to connect directly to the database with a slightly different `zenml connect` command:
+The ZenML client on the host machine can then be configured to connect directly to the database with a slightly different `zenml login` command:
 
 ```shell
-zenml connect --url mysql://127.0.0.1/zenml --username root --password password
+zenml login mysql://root:password@127.0.0.1/zenml
 ```
 
 > **Note** The `localhost` hostname will not work with MySQL databases. You need to use the `127.0.0.1` IP address instead.
@@ -438,7 +438,7 @@ docker compose -f /path/to/docker-compose.yml -p zenml up -d
 You need to visit the ZenML dashboard at `http://localhost:8080` to activate the server by creating an initial admin account. You can then connect your client to the server with the web login flow:
 
 ```shell
-zenml connect --url http://localhost:8080
+zenml login http://localhost:8080
 ```
 
 Tearing down the installation is as simple as running:
@@ -493,7 +493,7 @@ You can check the logs of the container to verify if the server is up and, depen
 
 ### CLI Docker deployments
 
-If you used the `zenml up --docker` CLI command to deploy the Docker ZenML server, you can check the logs with the command:
+If you used the `zenml login --local --docker` CLI command to deploy the Docker ZenML server, you can check the logs with the command:
 
 ```shell
 zenml logs -f

docs/book/getting-started/deploying-zenml/deploy-with-helm.md

@@ -113,43 +113,16 @@ Immediately after deployment, the ZenML server needs to be activated before it c
 
 The Helm chart should print out a message with the URL of the deployed ZenML server. You can use the URL to open the ZenML UI in your browser.
 
-To connect your local client to the ZenML server, you can either pass the configuration as command line arguments or as a YAML file:
+To connect your local client to the ZenML server, you can run:
 
 ```bash
-zenml connect --url=https://zenml.example.com:8080 --no-verify-ssl
-```
-
-or
-
-```bash
-zenml connect --config=/path/to/zenml_server_config.yaml
-```
-
-The YAML file should have the following structure when connecting to a ZenML server:
-
-```yaml
-url: <The URL of the ZenML server>
-verify_ssl: |
-  <Either a boolean, in which case it controls whether the
-  server's TLS certificate is verified, or a string, in which case it
-  must be a path to a CA certificate bundle to use or the CA bundle
-  value itself>
-```
-
-Example of a ZenML server YAML configuration file:
-
-```yaml
-url: https://ac8ef63af203226194a7725ee71d85a-7635928635.us-east-1.elb.amazonaws.com/zenml
-verify_ssl: |
-  -----BEGIN CERTIFICATE-----
-...
-  -----END CERTIFICATE-----
+zenml login https://zenml.example.com:8080 --no-verify-ssl
 ```
 
 To disconnect from the current ZenML server and revert to using the local default database, use the following command:
 
 ```bash
-zenml disconnect
+zenml logout
 ```
 
 ## ZenML Helm Deployment Scenarios
@@ -171,7 +144,7 @@ Once deployed, you have to use port-forwarding to access the ZenML server and to
 
 ```bash
 kubectl -n zenml-server port-forward svc/zenml-server 8080:8080
-zenml connect --url=http://localhost:8080
+zenml login http://localhost:8080
 ```
 
 This is just a simple example only fit for testing and evaluation purposes. For production deployments, you should use an external database and an Ingress service with TLS certificates to secure and expose the ZenML server to the internet.

docs/book/getting-started/installation.md

@@ -87,7 +87,7 @@ Though ZenML can run entirely as a pip package on a local system, complete with
 
 ```shell
 pip install "zenml[server]"
-zenml up  # opens the dashboard locally 
+zenml login --local  # opens the dashboard locally 
 ```
 
 However, advanced ZenML features are dependent on a centrally-deployed ZenML server accessible to other MLOps stack components. You can read more about it [here](deploying-zenml/README.md).

docs/book/how-to/advanced-topics/control-logging/view-logs-on-the-dasbhoard.md

@@ -16,7 +16,7 @@ def my_step() -> None:
 These logs are stored within the respective artifact store of your stack. This means that you can only view these logs in the dashboard
 *if the deployed ZenML server has direct access to the underlying artifact store*. There are two cases in which this will be true:
 
-* In case of a local ZenML server (via `zenml up`), both local and remote artifact stores may be accessible, depending on configuration of the client.
+* In case of a local ZenML server (via `zenml login --local`), both local and remote artifact stores may be accessible, depending on configuration of the client.
 * In case of a deployed ZenML server, logs for runs on a [local artifact store](../../component-guide/artifact-stores/local.md) will not be accessible. Logs
 for runs using a [remote artifact store](../../user-guide/production-guide/remote-storage.md) **may be** accessible, if the artifact store has been configured
 with a [service connector](../auth-management/service-connectors-guide.md). Please read [this chapter](../../user-guide/production-guide/remote-storage.md) of

docs/book/how-to/advanced-topics/manage-zenml-server/troubleshoot-your-deployed-server.md

@@ -44,7 +44,7 @@ real-time.
 {% tab title="Docker" %}
 If you are using Docker, you can view the logs of the ZenML server using the following method:
 
-* If you used the `zenml up --docker` CLI command to deploy the Docker ZenML server, you can check the logs with the
+* If you used the `zenml login --local --docker` CLI command to deploy the Docker ZenML server, you can check the logs with the
   command:
 
   ```shell

docs/book/how-to/debug-and-solve-issues.md

@@ -149,7 +149,7 @@ RuntimeError: Error initializing rest store with URL 'http://127.0.0.1:8237': HT
 NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f9abb198550>: Failed to establish a new connection: [Errno 61] Connection refused'))
 ```
 
-If you restarted your machine after deploying ZenML then you have to run `zenml up` again after each restart. Local ZenML deployments don't survive machine restarts.
+If you restarted your machine after starting the local ZenML server with `zenml login --local`, then you have to run `zenml login --local` again after each restart. Local ZenML deployments don't survive machine restarts.
 
 #### Column 'step\_configuration' cannot be null
 

docs/book/how-to/infrastructure-deployment/auth-management/best-security-practices.md

@@ -51,7 +51,7 @@ Most cloud providers and their associated Service Connector Types include some f
 
 There are a few caveats that you should be aware of when choosing an implicit authentication method. It may seem like the easiest way out, but it carries with it some implications that may impact portability and usability later down the road:
 
-* when used with a local ZenML deployment, like the default deployment, or [a local ZenML server started with `zenml up`](../../user-guide/production-guide/README.md), the implicit authentication method will use the configuration files and credentials or environment variables set up _on your local machine_. These will not be available to anyone else outside your local environment and will also not be accessible to workloads running in other environments on your local host. This includes for example local K3D Kubernetes clusters and local Docker containers.
+* when used with a local ZenML deployment, like the default deployment, or [a local ZenML server started with `zenml login --local`](../../user-guide/production-guide/README.md), the implicit authentication method will use the configuration files and credentials or environment variables set up _on your local machine_. These will not be available to anyone else outside your local environment and will also not be accessible to workloads running in other environments on your local host. This includes for example local K3D Kubernetes clusters and local Docker containers.
 * when used with a remote ZenML server, the implicit authentication method only works if your ZenML server is deployed in the same cloud as the one supported by the Service Connector Type that you are using. For instance, if you're using the AWS Service Connector Type, then the ZenML server must also be deployed in AWS (e.g. in an EKS Kubernetes cluster). You may also need to manually adjust the cloud configuration of the remote cloud workload where the ZenML server is running to allow access to resources (e.g. add permissions to the AWS IAM role attached to the EC2 or EKS node, add roles to the GCP service account attached to the GKE cluster nodes).
 
 <details>

docs/book/how-to/infrastructure-deployment/stack-deployment/deploy-a-cloud-stack-with-terraform.md

@@ -33,9 +33,11 @@ you can also use [the stack wizard to seamlessly register your stack](../../how-
 
 To use this feature, you need a deployed ZenML server instance that is reachable
 from the cloud provider where you wish to have the stack provisioned (this can't
-be a local server started via `zenml up`). If you do not already have one set up,
-you [can register for a free ZenML Pro account](https://cloud.zenml.io/signup)
-or you can learn about self-hosting a ZenML server [here](../../getting-started/deploying-zenml/README.md).
+be a local server started via `zenml login --local`). If you do not already have
+one set up, you can fast-track to trying out a ZenML Pro server by simply
+running `zenml login --pro` or [register for a free ZenML Pro account](https://cloud.zenml.io/signup).
+If you prefer to host your own, you can learn about self-hosting a ZenML server
+[here](../../getting-started/deploying-zenml/README.md).
 
 Once you are connected to your deployed ZenML server, you need to create
 a service account and an API key for it. You will use the API key to give the
@@ -58,8 +60,10 @@ The API key value is: 'ZENKEY_...'
 Please store it safely as it will not be shown again.
 To configure a ZenML client to use this API key, run:
 
-zenml connect --url https://842ed6a9-zenml.staging.cloudinfra.zenml.io --api-key \
-    'ZENKEY_...'
+zenml login https://842ed6a9-zenml.staging.cloudinfra.zenml.io --api-key
+
+and enter the following API key when prompted:
+ZENKEY_...
 ```
 
 Finally, you will need the following on the machine where you will be running

docs/book/how-to/infrastructure-deployment/stack-deployment/deploy-a-cloud-stack.md

@@ -28,7 +28,7 @@ you can also use [the stack wizard to seamlessly register your stack](../../how-
 ## How to use the 1-click deployment tool?
 
 The first thing that you need in order to use this feature is a
-deployed instance of ZenML (not a local server via `zenml up`). If you do 
+deployed instance of ZenML (not a local server via `zenml login --local`). If you do 
 not already have it set up for you, feel free to learn how to do so
 [here](../../getting-started/deploying-zenml/README.md).
 

docs/book/how-to/pipeline-development/build-pipelines/fetching-pipelines.md

@@ -260,7 +260,7 @@ output.visualize()
 ![output.visualize() Output](../../.gitbook/assets/artifact\_visualization\_evidently.png)
 
 {% hint style="info" %}
-If you're not in a Jupyter notebook, you can simply view the visualizations in the ZenML dashboard by running `zenml up` and clicking on the respective artifact in the pipeline run DAG instead. Check out the [artifact visualization page](../visualize-artifacts/README.md) to learn more about how to build and view artifact visualizations in ZenML!
+If you're not in a Jupyter notebook, you can simply view the visualizations in the ZenML dashboard by running `zenml login --local` and clicking on the respective artifact in the pipeline run DAG instead. Check out the [artifact visualization page](../visualize-artifacts/README.md) to learn more about how to build and view artifact visualizations in ZenML!
 {% endhint %}
 
 ## Fetching information during run execution

docs/book/how-to/project-setup-and-management/connecting-to-zenml/connect-in-with-your-user-interactive.md

@@ -3,7 +3,7 @@
 You can authenticate your clients with the ZenML Server using the ZenML CLI and the web based login. This can be executed with the command:
 
 ```bash
-zenml connect --url https://...
+zenml login https://...
 ```
 
 This command will start a series of steps to validate the device from where you are connecting that will happen in your browser. You can choose whether to mark your respective device as trusted or not. If you choose not to click `Trust this device`, a 24-hour token will be issued for authentication services. Choosing to trust the device will issue a 30-day token instead.
@@ -28,7 +28,7 @@ zenml authorized-device lock <DEVICE_ID>
 
 To keep things simple, we can summarize the steps:
 
-1. Use the `zenml connect --url` command to start a device flow and connect to a zenml server.
+1. Use the `zenml login <URL>` command to start a device flow and connect to a zenml server.
 2. Choose whether to trust the device when prompted.
 3. Check permitted devices with `zenml devices list`.
 4. Invalidate a token with `zenml device lock ...`.

docs/book/how-to/project-setup-and-management/connecting-to-zenml/connect-with-a-service-account.md

@@ -11,7 +11,8 @@ This command creates a service account and an API key for it. The API key is dis
 * using the CLI:
 
 ```bash
-zenml connect --url https://... --api-key <API_KEY>
+# This command will prompt you to enter the API key
+zenml login https://... --api-key
 ```
 
 * setting the `ZENML_STORE_URL` and `ZENML_STORE_API_KEY` environment variables when you set up your ZenML client for the first time. This method is particularly useful when you are using the ZenML client in an automated CI/CD workload environment like GitHub Actions or GitLab CI or in a containerized environment like Docker or Kubernetes:
@@ -63,7 +64,7 @@ Deactivating a service account or an API key will prevent it from being used to
 To keep things simple, we can summarize the steps:
 
 1. Use the `zenml service-account create` command to create a service account and an API key.
-2. Use the `zenml connect --url <url> --api-key <api-key>` command to connect your ZenML client to the server using the API key.
+2. Use the `zenml login <url> --api-key` command to connect your ZenML client to the server using the API key.
 3. Check configured service accounts with `zenml service-account list`.
 4. Check configured API keys with `zenml service-account api-key <SERVICE_ACCOUNT_NAME> list`.
 5. Regularly rotate API keys with `zenml service-account api-key <SERVICE_ACCOUNT_NAME> rotate`.

docs/book/how-to/project-setup-and-management/develop-locally/keep-your-dashboard-server-clean.md

@@ -11,18 +11,17 @@ options:
 ## Run locally
 
 One of the easiest ways to avoid cluttering a shared server / dashboard is to
-disconnect from the server and simply spin up a local server:
+disconnect your client from the remote server and simply spin up a local server:
 
 ```bash
-zenml disconnect
-zenml up
+zenml login --local
 ```
 
 Note that there are some limitations to this approach, particularly if you want
 to use remote infrastructure, but if there are local runs that you can do
 without the need for remote infrastructure, this can be a quick and easy way to
 keep things clean. When you're ready to reconnect to the server to continue with
-your shared runs, you can simply run `zenml connect ...` again.
+your shared runs, you can simply run `zenml login <remote-url>` again.
 
 ## Pipeline Runs
 

docs/book/reference/api-reference.md

@@ -9,7 +9,7 @@ The ZenML server is a FastAPI application, therefore the OpenAPI-compliant docs
 of your ZenML server:
 
 {% hint style="info" %}
-In the local case (i.e. using `zenml up`, the docs are available on `http://127.0.0.1:8237/docs`)
+In the local case (i.e. using `zenml login --local`, the docs are available on `http://127.0.0.1:8237/docs`)
 {% endhint %}
 
 ![ZenML API docs](../.gitbook/assets/zenml_api_docs.png)

docs/book/user-guide/production-guide/ci-cd.md

@@ -95,8 +95,8 @@ jobs:
   run-staging-workflow:
     runs-on: run-zenml-pipeline
     env:
-      ZENML_HOST: ${{ secrets.ZENML_HOST }}  # Put your server url here
-      ZENML_API_KEY: ${{ secrets.ZENML_API_KEY }}  # Retrieves the api key for use  
+      ZENML_STORE_URL: ${{ secrets.ZENML_HOST }}  # Put your server url here
+      ZENML_STORE_API_KEY: ${{ secrets.ZENML_API_KEY }}  # Retrieves the api key for use  
       ZENML_STACK: stack_name  #  Use this to decide which stack is used for staging
       ZENML_GITHUB_SHA: ${{ github.event.pull_request.head.sha }}
       ZENML_GITHUB_URL_PR: ${{ github.event.pull_request._links.html.href }}
@@ -119,9 +119,9 @@ steps:
     run: |
       pip3 install -r requirements.txt
 
-  - name: Connect to ZenML server
+  - name: Confirm ZenML client is connected to ZenML server
     run: |
-      zenml connect --url $ZENML_HOST --api-key $ZENML_API_KEY
+      zenml status
 
   - name: Set stack
     run: |