Updating projects with the new zenml login flow

classifier-e2e/run_full.ipynb

@@ -38,7 +38,7 @@
    "source": [
     "! pip3 install -r requirements.txt\n",
     "! zenml integration install sklearn xgboost -y\n",
-    "! zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io \n",
+    "! zenml login https://1cf18d95-zenml.cloudinfra.zenml.io \n",
     "\n",
     "import IPython\n",
     "IPython.Application.instance().kernel.do_shutdown(restart=True)"

classifier-e2e/run_skip_basics.ipynb

@@ -38,7 +38,7 @@
    "source": [
     "! pip3 install -r requirements.txt\n",
     "! zenml integration install sklearn xgboost -y\n",
-    "! zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io \n",
+    "! zenml login https://1cf18d95-zenml.cloudinfra.zenml.io \n",
     "\n",
     "import IPython\n",
     "IPython.Application.instance().kernel.do_shutdown(restart=True)"

customer-satisfaction/README.md

@@ -46,7 +46,7 @@ In case you already have an account, here is how you connect to a deployed
 server.
 
 ```bash
-zenml connect -u <INSERT_SERVER_URL_HERE>
+zenml login <INSERT_SERVER_URL_HERE>
 ```
 
 To run locally, you need
@@ -55,7 +55,7 @@ but first you must install the optional dependencies for the ZenML server:
 
 ```bash
 pip install zenml["server"]
-zenml up
+zenml login --local
 ```
 
 If you are running the `run_deployment.py` script, you will also need to install
@@ -237,7 +237,7 @@ A browser window should open for you and let you configure a product to run a pr
     zenml integration install mlflow -y
     ```
 
-2. If you are trying to start the ZenML server with `zenml up`, if you're running 
+2. If you are trying to start the ZenML server with `zenml login --local`, if you're running 
 on a Mac, you might want to set the following environment variable in your `.zshrc` 
 file or in the environment in which you're running the pipeline:
 

databricks-demo/README.md

@@ -51,9 +51,8 @@ source .venv/bin/activate
 make setup
 # Optionally, provision default local stack
 make install-stack-local
-# Start the ZenML UI locally (recommended, but optional);
-# the default username is "admin" with an empty password
-zenml up
+# Start the ZenML UI locally (recommended, but optional)
+zenml login --local
 # Run the pipeline included in the project
 python run.py
 ```

end-to-end-computer-vision/README.md

@@ -55,7 +55,7 @@ export WANDB_DISABLED=True
 ### Connect to your deployed ZenML instance
 
 ```bash
-zenml connect --url <INSERT_ZENML_URL_HERE>
+zenml login <INSERT_ZENML_URL_HERE>
 ```
 
 ## Cloud Provider

explainability-shap/run.ipynb

@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!zenml connect --url=https://d13d987c-zenml.cloudinfra.zenml.io"
+    "!zenml login https://d13d987c-zenml.cloudinfra.zenml.io"
    ]
   },
   {

flux-dreambooth/walkthrough.ipynb

@@ -37,7 +37,7 @@
    "outputs": [],
    "source": [
     "# Connect to ZenML server\n",
-    "!zenml connect --url <your-zenml-server-url>"
+    "!zenml login <your-zenml-server-url>"
    ]
   },
   {

label_studio_annotation/README.md

@@ -32,7 +32,7 @@ cd label_studio_annotation
 zenml init
 
 # Start the ZenServer to enable dashboard access
-zenml up
+zenml login --local
 ```
 
 You will need to install and start Label Studio locally:

llm-finetuning-simple/README.md

@@ -45,7 +45,7 @@ zenml integration install lightning s3 aws -y
 
 # Initialize and connect to a deployed ZenML server
 zenml init
-zenml connect --url <MYZENMLSERVERURL>
+zenml login <MYZENMLSERVERURL>
 ```
 
 ## Running the Pipeline

native-experiment-tracking/README.md

@@ -41,7 +41,7 @@ zenml integration install sklearn pandas -y
 zenml init
 
 # Connect to your ZenML server
-zenml connect --url ...
+zenml login ...
 
 python run.py --parallel
 ```