Removes redunant variables from code blocks in add-w-and-b-to-your-code.md (#1187)

This PR:
- removes the unused **run** variables from the code blocks, and
- fixes a minor typo ("," -> ":") in one of the code blocks

---------

Co-authored-by: Matt Linville <matt.linville@wandb.com>

Author: SinaMostafanejad

content/guides/models/sweeps/add-w-and-b-to-your-code.md

@@ -8,7 +8,7 @@ title: Add W&B (wandb) to your code
 weight: 2
 ---
 
-There are numerous ways to add the W&B Python SDK to your script or Jupyter Notebook. Outlined below is a "best practice" example of how to integrate the W&B Python SDK into your own code.
+There are numerous ways to add the W&B Python SDK to your script or notebook. This section provides a "best practice" example that shows how to integrate the W&B Python SDK into your own code.
 
 ### Original training script
 
@@ -51,12 +51,13 @@ def main():
 
 ### Training script with W&B Python SDK
 
-The following code examples demonstrate how to add the W&B Python SDK into your code. If you start W&B Sweep jobs in the CLI, you will want to explore the CLI tab. If you start W&B Sweep jobs within a Jupyter notebook or Python script, explore the Python SDK tab.
+The following code examples demonstrate how to add the W&B Python SDK into your
+code. If you start W&B Sweep jobs in the CLI, you will want to explore the CLI
+tab. If you start W&B Sweep jobs within a Jupyter notebook or Python script,
+explore the Python SDK tab.
 
-
-{{< tabpane text=true >}}
-    {{% tab header="Python script or notebook" %}}
-  To create a W&B Sweep, we added the following to the code example:
+{{< tabpane text=true >}} {{% tab header="Python script or notebook" %}} To
+create a W&B Sweep, we added the following to the code example:
 
 1. Import the Weights & Biases Python SDK.
 2. Create a dictionary object where the key-value pairs define the sweep configuration. In the proceeding example, the batch size (`batch_size`), epochs (`epochs`), and the learning rate (`lr`) hyperparameters are varied during each sweep. For more information on how to create a sweep configuration, see [Define sweep configuration]({{< relref "/guides/models/sweeps/define-sweep-configuration/" >}}).
@@ -72,26 +73,10 @@ import wandb
 import numpy as np
 import random
 
-# Define sweep config
-sweep_configuration = {
-    "method": "random",
-    "name": "sweep",
-    "metric": {"goal": "maximize", "name": "val_acc"},
-    "parameters": {
-        "batch_size": {"values": [16, 32, 64]},
-        "epochs": {"values": [5, 10, 15]},
-        "lr": {"max": 0.1, "min": 0.0001},
-    },
-}
-
-# Initialize sweep by passing in config.
-# (Optional) Provide a name of the project.
-sweep_id = wandb.sweep(sweep=sweep_configuration, project="my-first-sweep")
-
 
 # Define training function that takes in hyperparameter
 # values from `wandb.config` and uses them to train a
-# model and return metric
+# model and return the metrics
 def train_one_epoch(epoch, lr, bs):
     acc = 0.25 + ((epoch / 30) + (random.random() / 10))
     loss = 0.2 + (1 - ((epoch - 1) / 10 + random.random() / 5))
@@ -104,38 +89,72 @@ def evaluate_one_epoch(epoch):
     return acc, loss
 
 
-def main():
-    run = wandb.init()
-
-    # note that we define values from `wandb.config`
-    # instead of defining hard values
-    lr = wandb.config.lr
-    bs = wandb.config.batch_size
-    epochs = wandb.config.epochs
-
-    for epoch in np.arange(1, epochs):
-        train_acc, train_loss = train_one_epoch(epoch, lr, bs)
-        val_acc, val_loss = evaluate_one_epoch(epoch)
+# Define a sweep config dictionary
+sweep_configuration = {
+    "method": "random",
+    "name": "sweep",
+    "metric": {"goal": "maximize", "name": "val_acc"},
+    "parameters": {
+        "batch_size": {"values": [16, 32, 64]},
+        "epochs": {"values": [5, 10, 15]},
+        "lr": {"max": 0.1, "min": 0.0001},
+    },
+}
 
-        wandb.log(
-            {
-                "epoch": epoch,
-                "train_acc": train_acc,
-                "train_loss": train_loss,
-                "val_acc": val_acc,
-                "val_loss": val_loss,
-            }
-        )
+# (Optional) Provide a name for the project.
+project = "my-first-sweep"
 
+def main():
+    # Use the `with` context manager statement to automatically end the run.
+    # This is equivalent to using `run.finish()` at the end of each run
+    with wandb.init(project=project) as run:
+
+        # This code fetches the hyperparameter values from `wandb.config`
+        # instead of defining them explicitly
+        lr = run.config["lr"]
+        bs = run.config["batch_size"]
+        epochs = run.config["epochs"]
+
+        # Execute the training loop and log the performance values to W&B
+        for epoch in np.arange(1, epochs):
+            train_acc, train_loss = train_one_epoch(epoch, lr, bs)
+            val_acc, val_loss = evaluate_one_epoch(epoch)
+
+            run.log(
+                {
+                    "epoch": epoch,
+                    "train_acc": train_acc,
+                    "train_loss": train_loss,
+                    "val_acc": val_acc,
+                    "val_loss": val_loss,
+                }
+            )
+
+
+if __name__ == "__main__":
+    # Initialize the sweep by passing in the config dictionary
+    sweep_id = wandb.sweep(sweep=sweep_configuration, project=project)
+
+    # Start the sweep job
+    wandb.agent(sweep_id, function=main, count=4)
 
-# Start sweep job.
-wandb.agent(sweep_id, function=main, count=4)
 ```
 
-{{% /tab %}}
-{{% tab header="CLI" %}}
+{{% alert %}} The preceding code snippet shows how to initialize a
+[`wandb.init()`]({{< relref "/ref/python/init.md" >}}) API within a `with`
+context manager statement to generate a background process to sync and log data
+as a [W&B Run]({{< relref "/ref/python/run.md" >}}). This ensures the run is
+properly terminated after uploading the logged values. An alternative approach
+is to call `wandb.init()` and `wandb.finish()` at the beginning and end of the
+training script, respectively.
+{{% /alert %}}
+
+{{% /tab %}} {{% tab header="CLI" %}}
 
-To create a W&B Sweep, we first create a YAML configuration file. The configuration file contains he hyperparameters we want the sweep to explore. In the proceeding example, the batch size (`batch_size`), epochs (`epochs`), and the learning rate (`lr`) hyperparameters are varied during each sweep.
+To create a W&B Sweep, we first create a YAML configuration file. The
+configuration file contains he hyperparameters we want the sweep to explore. In
+the proceeding example, the batch size (`batch_size`), epochs (`epochs`), and
+the learning rate (`lr`) hyperparameters are varied during each sweep.
 
 ```yaml
 # config.yaml
@@ -146,8 +165,8 @@ metric:
   goal: maximize
   name: val_acc
 parameters:
-  batch_size: 
-    values: [16,32,64]
+  batch_size:
+    values: [16, 32, 64]
   lr:
     min: 0.0001
     max: 0.1
@@ -157,7 +176,8 @@ parameters:
 
 For more information on how to create a W&B Sweep configuration, see [Define sweep configuration]({{< relref "/guides/models/sweeps/define-sweep-configuration/" >}}).
 
-Note that you must provide the name of your Python script for the `program` key in your YAML file.
+You must provide the name of your Python script for the `program` key
+in your YAML file.
 
 Next, we add the following to the code example:
 
@@ -192,7 +212,7 @@ def main():
     with open("./config.yaml") as file:
         config = yaml.load(file, Loader=yaml.FullLoader)
 
-    run = wandb.init(config=config)
+    wandb.init(config=config)
 
     # Note that we define values from `wandb.config`
     # instead of  defining hard values
@@ -219,7 +239,9 @@ def main():
 main()
 ```
 
-Navigate to your CLI. Within your CLI, set a maximum number of runs the sweep agent should try. This is step optional. In the following example we set the maximum number to five.
+In your CLI, set a maximum number of runs for the sweep
+agent to try. This is optional. This example we set the
+maximum number to 5.
 
 ```bash
 NUM=5
@@ -231,24 +253,24 @@ Next, initialize the sweep with the [`wandb sweep`]({{< relref "/ref/cli/wandb-s
 wandb sweep --project sweep-demo-cli config.yaml
 ```
 
-This returns a sweep ID. For more information on how to initialize sweeps, see [Initialize sweeps]({{< relref "./initialize-sweeps.md" >}}).
+This returns a sweep ID. For more information on how to initialize sweeps, see
+[Initialize sweeps]({{< relref "./initialize-sweeps.md" >}}).
 
-Copy the sweep ID and replace `sweepID` in the proceeding code snippet to start the sweep job with the [`wandb agent`]({{< relref "/ref/cli/wandb-agent.md" >}}) command:
+Copy the sweep ID and replace `sweepID` in the proceeding code snippet to start
+the sweep job with the [`wandb agent`]({{< relref "/ref/cli/wandb-agent.md" >}})
+command:
 
 ```bash
 wandb agent --count $NUM your-entity/sweep-demo-cli/sweepID
 ```
 
-For more information on how to start sweep jobs, see [Start sweep jobs]({{< relref "./start-sweep-agents.md" >}}).
+For more information, see [Start sweep jobs]({{< relref "./start-sweep-agents.md" >}}).
 
-{{% /tab %}}
-{{< /tabpane >}}
+{{% /tab %}} {{< /tabpane >}}
 
+## Consideration when logging metrics
 
-
-## Consideration when logging metrics 
-
-Ensure to log the metric you specify in your sweep configuration explicitly to W&B. Do not log metrics for your sweep inside of a sub-directory. 
+Be sure to log the sweep's metric to W&B explicitly. Do not log metrics for your sweep inside a subdirectory.
 
 For example, consider the proceeding psuedocode. A user wants to log the validation loss (`"val_loss": loss`). First they pass the values into a dictionary. However, the dictionary passed to `wandb.log` does not explicitly access the key-value pair in the dictionary:
 
@@ -309,7 +331,7 @@ def train():
 def main():
     wandb.init(entity="<entity>", project="my-first-sweep")
     val_metrics = train()
-    wandb.log({"val_loss", val_metrics["val_loss"]})
+    wandb.log({"val_loss": val_metrics["val_loss"]})
 
 
 sweep_configuration = {
@@ -324,4 +346,4 @@ sweep_configuration = {
 sweep_id = wandb.sweep(sweep=sweep_configuration, project="my-first-sweep")
 
 wandb.agent(sweep_id, function=main, count=10)
-```
+```