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A run is a single unit of computation logged by W&B. You can think of a W&B Run as an atomic element of your whole project. In other words, each run is a record of a specific computation, such as training a model and logging the results, hyperparameter sweeps, and so forth. Common patterns for initiating a run include, but are not limited to:
  • Training a model
  • Changing a hyperparameter and conducting a new experiment
  • Conducting a new machine learning experiment with a different model
  • Logging data or a model as a W&B Artifact
  • Downloading a W&B Artifact
Each run has a unique identifier known as a run ID. You can specify a unique ID or let W&B randomly generate one for you. Each run also has a human-readable, non-unique run name. You can specify a name for your run or let W&B randomly generate one for you. You can rename a run after initializing it. W&B logs your run to a project. You specify the project when you initialize the run with wandb.init(project=). W&B creates a new project if the project does not exist. If the project does exist, W&B logs the run to the project specified.
If you do not specify a project name, W&B stores the run in a project called Uncategorized.
Use the run object returned by wandb.init() to log metrics, artifacts, and other information to the run. Anything you log with wandb.Run.log() is recorded to that run. Each run has a state that describes the current status of the run. See Run states for a full list of possible run states. View runs and their properties within the run’s project workspace on the W&B App. You can also programmatically access run properties with the wandb.Api.Run object. As an example, consider the following code snippet that initializes a W&B run and logs some metrics to it:
Pass your W&B entity to the entity variable in the code snippets below if you want to follow along. Your entity is your W&B username or team name. You can find it in the URL of your W&B App workspace. For example, if your workspace URL is https://wandb.ai/nico/awesome-project, then your entity is nico.
import wandb

entity = "nico"  # Replace with your W&B entity
project = "awesome-project"

with wandb.init(entity=entity, project=project) as run:
    run.log({"accuracy": 0.9, "loss": 0.1})
The first line imports the W&B Python SDK. The second line initializes a run in the project awesome-project under the entity nico. The third line logs the accuracy and loss of the model to that run. Within the terminal, W&B returns: 
wandb: Syncing run earnest-sunset-1
wandb: ⭐️ View project at https://wandb.ai/nico/awesome-project
wandb: 🚀 View run at https://wandb.ai/nico/awesome-project/runs/1jx1ud12
wandb:                                                                                
wandb: 
wandb: Run history:
wandb: accuracy ▁
wandb:     loss ▁
wandb: 
wandb: Run summary:
wandb: accuracy 0.9
wandb:     loss 0.5
wandb: 
wandb: 🚀 View run earnest-sunset-1 at: https://wandb.ai/nico/awesome-project/runs/1jx1ud12
wandb: ⭐️ View project at: https://wandb.ai/nico/awesome-project
wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: ./wandb/run-20241105_111006-1jx1ud12/logs
The URL W&B returns in the terminal to redirects you to the run’s workspace in the W&B App UI. Note that the panels generated in the workspace corresponds to the single point.
Single run workspace
Logging a metrics at a single point of time might not be that useful. A more realistic example in the case of training discriminative models is to log metrics at regular intervals. For example, consider the following code snippet: 
import wandb
import random

config = {
    "epochs": 10,
    "learning_rate": 0.01,
}

with wandb.init(project="awesome-project", config=config) as run:
    print(f"lr: {config['learning_rate']}")
      
    # Simulating a training run
    for epoch in range(config['epochs']):
      offset = random.random() / 5
      acc = 1 - 2**-epoch - random.random() / (epoch + 1) - offset
      loss = 2**-epoch + random.random() / (epoch + 1) + offset
      print(f"epoch={epoch}, accuracy={acc}, loss={loss}")
      run.log({"accuracy": acc, "loss": loss})
The training script calls wandb.Run.log() 10 times. Each time the script calls wandb.Run.log(), W&B logs the accuracy and loss for that epoch. Within your terminal, you should see output similar to the following: 
wandb: Syncing run jolly-haze-4
wandb: ⭐️ View project at https://wandb.ai/nico/awesome-project
wandb: 🚀 View run at https://wandb.ai/nico/awesome-project/runs/pdo5110r
lr: 0.01
epoch=0, accuracy=-0.10070974957523078, loss=1.985328507123956
epoch=1, accuracy=0.2884687745057535, loss=0.7374362314407752
epoch=2, accuracy=0.7347387967382066, loss=0.4402409835486663
epoch=3, accuracy=0.7667969248039795, loss=0.26176963846423457
epoch=4, accuracy=0.7446848791003173, loss=0.24808611724405083
epoch=5, accuracy=0.8035095836268268, loss=0.16169791827329466
epoch=6, accuracy=0.861349032371624, loss=0.03432578493587426
epoch=7, accuracy=0.8794926436276016, loss=0.10331872172219471
epoch=8, accuracy=0.9424839917077272, loss=0.07767793473500445
epoch=9, accuracy=0.9584880427028566, loss=0.10531971149250456
wandb: 🚀 View run jolly-haze-4 at: https://wandb.ai/nico/awesome-project/runs/pdo5110r
wandb: Find logs at: wandb/run-20241105_111816-pdo5110r/logs
W&B captures the simulated training loop within a single run called jolly-haze-4. This is because the script calls wandb.init() method only once. Copy and paste the URL that W&B prints from the previous output into your browser. The URL directs you to the run’s workspace in the W&B App UI. For example, the following image shows the workspace for the run jolly-haze-4:
Training run with logged metrics

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Add a note to a run

Notes that you add to a specific run appear on the run page in the Overview tab and in the table of runs on the project page.
  1. Navigate to your W&B project
  2. Select the Workspace tab from the project sidebar
  3. Select the run you want to add a note to from the run selector
  4. Choose the Overview tab
  5. Select the pencil icon next to the Description field and add your notes