pip install wandb

wandb login --host <YOUR W&B HOST URL> <YOUR API TOKEN>

wandb.login(host=os.getenv("WANDB_BASE_URL"), key=os.getenv("WANDB_API_KEY"))

import wandb

wandb.init(project="pytorch-lightning-e2e",   
           entity="wandb",	        # Team
           group='my_group',	        # for organizing runs (e.g. distributed training)
           job_type='training',		# for organizing runs (e.g. preprocessing vs. training)
           config={'hyperparam1': 24,   # Hyperparams and other config
		   'hyperparam2': 'resnet'})

wandb.log({"my_metric": some_scalar_value})

wandb.init()

for i in range(5):
    wandb.log({"my_metric": i})

wandb.summary["my_metric"] = 2 # 2 instead of the default 4

wandb.finish()

wandb.log({"my_image": wandb.Image("my_image.jpg"))

from matplotlib.pyplot import figure
import mpld3

fig = figure()
ax = fig.gca()
ax.plot([1,2,3,4])

mpld3.save_html(fig,"test.html")

wandb.log({"matplotlib_to_html": wandb.Html(open("test.html"), inject=False)})

# Confusion matrices
wandb.log({"conf_mat": wandb.plot.confusion_matrix(y_true=ground_truth, preds=predictions, class_names=class_names)})

# ROC Curves
wandb.log({"roc": wandb.plot.roc_curve(ground_truth, predictions)})

# PR Curves
wandb.log({"pr": wandb.plot.pr_curve(ground_truth, predictions)})

wandb.log({"my_chart_data": wandb.Table(dataframe=my_pandas_dataframe)})

# Add data as a list of lists or pandas dataframe
my_data = [
  [0, wandb.Image("img_0.jpg"), 0, 0],
  [1, wandb.Image("img_1.jpg"), 8, 0],
  [2, wandb.Image("img_2.jpg"), 7, 1],
  [3, wandb.Image("img_3.jpg"), 1, 1]
]
          
# create a wandb.Table() with corresponding columns
columns=["id", "image", "prediction", "truth"]
test_table = wandb.Table(data=my_data, columns=columns)

# Add data incrementally
for img_id, img in enumerate(mnist_test_data):
    true_label = mnist_test_data_labels[img_id]
    guess_label = my_model.predict(img)
    test_table.add_data(img_id, wandb.Image(img), \
                         guess_label, true_label)

wandb.log({"test_table": test_table})

wandb.init(project="pytorch-lightning-e2e", entity='wandb', job_type="upload_data")

# Create Artifact
training_images = wandb.Artifact(name='training_images', 
                                 type="training_data",
                                 description='MNIST training data')

# Add serialized data i.e. directories, files, plots, html, W&B Tables
training_images.add_dir('./sample_images')

# Add other assets to better contextualize your artifact
training_images.add(wandb.Html('my_plotly_figure.html'), 'data_distribution_plot')

# Log to W&B, automatic versioning
wandb.log_artifact(training_images)

with wandb.init(project="pytorch-lightning-e2e", entity='wandb', job_type="model_training"})
# Indicate we are using a dependency
training_imgs_artifact = wandb.use_artifact("training_images:latest")
training_images_dir = training_imgs_artifact.download()

wandb.init()

for i in range(epochs):
   wandb.log({"my_table": wandb.Table(...)})

wandb.init(project='pytorch-lightning-e2e',
           entity='wandb',
           job_type='training',
           config={
                  "model_name": "resnet",
                  "batch_size": 16
           })

# Logs all checkpoints.
wandb_logger = WandbLogger(log_model='all', checkpoint_name=f'nature-{wandb.run.id}') 

checkpoint_callback = ModelCheckpoint(every_n_epochs=1)

model = NatureLitModule(model_name=wandb.config['model_name']) # Access hyperparameters downstream to instantiate models/datasets

trainer = Trainer(logger=wandb_logger,  # W&B integration
                  callbacks=[checkpoint_callback],
                  max_epochs=5,
                  log_every_n_steps=5)                          
trainer.fit(model, datamodule=nature_module)