Mayo Clinic Onboarding Guide

pip install wandb

wandb login --host https://mc.wandb.io <YOUR API TOKEN>

WANDB_BASE_URL = "https://mc.wandb.io"
wandb.login(host=os.getenv("WANDB_BASE_URL"), key=os.getenv("WANDB_API_KEY"))

import wandb

wandb.init(project="my-sample-project",   
           entity="<enter team name>",	        # 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"))

# 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})

# 1. Log a dataset version as an artifact
import wandb
import os

# Initialize a new W&B run to track this job
run = wandb.init(project="artifacts-quickstart", job_type="dataset-creation")

# Create a sample dataset to log as an artifact
f = open('my-dataset.txt', 'w')
f.write('Imagine this is a big dataset.')
f.close()

# Create a new artifact, which is a sample dataset
dataset = wandb.Artifact('my-dataset', type='dataset')
# Add files to the artifact, in this case a simple text file
dataset.add_file('my-dataset.txt')
# Log the artifact to save it as an output of this run
run.log_artifact(dataset)

wandb.finish()

import wandb
run = wandb.init()
# Indicate we are using a dependency
artifact = run.use_artifact('dummy-team/that_was_easy/my-dataset:v3', type='dataset')
artifact_dir = artifact.download()

import wandb
import random

# Start a new W&B run to track your experiment
run = wandb.init(project="registry_quickstart") 

# Simulate logging model metrics
run.log({"acc": random.random()})

# Create a simulated model file
with open("my_model.txt", "w") as f:
   f.write("Model: " + str(random.random()))

# log and link the model to the model registry inside W&B Registry
logged_artifact = run.log_artifact(artifact_or_path="./my_model.txt", name="gemma-finetuned-3twsov9e", type="model")
run.link_artifact(artifact=logged_artifact, target_path=f"<INSERT-ORG-NAME>/wandb-registry-model/registry-quickstart-collection"),

run.finish()

from lightning.pytorch.loggers import WandbLogger
from lightning.pytorch import Trainer
from lightning.pytorch.callbacks import ModelCheckpoint


checkpoint_callback = ModelCheckpoint(monitor='val_accuracy', mode='max')

wandb_logger = WandbLogger(project='MNIST', # group runs in "MNIST" project
                           entity='smle-demo',
                           log_model='all') # log all new checkpoints during training
trainer = Trainer(
    logger=wandb_logger,                    # W&B integration
    callbacks=[log_predictions_callback,    # logging of sample predictions
               checkpoint_callback],        # our model checkpoint callback
    accelerator="gpu",                      # use GPU
    max_epochs=5)        

wandb.init(name="my_awesome_run")

os.environ["WANDB_SILENT"] = "true"

%env WANDB_SILENT=true

WANDB_SILENT=true