for _ in range(RUN_COUNT):

  # 1️⃣ Initialize a new W&B run to track this job
  run = wandb.init(project=PROJECT, config=set_config())

  for epoch in range(5):
    # 2️⃣ Log metrics to W&B for each epoch of training
    run.log(get_metrics(epoch))

  # 3️⃣ At the end of training, save the model artifact
  # Name this artifact after the current run
  model_artifact_name = "demo_model_" + run.id
  # Create a new artifact 
  model = wandb.Artifact(model_artifact_name, type='model')
  # Add files to the artifact, in this case a simple text file
  model.add_file(get_model())
  # Log the model to W&B
  run.log_artifact(model)

  # Call finish if you're in a notebook, to mark the run as done
  run.finish()



https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb-model-registry/W%26B_Model_Registry_Quickstart.ipynb#scrollTo=CFXVyKSaRtUw

#@title 1) Run this cell to set up `wandb` and define helper functions


# INSTALL W&B LIBRARY
!pip install wandb -qqq

import wandb
import os
import math
import random

# FORM VARIABLES
PROJECT = "Model_Registry_Quickstart" #@param {type:"string"}
RUN_COUNT = 3 #@param {type:"integer"}


# HELPER FUNCTIONS
# Create fake data to simulate training a model.

# Simulate setting up hyperparameters
# Return: A dict of params to log as config to W&B
def set_config():
  config={
    "learning_rate": 0.01 + 0.1 * random.random(),
    "batch_size": 128,
    "architecture": "CNN",
  }
  return config

# Simulate training a model
# Return: A model file to log as an artifact to W&B
def get_model():
  file_name = "demo_model.h5"
  model_file = open(file_name, 'w')
  model_file.write('Imagine this is a big model file! ' + str(random.random()))
  model_file.close()
  return file_name

# Simulate logging metrics from model training
# Return: A dictionary of metrics to log to W&B
def get_metrics(epoch):
  metrics = {
    "acc": .8 + 0.04 * (math.log(1 + epoch + random.random()) + (0.3 * random.random())),
    "val_acc": .75 + 0.04 * (math.log(1 + epoch + random.random()) - (0.3 * random.random())),
    "loss": .1 + 0.1 * (4 - math.log(1 + epoch + random.random()) + (0.3 * random.random())),
    "val_loss": .1 + 0.16 * (5 - math.log(1 + epoch + random.random()) - (0.3 * random.random())),
  }
  return metrics

run.id
saved_model_weights.pt