Create Em CPT

Em CPT

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+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