kernel restart error in jupyter notebook

jupyter-minimal_ipynb

# from
# https://www.tensorflow.org/beta/tutorials/estimators/boosted_trees
import numpy as np
import pandas as pd
from IPython.display import clear_output
from matplotlib import pyplot as plt
import platform
try:
    %tensorflow_version 2.x
except Exception:
    pass
import tensorflow as tf
tf.random.set_seed(123)
print(platform.python_version())
print(tf.version.GIT_VERSION)
print(tf.version.VERSION)
print(tf.__version__)
#### output of above
# 3.7.4
# v2.0.0-rc2-26-g64c3d38
# 2.0.0
# 2.0.0
# Load dataset.
dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')
dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')
y_train = dftrain.pop('survived')
y_eval = dfeval.pop('survived')
fc = tf.feature_column
CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck', 'embark_town', 'alone']
NUMERIC_COLUMNS = ['age', 'fare']

def one_hot_cat_column(feature_name, vocab):
    return tf.feature_column.indicator_column(
        tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocab))

feature_columns = []
for feature_name in CATEGORICAL_COLUMNS:
    # Need to one-hot encode categorical features.
    vocabulary = dftrain[feature_name].unique()
    feature_columns.append(one_hot_cat_column(feature_name, vocabulary))

for feature_name in NUMERIC_COLUMNS:
    feature_columns.append(tf.feature_column.numeric_column(feature_name,
                                           dtype=tf.float32))
example = dict(dftrain.head(1))
example
class_fc = tf.feature_column.indicator_column( 
    tf.feature_column.categorical_column_with_vocabulary_list(
        'class', 
        ('First', 'Second', 'Third')))
class_fc

print('Feature value: "{}"'.format(example['class'].iloc[0]))
print('One-hot encoded: ', tf.keras.layers.DenseFeatures([class_fc])(example).numpy())

# Use entire batch since this is such a small dataset.
NUM_EXAMPLES = len(y_train)

def make_input_fn(X, y, n_epochs=None, shuffle=True):
    def input_fn():
        dataset = tf.data.Dataset.from_tensor_slices((dict(X), y))
        if shuffle:
            dataset = dataset.shuffle(NUM_EXAMPLES)
        # For training, cycle thru dataset as many times as need (n_epochs=None).
        dataset = dataset.repeat(n_epochs)
        # In memory training doesn't use batching.
        dataset = dataset.batch(NUM_EXAMPLES)
        return dataset
    return input_fn
    
# Training and evaluation input functions.
train_input_fn = make_input_fn(dftrain, y_train)
eval_input_fn = make_input_fn(dfeval, y_eval, shuffle=False, n_epochs=1)

# Since data fits into memory, use entire dataset per layer. It will be faster.
# Above one batch is defined as the entire dataset.
n_batches = 1
est = tf.estimator.BoostedTreesClassifier(feature_columns,
                                          n_batches_per_layer=n_batches)
                                          
# The model will stop training once the specified number of trees is built, not
# based on the number of steps.
##########
# In jupyter this will crash the kernel
############
est.train(train_input_fn, max_steps=100)

#### output of above
# INFO:tensorflow:Calling model_fn.
# WARNING:tensorflow:From /home/davis/anaconda3/envs/py3tf2/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/boosted_trees.py:214: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
# Instructions for updating:
# Use `tf.cast` instead.
# INFO:tensorflow:Done calling model_fn.
# INFO:tensorflow:Create CheckpointSaverHook.
# WARNING:tensorflow:Issue encountered when serializing resources.
# Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
# '_Resource' object has no attribute 'name'
# INFO:tensorflow:Graph was finalized.
# INFO:tensorflow:Running local_init_op.
# INFO:tensorflow:Done running local_init_op.
# WARNING:tensorflow:Issue encountered when serializing resources.
# Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
# '_Resource' object has no attribute 'name'
# INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpokrqlmdl/model.ckpt.
# WARNING:tensorflow:Issue encountered when serializing resources.
# Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
# '_Resource' object has no attribute 'name'
# INFO:tensorflow:loss = 0.6931468, step = 0
# WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.