Skip to content
Advertisement

cannot load pickle files for xgboost images of version > 1.2-2 in sagemaker – UnpicklingError

I can train a XGBoost model using Sagemaker images like so:

import boto3
import sagemaker
from sagemaker.inputs import TrainingInput
import os

folder = r"C:Somewhere"
os.chdir(folder)

s3_prefix = 'some_model'
s3_bucket_name = 'the_bucket'
train_file_name = 'train.csv'
val_file_name = 'val.csv'
role_arn = 'arn:aws:iam::482777693429:role/bla_instance_role'

region_name = boto3.Session().region_name

s3_input_train = TrainingInput(s3_data='s3://{}/{}/{}'.format(s3_bucket_name, s3_prefix, train_file_name), content_type='csv')
s3_input_val = TrainingInput(s3_data='s3://{}/{}/{}'.format(s3_bucket_name, s3_prefix, val_file_name), content_type='csv')

print(type(s3_input_train))

hyperparameters = {
        "max_depth":"13",
        "eta":"0.15",
        "gamma":"4",
        "min_child_weight":"6",
        "subsample":"0.7",
        "objective":"reg:squarederror",
        "num_round":"50"}

output_path = 's3://{}/{}/output'.format(s3_bucket_name, s3_prefix)

# 1.5-1
# 1.3-1
estimator = sagemaker.estimator.Estimator(image_uri=sagemaker.image_uris.retrieve("xgboost", region_name, "1.2-2"), 
                                          hyperparameters=hyperparameters,
                                          role=role_arn,
                                          instance_count=1, 
                                          instance_type='ml.m5.2xlarge',
                                          #instance_type='local', 
                                          volume_size=1, # 1 GB 
                                          output_path=output_path)

estimator.fit({'train': s3_input_train, 'validation': s3_input_val})

This work for all versions 1.2-2, 1.3-1 and 1.5-1. Unfortunately the following code only works for version 1.2-2:

import boto3
import os
import pickle as pkl 
import tarfile
import pandas as pd
import xgboost as xgb

folder = r"C:Somewhere"
os.chdir(folder)

s3_prefix = 'some_model'
s3_bucket_name = 'the_bucket'
model_path = 'output/sagemaker-xgboost-2022-04-30-10-52-29-877/output/model.tar.gz'
session = boto3.Session(profile_name='default')
session.resource('s3').Bucket(s3_bucket_name).download_file('{}/{}'.format(s3_prefix, model_path), 'model.tar.gz')
t = tarfile.open('model.tar.gz', 'r:gz')
t.extractall()

model_file_name = 'xgboost-model'
with open(model_file_name, "rb") as input_file:
e = pkl.load(input_file) 

Otherwise I get a:

_pickle.UnpicklingError: unpickling stack underflow

Am I missing something? Is my “pickle loading code wrong”?

The version of xgboost is 1.6.0 where I run the pickle code.

Advertisement

Answer

I found the solution here. I will leave it in case someone come accross the same issue.

7 People found this is helpful
Advertisement