XGBoost（免费套餐）的 Amazon Sagemaker ResourceLimitExceeded 错误

Question

我正在尝试在免费套餐 AWS Sagemaker 中创建 XGBoost 模型。我收到以下错误：

"ResourceLimitExceeded：调用 CreateEndpoint 操作时发生错误 (ResourceLimitExceeded)：账户级服务限制 'ml.m5.xlarge for endpoint usage' 为 0 Instances，当前利用率为 0 Instances 和一个请求1 个实例的增量。”.

我应该使用什么正确的 train_instance_type？

这是我的代码：

# import libraries
import boto3, re, sys, math, json, os, sagemaker, urllib.request
from sagemaker import get_execution_role
import numpy as np                                
import pandas as pd                               
import matplotlib.pyplot as plt                   
from IPython.display import Image                 
from IPython.display import display               
from time import gmtime, strftime                 
from sagemaker.predictor import csv_serializer   

# Define IAM role
role = get_execution_role()
prefix = 'sagemaker/DEMO-xgboost-dm'
containers = {'us-west-2': '433757028032.dkr.ecr.us-west-2.amazonaws.com/xgboost:latest',
              'us-east-1': '811284229777.dkr.ecr.us-east-1.amazonaws.com/xgboost:latest',
              'us-east-2': '825641698319.dkr.ecr.us-east-2.amazonaws.com/xgboost:latest',
              'eu-west-1': '685385470294.dkr.ecr.eu-west-1.amazonaws.com/xgboost:latest'} # each region has its XGBoost container
my_region = boto3.session.Session().region_name # set the region of the instance

# Create an instance of the XGBoost model (an estimator), and define the model’s hyperparameters.
# Note: train_instance_type='ml.m5.large' has 0 free credits! Use one of https://aws.amazon.com/sagemaker/pricing/ 
sess = sagemaker.Session()
xgb = sagemaker.estimator.Estimator(containers[my_region],role, train_instance_count=1, train_instance_type='ml.m5.xlarge',output_path='s3://{}/{}/output'.format('my_s3_bucket', prefix),sagemaker_session=sess)
xgb.set_hyperparameters(max_depth=1,eta=0.2,gamma=4,min_child_weight=6,subsample=0.8,silent=0,objective='binary:logistic',num_round=100)
# Train the model using gradient optimization on a ml.m4.xlarge instance
# After a few minutes, you should start to see the training logs being generated.
xgb.fit({'train': s3_input_train})

在这一步，我看到的是：

2019-10-22 06:32:51 Starting - Starting the training job...
2019-10-22 06:33:00 Starting - Launching requested ML instances......
2019-10-22 06:33:54 Starting - Preparing the instances for training...
2019-10-22 06:34:41 Downloading - Downloading input data...
2019-10-22 06:35:22 Training - Training image download completed. Training in progress..Arguments: train
[2019-10-22:06:35:22:INFO] Running standalone xgboost training.
[2019-10-22:06:35:22:INFO] Path /opt/ml/input/data/validation does not exist!
[2019-10-22:06:35:22:INFO] File size need to be processed in the node: 3.38mb. Available memory size in the node: 8089.9mb
[2019-10-22:06:35:22:INFO] Determined delimiter of CSV input is ','
[06:35:22] S3DistributionType set as FullyReplicated
[06:35:22] 28831x59 matrix with 1701029 entries loaded from /opt/ml/input/data/train?format=csv&label_column=0&delimiter=,
[06:35:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1
[0]#011train-error:0.102182
[06:35:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1
[1]#011train-error:0.102182
[06:35:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1
[2]#011train-error:0.102182
[06:35:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1
[3]#011train-error:0.102182
[06:35:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1
[4]#011train-error:0.102182
[06:35:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1
[5]#011train-error:0.102182
[06:35:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1
[6]#011train-error:0.102182
[06:35:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1
[7]#011train-error:0.10839
[06:35:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1
[8]#011train-error:0.102737
[06:35:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2 extra nodes, 0 pruned nodes, max_depth=1
[9]#011train-error:0.107697

然后当我部署这个时：

# Deploy the model on a server and create an endpoint that you can access
xgb_predictor = xgb.deploy(initial_instance_count=1,instance_type='ml.m5.xlarge')
---------------------------------------------------------------------------
ResourceLimitExceeded                     Traceback (most recent call last)
<ipython-input-38-6d149f3edc98> in <module>()
      1 # Deploy the model on a server and create an endpoint that you can access
----> 2 xgb_predictor = xgb.deploy(initial_instance_count=1,instance_type='ml.m5.xlarge')

~/anaconda3/envs/python3/lib/python3.6/site-packages/sagemaker/estimator.py in deploy(self, initial_instance_count, instance_type, accelerator_type, endpoint_name, use_compiled_model, update_endpoint, wait, model_name, kms_key, **kwargs)
    559             tags=self.tags,
    560             wait=wait,
--> 561             kms_key=kms_key,
    562         )
    563 

~/anaconda3/envs/python3/lib/python3.6/site-packages/sagemaker/model.py in deploy(self, initial_instance_count, instance_type, accelerator_type, endpoint_name, update_endpoint, tags, kms_key, wait)
    464         else:
    465             self.sagemaker_session.endpoint_from_production_variants(
--> 466                 self.endpoint_name, [production_variant], tags, kms_key, wait
    467             )
    468 

~/anaconda3/envs/python3/lib/python3.6/site-packages/sagemaker/session.py in endpoint_from_production_variants(self, name, production_variants, tags, kms_key, wait)
   1361 
   1362             self.sagemaker_client.create_endpoint_config(**config_options)
-> 1363         return self.create_endpoint(endpoint_name=name, config_name=name, tags=tags, wait=wait)
   1364 
   1365     def expand_role(self, role):

~/anaconda3/envs/python3/lib/python3.6/site-packages/sagemaker/session.py in create_endpoint(self, endpoint_name, config_name, tags, wait)
    975 
    976         self.sagemaker_client.create_endpoint(
--> 977             EndpointName=endpoint_name, EndpointConfigName=config_name, Tags=tags
    978         )
    979         if wait:

~/anaconda3/envs/python3/lib/python3.6/site-packages/botocore/client.py in _api_call(self, *args, **kwargs)
    355                     "%s() only accepts keyword arguments." % py_operation_name)
    356             # The "self" in this scope is referring to the BaseClient.
--> 357             return self._make_api_call(operation_name, kwargs)
    358 
    359         _api_call.__name__ = str(py_operation_name)

~/anaconda3/envs/python3/lib/python3.6/site-packages/botocore/client.py in _make_api_call(self, operation_name, api_params)
    659             error_code = parsed_response.get("Error", {}).get("Code")
    660             error_class = self.exceptions.from_code(error_code)
--> 661             raise error_class(parsed_response, operation_name)
    662         else:
    663             return parsed_response

ResourceLimitExceeded: An error occurred (ResourceLimitExceeded) when calling the CreateEndpoint operation: The account-level service limit 'ml.m5.xlarge for endpoint usage' is 0 Instances, with current utilization of 0 Instances and a request delta of 1 Instances. Please contact AWS support to request an increase for this limit.

编辑：尝试ml.m4.xlarge实例：

当我使用 ml.m4.xlarge 时，我收到相同的消息“ResourceLimitExceeded：调用 CreateEndpoint 操作时发生错误 (ResourceLimitExceeded)：帐户级服务限制 'ml.m4.xlarge 用于端点使用'是0 个实例，当前利用率为 0 个实例，请求增量为 1 个实例。请联系 AWS 支持以请求增加此限制。"

Answer 1

根据this AWS page，您将获得前两个月每月 50 小时的 m4.xlarge 培训和 每月 125 小时的 m4.xlarge 培训两个月。因此，如果您在前两个月内，ml.m4.xlarge 应该可以解决问题。

至于服务限制本身根据post新创建的帐户将 SageMaker 中的每个实例类型（t2 介质除外）限制为 0，而不是默认限制。

因此，您毕竟需要联系 AWS 支持并要求提高您的限制。此外，如果您自己不是管理员，这可能会受到您帐户管理员的限制。因此，在这种情况下，这应该是您的第一个停靠港。

Answer 2

请求增加 ml.m5.xlarge 限制的步骤

1. 访问 aws 控制台https://console.aws.amazon.com/
2. 点击右上角的支持
3. 点击创建案例（橙色按钮）
4. 选择服务限制增加单选按钮
5. 对于限制类型，搜索并选择 SageMaker 笔记本实例
6. 选择与亚马逊控制台右上角显示的区域相同的区域。
7. 写一个简短的用例描述
8. 对于限制，选择 ml.[x].[x]（在您的情况下为 ml.m5.xlarge）
9. 新的限值 1

此手动支持工单可能需要 48 小时才能周转。（对我来说，我在一天后收到了支持团队的回复，实例限制已更改为 1）