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AWS Sagemaker - ClientError: Data download failed
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2
Problem:
I am trying to setup a model in Sagemaker, however it fails when it comes to downloading the data.
Does anyone know what I am doing wrong?
What I did so far:
In order to avoid any mistakes on my side I decided to use the AWS tutorial:
tensorflow_iris_dnn_classifier_using_estimators
And I made only two changes:
- I copied the dataset to my own S3 instance. --> I tested if I could access / show the data and it worked.
- I edited the path to point to the new folder.
This is the AWS source code:
https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/tensorflow_iris_dnn_classifier_using_estimators
%%time
import boto3
# use the region-specific sample data bucket
region = boto3.Session().region_name
#train_data_location = 's3://sagemaker-sample-data-{}/tensorflow/iris'.format(region)
train_data_location = 's3://my-s3-bucket'
iris_estimator.fit(train_data_location)
And this is the error I get:
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in run_cell_magic(self, magic_name, line, cell)
2115 magic_arg_s = self.var_expand(line, stack_depth)
2116 with self.builtin_trap:
-> 2117 result = fn(magic_arg_s, cell)
2118 return result
2119
<decorator-gen-60> in time(self, line, cell, local_ns)
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/IPython/core/magic.pyc in <lambda>(f, *a, **k)
186 # but it's overkill for just that one bit of state.
187 def magic_deco(arg):
--> 188 call = lambda f, *a, **k: f(*a, **k)
189
190 if callable(arg):
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)
1191 else:
1192 st = clock2()
-> 1193 exec(code, glob, local_ns)
1194 end = clock2()
1195 out = None
<timed exec> in <module>()
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/tensorflow/estimator.pyc in fit(self, inputs, wait, logs, job_name, run_tensorboard_locally)
314 tensorboard.join()
315 else:
--> 316 fit_super()
317
318 @classmethod
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/tensorflow/estimator.pyc in fit_super()
293
294 def fit_super():
--> 295 super(TensorFlow, self).fit(inputs, wait, logs, job_name)
296
297 if run_tensorboard_locally and wait is False:
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/estimator.pyc in fit(self, inputs, wait, logs, job_name)
232 self.latest_training_job = _TrainingJob.start_new(self, inputs)
233 if wait:
--> 234 self.latest_training_job.wait(logs=logs)
235
236 def _compilation_job_name(self):
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/estimator.pyc in wait(self, logs)
571 def wait(self, logs=True):
572 if logs:
--> 573 self.sagemaker_session.logs_for_job(self.job_name, wait=True)
574 else:
575 self.sagemaker_session.wait_for_job(self.job_name)
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/session.pyc in logs_for_job(self, job_name, wait, poll)
1126
1127 if wait:
-> 1128 self._check_job_status(job_name, description, 'TrainingJobStatus')
1129 if dot:
1130 print()
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/session.pyc in _check_job_status(self, job, desc, status_key_name)
826 reason = desc.get('FailureReason', '(No reason provided)')
827 job_type = status_key_name.replace('JobStatus', ' job')
--> 828 raise ValueError('Error for {} {}: {} Reason: {}'.format(job_type, job, status, reason))
829
830 def wait_for_endpoint(self, endpoint, poll=5):
ValueError: Error for Training job sagemaker-tensorflow-2019-01-03-16-32-16-435: Failed Reason: ClientError: Data download failed:S3 key: s3://my-s3-bucket//sagemaker-tensorflow-2019-01-03-14-02-39-959/source/sourcedir.tar.gz has an illegal char sub-sequence '//' in it
python amazon-web-services tensorflow jupyter amazon-sagemaker
asked Jan 3 at 16:59
Ralf SürigRalf Sürig
153
add a comment |
2
Problem:
I am trying to setup a model in Sagemaker, however it fails when it comes to downloading the data.
Does anyone know what I am doing wrong?
What I did so far:
In order to avoid any mistakes on my side I decided to use the AWS tutorial:
tensorflow_iris_dnn_classifier_using_estimators
And I made only two changes:
- I copied the dataset to my own S3 instance. --> I tested if I could access / show the data and it worked.
- I edited the path to point to the new folder.
This is the AWS source code:
https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/tensorflow_iris_dnn_classifier_using_estimators
%%time
import boto3
# use the region-specific sample data bucket
region = boto3.Session().region_name
#train_data_location = 's3://sagemaker-sample-data-{}/tensorflow/iris'.format(region)
train_data_location = 's3://my-s3-bucket'
iris_estimator.fit(train_data_location)
And this is the error I get:
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in run_cell_magic(self, magic_name, line, cell)
2115 magic_arg_s = self.var_expand(line, stack_depth)
2116 with self.builtin_trap:
-> 2117 result = fn(magic_arg_s, cell)
2118 return result
2119
<decorator-gen-60> in time(self, line, cell, local_ns)
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/IPython/core/magic.pyc in <lambda>(f, *a, **k)
186 # but it's overkill for just that one bit of state.
187 def magic_deco(arg):
--> 188 call = lambda f, *a, **k: f(*a, **k)
189
190 if callable(arg):
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)
1191 else:
1192 st = clock2()
-> 1193 exec(code, glob, local_ns)
1194 end = clock2()
1195 out = None
<timed exec> in <module>()
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/tensorflow/estimator.pyc in fit(self, inputs, wait, logs, job_name, run_tensorboard_locally)
314 tensorboard.join()
315 else:
--> 316 fit_super()
317
318 @classmethod
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/tensorflow/estimator.pyc in fit_super()
293
294 def fit_super():
--> 295 super(TensorFlow, self).fit(inputs, wait, logs, job_name)
296
297 if run_tensorboard_locally and wait is False:
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/estimator.pyc in fit(self, inputs, wait, logs, job_name)
232 self.latest_training_job = _TrainingJob.start_new(self, inputs)
233 if wait:
--> 234 self.latest_training_job.wait(logs=logs)
235
236 def _compilation_job_name(self):
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/estimator.pyc in wait(self, logs)
571 def wait(self, logs=True):
572 if logs:
--> 573 self.sagemaker_session.logs_for_job(self.job_name, wait=True)
574 else:
575 self.sagemaker_session.wait_for_job(self.job_name)
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/session.pyc in logs_for_job(self, job_name, wait, poll)
1126
1127 if wait:
-> 1128 self._check_job_status(job_name, description, 'TrainingJobStatus')
1129 if dot:
1130 print()
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/session.pyc in _check_job_status(self, job, desc, status_key_name)
826 reason = desc.get('FailureReason', '(No reason provided)')
827 job_type = status_key_name.replace('JobStatus', ' job')
--> 828 raise ValueError('Error for {} {}: {} Reason: {}'.format(job_type, job, status, reason))
829
830 def wait_for_endpoint(self, endpoint, poll=5):
ValueError: Error for Training job sagemaker-tensorflow-2019-01-03-16-32-16-435: Failed Reason: ClientError: Data download failed:S3 key: s3://my-s3-bucket//sagemaker-tensorflow-2019-01-03-14-02-39-959/source/sourcedir.tar.gz has an illegal char sub-sequence '//' in it
python amazon-web-services tensorflow jupyter amazon-sagemaker
asked Jan 3 at 16:59
Ralf SürigRalf Sürig
153
The script is expecting 'bucket' to be bucket = Session().default_bucket() or your own. Have you tried setting bucket equal to your personal bucket?
– Matthew Arthur
Jan 3 at 17:03
@MatthewArthur Colleague of Ralf's here. We were trying both, but your comment made us re-evaluate what we had been doing. The trouble seems to be that the script can't reach our S3 bucket; usingSession().default_bucket()works. We were letting the misleading error message (that suggests an error in a path configuration somewhere) get in the way of troubleshooting the right spot. Can you write your comment as an answer so Ralf can accept it?
– pinkgothic
Jan 4 at 9:09
add a comment |
2
2
2
Problem:
I am trying to setup a model in Sagemaker, however it fails when it comes to downloading the data.
Does anyone know what I am doing wrong?
What I did so far:
In order to avoid any mistakes on my side I decided to use the AWS tutorial:
tensorflow_iris_dnn_classifier_using_estimators
And I made only two changes:
- I copied the dataset to my own S3 instance. --> I tested if I could access / show the data and it worked.
- I edited the path to point to the new folder.
This is the AWS source code:
https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/tensorflow_iris_dnn_classifier_using_estimators
%%time
import boto3
# use the region-specific sample data bucket
region = boto3.Session().region_name
#train_data_location = 's3://sagemaker-sample-data-{}/tensorflow/iris'.format(region)
train_data_location = 's3://my-s3-bucket'
iris_estimator.fit(train_data_location)
And this is the error I get:
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in run_cell_magic(self, magic_name, line, cell)
2115 magic_arg_s = self.var_expand(line, stack_depth)
2116 with self.builtin_trap:
-> 2117 result = fn(magic_arg_s, cell)
2118 return result
2119
<decorator-gen-60> in time(self, line, cell, local_ns)
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/IPython/core/magic.pyc in <lambda>(f, *a, **k)
186 # but it's overkill for just that one bit of state.
187 def magic_deco(arg):
--> 188 call = lambda f, *a, **k: f(*a, **k)
189
190 if callable(arg):
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)
1191 else:
1192 st = clock2()
-> 1193 exec(code, glob, local_ns)
1194 end = clock2()
1195 out = None
<timed exec> in <module>()
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/tensorflow/estimator.pyc in fit(self, inputs, wait, logs, job_name, run_tensorboard_locally)
314 tensorboard.join()
315 else:
--> 316 fit_super()
317
318 @classmethod
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/tensorflow/estimator.pyc in fit_super()
293
294 def fit_super():
--> 295 super(TensorFlow, self).fit(inputs, wait, logs, job_name)
296
297 if run_tensorboard_locally and wait is False:
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/estimator.pyc in fit(self, inputs, wait, logs, job_name)
232 self.latest_training_job = _TrainingJob.start_new(self, inputs)
233 if wait:
--> 234 self.latest_training_job.wait(logs=logs)
235
236 def _compilation_job_name(self):
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/estimator.pyc in wait(self, logs)
571 def wait(self, logs=True):
572 if logs:
--> 573 self.sagemaker_session.logs_for_job(self.job_name, wait=True)
574 else:
575 self.sagemaker_session.wait_for_job(self.job_name)
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/session.pyc in logs_for_job(self, job_name, wait, poll)
1126
1127 if wait:
-> 1128 self._check_job_status(job_name, description, 'TrainingJobStatus')
1129 if dot:
1130 print()
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/session.pyc in _check_job_status(self, job, desc, status_key_name)
826 reason = desc.get('FailureReason', '(No reason provided)')
827 job_type = status_key_name.replace('JobStatus', ' job')
--> 828 raise ValueError('Error for {} {}: {} Reason: {}'.format(job_type, job, status, reason))
829
830 def wait_for_endpoint(self, endpoint, poll=5):
ValueError: Error for Training job sagemaker-tensorflow-2019-01-03-16-32-16-435: Failed Reason: ClientError: Data download failed:S3 key: s3://my-s3-bucket//sagemaker-tensorflow-2019-01-03-14-02-39-959/source/sourcedir.tar.gz has an illegal char sub-sequence '//' in it
python amazon-web-services tensorflow jupyter amazon-sagemaker
asked Jan 3 at 16:59
Ralf SürigRalf Sürig
153
Problem:
I am trying to setup a model in Sagemaker, however it fails when it comes to downloading the data.
Does anyone know what I am doing wrong?
What I did so far:
In order to avoid any mistakes on my side I decided to use the AWS tutorial:
tensorflow_iris_dnn_classifier_using_estimators
And I made only two changes:
- I copied the dataset to my own S3 instance. --> I tested if I could access / show the data and it worked.
- I edited the path to point to the new folder.
This is the AWS source code:
https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/tensorflow_iris_dnn_classifier_using_estimators
%%time
import boto3
# use the region-specific sample data bucket
region = boto3.Session().region_name
#train_data_location = 's3://sagemaker-sample-data-{}/tensorflow/iris'.format(region)
train_data_location = 's3://my-s3-bucket'
iris_estimator.fit(train_data_location)
And this is the error I get:
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in run_cell_magic(self, magic_name, line, cell)
2115 magic_arg_s = self.var_expand(line, stack_depth)
2116 with self.builtin_trap:
-> 2117 result = fn(magic_arg_s, cell)
2118 return result
2119
<decorator-gen-60> in time(self, line, cell, local_ns)
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/IPython/core/magic.pyc in <lambda>(f, *a, **k)
186 # but it's overkill for just that one bit of state.
187 def magic_deco(arg):
--> 188 call = lambda f, *a, **k: f(*a, **k)
189
190 if callable(arg):
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns)
1191 else:
1192 st = clock2()
-> 1193 exec(code, glob, local_ns)
1194 end = clock2()
1195 out = None
<timed exec> in <module>()
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/tensorflow/estimator.pyc in fit(self, inputs, wait, logs, job_name, run_tensorboard_locally)
314 tensorboard.join()
315 else:
--> 316 fit_super()
317
318 @classmethod
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/tensorflow/estimator.pyc in fit_super()
293
294 def fit_super():
--> 295 super(TensorFlow, self).fit(inputs, wait, logs, job_name)
296
297 if run_tensorboard_locally and wait is False:
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/estimator.pyc in fit(self, inputs, wait, logs, job_name)
232 self.latest_training_job = _TrainingJob.start_new(self, inputs)
233 if wait:
--> 234 self.latest_training_job.wait(logs=logs)
235
236 def _compilation_job_name(self):
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/estimator.pyc in wait(self, logs)
571 def wait(self, logs=True):
572 if logs:
--> 573 self.sagemaker_session.logs_for_job(self.job_name, wait=True)
574 else:
575 self.sagemaker_session.wait_for_job(self.job_name)
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/session.pyc in logs_for_job(self, job_name, wait, poll)
1126
1127 if wait:
-> 1128 self._check_job_status(job_name, description, 'TrainingJobStatus')
1129 if dot:
1130 print()
/home/ec2-user/anaconda3/envs/tensorflow_p27/lib/python2.7/site-packages/sagemaker/session.pyc in _check_job_status(self, job, desc, status_key_name)
826 reason = desc.get('FailureReason', '(No reason provided)')
827 job_type = status_key_name.replace('JobStatus', ' job')
--> 828 raise ValueError('Error for {} {}: {} Reason: {}'.format(job_type, job, status, reason))
829
830 def wait_for_endpoint(self, endpoint, poll=5):
ValueError: Error for Training job sagemaker-tensorflow-2019-01-03-16-32-16-435: Failed Reason: ClientError: Data download failed:S3 key: s3://my-s3-bucket//sagemaker-tensorflow-2019-01-03-14-02-39-959/source/sourcedir.tar.gz has an illegal char sub-sequence '//' in it
python amazon-web-services tensorflow jupyter amazon-sagemaker
python amazon-web-services tensorflow jupyter amazon-sagemaker
asked Jan 3 at 16:59
Ralf SürigRalf Sürig
153
asked Jan 3 at 16:59
Ralf SürigRalf Sürig
153
asked Jan 3 at 16:59
Ralf SürigRalf Sürig
153
asked Jan 3 at 16:59
Ralf SürigRalf Sürig
153
asked Jan 3 at 16:59
Ralf SürigRalf Sürig
153
153
The script is expecting 'bucket' to be bucket = Session().default_bucket() or your own. Have you tried setting bucket equal to your personal bucket?
– Matthew Arthur
Jan 3 at 17:03
@MatthewArthur Colleague of Ralf's here. We were trying both, but your comment made us re-evaluate what we had been doing. The trouble seems to be that the script can't reach our S3 bucket; usingSession().default_bucket()works. We were letting the misleading error message (that suggests an error in a path configuration somewhere) get in the way of troubleshooting the right spot. Can you write your comment as an answer so Ralf can accept it?
– pinkgothic
Jan 4 at 9:09
add a comment |
The script is expecting 'bucket' to be bucket = Session().default_bucket() or your own. Have you tried setting bucket equal to your personal bucket?
– Matthew Arthur
Jan 3 at 17:03
@MatthewArthur Colleague of Ralf's here. We were trying both, but your comment made us re-evaluate what we had been doing. The trouble seems to be that the script can't reach our S3 bucket; usingSession().default_bucket()works. We were letting the misleading error message (that suggests an error in a path configuration somewhere) get in the way of troubleshooting the right spot. Can you write your comment as an answer so Ralf can accept it?
– pinkgothic
Jan 4 at 9:09
The script is expecting 'bucket' to be bucket = Session().default_bucket() or your own. Have you tried setting bucket equal to your personal bucket?
– Matthew Arthur
Jan 3 at 17:03
The script is expecting 'bucket' to be bucket = Session().default_bucket() or your own. Have you tried setting bucket equal to your personal bucket?
– Matthew Arthur
Jan 3 at 17:03
@MatthewArthur Colleague of Ralf's here. We were trying both, but your comment made us re-evaluate what we had been doing. The trouble seems to be that the script can't reach our S3 bucket; using
Session().default_bucket() works. We were letting the misleading error message (that suggests an error in a path configuration somewhere) get in the way of troubleshooting the right spot. Can you write your comment as an answer so Ralf can accept it?– pinkgothic
Jan 4 at 9:09
@MatthewArthur Colleague of Ralf's here. We were trying both, but your comment made us re-evaluate what we had been doing. The trouble seems to be that the script can't reach our S3 bucket; using
Session().default_bucket() works. We were letting the misleading error message (that suggests an error in a path configuration somewhere) get in the way of troubleshooting the right spot. Can you write your comment as an answer so Ralf can accept it?– pinkgothic
Jan 4 at 9:09
add a comment |
2 Answers
2
active
oldest
votes
1
The script is expecting 'bucket' to be bucket = Session().default_bucket() or your own. Have you tried setting bucket equal to your personal bucket?
answered Jan 4 at 9:43
Matthew ArthurMatthew Arthur
10819
add a comment |
0
It looks like the full error message you received there was:
ClientError: Data download failed:S3 key: s3://my-s3-bucket//sagemaker-tensorflow-2019-01-03-14-02-39-959/source/sourcedir.tar.gz has an illegal char sub-sequence '//' in it
Does the problem persist even after fixing the key?
answered Jan 8 at 21:58
ArvindArvind
543
add a comment |
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2 Answers
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2 Answers
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The script is expecting 'bucket' to be bucket = Session().default_bucket() or your own. Have you tried setting bucket equal to your personal bucket?
answered Jan 4 at 9:43
Matthew ArthurMatthew Arthur
10819
add a comment |
1
The script is expecting 'bucket' to be bucket = Session().default_bucket() or your own. Have you tried setting bucket equal to your personal bucket?
answered Jan 4 at 9:43
Matthew ArthurMatthew Arthur
10819
add a comment |
1
1
1
The script is expecting 'bucket' to be bucket = Session().default_bucket() or your own. Have you tried setting bucket equal to your personal bucket?
answered Jan 4 at 9:43
Matthew ArthurMatthew Arthur
10819
The script is expecting 'bucket' to be bucket = Session().default_bucket() or your own. Have you tried setting bucket equal to your personal bucket?
answered Jan 4 at 9:43
Matthew ArthurMatthew Arthur
10819
answered Jan 4 at 9:43
Matthew ArthurMatthew Arthur
10819
answered Jan 4 at 9:43
Matthew ArthurMatthew Arthur
10819
answered Jan 4 at 9:43
Matthew ArthurMatthew Arthur
10819
10819
add a comment |
add a comment |
0
It looks like the full error message you received there was:
ClientError: Data download failed:S3 key: s3://my-s3-bucket//sagemaker-tensorflow-2019-01-03-14-02-39-959/source/sourcedir.tar.gz has an illegal char sub-sequence '//' in it
Does the problem persist even after fixing the key?
answered Jan 8 at 21:58
ArvindArvind
543
add a comment |
0
It looks like the full error message you received there was:
ClientError: Data download failed:S3 key: s3://my-s3-bucket//sagemaker-tensorflow-2019-01-03-14-02-39-959/source/sourcedir.tar.gz has an illegal char sub-sequence '//' in it
Does the problem persist even after fixing the key?
answered Jan 8 at 21:58
ArvindArvind
543
add a comment |
0
0
0
It looks like the full error message you received there was:
ClientError: Data download failed:S3 key: s3://my-s3-bucket//sagemaker-tensorflow-2019-01-03-14-02-39-959/source/sourcedir.tar.gz has an illegal char sub-sequence '//' in it
Does the problem persist even after fixing the key?
answered Jan 8 at 21:58
ArvindArvind
543
It looks like the full error message you received there was:
ClientError: Data download failed:S3 key: s3://my-s3-bucket//sagemaker-tensorflow-2019-01-03-14-02-39-959/source/sourcedir.tar.gz has an illegal char sub-sequence '//' in it
Does the problem persist even after fixing the key?
answered Jan 8 at 21:58
ArvindArvind
543
answered Jan 8 at 21:58
ArvindArvind
543
answered Jan 8 at 21:58
ArvindArvind
543
answered Jan 8 at 21:58
ArvindArvind
543
543
add a comment |
add a comment |
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The script is expecting 'bucket' to be bucket = Session().default_bucket() or your own. Have you tried setting bucket equal to your personal bucket?
– Matthew Arthur
Jan 3 at 17:03
@MatthewArthur Colleague of Ralf's here. We were trying both, but your comment made us re-evaluate what we had been doing. The trouble seems to be that the script can't reach our S3 bucket; using
Session().default_bucket()works. We were letting the misleading error message (that suggests an error in a path configuration somewhere) get in the way of troubleshooting the right spot. Can you write your comment as an answer so Ralf can accept it?– pinkgothic
Jan 4 at 9:09