Training with aws Sagemaker stuck if more than one epoch

[hjuhel-cdpq]

Describe the bug
Training the simpletransformers.ner model on AWS Sagemaker for more than one epoch results in the training idling for ever before the end of the first epoch. If trained for one epoch, the training ends normally.

While, idling, the GPU use drop to 0 and the disk utilization grows and began to plateau. (see attached picture)

It might also be a simpleTransformers issue, but the same procedure works perfectly when training the same model directly on a Sagemaker notebook.

To Reproduce

1. Generate a docker, based on the pytorch-training and with Apex and simpletransformers installed

FROM 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:1.4.0-gpu-py36-cu101-ubuntu16.04
RUN pip install simpletransformers
RUN git clone https://github.com/NVIDIA/apex

RUN pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./apex

2. Upload the Docker to ECR with a name <training_image>

3. Prepare a minimal training script and upload it to a Sagemaker Notebook

import os

from simpletransformers.ner import NERModel
import pandas as pd

if __name__ == "__main__":

 # Retrieve data and labels
 data = pd.read_csv(os.path.join(os.environ["SM_CHANNEL_TRAINING"], "training.csv"))
 labels = data.labels.unique().tolist()

 # Instanciate a NER model
 args = {
 "output_dir": os.environ["SM_MODEL_DIR"],
 "reprocess_input_data": True,
 "num_train_epochs": 2,
 "train_batch_size": 8,
 }
 model = NERModel("bert", "bert-base-uncased", args=args, labels=labels)

 model.train(data)

 return 0

4. Start the training from Sagemaker Notebook

import sagemaker
from sagemaker.pytorch import PyTorch
from sagemaker.estimator import Estimator
from sagemaker import get_execution_role

ROLE = get_execution_role()

pytorch_estimator = PyTorch(entry_point='train.py',
 image_name="<docker_Image>", 
 instance_type="ml.p3.2xlarge",
 train_instance_count=1,
 role = ROLE,
 )

pytorch_estimator.fit({"training":"<path_to_training.csv>"})

Expected behavior
I was expecting a larger number of epochs to works

Screenshots

Desktop (please complete the following information):

• OS : Ubuntu, from the base Sagemaker's pytorch Image

Additional context
Please, find attached thge logs. I'have remove the logs between the start of the first step of the first epoch and the last event received from the first epoch:

01:10:37
bash: cannot set terminal process group (-1): Inappropriate ioctl for device

01:10:37
bash: no job control in this shell

01:10:39
2020-03-12 01:10:38,648 sagemaker-containers INFO Imported framework sagemaker_pytorch_container.training

01:10:39
2020-03-12 01:10:38,727 sagemaker_pytorch_container.training INFO Block until all host DNS lookups succeed.

01:10:39
2020-03-12 01:10:38,728 sagemaker_pytorch_container.training INFO Invoking user training script.

01:10:39
2020-03-12 01:10:39,071 sagemaker-containers INFO Module default_user_module_name does not provide a setup.py.

01:10:39
Generating setup.py

01:10:39
2020-03-12 01:10:39,071 sagemaker-containers INFO Generating setup.cfg

01:10:39
2020-03-12 01:10:39,071 sagemaker-containers INFO Generating MANIFEST.in

01:10:39
2020-03-12 01:10:39,071 sagemaker-containers INFO Installing module with the following command:

01:10:39
/opt/conda/bin/python -m pip install .

01:10:40
Processing /tmp/tmpvf5izqsk/module_dir

01:10:40
Building wheels for collected packages: default-user-module-name Building wheel for default-user-module-name (setup.py): started Building wheel for default-user-module-name (setup.py): finished with status 'done' Created wheel for default-user-module-name: filename=default_user_module_name-1.0.0-py2.py3-none-any.whl size=4365 sha256=a7c076c4c020f4b8b9f85a40721d629b752f9b1006cdb0ba2bf27132f9b

01:10:40
Successfully built default-user-module-name

01:10:41
Installing collected packages: default-user-module-name

01:10:41
Successfully installed default-user-module-name-1.0.0

01:10:41
2020-03-12 01:10:41,498 sagemaker-containers INFO Invoking user script

01:10:41
Training Env:

01:10:41
{ "additional_framework_parameters": {}, "channel_input_dirs": { "training": "/opt/ml/input/data/training" }, "current_host": "algo-1", "framework_module": "sagemaker_pytorch_container.training:main", "hosts": [ "algo-1" ], "hyperparameters": { "n_gpu": 8, "batch_size": 32, "seed": 2, "lower": true, "epochs": 5

01:10:41
}
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01:10:41
Environment variables:

01:10:41
SM_HOSTS=["algo-1"]

01:10:41
SM_NETWORK_INTERFACE_NAME=eth0
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01:10:41
SM_HPS={"batch_size":32,"epochs":50,"lower":true,"n_gpu":8,"seed":2}

01:10:41
SM_USER_ENTRY_POINT=train.py

01:10:41
SM_FRAMEWORK_PARAMS={}
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01:10:41
SM_RESOURCE_CONFIG={"current_host":"algo-1","hosts":["algo-1"],"network_interface_name":"eth0"}

01:10:41
SM_INPUT_DATA_CONFIG={"training":{"RecordWrapperType":"None","S3DistributionType":"FullyReplicated","TrainingInputMode":"File"}}
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01:10:41
SM_OUTPUT_DATA_DIR=/opt/ml/output/data

01:10:41
SM_CHANNELS=["training"]

01:10:41
SM_CURRENT_HOST=algo-1

01:10:41
SM_MODULE_NAME=train

01:10:41
SM_LOG_LEVEL=20

01:10:41
SM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main

01:10:41
SM_INPUT_DIR=/opt/ml/input

01:10:41
SM_INPUT_CONFIG_DIR=/opt/ml/input/config

01:10:41
SM_OUTPUT_DIR=/opt/ml/output

01:10:41
SM_NUM_CPUS=64

01:10:41
SM_NUM_GPUS=8

01:10:41
SM_MODEL_DIR=/opt/ml/model

01:10:41
SM_MODULE_DIR=<path>

01:10:41
SM_TRAINING_ENV={"additional_framework_parameters":{},"channel_input_dirs":{"training":"/opt/ml/input/data/training"},"current_host":"algo-1","framework_module":"sagemaker_pytorch_container.training:main","hosts":["algo-1"],"hyperparameters":{"batch_size":32,"epochs":50,"lower":true,"n_gpu":8,"seed":2},"input_config_dir":"/opt/ml/input/config","input_data_config":{"training":{"RecordWrapperType":"

01:10:41
SM_USER_ARGS=["--batch_size","32","--epochs","50","--lower","True","--n_gpu","8","--seed","2"]

01:10:41
SM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate

01:10:41
SM_CHANNEL_TRAINING=/opt/ml/input/data/training

01:10:41
SM_HP_N_GPU=8

01:10:41
SM_HP_BATCH_SIZE=32

01:10:41
SM_HP_SEED=2

01:10:41
SM_HP_LOWER=true

01:10:41
SM_HP_EPOCHS=50

01:10:41
PYTHONPATH=/opt/ml/code:/opt/conda/bin:/opt/conda/lib/python36.zip:/opt/conda/lib/python3.6:/opt/conda/lib/python3.6/lib-dynload:/opt/conda/lib/python3.6/site-packages

01:10:41
Invoking script with the following command:

01:10:41
/opt/conda/bin/python train.py --batch_size 32 --epochs 50 --lower True --n_gpu 8 --seed 2

01:11:03
Converting to features started.

01:11:03
2020-03-12 01:10:45,583 [train.py ] INFO Starting training...

01:11:07
[2020-03-12 01:11:07.095 algo-1:102 INFO json_config.py:90] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.

01:11:07
2020-03-12 01:10:45,584 [train.py ] INFO No dataset provided for testing...training will be splitted

01:11:07
[2020-03-12 01:11:07.096 algo-1:102 INFO hook.py:152] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.

01:11:07
2020-03-12 01:10:45,584 [train.py ] INFO Reading training data at /opt/ml/input/data/training

01:11:07
[2020-03-12 01:11:07.096 algo-1:102 INFO hook.py:197] Saving to /opt/ml/output/tensors
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01:11:07
2020-03-12 01:10:45,584 [train.py ] INFO Reading file : 00_data_IOB.csv, located at /opt/ml/input/data/training/00_data_IOB.csv

01:11:07
[2020-03-12 01:11:07.116 algo-1:102 INFO hook.py:326] Monitoring the collections: losses
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01:11:07
train.py:95: SettingWithCopyWarning:

01:11:08
[2020-03-12 01:11:08.055 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.0.attention.self NoneType

01:11:08
A value is trying to be set on a copy of a slice from a DataFrame.

01:11:08
[2020-03-12 01:11:08.055 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.0.attention.self NoneType

01:11:08
Try using .loc[row_indexer,col_indexer] = value instead

01:11:08
[2020-03-12 01:11:08.055 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.0.attention.self NoneType

01:11:08
[2020-03-12 01:11:08.055 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.0.attention NoneType

01:11:08
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

01:11:08
[2020-03-12 01:11:08.064 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.0 NoneType training["words"] = training["words"].apply(str)

01:11:08
[2020-03-12 01:11:08.064 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.0 NoneType

01:11:08
train.py:96: SettingWithCopyWarning:

01:11:08
[2020-03-12 01:11:08.064 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.0 NoneType

01:11:08
A value is trying to be set on a copy of a slice from a DataFrame.

01:11:08
[2020-03-12 01:11:08.069 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.1.attention.self NoneType

01:11:08
Try using .loc[row_indexer,col_indexer] = value instead

01:11:08
[2020-03-12 01:11:08.069 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.1.attention.self NoneType

01:11:08
[2020-03-12 01:11:08.070 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.1.attention.self NoneType

01:11:08
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

01:11:08
[2020-03-12 01:11:08.070 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.1.attention NoneType testing["words"] = testing["words"].apply(str)

01:11:08
[2020-03-12 01:11:08.073 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.1 NoneType

01:11:08
train.py:99: SettingWithCopyWarning:

01:11:08
[2020-03-12 01:11:08.073 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.1 NoneType

01:11:08
A value is trying to be set on a copy of a slice from a DataFrame.

01:11:08
[2020-03-12 01:11:08.073 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.1 NoneType

01:11:08
Try using .loc[row_indexer,col_indexer] = value instead

01:11:08
[2020-03-12 01:11:08.078 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.2.attention.self NoneType

01:11:08
[2020-03-12 01:11:08.078 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.2.attention.self NoneType

01:11:08
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

01:11:08
[2020-03-12 01:11:08.078 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.2.attention.self NoneType training["words"] = training["words"].apply(lambda x: x.lower().strip())

01:11:08
[2020-03-12 01:11:08.079 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.2.attention NoneType

01:11:08
train.py:100: SettingWithCopyWarning:

01:11:08
[2020-03-12 01:11:08.081 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.2 NoneType

01:11:08
A value is trying to be set on a copy of a slice from a DataFrame.

01:11:08
[2020-03-12 01:11:08.081 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.2 NoneType

01:11:08
Try using .loc[row_indexer,col_indexer] = value instead

01:11:08
[2020-03-12 01:11:08.081 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.2 NoneType

01:11:08
[2020-03-12 01:11:08.087 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.3.attention.self NoneType

01:11:08
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

01:11:08
[2020-03-12 01:11:08.087 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.3.attention.self NoneType testing["words"] = testing["words"].apply(lambda x: x.lower().strip())

01:11:08
[2020-03-12 01:11:08.087 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.3.attention.self NoneType

01:11:08
2020-03-12 01:10:45,676 [train.py ] INFO Training datapoitns : 71712

01:11:08
[2020-03-12 01:11:08.087 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.3.attention NoneType

01:11:08
2020-03-12 01:10:45,676 [train.py ] INFO TraininTestingg datapoitns : 18001

01:11:08
[2020-03-12 01:11:08.090 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.3 NoneType

01:11:08
2020-03-12 01:10:45,681 [train.py ] INFO Training the model with label : ['O', 'B-E', 'I-E', 'B-C', 'I-C']

01:11:08
[2020-03-12 01:11:08.090 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.3 NoneType

01:11:08
2020-03-12 01:10:45,681 [train.py ] INFO Training the model with arguments : {'output_dir': '/opt/ml/model/model', 'reprocess_input_data': True, 'num_train_epochs': 50, 'train_batch_size': 32, 'fp16': False, 'save_eval_checkpoints': False, 'save_steps': 8223372036854775807, 'save_model_every_epoch': False, 'overwrite_output_dir': True, 'logging_steps': 500, 'silent': False, 'use_early_stopp

01:11:08
[2020-03-12 01:11:08.090 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.3 NoneType
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01:11:08
#015Downloading: 0%| | 0.00/361 [00:00<?, ?B/s]#015Downloading: 100%|██████████| 361/361 [00:00<00:00, 366kB/s]
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01:11:08
[2020-03-12 01:11:08.095 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.4.attention.self NoneType
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#015Downloading: 0%| | 0.00/440M [00:00<?, ?B/s]#015Downloading: 1%| | 4.70M/440M [00:00<00:09, 47.0MB/s]#015Downloading: 2%|▏ | 9.55M/440M [00:00<00:09, 47.4MB/s]#015Downloading: 3%|▎ | 14.4M/440M [00:00<00:08, 47.9MB/s]#015Downloading: 4%|▍ | 19.0M/440M [00:00<00:08, 47.2MB/s]#015Downloading: 5%|▌ | 23.5M/440M [00:00<00:08, 46.5MB/s]#
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01:11:08
[2020-03-12 01:11:08.095 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.4.attention.self NoneType
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01:11:08
#015Downloading: 0%| | 0.00/232k [00:00<?, ?B/s]#015Downloading: 100%|██████████| 232k/232k [00:00<00:00, 26.4MB/s]
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01:11:08
[2020-03-12 01:11:08.095 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.4.attention.self NoneType
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#015 0%| | 0/5234 [00:00<?, ?it/s]#015 0%| | 1/5234 [00:00<46:04, 1.89it/s]#015 57%|█████▋ | 3001/5234 [00:00<13:45, 2.70it/s]#015100%|██████████| 5234/5234 [00:00<00:00, 6824.67it/s]
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[2020-03-12 01:11:08.096 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.4.attention NoneType
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01:11:08
#015Epoch: 0%| | 0/50 [00:00<?, ?it/s]
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[2020-03-12 01:11:08.098 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.4 NoneType
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#015Current iteration: 0%| | 0/164 [00:00<?, ?it/s]#033[A
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[2020-03-12 01:11:08.099 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.4 NoneType
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#015Current iteration: 1%| | 1/164 [00:01<03:49, 1.41s/it]#033[A
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[2020-03-12 01:11:08.099 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.4 NoneType
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#015Current iteration: 1%| | 2/164 [00:01<02:51, 1.06s/it]#033[A
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[2020-03-12 01:11:08.104 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.5.attention.self NoneType
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#015Current iteration: 2%|▏ | 3/164 [00:01<02:10, 1.23it/s]#033[A

...



01:13:09
[2020-03-12 01:13:09.473 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.11 NoneType
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01:13:09
#015Current iteration: 82%|████████▏ | 134/164 [00:33<00:07, 4.15it/s]#033[A
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01:13:09
[2020-03-12 01:13:09.473 algo-1:102 WARNING hook.py:808] var is not Tensor or list or tuple of Tensors, module_name:bert.encoder.layer.11 NoneType

01:13:09
#015Current iteration: 82%|████████▏ | 135/164 [00:33<00:07, 4.14it/s]#033[A

The training is stuck after the last event received. At the same time, GPU use drops to 0.

* Testedon 3 differents Sagemaker's instance types

[mmaybeno]

I experienced something like this as well using the Pytorch 1.4.0 image. I am training some image fine tuning and it works on small subsets, but when I try and scale it up it starts to hang, even if I keep the batch sizes low. I've ran the same order of operations in a google colab and they've been fine.

[hjuhel-cdpq]

Hi @mmaybeno ,

Thanks for your answer ! Have you tried to run your program with à previous version of pytorch? I Will give it à try and keep you posted.

[mmaybeno]

I have not, I did however run it locally (not on aws, not their image) and it was running fine. The issues I'm having are all based on either their own image or a build I created from their dockerfile. I had to do my own build as I think there is a bug with their custom pytorch 1.4.0 python wheel.

aws/sagemaker-pytorch-training-toolkit#181

[mmaybeno]

I've been doing some experimenting on my custom built image. Using the CPU version of the image it will start successfully then crash without error. After reading #1225, I suspect the reason is because of a resource limit (my docker env was pinned to a max of 8GB). After increasing those limits (to 14GB), it wouldn't crash but it would hit ~12GB of ram usage and just hang there. If I run it outside of the container it progresses fine with ~8GB usage. I wonder if this hanging is similar to the GPU version as well.

[hjuhel-cdpq]

Hi @mmaybeno

I have tried with a custom image of pytorch build from one of Nvidia but it did not fix the problem, the training still get stuck before the end of the first eproch.

Regarding the resource limit : the dataset I am using is quite tiny with only 2k sentences, (all with less that 50 tokens) and the larger machine is used for training was the ml.p3dn.24xlarge (with 768G of RAM and 8 V100 GPU), so i'm doubtfull the probleme is related to resources. Plus, the training start to hang during the training of the first epoch IF the number of epochs is larger than one, but complete IF the total number of epochs is one. If resources starvation was, somehow, limitating the training, schould'nt it be independant from the number of epochs (modulo some caching) ?

[mmaybeno]

Well I found a few issues on my end that may be related. I was able to get my custom image working locally but without using the sagemaker-python-sdk. Turns out running locally uses the default docker shm-size of 64MB. If you manually create the container with a larger shm-size and run the training command from within the container it works fine. However AWS says they use larger shm-size volumes when they run the instances on their infrastructure.
pytorch/pytorch#2244
pytorch/pytorch#5040
#937

That lead me to looking at the dataloaders. pytorch/pytorch#1355 (comment).
I have to experiment with this more, but so far no luck. What you were mentioning is similar to what I'm experiencing. But for me, it doesn't even start the first epoch. GPU just spikes for several minutes then everything just drops.

[mmaybeno]

@hjuhel-cdpq have you tried seeing if it stops at a particular iteration in your training? I noticed it happened after about training 6325 images. If I created a small dataset it will continue with multiple iterations but still stop at that number, (epoch 0 - 5000 images, epoch 1 - 1325 images). If I only wanted 1 iteration but fell under that image threshold it would complete.

[hjuhel-cdpq]

Hi @mmaybeno

Great news, we managed to directly contact someone in AWS to get an update of this issue since they were not answering it on git. We have just be informed that they were aware of the issue and that they have fixed it in the last version of their docker image. The corresponding PR is here . The version of the container to use from now is here . Can you try by rebuilding your docker image first with a fresh version of the container ?.

[mmaybeno]

That's good to hear @hjuhel-cdpq. Unfortunately there is some other issues regarding the prebuilt images that have not been resolved on my end. Namely the torchvision::nms operator error. I'll try rebuilding based off these images to see if the epoch issue has been solved for me though. 🤞

[mmaybeno]

Just tried it with a custom image built on their updated image and indeed the epoch issue for me is resolved. I agree it was related to that PR with streaming the stderr. Thanks @hjuhel-cdpq

[yifeim]

Came across this discussion. I think now you can do this:

Default behavior and opting out
For TensorFlow, Keras, MXNet, PyTorch and XGBoost estimators, the DebuggerHookConfig is always initialized regardless of specification while initializing the estimator. This is done to minimize code changes needed to get useful debugging information.

To disable the hook initialization, you can do so by specifying False for value of debugger_hook_config in your framework estimator’s initialization:

estimator = TensorFlow(
role=role,
train_instance_count=1,
train_instance_type=train_instance_type,
debugger_hook_config=False
)

[hjuhel-cdpq]

Hi @yifeim
Thanks for your answer, since AWS released their fixed image, we don't have the bug anymore (april 2020) , I'am curious about you solution and will try it if I find a previous build of their image.

Thanks !