Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Model Parallel Troubleshooting

If you run into an error, you can use the following list to try to troubleshoot your training job. If the problem persists, contact Amazon Support.

Considerations for Using SageMaker Debugger with the SageMaker Model Parallelism Library

SageMaker Debugger is not available for the SageMaker model parallelism library. Debugger is enabled by default for all SageMaker TensorFlow and PyTorch training jobs, and you might see an error that looks like the following:

FileNotFoundError: [Errno 2] No such file or directory: '/opt/ml/checkpoints/metadata.json.sagemaker-uploading

To fix this issue, disable Debugger by passing debugger_hook_config=False when creating a framework estimator as shown in the following example.

bucket=sagemaker.Session().default_bucket()
base_job_name="sagemaker-checkpoint-test"
checkpoint_in_bucket="checkpoints"

# The S3 URI to store the checkpoints
checkpoint_s3_bucket="s3://{}/{}/{}".format(bucket, base_job_name, checkpoint_in_bucket)

estimator = TensorFlow(
    ...

    distribution={"smdistributed": {"modelparallel": { "enabled": True }}},
    checkpoint_s3_uri=checkpoint_s3_bucket,
    checkpoint_local_path="/opt/ml/checkpoints",
    debugger_hook_config=False
)

Saving Checkpoints

You might run into the following error when saving checkpoints of a large model on SageMaker:

InternalServerError: We encountered an internal error. Please try again

This could be caused by a SageMaker limitation while uploading the local checkpoint to Amazon S3 during training. To disable checkpointing in SageMaker, use the following example to explicitly upload the checkpoints.

If you run into the preceding error, do not use checkpoint_s3_uri with the SageMaker estimator call. While saving checkpoints for larger models, we recommend saving checkpoints to a custom directory and passing the same to the helper function (as a local_path argument).

import os

def aws_s3_sync(source, destination):
    """aws s3 sync in quiet mode and time profile"""
    import time, subprocess
    cmd = ["aws", "s3", "sync", "--quiet", source, destination]
    print(f"Syncing files from {source} to {destination}")
    start_time = time.time()
    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    p.wait()
    end_time = time.time()
    print("Time Taken to Sync: ", (end_time-start_time))
    return

def sync_local_checkpoints_to_s3(local_path="/opt/ml/checkpoints", s3_uri=os.path.dirname(os.path.dirname(os.getenv('SM_MODULE_DIR', '')))+'/checkpoints'):
    """ sample function to sync checkpoints from local path to s3 """

    import boto3
    #check if local path exists
    if not os.path.exists(local_path):
        raise RuntimeError("Provided local path {local_path} does not exist. Please check")

    #check if s3 bucket exists
    s3 = boto3.resource('s3')
    if not s3_uri.startswith("s3://"):
        raise ValueError(f"Provided s3 uri {s3_uri} is not valid.")

    s3_bucket = s3_uri.replace('s3://','').split('/')[0]
    print(f"S3 Bucket: {s3_bucket}")
    try:
        s3.meta.client.head_bucket(Bucket=s3_bucket)
    except Exception as e:
        raise e
    aws_s3_sync(local_path, s3_uri)
    return

def sync_s3_checkpoints_to_local(local_path="/opt/ml/checkpoints", s3_uri=os.path.dirname(os.path.dirname(os.getenv('SM_MODULE_DIR', '')))+'/checkpoints'):
    """ sample function to sync checkpoints from s3 to local path """

    import boto3
    #try to create local path if it does not exist
    if not os.path.exists(local_path):
        print(f"Provided local path {local_path} does not exist. Creating...")
        try:
            os.makedirs(local_path)
        except Exception as e:
            raise RuntimeError(f"Failed to create {local_path}")

    #check if s3 bucket exists
    s3 = boto3.resource('s3')
    if not s3_uri.startswith("s3://"):
        raise ValueError(f"Provided s3 uri {s3_uri} is not valid.")

    s3_bucket = s3_uri.replace('s3://','').split('/')[0]
    print(f"S3 Bucket: {s3_bucket}")
    try:
        s3.meta.client.head_bucket(Bucket=s3_bucket)
    except Exception as e:
        raise e
    aws_s3_sync(s3_uri, local_path)
    return

Usage of helper functions:

#base_s3_uri - user input s3 uri or save to model directory (default)
#curr_host - to save checkpoints of current host
#iteration - current step/epoch during which checkpoint is saved

# save checkpoints on every node using local_rank
if smp.local_rank() == 0:
    base_s3_uri = os.path.dirname(os.path.dirname(os.getenv('SM_MODULE_DIR', '')))
    curr_host = os.environ['SM_CURRENT_HOST']
    full_s3_uri = f'{base_s3_uri}/checkpoints/{curr_host}/{iteration}'
    sync_local_checkpoints_to_s3(local_path=checkpoint_dir, s3_uri=full_s3_uri)

Convergence Using Model Parallel and TensorFlow

When you use SageMaker multi-node training with TensorFlow and the model parallelism library, the loss may not converge as expected because the order of training input files may be different on each node. This may cause different ranks in the same model parallel group to work on different input files, causing inconsistencies. To prevent this, ensure the input files are ordered the same way in all the ranks before they get converted to TensorFlow datasets. One way to achieve this is to sort the input file names in the training script.

Stalling or Crashing Distributed Training Jobs

If your training job has stalling, crashing, or not responding issues, read the following troubleshooting items to identify what's the cause of the issue. If you need any further support, reach out to the SageMaker distributed training team through Amazon Support.

Receiving NCCL Error for a PyTorch Training Job

If you encountered the following error, it might be due to a process running out of GPU memory.

NCCL error in: ../torch/lib/c10d/ProcessGroupNCCL.cpp:825, unhandled system error, NCCL version 2.7.8
ncclSystemError: System call (socket, malloc, munmap, etc) failed.

You can resolve this by reducing the batch size or active_microbatches. If auto partitioning is not resulting in a well-balanced partitioning, you might have to consider manual partitioning. For more information, see Pipeline parallelism across nodes.

Receiving RecursionError for a PyTorch Training Job

The library does not support calling super.forward() inside a module's forward call. If you use super.forward(), you might receive the following error message.

RecursionError: maximum recursion depth exceeded

To fix the error, instead of calling super.forward(), you should call super()._orig_forward().