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Train mounts cloud storage into your container before the job starts and sets STORAGE_MOUNT_0_PATH, STORAGE_MOUNT_1_PATH, etc. as environment variables. Your training script reads from these paths, with no cloud-specific SDKs or storage code needed.

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Mount Configuration

Each entry in storage_mounts maps a cloud URI to a container path: 
"storage_mounts": [
  {
    "source_uri": "gs://my-bucket/training-data",
    "mount_path": "/mnt/data",
    "read_only": true
  },
  {
    "source_uri": "gs://my-bucket/output/run-1",
    "mount_path": "/mnt/output",
    "read_only": false
  }
]
Inside the container: 
STORAGE_MOUNT_0_PATH=/mnt/data
STORAGE_MOUNT_1_PATH=/mnt/output
Mounts are indexed in order, starting at zero.

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URI Formats

CloudFormatExample
GCPgs://bucket/pathgs://my-bucket/datasets/imagenet
AWSs3://bucket/paths3://my-bucket/datasets/imagenet
Azureazureml://datastores/name/paths/pathazureml://datastores/training/paths/imagenet
Azure storage mounts reference registered Azure ML datastores, not raw Blob Storage URLs. Datastores are configured in your Azure ML workspace.

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Accessing Mounts in Your Container

import os
import torch
from datasets import load_dataset

data_path = os.environ["STORAGE_MOUNT_0_PATH"]
output_path = os.environ["STORAGE_MOUNT_1_PATH"]

dataset = load_dataset(data_path)

# ... training loop ...
torch.save(model.state_dict(), f"{output_path}/checkpoint-epoch-{epoch}.pt")
This works identically on GCP, AWS, and Azure.

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Read vs. Write

Set read_only based on intent. Use true for input data and pretrained weights; false for outputs, checkpoints, and logs. Marking input mounts read-only prevents accidental writes and may improve performance on some backends.

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SageMaker: Input Data Config

SageMaker also supports input_data_config for S3 input channels. Unlike FUSE mounts, channels are downloaded to the instance before training starts, which can be faster for large datasets with random access patterns: 
"input_data_config": [{
  "channel_name": "train",
  "data_source": {
    "s3_data_source": {
      "s3_data_type": "S3Prefix",
      "s3_uri": "s3://my-bucket/datasets/train/",
      "s3_data_distribution_type": "FullyReplicated"
    }
  },
  "input_mode": "File"
}]
SageMaker makes this available at /opt/ml/input/data/train/. This coexists with storage_mounts.

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Next Steps