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Where to save and write files for Azure Machine Learning experiments

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APPLIES TO: Python SDK azureml v1

In this article, you learn where to save input files, and where to write output files from your experiments to prevent storage limit errors and experiment latency.

When you run training jobs on a compute target, they're isolated from outside environments. The purpose of this design is to ensure reproducibility and portability of the experiment. If you run the same script twice, on the same or another compute target, you receive the same results. With this design, you can treat compute targets as stateless computation resources, each having no affinity to the jobs that are running after they're finished.

Where to save input files

Before you can initiate an experiment on a compute target or your local machine, you must ensure that the necessary files are available to that compute target. For example, dependency files and data files your code needs to run.

Azure Machine Learning jobs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a .ignore file or don't include it in the source directory. Instead, access your data using a datastore.

The storage limit for experiment snapshots is 300 MB and/or 2,000 files.

For this reason, we recommend:

  • Storing your files in an Azure Machine Learning dataset. Using datasets prevents experiment latency issues, and has the advantages of accessing data from a remote compute target. Azure Machine Learning handles authentication and mounting of the dataset. Learn more about how to specify a dataset as your input data source in your training script with Train with datasets.

  • If you only need a couple data files and dependency scripts and can't use a datastore, place the files in the same folder directory as your training script. Specify this folder as your source_directory directly in your training script, or in the code that calls your training script.

Storage limits of experiment snapshots

For experiments, Azure Machine Learning automatically makes an experiment snapshot of your code based on the directory you suggest when you configure the job. For a pipeline, the directory is configured for each step.

This has a total limit of 300 MB and/or 2,000 files. If you exceed this limit, you see the following error:

While attempting to take snapshot of .
Your total snapshot size exceeds the limit of 300.0 MB

To resolve this error, store your experiment files on a datastore. If you can't use a datastore, the below table offers possible alternate solutions.

Experiment description Storage limit solution
Less than 2,000 files & can't use a datastore Override snapshot size limit with
azureml._restclient.snapshots_client.SNAPSHOT_MAX_SIZE_BYTES = 'insert_desired_size' and azureml._restclient.constants.SNAPSHOT_MAX_SIZE_BYTES = 'insert_desired_size'
This might take several minutes depending on the number and size of files.
Must use specific script directory To prevent unnecessary files from being included in the snapshot, make an ignore file (.gitignore or .amlignore) in the directory. Add the files and directories to exclude to this file. For more information on the syntax to use inside this file, see syntax and patterns for .gitignore. The .amlignore file uses the same syntax. If both files exist, the .amlignore file is used and the .gitignore file is unused.
Pipeline Use a different subdirectory for each step
Jupyter notebooks Create a .amlignore file or move your notebook into a new, empty, subdirectory, and run your code again.

Where to write files

Due to the isolation of training experiments, the changes to files that happen during jobs aren't necessarily persisted outside of your environment. If your script modifies the files local to compute, the changes aren't persisted for your next experiment job, and they're not propagated back to the client machine automatically. Therefore, the changes made during the first experiment job don't and shouldn't affect those in the second.

When writing changes, we recommend writing files to storage via an Azure Machine Learning dataset with an OutputFileDatasetConfig object. See how to create an OutputFileDatasetConfig.

Otherwise, write files to the ./outputs and/or ./logs folder.

Important

Two folders, outputs and logs, receive special treatment by Azure Machine Learning. During training, when you write files to./outputs and./logs folders, the files will automatically upload to your job history, so that you have access to them once your job is finished.

  • For output such as status messages or scoring results, write files to the ./outputs folder, so they're persisted as artifacts in job history. Be mindful of the number and size of files written to this folder, as latency might occur when the contents are uploaded to job history. If latency is a concern, writing files to a datastore is recommended.

  • To save written file as logs in job history, write files to ./logs folder. The logs are uploaded in real time, so this method is suitable for streaming live updates from a remote job.

Next steps