Reproducible AI Research Across Cloud Providers

This guide shows you how to structure and package your AI research so it runs identically on AWS, Azure, Google Cloud, or any other provider. You’ll learn to containerize your environment, pin dependencies, manage data, and automate the entire pipeline, so a colleague (or your future self) can reproduce your results with one command.

What You’ll Need

• Basic familiarity with the command line and Git.
• Docker installed locally (or access to a container registry).
• An account on at least one cloud provider (but the steps work across all).
• Your AI/ML codebase (Python, PyTorch, TensorFlow, etc.) in a Git repository.

Step-by-Step

1. Define the exact environment in a Dockerfile.
   Start from an official base image (e.g., python:3.11-slim). Install system packages, then copy your requirements.txt or environment.yml and install Python dependencies. Use exact version pins (e.g., numpy==1.26.0) rather than ranges. This ensures every cloud instance gets the same libraries.
2. Pin the base image digest.
   Instead of just python:3.11-slim, use the full digest (e.g., python:3.11-slim@sha256:abc123...). This locks the OS and Python patch version, preventing silent updates that could change behavior.
3. Make your code deterministic.
   Set random seeds for all libraries (Python’s random, NumPy, PyTorch, TensorFlow) at the start of your script. Also set environment variables like PYTHONHASHSEED=0 and TF_DETERMINISTIC_OPS=1 if using TensorFlow. For GPU, enable deterministic algorithms (e.g., torch.backends.cudnn.deterministic = True).
4. Version your data.
   Do not rely on cloud storage paths that might change. Use a data versioning tool like DVC (Data Version Control) or store a SHA-256 checksum of each dataset file. In your code, verify the checksum before training. If the data is large, store it in a cloud bucket and record the exact URI and version ID.
5. Use a configuration file for all hyperparameters.
   Create a config.yaml or config.json that holds every tunable: learning rate, batch size, epochs, model architecture, etc. Your code should read from this file, not have hardcoded values. This makes it easy to compare runs and ensures everyone uses the same settings.
6. Log every run with a metadata tracker.
   Use MLflow, Weights & Biases, or a simple CSV logger to record the config, code version (Git commit hash), data checksum, and the resulting metrics. This gives you a complete audit trail and makes comparisons across cloud providers meaningful.
7. Containerize the entire pipeline, not just training.
   Build a Docker image that includes preprocessing, training, and evaluation scripts. Use a single entrypoint that takes the config file as an argument. This way, the same container runs on any cloud provider’s container service (ECS, AKS, GKE, etc.).
8. Push the image to a public or private registry.
   Use Docker Hub, GitHub Container Registry, or a cloud-specific registry. Tag the image with the Git commit hash (e.g., myai:latest and myai:7f3a2b) so you can always pull the exact version.
9. Write an infrastructure-as-code script.
   Use Terraform or AWS CloudFormation to define the cloud resources (VMs, GPUs, storage) needed. This makes the environment reproducible too. For example, a Terraform script can spin up a VM with the right GPU driver and pull your Docker image.
10. Automate with a CI/CD pipeline.
    Set up GitHub Actions (or similar) to build the Docker image, run tests, and push to the registry on every commit. This ensures the image is always up-to-date and testable.
11. Document the full process.
    Write a README that explains how to run the experiment on any cloud provider. Include the exact commands to pull the image, run the container, and download the data. Also note any provider-specific quirks (e.g., different GPU driver versions).
12. Test on a second provider.
    Before publishing, run the entire pipeline on a different cloud provider than your primary one. This catches hidden dependencies (like hardcoded paths) and ensures true portability.

Common Mistakes

• Not pinning versions exactly. Using numpy>=1.20 can pull a newer version that changes results.
• Ignoring random seeds. Without seeding, each run gives different results, making comparison impossible.
• Hardcoding data paths. A path like /home/user/data will break on a different machine.
• Forgetting to version the data. If the dataset changes, your results are not comparable.
• Not testing on a second cloud. Something that works on AWS may fail on Azure due to minor differences.

Tips

• Use a Makefile or shell script to wrap common commands (build, run, test). This reduces human error.
• Store all outputs (models, logs) in a cloud-agnostic format like ONNX or MLflow’s model registry.
• Use environment variables for cloud-specific credentials (e.g., AWS_ACCESS_KEY_ID) so your code stays clean.
• For large datasets, consider using a data lake with a consistent URI scheme (e.g., s3:// vs az://) and abstract it with a library like cloudpathlib.
• Always run a small smoke test (e.g., 1 epoch) on a new provider before the full run.

Troubleshooting

• Different results on different clouds. Check the CPU/GPU architecture. A model trained on a V100 vs. an A100 may have slight numerical differences. Use deterministic algorithms and consider using CPU-only for exact reproducibility.
• Missing system libraries. If your code fails with an import error, you probably forgot a system package in the Dockerfile. Run ldd on the binary or check the error message and add the package.
• GPU driver mismatch. Each cloud provider has its own GPU driver version. Use a base image that includes the CUDA version you need, and test on the target provider early.
• Data download fails. If the data URI is not accessible from the new provider, set up a cross-cloud replication or use a public bucket for small datasets.
• Container runs out of memory. Adjust the memory limit in your cloud service configuration. Make sure your code doesn’t have a hardcoded memory assumption.
• Cannot reproduce due to non-determinism. Some operations (like certain GPU kernels) are non-deterministic by nature. Use deterministic flags, and if that fails, run on CPU for exact reproducibility.

Frequently Asked Questions

Do I need to use a specific cloud provider?

No, the steps are provider-agnostic. The key is to containerize and version everything, so any provider that can run Docker and has the required hardware will work.

How do I handle large datasets that are too big for a container?

Store the data in a cloud bucket (S3, Azure Blob, GCS) and use a data versioning tool like DVC. Your code should download the data at runtime, and you should record the exact URI and checksum.

What if my model uses GPUs and the cloud provider has a different GPU?

Use a base image that includes the CUDA version you need, and test on the target provider early. For exact numerical reproducibility, consider using CPU-only, as GPU results can vary slightly between architectures.

Can I use this approach for research that involves external APIs?

Yes, but you must pin the API version and record the exact request parameters. Also, be aware that external services can change over time, so you may need to save the responses for full reproducibility.

Is it enough to just share the Docker image?

No, you also need to share the data, the config, and the code version. The Docker image is just the environment; the other pieces are equally important.

How do I ensure my results are reproducible months later?

Pin everything: base image digest, library versions, data checksum, and code commit hash. Also, store the logs and metrics in a metadata tracker so you can verify the run later.

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