Using Terraform Provider Iterative for bespoke Machine Learning & Data Science on AWS, Azure, GCP and K8s Jupyter, TensorFlow/Keras, and TensorBoard are widely used in many Data Science & Machine Learning projects. Unfortunately, there are two main pain points often faced by scientists and engineers alike: Installing and maintaining your entire software stack (including GPU drivers and dependencies) is difficult and , and time-consuming You are you own (laptop or desktop specs). limited by the hardware In some cases, work-arounds for these problems include the free service as well as the notebook-based , but both have their own strict limitations on resources (CPU, GPU, RAM, disk storage, and even uptime). Binder Google Colab Instead, this short guide covers how to deploy an ML-ready Jupyter server and sync results with your preferred cloud compute provider. There are only 3 requirements: Download the CLI tool and say goodbye to (free), terraform ClickOps Get an account for port forwarding convenience (free), and ngrok Have cloud credentials, of course! ( , , , or .) AWS Azure GCP Kubernetes ⚙ No ClickOps? Show me some code then… To get a snazzy Jupyter workspace just like in the image above, download the accompanying code from this : GitHub repository git clone https://github.com/iterative/blog-tpi-jupyter
cd blog-tpi-jupyter
terraform init               # Setup local dependencies If you like, have a look at the — it contains all the you’d possibly want, such as custom hardware specs and spot price bidding. The default is an (with an NVIDIA Tesla T4 16GB GPU for a total cost of around $0.15/hour as of writing). Next, we need some environment variables: main.tf file config options AWS EC2 G4 instance export NGROK_TOKEN="..."     # Sign up for free at https://ngrok.com
export TF_LOG_PROVIDER=INFO  # (optional) Increase verbosity
export AWS_ACCESS_KEY_ID="..."         # assuming AWS cloud provider
export AWS_SECRET_ACCESS_KEY="..." See the for other cloud providers. authentication docs Now time for magic! 🎩 terraform apply In ⏱ this simple command: just a few minutes creates a cloud storage bucket, uploads your local working directory to the bucket, shared makes an auto-scaling group which in turn provisions the desired accelerated compute instance, installs an ML software stack (CUDA drivers, Python & TensorFlow/Keras) on the instance, and serves Jupyter Lab, Notebook, and TensorBoard over an ngrok tunnel. To see the logs (including the server URLs) at any point, simply run: terraform refresh ⏳ If the URLs at the bottom of the output are blank ( ), the instance isn’t ready yet. Wait a few minutes before running again. Eventually you’ll see: urls = [] terraform refresh Outputs:

urls = [
  "Jupyter Lab: https://8c62-54-173-120-3.ngrok.io/lab?token=...",
  "Jupyter Notebook: https://8c62-54-173-120-3.ngrok.io/tree?token=...",
  "TensorBoard: https://6d52-54-173-120-3.ngrok.io",
] Finally, when done experimenting, download the working directory, delete the cloud storage, and terminate the cloud instance with one simple command: shared terraform destroy But Why? It uses under-the-hood. There are a few distinct advantages to TPI: Terraform Provider Iterative (TPI) 💰 Lower cost: use your preferred cloud provider's existing pricing, including on-demand per-second billing and bulk discounts.
🔄 Auto-recovery: spot/preemptible instances are cheap but unreliable. TPI reliably and automatically respawns such interrupted instances, caching & restoring the working directory in the cloud even when you are offline.
👓 Custom spec: full control over hardware & software requirements via a single config file — including machine (CPU, GPU, RAM, storage) & . main.tf types images TL;DR summary You can self-provision DS & ML hardware & software environments in the cloud with . TPI ⚡️ It's easier than you think: git clone https://github.com/iterative/blog-tpi-jupyter
cd blog-tpi-jupyter
export NGROK_TOKEN="..."     # Sign up for free at https://ngrok.com
export TF_LOG_PROVIDER=INFO  # (optional) Increase verbosity
terraform init    # Setup local dependencies
terraform apply   # Create cloud resources & upload "shared" workdir
terraform refresh # Get Jupyter & TensorBoard URLs (rerun if blank)
# *=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*
# Click on the printed URLs and have fun!
# *=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*
# When done, download "shared" workdir & terminate cloud resources:
terraform destroy

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