TPI is a Terraform plugin built with machine learning in mind. This CLI tool offers full lifecycle management of computing resources (including GPUs and respawning spot instances) from several cloud vendors (AWS, Azure, GCP, K8s)... without needing to be a cloud expert.

• Lower cost with spot recovery: transparent data checkpoint/restore & auto-respawning of low-cost spot/preemptible instances
• No cloud vendor lock-in: switch between clouds with just one line thanks to unified abstraction
• No waste: auto-cleanup unused resources (terminate compute instances upon task completion/failure & remove storage upon download of results), pay only for what you use
• Developer-first experience: one-command data sync & code execution with no external server, making the cloud feel like a laptop

Supported cloud vendors include:

There are several reasons to use TPI instead of other related solutions (custom scripts and/or cloud orchestrators):

1. Reduced management overhead and infrastructure cost: TPI is a CLI tool, not a running service. It requires no additional orchestrating machine (control plane/head nodes) to schedule/recover/terminate instances. Instead, TPI runs (spot) instances via cloud-native scaling groups¹, taking care of recovery and termination automatically on the cloud provider's side. This design reduces management overhead & infrastructure costs. You can close your laptop while cloud tasks are running — auto-recovery happens even if you are offline.
2. Unified tool for data science and software development teams: TPI provides consistent tooling for both data scientists and DevOps engineers, improving cross-team collaboration. This simplifies compute management to a single config file, and reduces time to deliver ML models into production.
3. Reproducible, codified environments: Store hardware requirements in a single configuration file alongside the rest of your ML pipeline code.

TPI is used to power CML, bringing cloud providers to existing GitHub, GitLab & Bitbucket CI/CD workflows (repository).

• Install Terraform 1.0+, e.g.:
  • Brew (Homebrew/Mac OS): brew tap hashicorp/tap && brew install hashicorp/tap/terraform
  • Choco (Chocolatey/Windows): choco install terraform
  • Conda (Anaconda): conda install -c conda-forge terraform
  • Debian (Ubuntu/Linux):

    sudo apt-get update && sudo apt-get install -y gnupg software-properties-common curl
    curl -fsSL https://apt.releases.hashicorp.com/gpg | sudo apt-key add -
    sudo apt-add-repository "deb [arch=amd64] https://apt.releases.hashicorp.com $(lsb_release -cs) main"
    sudo apt-get update && sudo apt-get install terraform
    

• Create an account with any supported cloud vendor and expose its authentication credentials via environment variables

In a project root directory, create a file named main.tf with the following contents:

terraform {
  required_providers { iterative = { source = "iterative/iterative" } }
}
provider "iterative" {}

resource "iterative_task" "example" {
  cloud      = "aws" # or any of: gcp, az, k8s
  machine    = "m"   # medium. Or any of: l, xl, m+k80, xl+v100, ...
  spot       = 0     # auto-price. Default -1 to disable, or >0 for hourly USD limit
  disk_size  = -1    # GB. Default -1 for automatic

  storage {
    workdir = "."       # default blank (don't upload)
    output  = "results" # default blank (don't download). Relative to workdir
  }
  script = <<-END
    #!/bin/bash

    # create output directory if needed
    mkdir -p results
    # read last result (in case of spot/preemptible instance recovery)
    if test -f results/epoch.txt; then EPOCH="$(cat results/epoch.txt)"; fi
    EPOCH=$${EPOCH:-1}  # start from 1 if last result not found

    echo "(re)starting training loop from $EPOCH up to 1337 epochs"
    for epoch in $(seq $EPOCH 1337); do
      sleep 1
      echo "$epoch" | tee results/epoch.txt
    done
  END
}

See the reference for the full list of options for main.tf -- including more information on machine types with and without GPUs.

Run this once (in the directory containing main.tf) to download the required_providers:

terraform init
export TF_LOG_PROVIDER=INFO

terraform apply

This launches a machine in the cloud, uploads workdir, and runs the script. Upon completion (or error), the machine is terminated.

With spot/preemptible instances (spot >= 0), auto-recovery logic and persistent (disk_size) storage will be used to relaunch interrupted tasks.

Results and logs are periodically synced to persistent cloud storage. To query this status and view logs:

terraform refresh
terraform show

terraform destroy

This terminates the machine (if still running), downloads output, and removes the persistent disk_size storage.

• Run Jupyter & TensorBoard in the cloud with one command
• Move local ML experiments to the cloud

This diagram may help to see what TPI does under-the-hood:

flowchart LR
subgraph tpi [what TPI manages]
direction LR
    subgraph you [what you manage]
        direction LR
        A([Personal Computer])
    end
    B[("Cloud Storage (low cost)")]
    C{{"Cloud instance scaler (zero cost)"}}
    D[["Cloud (spot) Instance"]]
    A ---> |2. create cloud storage| B
    A --> |1. create cloud instance scaler| C
    A ==> |3. upload script & workdir| B
    A -.-> |"4. offline (lunch break)"| A
    C -.-> |"5. (re)provision instance"| D
    D ==> |7. run script| D
    B <-.-> |6. persistent workdir cache| D
    D ==> |8. script end,\nshutdown instance| B
    D -.-> |outage| C
    B ==> |9. download output| A
end
style you fill:#FFFFFF00,stroke:#13ADC7
style tpi fill:#FFFFFF00,stroke:#FFFFFF00,stroke-width:0px
style A fill:#13ADC7,stroke:#333333,color:#000000
style B fill:#945DD5,stroke:#333333,color:#000000
style D fill:#F46737,stroke:#333333,color:#000000
style C fill:#7B61FF,stroke:#333333,color:#000000

TPI is a CLI tool bringing the power of bare-metal cloud to a bare-metal local laptop. We're working on more featureful and visual interfaces. We'd also like to have more native support for distributed (multi-instance) training, more data sync optimisations & options, and tighter ecosystem integration with tools such as DVC. Plus of course more examples for Data Scientists and Machine Learning Engineers - from Jupyter, VSCode, and Codespaces to improving the live logging/monitoring/reporting experience.

The getting started guide has some more information. In case of errors, extra debugging information is available using TF_LOG_PROVIDER=DEBUG instead of INFO.

Feature requests and bugs can be reported via GitHub issues, while general questions and feedback are very welcome on our active Discord server.

Instead of using the latest stable release, a local copy of the repository must be used.

1. Install Go 1.17+
2. Clone the repository & build the provider

   git clone https://github.com/iterative/terraform-provider-iterative
   cd terraform-provider-iterative
   make install
   

3. Use source = "github.com/iterative/iterative" in your main.tf to use the local repository (source = "iterative/iterative" will download the latest release instead), and run terraform init --upgrade

This project and all contributions to it are distributed under