mimiry 0.3.0

Python SDK for serverless compute

mimiry — Python SDK for Mimiry GPU compute

Status: alpha — early access Backend: softlaunch.mimiry.com (beta)

Python-native interface for running serverless cloud GPU jobs on Mimiry, with full control over locality and providers.

Install

pip install mimiry

Or, for local development from a clone of this repo (editable install):

pip install -e .

Auth

Running jobs requires a Mimiry account. The SDK authenticates with SSH-JWT — the same SSH key you register on your account at the Mimiry portal. The fastest way to get set up is the interactive wizard, which generates a key (if needed), walks you through registering it in the portal, saves the key path to ~/.config/mimiry/config.toml so the SDK works right away (and in every future shell, no restart needed), and verifies the connection. It also exports MIMIRY_SSH_KEY to your shell profile for curl/shell use:

mimiry setup   # alias: mimiry init

This is a one-time step — you're set going forward.

To configure auth manually instead, point the SDK at your private key:

export MIMIRY_SSH_KEY=~/.ssh/mimiry

Or pass ssh_key_path= explicitly to mimiry.configure().

GPU types and providers

Mimiry sources GPUs from both local datacenters and cloud providers across Europe and the US, spanning entry-level cards up to the latest high-end accelerators. You control locality and hardware requirements, as well as which providers to use.

Always check what's currently available before selecting hardware:

mimiry availability

Filter with --gpu-family T4, --provider gcp, --location europe-west4-a, --min-vram 16, and/or --available-only.

Managing sessions

Run and manage GPU sessions entirely from the CLI — no Python required:

# Launch a job (omit --command for an interactive box; --wait blocks until it starts)
mimiry session create --image nvcr.io/nvidia/cuda:12.6.2-runtime-ubuntu24.04 \
    --gpu T4 --provider gcp --command "nvidia-smi" --wait

mimiry sessions                 # list recent sessions, newest first
mimiry sessions --active        # only running / provisioning (i.e. still billing)
mimiry session status <id>      # full detail (--events N for history, --wait to block until done)
mimiry session logs <id>        # container logs (--tail N, --timestamps, --follow to stream)
mimiry session ssh <id>         # interactive shell into a running session
mimiry session terminate <id>

mimiry session list is the long form of mimiry sessions; add --json for machine-readable output. session create also accepts --env KEY=VAL, --volume NAME:MOUNT, --gpu-count, and --auto-terminate {never,on_complete,on_success}.

Volumes

Persistent block storage that survives session termination:

mimiry volume create --name data --size-gb 100
mimiry volume list                       # hides deleted; --all to include them
mimiry volume status <id>
mimiry volume extend <id> --size-gb 200  # grow only (cannot shrink)
mimiry volume delete <id>

Attach one at launch: mimiry session create … --volume data:/mnt/data.

Account

mimiry balance        # remaining credit
mimiry quota          # usage limits
mimiry transactions   # credit/debit history
mimiry whoami         # verify auth end-to-end
mimiry config         # show resolved key path + API base (no network)

Python version

Your local Python major.minor must match the Python inside your container image. The SDK ships your function to the GPU with cloudpickle, which can't move code objects across Python versions — e.g. a function pickled on 3.12 won't load on 3.10.

You don't need to think about this with the default image: it ships Python 3.12, matching recent Ubuntu / Debian / Fedora. It only matters if you set image= yourself — pick one whose python3 matches your local interpreter. Confirm with python3 --version locally and inside the image; a mismatch shows up as a failure to deserialize your function.

Quickstart — one-shot function

import mimiry

@mimiry.function(
    # Uses default hardware; run `mimiry availability` to choose a GPU/provider.
    image="nvcr.io/nvidia/cuda:12.6.2-runtime-ubuntu24.04",
)
def gpu_info():
    import subprocess
    return subprocess.check_output(
        ["nvidia-smi", "--query-gpu=name,memory.total", "--format=csv"],
        text=True,
    )

print(gpu_info.remote())

Quickstart — raw bash command

import mimiry

result = mimiry.run(
    image="nvcr.io/nvidia/cuda:12.6.2-runtime-ubuntu24.04",
    command="nvidia-smi",
)
print(result.logs)

What works in this version

• @mimiry.function(gpu=..., image=...) decorator
• .remote(*args, **kwargs) — sync call, returns the function's return value
• .map(iterable) — runs the function over an iterable, sequentially
• Image.from_registry(uri).pip_install(...).apt_install(...) — basic image customisation (installs at container start; no real Dockerfile build)
• mimiry.run(image, gpu, command) — raw bash entrypoint
• SSH-JWT auth via existing key

Examples

See examples/:

• 01_hello.py — minimal nvidia-smi on a GPU
• 02_cuda_probe.py — probe the GPU (driver, CUDA, device count) and return a structured Python dict
• 03_bash_command.py — run an arbitrary shell command with mimiry.run()