Flow CLI & SDK

Python → Petaflops with one CLI. Flow procures GPUs through Mithril, spins up InfiniBand-connected instances, and runs your workloads with a compact CLI and SDK.

PyPI - Version Public repo

Background

There's a paradox in GPU infrastructure today: Massive GPU capacity sits idle, even as AI teams wait in queues—starved for compute. Mithril, the AI-compute omnicloud, dynamically allocates GPU resources from a global pool (spanning Mithril's first-party resources and 3rd-party partner cloud capacity) using efficient two-sided auctions, maximizing surplus and reducing costs. Mithril seamlessly supports both reserved-in-advance and just-in-time workloads—maximizing utilization, ensuring availability, and significantly reducing costs.

Infrastructure mode

flow instance create -i 8xh100 -N 20
╭─ Instance Configuration ────────────────────────────────╮
│                                                         │
│  Name           multinode-run                           │
│  Command        sleep infinity                          │
│  Image          nvidia/cuda:12.1.0-runtime-ubuntu22.04  │
│  Working Dir    /workspace                              │
│  Instance Type  8xh100                                  │
│  Num Instances  20                                      │
│  Max price      $12.29/hr                               │
│                                                         │
╰─────────────────────────────────────────────────────────╯

flow instance list
╭─────────────────────────────── ❊ Flow ────────────────────────────────╮
│                                                                       │
│     #     Status     Instance               GPU       Owner     Age   │
│     1   ● running    multinode-run        8×H100·80G  alex       0m   │
│     2   ● running    interactive-77c31e   A100·80G    noam       5h   │
│     3   ○ cancelled  dev-a100-test        A100·80G    alex       1d   │
│                                                                       │
╰───────────────────────────────────────────────────────────────────────╯

Research mode (in early preview)

flow submit "python train.py" # -i 8xh100
⠋ Bidding for best‑price GPU node (8×H100) with $12.29/h100-hr limit_price…
✓ Launching on NVIDIA H100-80GB for $1/h100-hr

Quick Start

# Optional: install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install via uv or pipx (or see installer scripts in scripts/)
uv tool install flow-compute
# or: pipx install flow-compute

flow setup  # Requires a Mithril account, API key, and project access
flow dev   # Interactive GPU development after initial VM setup

Optional assistant integrations:

flow claude  # Install Flow skills for Claude Code
flow codex   # Install Flow skills for Codex

Why choose Flow

Status quo GPU provisioning involves quotas, complex setups, and queue delays, even as GPUs sit idle elsewhere or in recovery processes. Flow addresses this:

Dynamic Market Allocation – Efficient two-sided auctions ensure you pay the lowest market-driven prices rather than inflated rates.

Simplified Batch Execution – An intuitive interface designed for cost-effective, high-performance batch workloads without complex infrastructure management.

Provision from 1 to thousands of GPUs for long-term reservations, short-term "micro-reservations" (minutes to weeks), or spot/on-demand needs—all interconnected via InfiniBand. High-performance persistent storage and built-in Docker support further streamline workloads, ensuring rapid data access and reproducibility.

Why Flow + Mithril?

Pillar	Outcome	How
Iteration Velocity and Ease	Fresh containers quickly once capacity is allocated.	`flow dev` for DevBox or `flow submit` to programmatically launch tasks
Best price-performance via market-based pricing	Preemptible secure jobs for $1/h100-hr	Blind two-sided second-price auction; client-side bid capping
Availability and Elasticity	Self-serve access to spot and reserved capacity, subject to account access and market availability.	Spot capacity plus overflow capacity from partner clouds
Abstraction and Simplification	InfiniBand VMs, CUDA drivers, auto-managed healing buffer—all pre-arranged.	Mithril virtualization and base images preconfigured + Mithril capacity management.

"The tremendous demand for AI compute and the large fraction of idle time makes sharing a perfect solution, and Mithril's innovative market is the right approach." — Paul Milgrom, Nobel Laureate (Auction Theory and Mechanism Design)

Key Concepts to Get Started

Core Workflows

Infrastructure mode

flow instance create -i 8xh100 -N 20 → request a 20-node GPU cluster
flow volume create -s 10000 -i file → provision 10 TB of persistent, high-speed storage
flow ssh instance -- nvidia-smi → run across all nodes in parallel

In research preview

Research mode

flow dev → interactive development on allocated GPU instances.
flow submit → reproducible batch jobs.
Python API → easy pipelines and orchestration.

Examples

# Launch a batch job on discounted H100s
flow submit "python train.py" -i 8xh100

# Frictionlessly leverage an existing SLURM script
flow submit job.slurm

# Serverless‑style decorator
@app.function(gpu="a100")

Ideal Use Cases

Rapid Experimentation – Quick iterations for research sprints.
Instant Elasticity – Scale rapidly from one to thousands of GPUs.
Collaborative Research – Shared dev environments with per-task cost controls.

Flow is not yet ideal for: always‑on ≤100 ms inference, strictly on‑prem regulated data, or models that fit on laptop or consumer-grade GPUs.

Architecture (30‑s view)

Your intent ⟶ Flow Execution Layer ⟶ Global GPU Fabric

Flow SDK abstracts complex GPU auctions, InfiniBand clusters, and multi-cloud management into a single seamless and unified developer interface.

Installation

Requirements

Python 3.10 or later
Recommended: use uv to auto-manage a compatible Python when installing the CLI
Linux or macOS for native CLI workflows. On Windows, use WSL2; native Windows is not fully validated.

1) Install uv — optional but recommended

Installation guide: docs.astral.sh/uv/getting-started/installation

macOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows: use WSL2 for the Flow CLI, then run the macOS/Linux command inside WSL.
```
curl -LsSf https://astral.sh/uv/install.sh | sh
```

2) Install Flow

Global CLI (uv):

uv tool install flow-compute
flow setup

Global CLI (pipx):
```
pipx install flow-compute
flow setup
```

Under the Hood (Advanced)

Bid Caps – Protect budgets automatically.
Self-Healing – Spot nodes dynamically migrate tasks.
Docker/Conda – Pre-built images or dynamic install.
Multi-cloud Ready – Mithril (with Oracle, Nebius integrations internal to Mithril), and more coming
SLURM Compatible – Run #SBATCH scripts directly.

Python SDK (Research Preview)

Advanced Task Configuration

# Distributed training example (32 GPUs, Mithril groups for InfiniBand connectivity by default)
task = flow.run(
    "torchrun --nproc_per_node=8 train.py",
    instance_type="8xa100",
    num_instances=4,  # Total of 32 GPUs (4 nodes × 8 GPUs each)
    env={"NCCL_DEBUG": "INFO"}
)

# Mount S3 data + persistent volumes
task = flow.run(
    "python analyze.py",
    gpu="a100",
    mounts={
        "/datasets": "s3://ml-bucket/imagenet",  # S3 via s3fs
        "/models": "volume://pretrained-models"   # Persistent storage
    }
)

Key Features Summary

Distributed Training – Multi-node InfiniBand clusters auto-configured
Code Upload – Automatic with .flowignore (or .gitignore fallback)
Live Debugging – SSH into running instances (flow ssh)
Cost Protection – Built-in max_price_per_hour safeguards
Jupyter Integration – Connect notebooks to GPU instances

Documentation: https://docs.mithril.ai/cli-and-sdk/quickstart

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