flow-compute 0.0.6

Simplified SDK for Mithril - GPU compute made simple

Mithril Flow SDK

Python → Petaflops in 15 seconds. Flow procures GPUs through Mithril, spins InfiniBand-connected instances, and runs your workloads—zero friction, no hassle.

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.

pip install flow-compute
flow run -c "python train.py"
⠋ Bidding for best‑price GPU node (8×H100) with $12.29/h100-hr limit_price…
✓ Launching on NVIDIA H100-80GB for $1/h100-hr

---

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 in seconds; from idea to training or serving instantly.	flow dev for DevBox or flow run 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	GPUs always available, self-serve; no haggling, no calls.	Uncapped spot + 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)

---

Pricing & Auctions

How Flow leverages Mithril's Second-Price Auction:

You express your limit price (or leverage flow defaults); GPUs provision instantly at the fair market clearing rate.

Your Bid's Limit Price	Current Spot Price	You Pay
$3.00	$1.00	$1.00
$3.00	$3.50 (spike)	No allocation

• Your billing price = highest losing bid.
• Limit price protects from surprises.
• Resell unused reservations into the auction to recoup costs.

Full Auction Mechanics →

---

Quick Start

Get an API key → app.mithril.ai

pip install flow-compute
flow init  # Sets up your authentication and configuration
flow dev -c 'python train.py'  # sub-5-second dev loop after initial VM config

---

Key Concepts to Get Started

Auctions & Limit Prices

Flow uses Mithril spot instances via second-price auctions. See auction mechanics.

Core Workflows

• flow dev → interactive loops in seconds.
• flow run → reproducible batch jobs (default 10 minutes).
• Python API → easy pipelines and orchestration.

Examples

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

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

# Serverless‑style decorator
@flow.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

uv tool install flow-compute
flow init
# after init and configuration
# option 1 -- launch a "job"
flow example gpu-test
# option 2 -- launch an interactive DevBox
flow dev

---

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.

---

Developer Deep Dive

Advanced Task Configuration

# Distributed training example (32 GPUs, Mithril groups for InfiniBand connectivity by default)
task = flow.run(
    command="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
    }
)

SLURM Migration

Flow seamlessly runs existing SLURM scripts:

# Your existing script works unchanged
flow run job.slurm

# SLURM → Flow mapping:
# #SBATCH --gpus=8        → instance_type="8xa100"
# #SBATCH --time=24:00:00 → max_run_time_hours=24
# squeue                  → flow status
# scancel                 → flow cancel

Zero-Import Remote Execution

Run existing Python functions on GPUs without code changes:

# Execute any function from any file remotely
from flow import invoke

result = invoke(
    "train.py",           # Your existing file
    "train_model",        # Function name  
    args=["dataset.csv"], # Arguments
    gpu="a100"           # GPU type
)

Persistent Volumes & Docker Caching

# Create reusable Docker cache (10x faster container starts)
cache = flow.create_volume(size_gb=100, name="docker-cache")

task = flow.run(
    "python train.py",
    instance_type="a100",
    image="pytorch/pytorch:2.3.0-cuda12.1-cudnn8",
    volumes=[{
        "volume_id": cache.volume_id,
        "mount_path": "/var/lib/docker"
    }]
)
# First run: ~5 min (downloads image)
# Next runs: ~30 sec (uses cache)

Dynamic Volume Mounting

Attach persistent storage to launched tasks without needing to self-coordinate restarts. Once mounted, the volume will be available for use at the specified path.

# Mount by names:
flow mount training-data gpu-job-1

# Mount by IDs:
flow mount vol_abc123 task_xyz789

# Volume is accessible after restart at /mnt/training-data

Key Features Summary

• Distributed Training – Multi-node InfiniBand clusters auto-configured
• Code Upload – Automatic with .flowignore support
• Container Environments – Custom Docker images with caching
• Live Debugging – SSH into running instances (flow ssh)
• Cost Protection – Built-in max_price_per_hour safeguards
• Google Colab Integration – Connect notebooks to GPU instances
• Private Registries – ECR/GCR with auto-authentication

Full API Documentation: github.com/mithril-ai/flow-sdk

Further Reading

• Restoring the Promise of Public Cloud for AI
• Introducing Mithril
• Spot Auction Mechanics