cloudfit-core 0.1.3

Cloud-agnostic machine type scoring engine for computational workloads

cloudfit-core

Cloud-agnostic machine type scoring engine for computational workloads.

cloudfit-core is the foundation of the cloudfit ecosystem — a pure Python library that, given a workload profile, scores and ranks available cloud instances across providers. No cloud credentials required. No API calls. Just a workload spec in, ranked recommendations out.

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The problem

Teams hardcode instance types (c2-standard-60, c7i.16xlarge) in infrastructure-as-code. When providers deprecate them or release better generations, nothing updates — costs drift and performance degrades silently. There is no open-source tool that takes a workload description and returns the best available instance across AWS, GCP, and Azure with explainable scoring.

cloudfit-core is that scoring engine.

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Installation

pip install cloudfit-core

Requires Python 3.9+.

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Quick start

from cloudfit import WorkloadProfile, MachineType, rank

# Define your workload
profile = WorkloadProfile(
    vcpu=60,
    ram_gb=224,
    workload="io-intensive",
    archetype="io",            # io | cpu | mem | gpu | burst
    optimize_for="balanced",   # cost | performance | availability | balanced
)

# Provide candidate instances (from a cloudfit-provider-* package or your own list)
candidates = [
    MachineType(id="c2-standard-60",       provider="gcp", vcpu=60, ram_gb=240, price_hr=3.13),
    MachineType(id="c3d-standard-60-lssd", provider="gcp", vcpu=60, ram_gb=240, price_hr=3.39),
    MachineType(id="t2d-standard-60",      provider="gcp", vcpu=60, ram_gb=240, price_hr=2.31),
    MachineType(id="c7i.24xlarge",         provider="aws", vcpu=96, ram_gb=192, price_hr=4.28),
]

# Score and rank
results = rank(profile, candidates)
for r in results:
    print(f"{r.instance.id:30s}  score={r.score:.2f}  ${r.instance.price_hr:.2f}/hr")

Output:

t2d-standard-60                 score=0.81  $2.31/hr
c2-standard-60                  score=0.81  $3.13/hr
c3d-standard-60-lssd            score=0.80  $3.39/hr
c7i.24xlarge                    score=0.00  $4.28/hr

c7i.24xlarge scores 0.00 and ranks last because its 192 GB RAM is below the requested 224 GB — it's eliminated by the hard floor filter, not just ranked low (see How scoring works).

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How scoring works

Every recommendation runs through the same weighted scoring function:

score = w_cost × cost_score + w_perf × perf_score + w_avail × avail_score

The optimize_for mode sets the weights:

Mode	w_cost	w_perf	w_avail	Best for
cost	0.70	0.20	0.10	Batch jobs, dev environments
balanced	0.33	0.34	0.33	Default — production workloads
performance	0.10	0.80	0.10	Latency-sensitive, GPU inference
availability	0.10	0.20	0.70	Long-running jobs, deprecation risk

Hard floor filters run before scoring — instances that don't meet minimum RAM, vCPU, or GPU requirements are eliminated entirely, not just ranked low.

Advanced users can override weights directly:

profile = WorkloadProfile(
    vcpu=60,
    ram_gb=224,
    # Both short and long key spellings are accepted:
    # short: {"cost": 0.5, "perf": 0.4, "avail": 0.1}
    # long:  {"cost": 0.5, "performance": 0.4, "availability": 0.1}
    weights={"cost": 0.5, "performance": 0.4, "availability": 0.1}
)

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Workload archetypes

cloudfit-core understands five resource archetypes, each reflecting a different dominant constraint:

Archetype	Dominant constraint	Typical workloads
io	Disk throughput	Sequencing demultiplexing, short-read alignment
cpu	Thread parallelism	Variant calling, de novo assembly, quantification
mem	RAM capacity	Metagenomics classification, single-cell RNA-seq, Hi-C
gpu	GPU VRAM	Protein structure prediction, GPU variant calling, basecalling
burst	Fleet × small instances	Nextflow pipelines, Snakemake DAGs, WDL scatter-gather

In this release the archetype is recorded on the workload profile for classification and downstream tooling; scoring weights are driven by optimize_for. Archetype-aware weighting and fleet-vs-single-instance recommendations (e.g. many small spot instances for burst) are planned for a future release.

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Dynamic disk sizing

For sequencing workloads, disk requirements scale with experiment parameters rather than being fixed. cloudfit-core computes disk from first principles:

from cloudfit import compute_disk_tb, WorkloadProfile, DiskSpec

disk_tb = compute_disk_tb(
    sequencer="novaseq_6000",
    flowcell="s4",
    lanes=4,
    retain_input=False,        # if True, raw input files are kept post-run
    keep_undetermined=False,   # if True, unmatched reads written to disk (+8%)
    safety_margin=0.20,
)
# → 15.84 TB

# Use the result when building your workload profile
profile = WorkloadProfile(
    vcpu=60,
    ram_gb=224,
    workload="io-intensive",
    archetype="io",
    disk=DiskSpec(sizing="static", scratch_tb=disk_tb),
)

compute_disk_tb is a standalone helper — call it before constructing your WorkloadProfile and pass the result into DiskSpec.scratch_tb.

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Workload YAML schema

workload:
  type: io-intensive
  archetype: io
  parallelism: lane        # lane | sample | interval | process | rule

  resources:
    vcpu: 60
    ram_gb: 224
    disk:
      sizing: dynamic      # "dynamic" computes from experiment params; "static" uses scratch_tb
      preferred: local_ssd_first
    gpu:
      required: false

  scheduling:
    spot: false
    restart_tolerant: false

  optimize_for: balanced   # cost | performance | availability | balanced
  providers:
    - gcp
    - aws

Load from file:

from cloudfit import from_yaml

profile = from_yaml("my-workload.yaml")
results = rank(profile, candidates)

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Provider plugins

cloudfit-core is the scoring engine only — it scores whatever instances you give it. Provider plugins fetch live instance data from cloud APIs on a schedule and feed the registry:

pip install cloudfit-provider-gcp   # fetches GCP Compute Engine machine types
pip install cloudfit-provider-aws   # fetches AWS EC2 instance specs and pricing

Each provider implements a simple interface:

from cloudfit.providers.base import Provider

class MyProvider(Provider):
    def fetch_instances(self, region: str) -> list[MachineType]: ...
    def get_pricing(self, instance_id: str, region: str) -> float: ...
    def get_availability(self, instance_id: str, region: str) -> float: ...

Want to add a provider? See CONTRIBUTING.md.

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Terraform / OpenTofu integration

Once cloudfit-api is running, use the Terraform provider to resolve instance types at plan time:

data "cloudfit_recommendation" "demux_worker" {
  vcpu         = 60
  ram_gb       = 224
  workload     = "sequencing-demux"
  optimize_for = "balanced"
}

resource "google_compute_instance" "worker" {
  machine_type = data.cloudfit_recommendation.demux_worker.machine_type
}

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Known limitations

cloudfit-core is at v0.1.0 and ships with documented gaps. Listed here in priority order, with planned mitigations. The math is open and auditable; these are not surprises, they are the v0.2 backlog.

Limitation	Impact	v0.2 plan
Performance scorer caps at 2× the requested size. With optimize_for: performance, top picks cluster at 2× capacity even when the workload would fit at 1×. Use optimize_for: cost for exact-fit sizing today.	Over-recommends headroom in performance mode	Smoother saturation curve. Factor in CPU generation, clock speed, memory bandwidth, network interconnect
GCP-only provider. No AWS, Azure, or other cloud catalogs yet.	Cannot rank AWS/Azure instances	cloudfit-provider-aws is the next planned provider (DescribeInstanceTypes + Pricing API)
No commitments awareness. CUDs, Savings Plans, and Reserved Instances are not factored. Recommendations are based on on-demand prices.	Inflated effective cost for customers with committed spend	Caller-provided commitments payload, computed effective_price_hr
No quota / capacity awareness. A recommendation may be technically valid but unlaunchable in a region with exhausted quota.	"Stuck in queue" failures	Optional quota_snapshot payload that hard-floors candidates exceeding remaining quota
No GPU type discrimination. Only gpu_count and gpu_vram_gb are scored. A100 vs H100 vs L4 vs T4 look the same if VRAM matches.	GPU recommendations may miss the right SKU for modern ML	GPU SKU as a scored dimension with TFLOPS and memory-bandwidth lookups
No CPU generation factor. A first-gen and third-gen instance with the same vCPU count score identically on perf.	Underweights modern instances that deliver more work per core	Add generation and architecture multipliers to the perf scorer
Bundled snapshots are static. cloudfit-api ships with a 267-instance JSON refreshed manually via cloudfit-provider-gcp.	Pricing drifts over time	Live registry refreshed hourly, versioned with provenance (fetched_at, source_etag)
No empirical validation. The scoring model is documented and auditable but has not been backtested against historical batch outcomes.	Recommendations are model predictions, not evidence-backed claims	Backtest harness ingesting Nextflow / Cromwell run history to compare cloudfit picks against actual run results

A complete self-audit covering UX, operations, scoring methodology, and the v0.2 roadmap will be published alongside the next release. Issues and PRs that surface additional gaps are welcome — see CONTRIBUTING.md.

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Citing cloudfit-core

If you use cloudfit-core in your research, please cite it:

@software{kasaraneni2026cloudfit,
  author    = {Kasaraneni, Chaitanya Krishna},
  title     = {cloudfit-core: Cloud-agnostic machine type scoring engine
               for computational workloads},
  year      = {2026},
  publisher = {GitHub},
  url       = {https://github.com/cloudfit-io/cloudfit-core},
  orcid     = {0000-0001-5792-1095}
}

GitHub also shows a Cite this repository button in the sidebar (powered by CITATION.cff).

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Related publications

• Kasaraneni, C.K. et al. (2025). AI-Driven Drug Repurposing: A Graph Neural Network and Self-Supervised Learning Approach. IEEE CIACON. doi:10.1109/CIACON65473.2025.11189545
• Kasaraneni, C.K. et al. (2025). Multi-modality Medical Image Fusion Using Machine Learning/Deep Learning. Springer. doi:10.1007/978-3-031-98728-1_16

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Related projects

• samplesheet-parser — Format-agnostic Illumina SampleSheet parser (BCLConvert V2 + IEM V1)
• clinops — Clinical ML data quality library

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Repository structure

cloudfit-core/
├── README.md               # first thing every visitor reads
├── CITATION.cff            # GitHub "Cite this repository" button — ORCID linked
├── pyproject.toml          # packaging, dependencies, PyPI metadata
├── CONTRIBUTING.md         # provider plugin interface guide
├── LICENSE                 # Apache 2.0
├── .gitignore
│
├── cloudfit/
│   ├── __init__.py         # exports rank, recommend, key models
│   ├── models.py           # WorkloadProfile, MachineType, ScoredInstance (pydantic v2)
│   ├── scorer.py           # rank(), score_instance(), weight matrix
│   ├── filter.py           # hard_floor_check() — RAM, vCPU, GPU hard filters
│   ├── disk.py             # compute_disk_tb() — dynamic disk sizing formula
│   ├── yaml_loader.py      # from_yaml() — loads workload YAML schema
│   └── providers/
│       ├── __init__.py
│       └── base.py         # abstract Provider class — plugin contract
│
└── tests/
    ├── test_scorer.py      # rank, scores, weight modes, hard floors
    ├── test_disk.py        # disk formula, CBCL vs BCL factor, sequencer profiles
    └── test_yaml.py        # from_yaml() loads profiles correctly

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Contributing

See CONTRIBUTING.md. Issues and pull requests are welcome — especially provider plugins for new cloud platforms (Azure, Hetzner, Oracle Cloud).

License

Apache 2.0 — see LICENSE.

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_{Author: Chaitanya Krishna Kasaraneni  ·  Google Scholar  ·  ORCID 0000-0001-5792-1095}