API Stability Promise

Applies to: mlsysim v0.1.x

This document defines which parts of the mlsysim API are stable, which are experimental, and what guarantees you can rely on when building on top of the framework.

Versioning Policy

mlsysim follows Semantic Versioning with one important caveat: we are pre-1.0. Under semver, this means:

Version bump What it means
0.1.x -> 0.1.y (patch) Bug fixes only. No API changes. Safe to upgrade.
0.1.x -> 0.2.0 (minor) Breaking changes allowed. Read the changelog before upgrading.
1.0.0 Full stability guarantee begins. Breaking changes require a major bump.

In practice: if you pin to mlsysim ~= 0.1.0 (any 0.1.x), your code will not break. If you upgrade to 0.2.0, expect to update imports and possibly adjust call signatures.

Stable API (will not break in v0.1.x)

These interfaces are locked for the entire 0.1.x series. Bug fixes may change return values (e.g., correcting a formula), but signatures and field names will not change.

Core Engine

from mlsysim import Engine

result = Engine.solve(
    model=...,        # ModelSpec or registry name
    hardware=...,     # HardwareSpec or registry name
    batch_size=32,    # int
    precision="fp16", # str: "fp32", "fp16", "bf16", "int8", "int4"
    efficiency=0.45,  # float: 0.0-1.0
)

All five parameters to Engine.solve() are stable. Their names, types, and positions will not change.

Hardware Registry

from mlsysim import Hardware

gpu = Hardware.H100            # All current entries are stable
gpu = Hardware.A100
gpu = Hardware.Cloud.H100
# ... every entry shipping in 0.1.x

New entries may be added in patch releases, but existing entries will not be removed or renamed.

Model Registry

from mlsysim import Models

model = Models.Llama3_70B      # All current entries are stable
model = Models.GPT2
# ... every entry shipping in 0.1.x

Same guarantee as Hardware: additions are allowed, removals are not.

Scenario Registry

from mlsysim import Scenarios

All scenarios shipping in 0.1.0 are stable. Their names, parameters, and behavior are fixed for the 0.1.x series.

PerformanceProfile Fields

The following fields on the result object returned by Engine.solve() are stable:

Field Type Description
latency pint.Quantity Wall-clock time for one forward pass
throughput pint.Quantity Tokens/sec or samples/sec
bottleneck str "Compute" or "Memory"
mfu float Model FLOPs Utilization (0.0-1.0)
feasible bool Whether the workload fits in memory
energy pint.Quantity Energy consumption per forward pass

Unit Registry

from mlsysim import ureg

The Pint unit registry instance is stable. All quantities returned by the engine use this registry.

Experimental API (may change in v0.2.0)

These interfaces work today but are not yet finalized. Use them freely for exploration, but do not build production tooling against them without pinning to an exact version.

Individual Solver Classes

from mlsysim.solvers import ForwardModel, DistributedModel, ServingModel

The solver class hierarchy, their constructors, and their method signatures may change. The Engine.solve() facade insulates you from these changes – prefer it over direct solver instantiation.

Top-level convenience imports such as from mlsysim import ServingModel are kept working throughout the 0.1.x series because the tutorials use them. For library code, prefer mlsysim.solvers so the import path makes the dependency on solver-specific behavior explicit.

Training Mode Parameter

Engine.solve(..., is_training=True)  # experimental

The is_training flag will likely be replaced by separate Engine.train() and Engine.infer() methods in v0.2.0, or by a more expressive workload specification.

Pipeline Composition API

The API for composing multiple solver stages into a pipeline (e.g., prefill + decode, or TP + PP) is experimental. The abstraction is correct but the interface is still being refined.

Design Space Exploration (DSE) API

The search/sweep API for exploring hardware-model combinations is experimental. Parameter names and result formats may change.

CLI Commands and Flags

All mlsysim CLI command names, subcommands, and flags are experimental. Shell scripts that call the CLI should pin to an exact version.

Solver-Specific Result Fields

Fields on specialized result types (DistributedResult, ServingResult, etc.) beyond the six stable PerformanceProfile fields listed above are experimental. They may be renamed, reorganized, or moved to nested objects.

Deprecated (will be removed in v0.2.0)

These interfaces still work in v0.1.x but emit deprecation warnings and will be removed in the next minor release.

No public import path is deprecated in 0.1.2. Deprecations will be listed here and in the changelog before the next minor release.

How to Protect Your Code

  1. Pin your dependency: mlsysim ~= 0.1.0 (allows 0.1.x patches, blocks 0.2.0).
  2. Use Engine.solve() as your primary interface. It is the most stable entry point.
  3. Use mlsysim.solvers only when you need solver-specific features. The engine facade covers most use cases.
  4. Run with warnings enabled (python3 -W default) to catch deprecation notices early.
  5. Read the changelog before any minor version upgrade.
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