stable-pretraining#

AI is moving beyond labels. Today’s models learn through self-supervision and multimodal alignment, extracting knowledge from raw data to build general-purpose representations that work across tasks. These foundation models are then deployed at scale, often after finetuning, to solve tasks in zero or few shot.

stable-pretraining is a PyTorch framework built on top of Lightning for this new paradigm. What sets us apart is real-time visibility into training quality through extensive logging and monitoring. Our callback ecosystem (OnlineProbe, OnlineKNN, RankMe, and many more) provides insights into feature collapse, training dynamics, and downstream performance. Data flows as dictionaries through model components, metrics, and callbacks, making any intermediate value accessible and debuggable. With stable-pretraining: track everything, debug faster, iterate sooner.

Join our Discord: https://discord.gg/8M6hT39X

How?#

To reach flexibility, scalability and stability, we rely on battle-tested third party libraries: PyTorch, Lightning, HuggingFace, TorchMetrics amongst a few others. Those dependencies allow us to focus on assembling everything into a powerful ML framework. stable-pretraining adopts a flexible and modular design for seamless integration of components from external libraries, including architectures, loss functions, evaluation metrics, and augmentations.

Quick setup#

# Clone the repository
git clone https://github.com/galilai-group/stable-pretraining.git

# Install the framework
cd stable-pretraining
pip install -e .

For advanced installation options, see Installation below.

Core Structure#

stable-pretraining simplifies complex ML workflows into 4 intuitive components:

1 - Data#

Your dataset must follow a dictionary-structured format where each sample is a dictionary with named fields (e.g., {"image": ..., "label": ...}). This ensures consistent behavior across all components. You have multiple options for creating datasets:

HuggingFace datasets (if available on the Hub):

import stable_pretraining as spt
train_dataset = spt.data.HFDataset(
    path="frgfm/imagenette",
    name="160px",
    split="train",
    transform=train_transform,
)

From PyTorch datasets:

train_dataset = spt.data.FromTorchDataset(
    torchvision_dataset,
    names=["image", "label"],  # Map tuple outputs to dictionary keys
    transform=train_transform,
)

Custom datasets: Any dataset that returns dictionaries

datamodule = spt.data.DataModule(train=train_dataloader, val=val_dataloader)

2 - Module#

The key differentiator from PyTorch Lightning - you only define the forward function, not training_step! This unified approach computes losses and generates useful quantities that can be retrieved for monitoring and analysis:

# Use the pre-built forward functions from stable_pretraining
from stable_pretraining import forward

# Simply use the appropriate forward for your method
module = spt.Module(
    backbone=backbone,
    projector=projector,
    forward=forward.simclr_forward,  # Or byol_forward, vicreg_forward, etc.
    simclr_loss=spt.losses.NTXEntLoss(temperature=0.5),
    optim={
        "optimizer": {"type": "Adam", "lr": 0.001},
        "scheduler": {"type": "CosineAnnealingLR"},
        "interval": "epoch"
    }
)

Or define your own custom forward:

def forward(self, batch, stage):
    out = {}

    if isinstance(batch, list):
        # Multi-view training - batch is a list of view dicts
        embeddings = [self.backbone(view["image"]) for view in batch]
        out["embedding"] = torch.cat(embeddings, dim=0)

        if self.training:
            projections = [self.projector(emb) for emb in embeddings]
            out["loss"] = self.simclr_loss(projections[0], projections[1])
    else:
        # Single-view validation
        out["embedding"] = self.backbone(batch["image"])

    return out

Key points:

The forward method defines both the loss and any quantities to monitor
No need to override training_step, validation_step, etc.
Return a dictionary with a "loss" key for training
All model components are passed as kwargs to spt.Module

3 - Callbacks#

Monitor and evaluate your models in real-time during training. Callbacks are key ingredients of stable-pretraining, providing rich insights without interrupting your training flow.

Evaluation & Monitoring#

Callback	Description
`OnlineProbe`	Trains a lightweight linear probe on frozen representations to track downstream task accuracy in real-time. Maintains its own optimizer and training loop.
`OnlineKNN`	Non-parametric k-nearest neighbors evaluator using a rolling queue of cached embeddings. Zero training cost.
`RankMe`	Tracks effective rank of feature representations via singular values. A rank drop signals dimensional collapse.
`LiDAR`	Linear Discriminant Analysis Rank over surrogate classes of augmented views.
`CLIPZeroShot`	Zero-shot classification for CLIP-style models. Compares image embeddings against text-encoded class names.
`ImageRetrieval`	Image retrieval evaluator following the DINO protocol with query/gallery splits.
`LatentViz`	Online 2D visualization of the latent space. Learns a neighborhood-preserving projection and periodically plots it.
`EpochMilestones`	Early-stops training if a metric fails to reach a threshold by a given epoch.

Training Utilities#

Callback	Description
`TeacherStudentCallback`	Auto-discovers `TeacherStudentWrapper` instances and performs EMA teacher updates at configurable frequency.
`WeightDecayUpdater`	Updates weight decay on a per-batch schedule (constant, linear, cosine, or exponential).
`EmbeddingCache`	Hooks into named submodules to cache intermediate embeddings for downstream use.

Checkpointing & Export#

Callback	Description
`SklearnCheckpoint`	Saves and restores scikit-learn models (probes, classifiers) inside Lightning checkpoints.
`WandbCheckpoint`	Uploads checkpoints to Weights & Biases as artifacts with run-resume support.
`StrictCheckpointCallback`	Controls strict/non-strict checkpoint loading with detailed mismatch reporting.
`HuggingFaceCheckpointCallback`	Exports HuggingFace-compatible checkpoints for any `PreTrainedModel` submodule (zero-knowledge reload).

System & Logging#

Callback	Description
`LoggingCallback`	Displays validation metrics in a color-coded formatted table after each epoch.
`ModuleSummary`	Logs detailed parameter statistics (trainable, frozen, per-layer) at the start of training.
`TrainerInfo`	Links trainer to DataModule and logs trainer configuration.
`SLURMInfo`	Extracts and logs SLURM environment information (job ID, partition, resources).
`EnvironmentDumpCallback`	Dumps Python version, CUDA info, installed packages, git state, and env vars to `environment.json` for exact reproducibility.
`LogUnusedParametersOnce`	Reports parameters that receive no gradient after the first backward pass. Useful for catching wiring bugs.
`CleanUpCallback`	Removes selected training artifacts (SLURM logs, Hydra files, checkpoints, etc.) after successful training. Keeps everything on failure for debugging.
`ModuleRegistryCallback`	Registers the module for global logging access. Enables `spt.log()` and `spt.log_dict()` from anywhere.

Intelligent Queue System#

Callbacks that need rolling feature stores (OnlineKNN, RankMe, LiDAR, LatentViz) share memory through an automatic queue management system. If two callbacks monitor the same key with different queue lengths, a single queue is allocated at the maximum length and shared, eliminating redundant computation.

Why callbacks matter: Get real-time feedback on representation quality, catch issues like collapse early, and track multiple metrics simultaneously. For detailed usage and practical considerations, see the Callback guide.

Example:

# Monitor SSL representations with a linear classifier
linear_probe = spt.callbacks.OnlineProbe(
    module,
    name="linear_probe",
    input="embedding",
    target="label",
    probe=torch.nn.Linear(512, 10),
    loss_fn=torch.nn.CrossEntropyLoss(),
    metrics={
        "top1": torchmetrics.classification.MulticlassAccuracy(10),
        "top5": torchmetrics.classification.MulticlassAccuracy(10, top_k=5),
    },
)

# Track representation quality with KNN evaluation
knn_probe = spt.callbacks.OnlineKNN(
    name="knn_probe",
    input="embedding",
    target="label",
    queue_length=20000,
    k=10,
)

4 - Trainer#

Orchestrate everything together with PyTorch Lightning’s Trainer:

trainer = pl.Trainer(
    max_epochs=10,
    num_sanity_val_steps=1,
    callbacks=[linear_probe, knn_probe, rankme],  # Your monitoring callbacks
    precision="16-mixed",
    logger=False,
    enable_checkpointing=False,
)
manager = spt.Manager(trainer=trainer, module=module, data=data)
manager()

Once configured, the Manager connects all components and handles the training loop with precise logging and monitoring (optional).

Global Configuration#

Instead of scattering options across environment variables, callback constructors, and factory functions, stable-pretraining provides a single entry-point to configure library-wide behavior:

import stable_pretraining as spt

spt.set(
    verbose="WARNING",                          # Global log level (also controls callback verbosity)
    progress_bar="rich",                        # "auto", "rich", "simple", or "none"
    cleanup={"checkpoints": False, "slurm": False},  # What CleanUpCallback removes
    log_rank="all",                             # Which distributed rank(s) may log (default: 0)
    default_callbacks={"env_dump": False},       # Toggle individual default callbacks on/off
)

# Inspect the current configuration
print(spt.get_config())

Setting	Type	Default	Description
`verbose`	`str` or `int`	`"INFO"`	Loguru log level. Accepts `"DEBUG"`, `"INFO"`, `"WARNING"`, etc., or Python logging ints (`10`, `20`, `30`). Also controls the `verbose` flag on all callbacks when left at their default.
`progress_bar`	`str`	`"auto"`	Progress bar style. `"auto"` picks `"rich"` for TTYs and `"simple"` for non-interactive environments. `"none"` disables it.
`cleanup`	`dict`	keeps checkpoints & logs	Controls which artifact categories `CleanUpCallback` removes after training. Keys: `"checkpoints"`, `"logs"`, `"hydra"`, `"slurm"`, `"env_dump"`, `"callback_artifacts"`. Values are bools (`True` = keep, `False` = delete).
`log_rank`	`int` or `"all"`	`0`	Which distributed rank(s) may produce log output.
`default_callbacks`	`dict`	all enabled	Toggle individual default callbacks: `"progress_bar"`, `"registry"`, `"logging"`, `"env_dump"`, `"trainer_info"`, `"sklearn_checkpoint"`, `"wandb_checkpoint"`, `"module_summary"`, `"slurm_info"`, `"unused_params"`, `"hf_checkpoint"`.
`default_loggers`	`dict`	all enabled	Toggle default loggers: `"registry"` (SQLite run registry + per-step CSV logger, added as a pair).
`cache_dir`	`str` or `None`	`None` (or `SPT_CACHE_DIR` env var)	Root directory for all training outputs. See Output Directory below.
`requeue_checkpoint`	`bool`	`True`	Auto-add a `last.ckpt` checkpoint every epoch for SLURM requeue. Set to `False` to save time/disk when preemption is not a concern. Only applies when `cache_dir` is set.

Settings apply immediately and persist for the process lifetime. spt.set() can be called multiple times; only the settings you pass are updated.

Output Directory (`cache_dir`)#

By default, Lightning and Hydra scatter training outputs (checkpoints, logs, wandb data) based on the current working directory or Hydra’s run.dir. This causes collisions when multiple sweep jobs start at the same time and resolve to the same path.

stable-pretraining solves this with a centralized cache_dir. When set, every run gets its own unique directory and all outputs are routed there automatically:

{cache_dir}/runs/{YYYYMMDD}/{HHMMSS}/{run_id}/
├── checkpoints/last.ckpt
├── wandb_resume.json
├── run_meta.json
├── environment.json
└── ...

Enabling `cache_dir`#

import stable_pretraining as spt

# Option 1: in Python
spt.set(cache_dir="~/.cache/stable_pretraining")

# Option 2: via environment variable (e.g. in ~/.bashrc)
# export SPT_CACHE_DIR=~/.cache/stable_pretraining

When cache_dir is set, the Manager:

Creates a unique run directory under cache_dir/runs/.
Sets the Trainer’s default_root_dir to that directory (before instantiation).
Redirects all ModelCheckpoint callbacks to run_dir/checkpoints/ (preserving their filename, monitor, and other settings).
Adds a requeue checkpoint (last.ckpt, saved every epoch) for seamless SLURM preemption recovery. You never need to add one yourself.
Routes all callback outputs (environment dumps, latent visualizations, HuggingFace exports, etc.) there.

If preemption is not a concern and you want to skip the requeue checkpoint overhead:

spt.set(cache_dir="/scratch/runs", requeue_checkpoint=False)

When cache_dir is not set (None, the default), the library behaves exactly as before.

How the run ID is generated#

The run ID is chosen to be deterministic across all ranks of the same job, so multi-GPU training always agrees on a single directory:

Environment	Run ID	Example
SLURM	`SLURM_JOB_ID`	`99999`
SLURM array job	`SLURM_JOB_ID_SLURM_ARRAY_TASK_ID`	`99999_3`
torchrun	`TORCHELASTIC_RUN_ID`	`abc123`
Local / other	Random UUID (12 hex chars)	`a1b2c3d4e5f6`

`ckpt_path` is for loading only#

ckpt_path and cache_dir serve different purposes:

ckpt_path = where to load weights from (one-time, read-only).
cache_dir = where to save everything going forward.

manager = spt.Manager(
    trainer=trainer_cfg,
    module=module,
    data=data,
    ckpt_path="/old/run/pretrained.ckpt",  # Load from here once
)
# New checkpoints, logs, wandb data → cache_dir/runs/.../

If you don’t pass ckpt_path, the system checks run_dir/checkpoints/last.ckpt automatically. This means SLURM requeue works without any user configuration: the job is preempted, restarted with the same SLURM_JOB_ID, finds its previous run directory, and resumes from the last checkpoint.

Hydra compatibility#

When cache_dir is active, Hydra’s run.dir, sweep.dir, and job.chdir settings are ignored for trainer outputs (a warning is logged). Hydra still manages its own .hydra/ config dumps as usual. Note that SLURM .out/.err files are created by the scheduler before Python starts and cannot be redirected into the run directory.

Run Registry#

When cache_dir is set, stable-pretraining automatically maintains a filesystem-backed run registry that indexes every run. Think of it as a local, offline, instant-query alternative to the wandb dashboard — designed for large sweeps on HPC clusters.

There is nothing to configure — if cache_dir is set, the registry is active:

import stable_pretraining as spt

spt.set(cache_dir="/scratch/runs")
# That's it. Every run writes a sidecar.json into its run directory.

Architecture#

The registry uses a filesystem-first sidecar pattern. During training, each run writes only plain files — no SQLite, no network I/O:

{run_dir}/
  sidecar.json    ← atomic JSON snapshot (hparams, summary, status, tags)
  heartbeat       ← empty file, mtime touched every flush (liveness signal)
  metrics.csv     ← CSVLogger per-step time series
  hparams.yaml    ← CSVLogger hparams
  checkpoints/    ← Lightning checkpoints

The sidecar.json is the source of truth for each run. It is atomically rewritten (tmp + fsync + rename) so readers never see a partial file. A separate scanner (spt registry scan) walks {cache_dir}/runs/**/sidecar.json and builds a SQLite cache (registry.db) for fast querying. This cache is fully derived — deleting it is harmless; run spt registry scan --full to rebuild.

This design eliminates all SQLite contention during training: thousands of concurrent SLURM jobs write only to their own run directory and never touch a shared database.

What gets stored#

The registry captures three categories of data per run, all automatically:

Config / hparams — the full Hydra config (trainer, module, data) is flattened into dot-separated keys (e.g. module.optim.optimizer.lr, trainer.max_epochs) and stored as both config and hparams. This works the same way as wandb’s config: the Manager flattens the Hydra DictConfig and injects it into module.save_hyperparameters() before trainer.fit(), so Lightning’s built-in _log_hyperparams sends it to all loggers (wandb, CSV, TensorBoard, and the registry) automatically.
Summary — every self.log() call in your LightningModule accumulates into a wandb-style summary dict (last value per metric key). At the end of training, the final summary (e.g. {"val_acc": 0.85, "train_loss": 0.12}) is written to the sidecar.
Metadata — run ID, status (running/completed/failed/orphaned), liveness (heartbeat-based), tags, notes, run_dir path, and best checkpoint path.

Grouping with tags#

All grouping is done through tags — a flat list of strings attached to each run. There is no separate “project” or “group” concept; cache_dir already acts as the project, and tags handle everything else.

For SLURM array jobs, a "sweep:<SLURM_ARRAY_JOB_ID>" tag is automatically added so that all tasks in the same array are queryable as a group. You can add your own tags in YAML:

logger:
  - _target_: stable_pretraining.registry.RegistryLogger
    tags: [resnet50, simclr, ablation-v2]
    notes: "Testing higher learning rates"

Querying runs#

open_registry() triggers an incremental scan of the filesystem before returning, so the cache reflects the current state. A short in-process TTL ensures back-to-back queries don’t re-scan.

import stable_pretraining as spt

spt.set(cache_dir="/scratch/runs")
reg = spt.open_registry()

# All completed runs from a SLURM array sweep
best = reg.query(tag="sweep:12345", status="completed", sort_by="summary.val_acc", limit=5)
for r in best:
    print(f"{r.run_id}: val_acc={r.summary['val_acc']:.3f}  lr={r.hparams['module.optim.optimizer.lr']}")

# Load the best checkpoint directly
import torch
ckpt = torch.load(best[0].checkpoint_path)

# Filter by any Hydra config key (deeply nested keys work)
lars_runs = reg.query(hparams={"module.optim.optimizer.type": "LARS"})

# Check which runs are still alive (heartbeat-based)
active = reg.query(alive=True)

# All resnet runs as a pandas DataFrame
df = reg.to_dataframe(tag="resnet50")
# Columns include flattened hparams and summary:
#   run_id, status, alive, tags, notes, checkpoint_path,
#   hparams.module.optim.optimizer.lr, hparams.trainer.max_epochs,
#   summary.val_acc, summary.train_loss, ...

# Quick analysis
df[["run_id", "hparams.module.optim.optimizer.lr", "summary.val_acc"]].sort_values(
    "summary.val_acc", ascending=False
).head(10)

Concurrency and SLURM requeue#

Since training jobs only write to their own run directory (plain files, no shared database), there is zero SQLite contention — thousands of concurrent SLURM jobs run safely with no coordination. The scanner is the sole SQLite writer and uses WAL mode so concurrent readers never block it. On SLURM requeue, the run ID is deterministic (derived from SLURM_JOB_ID), so the requeued job finds its previous sidecar and resumes seamlessly — the same mechanism used for wandb run resumption and checkpoint recovery.

Liveness detection#

The registry tracks whether a run is alive via a heartbeat file (an empty file whose mtime is touched on every log_metrics call). The scanner considers a run alive if its heartbeat is newer than 180 seconds and its status is not terminal. This lets you distinguish running, stalled, and dead jobs without contacting SLURM:

# Only alive runs
spt registry ls --alive

# Only dead/finished runs
spt registry ls --dead

CLI#

The spt registry command lets you query runs from the terminal:

# List all runs
spt registry ls

# Filter by tag, status, or liveness
spt registry ls --tag resnet50 --status completed
spt registry ls --alive

# Top 5 runs by a metric (use --asc for losses)
spt registry best val_acc
spt registry best train_loss --asc -n 10

# Show full details for a run (config, summary, tags)
spt registry show <run_id>

# Export to CSV or Parquet
spt registry export sweep_results.csv --tag sweep:12345

# Manually refresh the SQLite cache from sidecars
spt registry scan
spt registry scan --full  # re-ingest everything (schema migration, DB rebuild)

# Migrate from a legacy server-backed registry.db
spt registry migrate /path/to/old/registry.db --cache-dir /scratch/runs

By default the CLI uses $SPT_CACHE_DIR/registry.db (or the spt.set(cache_dir=...) value). Pass --db /path/to/registry.db and/or --cache-dir to override.

Disabling the registry#

spt.set(default_loggers={"registry": False})

Built-in Methods#

stable-pretraining ships with ready-to-use forward functions and matching loss functions for popular self-supervised learning methods:

Method	Forward	Loss	Description
Supervised	`forward.supervised_forward`	any	Standard supervised training with labels
SimCLR	`forward.simclr_forward`	`NTXEntLoss`	Contrastive learning with 2 augmented views
BYOL	`forward.byol_forward`	`BYOLLoss`	Momentum-based self-distillation without negatives
VICReg	`forward.vicreg_forward`	`VICRegLoss`	Variance-invariance-covariance regularization
Barlow Twins	`forward.barlow_twins_forward`	`BarlowTwinsLoss`	Cross-correlation matrix alignment to identity
SwAV	`forward.swav_forward`	`sinkhorn_knopp`	Online clustering with Sinkhorn-Knopp normalization
NNCLR	`forward.nnclr_forward`	`NTXEntLoss`	Nearest-neighbor contrastive learning
DINO	`forward.dino_forward`	`DINOv1Loss`	Self-distillation with multi-crop and centering
DINOv2	`forward.dinov2_forward`	`DINOv2Loss`	DINO + iBOT masked patch prediction

Backbones#

Load architectures from popular libraries or use built-in components:

# From torchvision
backbone = spt.backbone.from_torchvision("resnet50")

# From timm (thousands of pretrained models)
backbone = spt.backbone.from_timm("vit_base_patch16_224")

# From HuggingFace
backbone = spt.backbone.vit_hf("google/vit-base-patch16-224")

Additional building blocks: MLP, ConvMixer, Resnet9, TeacherStudentWrapper (EMA), MAEDecoder, MaskedEncoder, FlexibleTransformer, PatchMasking, IJEPAMasking, MultiBlockMasking, LinearProbe, AutoLinearClassifier, AutoTuneMLP, and more.

Optimizers & Schedulers#

Component	Description
`LARS`	Layer-wise Adaptive Rate Scaling - the standard optimizer for SSL
`LinearWarmupCosineAnnealing`	Linear warmup followed by cosine decay
`LinearWarmupCyclicAnnealing`	Linear warmup followed by cyclic cosine decay
`CosineDecayer`	Pure cosine decay schedule
`create_optimizer` / `create_scheduler`	Factory functions that accept string names, dicts, or partial objects

module = spt.Module(
    ...,
    optim={
        "optimizer": {"type": "LARS", "lr": 5, "weight_decay": 1e-6},
        "scheduler": {"type": "LinearWarmupCosineAnnealing"},
        "interval": "epoch",
    },
)

Complete Example#

SimCLR on CIFAR-10

This example demonstrates the key features of stable-pretraining: dictionary-structured data, unified forward function, and rich monitoring through callbacks.

import lightning as pl
import torch
import torchmetrics
import torchvision
from torch import nn
from lightning.pytorch.loggers import WandbLogger

import stable_pretraining as spt
from stable_pretraining import forward
from stable_pretraining.data import transforms

# Define augmentations for SimCLR (creates 2 views of each image)
simclr_transform = transforms.MultiViewTransform(
    [
        transforms.Compose(
            transforms.RGB(),
            transforms.RandomResizedCrop((32, 32), scale=(0.2, 1.0)),
            transforms.RandomHorizontalFlip(p=0.5),
            transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1, p=0.8),
            transforms.RandomGrayscale(p=0.2),
            transforms.ToImage(**spt.data.static.CIFAR10),
        ),
        # Second view with slightly different augmentations
        transforms.Compose(
            transforms.RGB(),
            transforms.RandomResizedCrop((32, 32), scale=(0.08, 1.0)),
            transforms.RandomHorizontalFlip(p=0.5),
            transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1, p=0.8),
            transforms.RandomGrayscale(p=0.2),
            transforms.RandomSolarize(threshold=0.5, p=0.2),
            transforms.ToImage(**spt.data.static.CIFAR10),
        ),
    ]
)

# Load CIFAR-10 and wrap in dictionary format
cifar_train = torchvision.datasets.CIFAR10(root="./data", train=True, download=True)
cifar_val = torchvision.datasets.CIFAR10(root="./data", train=False, download=True)

train_dataset = spt.data.FromTorchDataset(
    cifar_train,
    names=["image", "label"],  # Convert tuple to dictionary
    transform=simclr_transform,
)

val_dataset = spt.data.FromTorchDataset(
    cifar_val,
    names=["image", "label"],
    transform=transforms.Compose(
        transforms.RGB(),
        transforms.Resize((32, 32)),
        transforms.ToImage(**spt.data.static.CIFAR10),
    ),
)

# Create dataloaders - MultiViewTransform handles the view creation
train_dataloader = torch.utils.data.DataLoader(
    dataset=train_dataset,
    batch_size=256,
    num_workers=8,
    drop_last=True,
    shuffle=True,  # Simple shuffle, no RepeatedRandomSampler needed
)

val_dataloader = torch.utils.data.DataLoader(
    dataset=val_dataset,
    batch_size=256,
    num_workers=10,
)

data = spt.data.DataModule(train=train_dataloader, val=val_dataloader)

# Build model components
backbone = spt.backbone.from_torchvision("resnet18", low_resolution=True)
backbone.fc = torch.nn.Identity()  # Remove classification head

projector = nn.Sequential(
    nn.Linear(512, 2048),
    nn.BatchNorm1d(2048),
    nn.ReLU(inplace=True),
    nn.Linear(2048, 2048),
    nn.BatchNorm1d(2048),
    nn.ReLU(inplace=True),
    nn.Linear(2048, 256),
)

# Create the module using the built-in SimCLR forward function
module = spt.Module(
    backbone=backbone,
    projector=projector,
    forward=forward.simclr_forward,  # Use the built-in forward function
    simclr_loss=spt.losses.NTXEntLoss(temperature=0.5),
    optim={
        "optimizer": {"type": "LARS", "lr": 5, "weight_decay": 1e-6},
        "scheduler": {"type": "LinearWarmupCosineAnnealing"},
        "interval": "epoch",
    },
)

# Add callbacks for monitoring performance during training
linear_probe = spt.callbacks.OnlineProbe(
    module,
    name="linear_probe",
    input="embedding",
    target="label",
    probe=torch.nn.Linear(512, 10),
    loss_fn=torch.nn.CrossEntropyLoss(),
    metrics={
        "top1": torchmetrics.classification.MulticlassAccuracy(10),
        "top5": torchmetrics.classification.MulticlassAccuracy(10, top_k=5),
    },
)

knn_probe = spt.callbacks.OnlineKNN(
    name="knn_probe",
    input="embedding",
    target="label",
    queue_length=20000,
    metrics={"accuracy": torchmetrics.classification.MulticlassAccuracy(10)},
    input_dim=512,
    k=10,
)

# Configure training
trainer = pl.Trainer(
    max_epochs=1000,
    callbacks=[knn_probe, linear_probe],  # Monitor SSL quality in real-time
    precision="16-mixed",
    logger=WandbLogger(project="cifar10-simclr"),
)

# Launch training
manager = spt.Manager(trainer=trainer, module=module, data=data)
manager()

Quick Start with `spt` CLI#

The spt command launches training from YAML configuration files using Hydra.

Note: spt requires YAML configs. If you have Python-based configs, you can:

Convert them to YAML format where each component uses _target_ to specify the importable class/function
See examples/simclr_cifar10_config.yaml for the structure and syntax

Local Training#

# Run with a config file
spt examples/simclr_cifar10_config.yaml

# With parameter overrides
spt examples/simclr_cifar10_config.yaml trainer.max_epochs=50 module.optim.lr=0.01

# Run from any directory - supports absolute and relative paths
spt ../configs/my_config.yaml
spt /path/to/config.yaml

SLURM Cluster Training#

For training on SLURM clusters, use the -m flag to enable multirun mode:

# Use the provided SLURM template (customize partition/QOS in the file)
spt examples/simclr_cifar10_slurm.yaml -m

# Override SLURM parameters via command line
spt examples/simclr_cifar10_slurm.yaml -m \
    hydra.launcher.partition=gpu \
    hydra.launcher.qos=normal \
    hydra.launcher.timeout_min=720

The SLURM template (examples/simclr_cifar10_slurm.yaml) includes placeholders for cluster-specific settings. Either modify the file directly or override values via command line.

Installation#

The library is not yet available on PyPI. You can install it from the source code, as follows.

conda (optional)

First use your favorite environment manager and install your favorite pytorch version, we provide an example with conda
```
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
```
follow installation instructions… once completed, create your environment
```
conda create -n my_env python=3.11
```
with your environment name (here my_env) and your favorite Python version (here, 3.11). Once completed, make sure to activate your environment (conda activate my_env) before proceeding to the next steps!

Pytorch and our library (we recommend using uv for quicker package management):
```
pip3 install uv
uv pip install torch torchvision torchaudio
uv pip install -e .  # Core dependencies only
```
For optional features (vision models, experiment tracking, cluster support, etc.):
```
uv pip install -e ".[vision,tracking]"  # Example: add vision models and wandb
uv pip install -e ".[all]"  # Or install all optional dependencies
```
See pyproject.toml for available dependency groups (vision, tracking, cluster, visualization, datasets, extras, dev, doc).

If you do not want to use uv, simply remove it from the above commands.
API login (optional)
```
wandb login
huggingface-cli login
```

LaTeX support in Matplotlib (optional)

Click to expand setup instructions

Install Computer Modern fonts:

mkdir -p ~/.local/share/fonts
cp assets/cm-unicode-0.7.0\ 2/*.ttf ~/.local/share/fonts/
fc-cache -f -v
# verify: fc-list | grep cmu

Clear matplotlib font cache:

python -c "import shutil, matplotlib; shutil.rmtree(matplotlib.get_cachedir())"

Install TeX Live (minimal, no sudo):

cd /tmp
wget https://mirror.ctan.org/systems/texlive/tlnet/install-tl-unx.tar.gz
tar xzf install-tl-unx.tar.gz
cd install-tl-*/
./install-tl --texdir ~/texlive --no-interaction --scheme=scheme-basic
~/texlive/bin/x86_64-linux/tlmgr install type1cm cm-super dvipng collection-fontsrecommended amsmath amssymb bm underscore xcolor

Add to your ~/.bashrc (or equivalent):

export PATH="$HOME/texlive/bin/x86_64-linux:$PATH"

Verify:

python -c "
import matplotlib; matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.figure(); plt.title(r'\$\sum_{i=1}^n x_i\$')
plt.savefig('/tmp/tex_test.png')
print('Success!')
"

Ways You Can Contribute:#

If you’d like to contribute new features, bug fixes, or improvements to the documentation, please refer to our contributing guide for detailed instructions on how to get started.
You can also contribute by adding new methods, datasets, or configurations that improve the current performance of a method in the benchmark section.

Citation#

If you use stable-pretraining in your research, please cite:

@article{balestriero2025stable,
  title={stable-pretraining-v1: Foundation Model Research Made Simple},
  author={Balestriero, Randall and Van Assel, Hugues and BuGhanem, Sami and Maes, Lucas},
  journal={arXiv preprint arXiv:2511.19484},
  year={2025}
}

Contributors#

Core contributors (in order of joining the project):

API

stable-pretraining

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