Building a Gin Config Controlled PyTorch Pipeline with Configurable MLP Variants, Cosine Scheduling, and Runtime Parameter Overrides
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In this tutorial, we implement a Gin Config–controlled PyTorch experiment pipeline in which the executable training code remains stable. At the same time, the experimental degrees of freedom are moved into declarative configuration files. We construct a nonlinear spiral binary classification task, define a configurable MLP with scoped architectural variants, and expose parameters for the optimizer, scheduler, loss, batching, seeding, and training loop via @gin.configurable bindings. We use Gin’s scoped references to instantiate separate model configurations, runtime bindings to override selected parameters without editing source code, and operative config export to capture the exact resolved configuration that produces each training run.
Installing Gin Config and Building the Spiral Dataset
!pip -q install gin-config
import os
import json
import math
import random
import textwrap
from pathlib import Path
import gin
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import TensorDataset, DataLoader
import matplotlib.pyplot as plt
ROOT = Path("/content/gin_config_sharp_tutorial")
CONFIG_DIR = ROOT / "configs"
RUN_DIR = ROOT / "runs"
CONFIG_DIR.mkdir(parents=True, exist_ok=True)
RUN_DIR.mkdir(parents=True, exist_ok=True)
gin.clear_config()
@gin.configurable
def seed_everything(seed=42):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
return seed
@gin.configurable
def make_spiral_dataset(
n_per_class=gin.REQUIRED,
noise=0.18,
rotations=1.75,
train_fraction=0.8,
seed=0,
):
rng = np.random.default_rng(seed)
radius_0 = np.linspace(0.05, 1.0, n_per_class)
theta_0 = rotations * 2 * np.pi * radius_0
theta_0 += rng.normal(0.0, noise, size=n_per_class)
x0 = np.stack(
[
radius_0 * np.cos(theta_0),
radius_0 * np.sin(theta_0),
],
axis=1,
)
radius_1 = np.linspace(0.05, 1.0, n_per_class)
theta_1 = rotations * 2 * np.pi * radius_1 + np.pi
theta_1 += rng.normal(0.0, noise, size=n_per_class)
x1 = np.stack(
[
radius_1 * np.cos(theta_1),
radius_1 * np.sin(theta_1),
],
axis=1,
)
x = np.concatenate([x0, x1], axis=0).astype(np.float32)
y = np.concatenate(
[
np.zeros((n_per_class, 1)),
np.ones((n_per_class, 1)),
],
axis=0,
).astype(np.float32)
order = rng.permutation(len(x))
x = x[order]
y = y[order]
split = int(train_fraction * len(x))
x_train, y_train = x[:split], y[:split]
x_val, y_val = x[split:], y[split:]
mean = x_train.mean(axis=0, keepdims=True)
std = x_train.std(axis=0, keepdims=True) + 1e-8
x_train = (x_train - mean) / std
x_val = (x_val - mean) / std
return {
"train": (
torch.tensor(x_train),
torch.tensor(y_train),
),
"val": (
torch.tensor(x_val),
torch.tensor(y_val),
),
"metadata": {
"n_train": int(len(x_train)),
"n_val": int(len(x_val)),
"n_features": int(x_train.shape[1]),
"noise": float(noise),
"rotations": float(rotations),
"seed": int(seed),
},
}
@gin.configurable(denylist=["x", "y"])
def make_loader(
x,
y,
batch_size=128,
shuffle=True,
seed=0,
):
generator = torch.Generator()
generator.manual_seed(seed)
dataset = TensorDataset(x, y)
return DataLoader(
dataset,
batch_size=batch_size,
shuffle=shuffle,
generator=generator,
drop_last=False,
)
We start by installing Gin Config and importing the core Python libraries, PyTorch, NumPy, and the plotting libraries required for the experiment. We create a clean project directory structure and reset Gin’s global configuration state so the notebook runs reproducibly. We then define the seed function, generate a nonlinear spiral dataset, and build a configurable DataLoader that Gin can control through external bindings.
Defining a Gin-Configurable MLP, Optimizer, and Scheduler
def activation_layer(name):
name = name.lower()
if name == "relu":
return nn.ReLU()
if name == "gelu":
return nn.GELU()
if name == "tanh":
return nn.Tanh()
if name == "silu":
return nn.SiLU()
raise ValueError(f"Unknown activation: {name}")
@gin.configurable
class MLP(nn.Module):
def __init__(
self,
input_dim=gin.REQUIRED,
hidden_dims=(64, 64),
output_dim=1,
activation="gelu",
dropout=0.0,
use_layernorm=False,
):
super().__init__()
layers = []
current_dim = input_dim
for hidden_dim in hidden_dims:
layers.append(nn.Linear(current_dim, hidden_dim))
if use_layernorm:
layers.append(nn.LayerNorm(hidden_dim))
layers.append(activation_layer(activation))
if dropout > 0:
layers.append(nn.Dropout(dropout))
current_dim = hidden_dim
layers.append(nn.Linear(current_dim, output_dim))
self.network = nn.Sequential(*layers)
def forward(self, x):
return self.network(x)
@gin.configurable(denylist=["params"])
def make_optimizer(
params,
name="adamw",
lr=3e-3,
weight_decay=1e-3,
momentum=0.9,
):
name = name.lower()
if name == "adamw":
return torch.optim.AdamW(
params,
lr=lr,
weight_decay=weight_decay,
)
if name == "sgd":
return torch.optim.SGD(
params,
lr=lr,
momentum=momentum,
weight_decay=weight_decay,
)
raise ValueError(f"Unknown optimizer: {name}")
@gin.configurable(denylist=["optimizer"])
def make_cosine_scheduler(
optimizer,
total_epochs=60,
warmup_epochs=5,
min_lr_factor=0.05,
):
def lr_lambda(epoch):
if epoch < warmup_epochs:
return float(epoch + 1) / float(max(1, warmup_epochs))
progress = (epoch - warmup_epochs) / float(
max(1, total_epochs - warmup_epochs)
)
cosine = 0.5 * (1.0 + math.cos(math.pi * progress))
return min_lr_factor + (1.0 - min_lr_factor) * cosine
return torch.optim.lr_scheduler.LambdaLR(
optimizer,
lr_lambda=lr_lambda,
)
@gin.configurable
def bce_with_logits_loss(
logits,
targets,
label_smoothing=0.0,
):
if label_smoothing > 0:
targets = targets * (1.0 - label_smoothing) + 0.5 * label_smoothing
return F.binary_cross_entropy_with_logits(logits, targets)
@torch.no_grad()
def evaluate(model, loader, loss_fn, device):
model.eval()
total_loss = 0.0
total_correct = 0
total_count = 0
for x, y in loader:
x = x.to(device)
y = y.to(device)
logits = model(x)
loss = loss_fn(logits, y)
probs = torch.sigmoid(logits)
preds = (probs >= 0.5).float()
total_loss += loss.item() * len(x)
total_correct += (preds == y).sum().item()
total_count += len(x)
return {
"loss": total_loss / total_count,
"accuracy": total_correct / total_count,
}
We define the neural network building blocks that form the configurable model and the training utilities. We create an MLP class whose architecture, activation function, dropout, and layer normalization behavior are controlled through Gin rather than hardcoded values. We also implement configurable optimizer, scheduler, loss, and evaluation functions so the training pipeline remains modular and experiment-ready.
Implementing the Training Loop and Experiment Runner
@gin.configurable(
denylist=[
"model",
"optimizer",
"scheduler",
"train_loader",
"val_loader",
"device",
]
)
def fit(
model,
optimizer,
scheduler,
train_loader,
val_loader,
device,
epochs=60,
grad_clip_norm=1.0,
log_every=10,
loss_fn=bce_with_logits_loss,
):
history = []
for epoch in range(1, epochs + 1):
model.train()
for x, y in train_loader:
x = x.to(device)
y = y.to(device)
optimizer.zero_grad(set_to_none=True)
logits = model(x)
loss = loss_fn(logits, y)
loss.backward()
if grad_clip_norm is not None:
nn.utils.clip_grad_norm_(
model.parameters(),
grad_clip_norm,
)
optimizer.step()
if scheduler is not None:
scheduler.step()
train_metrics = evaluate(
model,
train_loader,
loss_fn,
device,
)
val_metrics = evaluate(
model,
val_loader,
loss_fn,
device,
)
lr = optimizer.param_groups[0]["lr"]
row = {
"epoch": epoch,
"lr": lr,
"train_loss": train_metrics["loss"],
"train_accuracy": train_metrics["accuracy"],
"val_loss": val_metrics["loss"],
"val_accuracy": val_metrics["accuracy"],
}
history.append(row)
if epoch == 1 or epoch % log_every == 0 or epoch == epochs:
print(
f"epoch={epoch:03d} | "
f"lr={lr:.6f} | "
f"train_loss={row['train_loss']:.4f} | "
f"train_acc={row['train_accuracy']:.3f} | "
f"val_loss={row['val_loss']:.4f} | "
f"val_acc={row['val_accuracy']:.3f}"
)
return history
@gin.configurable
def run_experiment(
tag=gin.REQUIRED,
model=gin.REQUIRED,
dataset_fn=make_spiral_dataset,
optimizer_factory=make_optimizer,
scheduler_factory=make_cosine_scheduler,
prefer_gpu=True,
):
seed_everything()
device = "cuda" if prefer_gpu and torch.cuda.is_available() else "cpu"
data = dataset_fn()
x_train, y_train = data["train"]
x_val, y_val = data["val"]
train_loader = make_loader(
x_train,
y_train,
shuffle=True,
)
val_loader = make_loader(
x_val,
y_val,
shuffle=False,
)
model = model.to(device)
optimizer = optimizer_factory(model.parameters())
scheduler = None
if scheduler_factory is not None:
scheduler = scheduler_factory(optimizer)
print("n" + "=" * 80)
print(f"Experiment: {tag}")
print("=" * 80)
print(f"Device: {device}")
print(f"Dataset: {data['metadata']}")
print(f"Parameters: {sum(p.numel() for p in model.parameters()):,}")
history = fit(
model=model,
optimizer=optimizer,
scheduler=scheduler,
train_loader=train_loader,
val_loader=val_loader,
device=device,
)
result = {
"tag": tag,
"device": device,
"metadata": data["metadata"],
"parameters": sum(p.numel() for p in model.parameters()),
"final": history[-1],
"history": history,
}
return result
We implement the main training loop, in which the model performs forward passes, computes binary cross-entropy loss, backpropagates gradients, applies gradient clipping, and updates parameters. We evaluate the model after each epoch on both the training and validation sets, while storing loss, accuracy, and learning rate history. We then define the top-level experiment runner that connects the dataset, model, optimizer, scheduler, and training loop through Gin-managed dependencies.
Writing Gin Config Files with Scoped Bindings and Runtime Overrides
BASE_CONFIG = CONFIG_DIR / "base.gin"
COMPACT_CONFIG = CONFIG_DIR / "compact_adamw.gin"
WIDE_CONFIG = CONFIG_DIR / "wide_sgd.gin"
BASE_CONFIG.write_text(
textwrap.dedent(
"""
SEED = 123
N_PER_CLASS = 900
EPOCHS = 50
BATCH = 128
seed_everything.seed = %SEED
make_spiral_dataset.n_per_class = %N_PER_CLASS
make_spiral_dataset.noise = 0.20
make_spiral_dataset.rotations = 1.85
make_spiral_dataset.train_fraction = 0.80
make_spiral_dataset.seed = %SEED
make_loader.batch_size = %BATCH
make_loader.seed = %SEED
MLP.input_dim = 2
MLP.output_dim = 1
MLP.activation = 'gelu'
MLP.dropout = 0.05
MLP.use_layernorm = True
make_optimizer.name = 'adamw'
make_optimizer.lr = 0.003
make_optimizer.weight_decay = 0.001
make_optimizer.momentum = 0.9
make_cosine_scheduler.total_epochs = %EPOCHS
make_cosine_scheduler.warmup_epochs = 5
make_cosine_scheduler.min_lr_factor = 0.05
bce_with_logits_loss.label_smoothing = 0.02
fit.epochs = %EPOCHS
fit.grad_clip_norm = 1.0
fit.log_every = 10
fit.loss_fn = @bce_with_logits_loss
run_experiment.dataset_fn = @make_spiral_dataset
run_experiment.optimizer_factory = @make_optimizer
run_experiment.scheduler_factory = @make_cosine_scheduler
run_experiment.prefer_gpu = True
"""
).strip()
)
COMPACT_CONFIG.write_text(
textwrap.dedent(
f"""
include '{BASE_CONFIG.as_posix()}'
run_experiment.tag = 'compact_gelu_adamw'
run_experiment.model = @compact/MLP()
compact/MLP.hidden_dims = (64, 64, 64)
compact/MLP.dropout = 0.05
compact/MLP.use_layernorm = True
make_optimizer.name = 'adamw'
make_optimizer.lr = 0.003
make_optimizer.weight_decay = 0.001
"""
).strip()
)
WIDE_CONFIG.write_text(
textwrap.dedent(
f"""
include '{BASE_CONFIG.as_posix()}'
run_experiment.tag = 'wide_relu_sgd'
run_experiment.model = @wide/MLP()
wide/MLP.hidden_dims = (128, 128, 128, 64)
wide/MLP.activation = 'relu'
wide/MLP.dropout = 0.02
wide/MLP.use_layernorm = True
make_optimizer.name = 'sgd'
make_optimizer.lr = 0.035
make_optimizer.momentum = 0.92
make_optimizer.weight_decay = 0.0005
bce_with_logits_loss.label_smoothing = 0.0
"""
).strip()
)
def run_from_gin_file(config_path, runtime_bindings=None):
runtime_bindings = runtime_bindings or []
gin.clear_config()
gin.parse_config_files_and_bindings(
config_files=[str(config_path)],
bindings=runtime_bindings,
skip_unknown=False,
finalize_config=True,
)
print("nLoaded config file:")
print(config_path)
print("nSelected queried parameters:")
print("fit.epochs =", gin.query_parameter("fit.epochs"))
print("make_loader.batch_size =", gin.query_parameter("make_loader.batch_size"))
print("make_spiral_dataset.noise =", gin.query_parameter("make_spiral_dataset.noise"))
try:
gin.bind_parameter("fit.epochs", 999)
except RuntimeError as error:
print("nConfig lock check:")
print(str(error).splitlines()[0])
result = run_experiment()
tag = result["tag"]
out_dir = RUN_DIR / tag
out_dir.mkdir(parents=True, exist_ok=True)
result_path = out_dir / "result.json"
operative_path = out_dir / "operative_config.gin"
result_path.write_text(json.dumps(result, indent=2))
operative_path.write_text(gin.operative_config_str())
print("nSaved:")
print(result_path)
print(operative_path)
return result, operative_path
compact_result, compact_operative = run_from_gin_file(
COMPACT_CONFIG,
runtime_bindings=[
"fit.epochs = 45",
"make_spiral_dataset.noise = 0.18",
"run_experiment.tag = 'compact_gelu_adamw_runtime_override'",
],
)
wide_result, wide_operative = run_from_gin_file(
WIDE_CONFIG,
runtime_bindings=[
"fit.epochs = 45",
"make_spiral_dataset.noise = 0.18",
"run_experiment.tag = 'wide_relu_sgd_runtime_override'",
],
)
We create the actual Gin configuration files that control the experiment without modifying the Python source code. We define a shared base configuration and then compose two scoped experiments: a compact GELU-based AdamW model and a wider ReLU-based SGD model. We also demonstrate runtime overrides, parameter queries, config locking, result serialization, and operative config export for reproducible experiment tracking.
Comparing Results and Exporting the Operative Config
def plot_metric(results, metric, title):
plt.figure(figsize=(9, 4))
for result in results:
epochs = [row["epoch"] for row in result["history"]]
values = [row[metric] for row in result["history"]]
plt.plot(epochs, values, label=result["tag"])
plt.xlabel("Epoch")
plt.ylabel(metric)
plt.title(title)
plt.grid(True, alpha=0.3)
plt.legend()
plt.tight_layout()
plt.show()
plot_metric(
[compact_result, wide_result],
"val_loss",
"Validation Loss Controlled by Gin Config",
)
plot_metric(
[compact_result, wide_result],
"val_accuracy",
"Validation Accuracy Controlled by Gin Config",
)
summary = [
{
"tag": compact_result["tag"],
"params": compact_result["parameters"],
"val_loss": compact_result["final"]["val_loss"],
"val_accuracy": compact_result["final"]["val_accuracy"],
},
{
"tag": wide_result["tag"],
"params": wide_result["parameters"],
"val_loss": wide_result["final"]["val_loss"],
"val_accuracy": wide_result["final"]["val_accuracy"],
},
]
print("n" + "=" * 80)
print("Final comparison")
print("=" * 80)
for row in summary:
print(
f"{row['tag']} | "
f"params={row['params']:,} | "
f"val_loss={row['val_loss']:.4f} | "
f"val_acc={row['val_accuracy']:.3f}"
)
print("n" + "=" * 80)
print("Compact experiment operative config preview")
print("=" * 80)
print(compact_operative.read_text()[:2500])
print("n" + "=" * 80)
print("Generated files")
print("=" * 80)
for path in sorted(ROOT.rglob("*")):
if path.is_file():
print(path)
We visualize the validation loss and validation accuracy curves for both Gin-controlled experiments. We summarize the final parameter counts, validation losses, and validation accuracies to clearly compare the two configurations. We also print the operative configuration and the generated files, which provide a complete record of the exact settings used during execution.
Conclusion
In conclusion, we have a reproducible experiment-management workflow that demonstrates how Gin Config improves control, traceability, and modularity in PyTorch projects. We ran multiple scoped experiments from composed .gin files, compared AdamW and SGD training behavior under controlled dataset and epoch settings, verified configuration locking after parsing, and saved both metrics and operative configs for later inspection. It gives us a pattern for scaling Colab experiments into research-grade pipelines, in which model architecture, optimization strategy, data generation, and training schedules must be systematically adjusted without breaking the core implementation.
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The post Building a Gin Config Controlled PyTorch Pipeline with Configurable MLP Variants, Cosine Scheduling, and Runtime Parameter Overrides appeared first on MarkTechPost.
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