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`pillow` and `soundfile` are the media dependencies used by the Image and Audio examples in this skill. `swanlab[dashboard]` adds the local dashboard dependency required by `mode="local"` and `swanlab watch`.

import swanlab

run = swanlab.init(
    project="my-project",
    experiment_name="baseline",
    config={
        "learning_rate": 1e-3,
        "epochs": 10,
        "batch_size": 32,
        "model": "resnet18",
    },
)

for epoch in range(run.config.epochs):
    train_loss = train_epoch()
    val_loss = validate()

    swanlab.log(
        {
            "train/loss": train_loss,
            "val/loss": val_loss,
            "epoch": epoch,
        }
    )

run.finish()

import torch
import torch.nn as nn
import torch.optim as optim
import swanlab

run = swanlab.init(
    project="pytorch-demo",
    experiment_name="mnist-mlp",
    config={
        "learning_rate": 1e-3,
        "batch_size": 64,
        "epochs": 10,
        "hidden_size": 128,
    },
)

model = nn.Sequential(
    nn.Flatten(),
    nn.Linear(28 * 28, run.config.hidden_size),
    nn.ReLU(),
    nn.Linear(run.config.hidden_size, 10),
)
optimizer = optim.Adam(model.parameters(), lr=run.config.learning_rate)
criterion = nn.CrossEntropyLoss()

for epoch in range(run.config.epochs):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        logits = model(data)
        loss = criterion(logits, target)
        loss.backward()
        optimizer.step()

        if batch_idx % 100 == 0:
            swanlab.log(
                {
                    "train/loss": loss.item(),
                    "train/epoch": epoch,
                    "train/batch": batch_idx,
                }
            )

run.finish()

import swanlab

run = swanlab.init(
    project="image-classification",
    experiment_name="resnet18-seed42",
    description="Baseline run on ImageNet subset",
    tags=["baseline", "resnet18"],
    config={
        "model": "resnet18",
        "seed": 42,
        "batch_size": 64,
        "learning_rate": 3e-4,
    },
)

print(run.id)
print(run.config.learning_rate)

config = {
    "model": "resnet18",
    "seed": 42,
    "batch_size": 64,
    "learning_rate": 3e-4,
    "epochs": 20,
}

run = swanlab.init(project="my-project", config=config)

learning_rate = run.config.learning_rate
batch_size = run.config.batch_size

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import numpy as np
import swanlab

```python

See [references/visualization.md](references/visualization.md) for more chart and media patterns.

import os
import swanlab

```bash

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from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=8,
    evaluation_strategy="epoch",
    logging_steps=50,
    report_to="swanlab",
    run_name="bert-finetune",
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

trainer.train()

import pytorch_lightning as pl
from swanlab.integration.pytorch_lightning import SwanLabLogger

swanlab_logger = SwanLabLogger(
    project="lightning-demo",
    experiment_name="mnist-classifier",
    config={"batch_size": 64, "max_epochs": 10},
)

trainer = pl.Trainer(
    logger=swanlab_logger,
    max_epochs=10,
    accelerator="auto",
)

trainer.fit(model, train_loader, val_loader)

from fastai.vision.all import accuracy, resnet34, vision_learner
from swanlab.integration.fastai import SwanLabCallback

learn = vision_learner(dls, resnet34, metrics=accuracy)
learn.fit(
    5,
    cbs=[
        SwanLabCallback(
            project="fastai-demo",
            experiment_name="pets-classification",
            config={"arch": "resnet34", "epochs": 5},
        )
    ],
)

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run = swanlab.init(
    project="image-classification",
    experiment_name="resnet18-baseline",
    config={
        "model": "resnet18",
        "learning_rate": 3e-4,
        "batch_size": 64,
        "seed": 42,
    },
)

import torch
import swanlab

checkpoint_path = "checkpoints/best.pth"
torch.save(model.state_dict(), checkpoint_path)

swanlab.log(
    {
        "best/val_accuracy": best_val_accuracy,
        "artifacts/checkpoint_path": swanlab.Text(checkpoint_path),
    }
)

run = swanlab.init(project="offline-demo", mode="local", logdir="./swanlog")

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