Add wandb to any library

torch==1.8.0
...
wandb==0.13.*

try:
    import wandb
except ImportError:
    raise ImportError(
        "You are trying to use wandb which is not currently installed."
        "Please install it using pip install wandb"
    )

[project]
name = "my_awesome_lib"
version = "0.1.0"
dependencies = [
    "torch",
    "sklearn"
]

[project.optional-dependencies]
dev = [
    "wandb"
]

run = wandb.init()

run = wandb.init(project=wandb_project, entity=wandb_entity)

# 이 블록이 종료되면 run.finish()를 자동으로 호출합니다.
# 예외로 인해 종료되면 run.finish(exit_code=1)을 사용하여 Run을 실패로 표시합니다.
with wandb.init() as run:
    ...

config = {"batch_size": 32, ...}
wandb.init(..., config=config)

run.config.update({"model_parameters": 3500})

for epoch in range(NUM_EPOCHS):
    for input, ground_truth in data:
        prediction = model(input)
        loss = loss_fn(prediction, ground_truth)
        metrics = { "loss": loss }
        run.log(metrics)

metrics = {
    "train/loss": 0.4,
    "train/learning_rate": 0.4,
    "val/loss": 0.5,
    "val/accuracy": 0.7
}
run.log(metrics)

with wandb.init(...) as run:
    run.define_metric("*", step_metric="global_step")

run.define_metric("train/loss", step_metric="global_step")

for step, (input, ground_truth) in enumerate(data):
    ...
    run.log({"global_step": step, "train/loss": 0.1})
    run.log({"global_step": step, "eval/loss": 0.2})

metadata = {"eval/accuracy": 0.8, "train/steps": 800}

artifact = wandb.Artifact(
                name=f"model-{run.id}",
                metadata=metadata,
                type="model"
                )
artifact.add_dir("output_model") # 모델 가중치가 저장되는 로컬 디렉토리

aliases = ["best", "epoch_10"]
run.log_artifact(artifact, aliases=aliases)

artifact_input_data = wandb.Artifact(name="flowers", type="dataset")
artifact_input_data.add_file("flowers.npy")
run.use_artifact(artifact_input_data)

artifact = run.use_artifact("user/project/artifact:latest")
local_path = artifact.download("./tmp")

artifact = wandb.Api().artifact("user/project/artifact:latest")
local_path = artifact.download()