W&B Models 완전 활용 가이드 1: 실험 관리부터 리포트 자동화까지

import wandb

config = dict (
  learning_rate = 0.01,
  momentum = 0.2,
  architecture = "CNN",
  dataset_id = "cats-0192"
)
with wandb.init(project="detect-cats",
		notes="change learning late",
		tags=["baseline","test"],
		job_type="training",
		config=config) as run:
    config = wandb.config
    ...
    run.log()
    ...

with wandb.init(project="detect-cats",
		notes="change learning late",
		tags=["baseline","test"],
		job_type="training",
		config=config) as run:
    config = wandb.config
    ...
    run.log()
    ...

wandb.log({"my_metric": some_scalar_value})

wandb.init()

for i in range(5):
    wandb.log({"my_metric": i})

wandb.summary["my_metric"] = 2 # 2 instead of the default 4

wandb.finish()

WANDB_API_KEY=$YOUR_API_KEY
WANDB_ENTITY=$entity_name
WANDB_PROJECT=$project_name
WANDB_MODE=offline

text = f"Accuracy {acc} is below acceptable threshold {thresh}"
wandb.alert(title="Low accuracy", text=text)

with wandb.init(project="artifacts-example", job_type="add-dataset") as run:
	# Create an artifact object with the wandb.Artifact API.
	artifact = wandb.Artifact(name="my_data", type="dataset")
	# Add one or more files, such as a model file or dataset, to your artifact object.
	artifact.add_dir(local_path="./dataset.h5")  # Add dataset directory to artifact
	# log your artifact to W&B.
	run.log_artifact(artifact)  # Logs the artifact version "my_data:v0"

s3://my-bucket
+-- datasets/
|       +-- mnist/
+-- models/
        +-- cnn/

import wandb

run = wandb.init()
artifact = wandb.Artifact("mnist", type="dataset")
artifact.add_reference("s3://my-bucket/datasets/mnist")
run.log_artifact(artifact)

import wandb

run = wandb.init()
artifact = run.use_artifact("mnist:latest", type="dataset")
artifact_dir = artifact.download()

# run 중에 사용하는 방법
with wandb.init() as run:
    artifact = wandb.Artifact("artifact_name", "artifact_type")

    # Add Files and Assets to the artifact using
    # `.add`, `.add_file`, `.add_dir`, and `.add_reference`
    artifact.add_file("image1.png")
    run.log_artifact(artifact)


# run 밖에서 사용하는 방
artifact = wandb.Artifact("artifact_name", "artifact_type")
# Add Files and Assets to the artifact using
# `.add`, `.add_file`, `.add_dir`, and `.add_reference`
artifact.add_file("image1.png")
artifact.save()

# WandB Public API를 사용하여 incremental 변경을 실행할 아티팩트 버전을 가져옵니다.
client = wandb.Api()
saved_artifact = client.artifact("my_artifact:latest")

# 이하와 같이 드래프트를 작성합니다.
draft_artifact = saved_artifact.new_draft()

# 새 버전에서 보고 싶은 임의의 incremental 변경을 실행합니다. 기존의 데이터를 추가, 삭제, 또는 변경하는 것이 가능합니다.
## add
draft_artifact.add_file("file_to_add.txt")
## remove
draft_artifact.remove("file_to_remove.txt") 
## modify
draft_artifact.remove("modified_file.txt")
draft_artifact.add_file("modified_file.txt")

# 마지막으로, 변경을 기록 또는 저장합니다.이하의 두 줄은 W&B run의 안팎으로 변경을 보존하는 방법을 의미합니다.
## run 안에 실행
run.log_artifact(draft_artifact)

## run 밖에 실행
draft_artifact.save()

######  Run1  ######
with wandb.init() as run:
    artifact = wandb.Artifact("artifact_name", "artifact_type")
    # 아티팩트에 파일이나 어셋을 추가하는 방법은
    # `.add`, `.add_file`, `.add_dir`, 그리고 `.add_reference` 를 사용합니다.
    artifact.add_file("image1.png")
    run.upsert_artifact(artifact, distributed_id="my_dist_artifact")



######  Run2  ######
with wandb.init() as run:
    artifact = wandb.Artifact("artifact_name", "artifact_type")
    # 아티팩트에 파일이나 어셋을 추가하는 방법은
    # `.add`, `.add_file`, `.add_dir`, 그리고 `.add_reference` 를 사용합니다.
    artifact.add_file("image2.png")
    run.upsert_artifact(artifact, distributed_id="my_dist_artifact")



######  Run3  ######
with wandb.init() as run:
    artifact = wandb.Artifact("artifact_name", "artifact_type")
    # 아티팩트에 파일이나 어셋을 추가하는 방법은
    # `.add`, `.add_file`, `.add_dir`, 그리고 `.add_reference` 를 사용합니다.
    artifact.add_file("image3.png")
    run.finish_artifact(artifact, distributed_id="my_dist_artifact")

# Pattern1
with wandb.init(project="table-test") as run:
    # wandb.Table(): Create a new table object.
    my_table = wandb.Table(columns=["a", "b"], data=[["a1", "b1"], ["a2", "b2"]])
    # run.log(): Log the table to save it to W&B.
    run.log({"Table Name": my_table})

# Pattern2
with wandb.init(project="table-test") as run:
    my_table = wandb.Table(columns=["Image", "A", "B"])
    my_table.add_data(wandb.Image(image), <value>, <value>)

# Pattern3
with wandb.init(project="table-test") as run:
    df = pd.read_csv("my_data.csv")
    my_table = wandb.Table(dataframe=df)
    wandb.log({"Table Name": my_table})

# Create and log a new table.
with wandb.init() as r:
    artifact = wandb.Artifact("my_dataset", type="dataset")
    table = wandb.Table(
        columns=["a", "b", "c"], data=[(i, i * 2, 2**i) for i in range(10)]
    )
    artifact.add(table, "my_table")
    wandb.log_artifact(artifact)

# Retrieve the created table using the artifact you created.
with wandb.init() as r:
    artifact = r.use_artifact("my_dataset:latest")
    table = artifact.get("my_table")

# Convert artifact to Dataframe
# Then, convert the table into a dataframe:
#  Following from the last code example:

df = table.get_dataframe()

import wandb
import wandb.apis.reports as wr

report = wr.Report(
    project="report-editing",
    title="An amazing title",
    description="A descriptive description.",
)

blocks = [
    wr.PanelGrid(
        panels=[
            wr.LinePlot(x="time", y="velocity"),
            wr.ScatterPlot(x="time", y="acceleration"),
        ]
    )
]

report.blocks = blocks
report.save()