AI is easy to productionize
자신 있게 AI 애플리케이션과 모델을 구축할 수 있는 AI 개발자 플랫폼
W&B Weave: AI 애플리케이션 개발
import weave
weave.init("quickstart")
@weave.op()
def llm_app(prompt):
pass # Track LLM calls, document retrieval, agent steps
W&B Models: AI 모델 개발
import wandb
run = wandb.init(project="my-model-training-project")
run.config = {"epochs": 1337, "learning_rate": 3e-4}
run.log({"metric": 42})
my_model_artifact = run.log_artifact("./my_model.pt", type="model")
세계 최고의 AI 팀이 Weights & Biases를 신뢰하고 있습니다
Weights & Biases AI 개발자 플랫폼
한 줄의 코드로 시작하기
“저는 여러 가지 이유로 Weave를 좋아합니다. 이미 잘 사용 중인 Weights & Biases에서 우리 제품의 GenAI 부분에 대한 수많은 정보를 얻기 위해 단 한줄만 추가하면 된다는 점이 가장 큰 장점입니다. AI의 성능 관련해서 관찰하고 있는 모든 것들을 이제 Weave를 통해 빠르고 쉽게 보고할 수 있습니다.”
Mike Maloney, Co-founder and CDO
Neuralift
import openai, weave
weave.init("weave-intro")
@weave.op
def correct_grammar(user_input):
client = openai.OpenAI()
response = client.chat.completions.create(
model="o1-mini",
messages=[{
"role": "user",
"content": "Correct the grammar:\n\n" +
user_input,
}],
)
return response.choices[0].message.content.strip()
result = correct_grammar("That was peace of cake!")
print(result)
import weave
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
# Initialize Weave with your project name
weave.init("langchain_demo")
llm = ChatOpenAI()
prompt = PromptTemplate.from_template("1 + {number} = ")
llm_chain = prompt | llm
output = llm_chain.invoke({"number": 2})
print(output)
import weave
from llama_index.core.chat_engine import SimpleChatEngine
# Initialize Weave with your project name
weave.init("llamaindex_demo")
chat_engine = SimpleChatEngine.from_defaults()
response = chat_engine.chat(
"Say something profound and romantic about fourth of July"
)
print(response)
import wandb
# 1. Start a new run
run = wandb.init(project="gpt5")
# 2. Save model inputs and hyperparameters
config = run.config
config.dropout = 0.01
# 3. Log gradients and model parameters
run.watch(model)
for batch_idx, (data, target) in enumerate(train_loader):
...
if batch_idx % args.log_interval == 0:
# 4. Log metrics to visualize performance
run.log({"loss": loss})
import wandb
# 1. Define which wandb project to log to and name your run
run = wandb.init(project="gpt-5",
run_name="gpt-5-base-high-lr")
# 2. Add wandb in your `TrainingArguments`
args = TrainingArguments(..., report_to="wandb")
# 3. W&B logging will begin automatically when your start training your Trainer
trainer = Trainer(..., args=args)
trainer.train()
from lightning.pytorch.loggers import WandbLogger
# initialise the logger
wandb_logger = WandbLogger(project="llama-4-fine-tune")
# add configs such as batch size etc to the wandb config
wandb_logger.experiment.config["batch_size"] = batch_size
# pass wandb_logger to the Trainer
trainer = Trainer(..., logger=wandb_logger)
# train the model
trainer.fit(...)
import wandb
# 1. Start a new run
run = wandb.init(project="gpt4")
# 2. Save model inputs and hyperparameters
config = wandb.config
config.learning_rate = 0.01
# Model training here
# 3. Log metrics to visualize performance over time
with tf.Session() as sess:
# ...
wandb.tensorflow.log(tf.summary.merge_all())
import wandb
from wandb.keras import (
WandbMetricsLogger,
WandbModelCheckpoint,
)
# 1. Start a new run
run = wandb.init(project="gpt-4")
# 2. Save model inputs and hyperparameters
config = wandb.config
config.learning_rate = 0.01
... # Define a model
# 3. Log layer dimensions and metrics
wandb_callbacks = [
WandbMetricsLogger(log_freq=5),
WandbModelCheckpoint("models"),
]
model.fit(
X_train, y_train, validation_data=(X_test, y_test),
callbacks=wandb_callbacks,
)
import wandb
wandb.init(project="visualize-sklearn")
# Model training here
# Log classifier visualizations
wandb.sklearn.plot_classifier(clf, X_train, X_test, y_train, y_test, y_pred, y_probas, labels,
model_name="SVC", feature_names=None)
# Log regression visualizations
wandb.sklearn.plot_regressor(reg, X_train, X_test, y_train, y_test, model_name="Ridge")
# Log clustering visualizations
wandb.sklearn.plot_clusterer(kmeans, X_train, cluster_labels, labels=None, model_name="KMeans")
import wandb
from wandb.xgboost import wandb_callback
# 1. Start a new run
run = wandb.init(project="visualize-models")
# 2. Add the callback
bst = xgboost.train(param, xg_train, num_round, watchlist, callbacks=[wandb_callback()])
# Get predictions
pred = bst.predict(xg_test)