“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 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)
