import wandb
wandb.init(project='gpt3')
config = wandb.config
config.learning_rate = 0.01
wandb.log({"loss": loss})
import wandb
wandb.init(project='gpt3')
config = wandb.config
config.learning_rate = 0.01
with tf.Session() as sess:
# ...
wandb.tensorflow.log(tf.summary.merge_all())
import wandb
wandb.init(project="gpt-3")
config = wandb.config
config.learning_rate = 0.01
wandb.watch(model)
for batch_idx, (data, target) in enumerate(train_loader):
...
if batch_idx % args.log_interval == 0:
wandb.log({"loss": loss})
import wandb
from wandb.keras import WandbCallback
wandb.init(project="gpt-3")
config = wandb.config
config.learning_rate = 0.01
model.fit(X_train, y_train, validation_data=(X_test, y_test),
callbacks=[WandbCallback()])
import wandb
wandb.init(project="visualize-sklearn")
wandb.sklearn.plot_classifier(clf, X_train, X_test, y_train, y_test, y_pred, y_probas, labels, model_name='SVC', feature_names=None)
wandb.sklearn.plot_regressor(reg, X_train, X_test, y_train, y_test, model_name='Ridge')
wandb.sklearn.plot_clusterer(kmeans, X_train, cluster_labels, labels=None, model_name='KMeans')
pip install wandb
python run_glue.py \
--model_name_or_path bert-base-uncased \
--task_name MRPC \
--data_dir $GLUE_DIR/$TASK_NAME \
--do_train \
--evaluate_during_training \
--max_seq_length 128 \
--per_gpu_train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3 \
--output_dir /tmp/$TASK_NAME/ \
--overwrite_output_dir \
--logging_steps 50
import wandb
wandb.init(project="visualize-models", name="xgboost")
bst = xgboost.train(param, xg_train, num_round, watchlist, callbacks=[wandb.xgboost.wandb_callback()])
pred = bst.predict(xg_test)
import wandb
import numpy as np
import xgboost as xgb
wandb.init(project="visualize-models", name="xgboost")
bst = gbm = lgb.train(params,
lgb_train,
num_boost_round=20,
valid_sets=lgb_eval,
valid_names=('validation'),
callbacks=[wandb.lightgbm.callback()])
# Get prediction
pred = bst.predict(xg_test)
import wandb
fastai2.callback.wandb import WandbCallback
wandb.init(project="gpt-3")
learn.fit(..., cbs=WandbCallback())