Split rllib ray tune examples

integrations/model-training/hugging_face/notebooks/Comet_with_Hugging_Face_Trainer.ipynb

@@ -242,7 +242,7 @@
     "\n",
     "\n",
     "def compute_metrics(pred):\n",
-    "    experiment = comet_ml.get_global_experiment()\n",
+    "    experiment = comet_ml.get_running_experiment()\n",
     "\n",
     "    labels = pred.label_ids\n",
     "    preds = pred.predictions.argmax(-1)\n",

integrations/model-training/hugging_face/transformers-distilbert-fine-tuning/transformers-distilbert-fine-tuning.py

@@ -51,7 +51,7 @@ def preprocess(texts, labels):
 
 
 def compute_metrics(pred):
-    experiment = comet_ml.get_global_experiment()
+    experiment = comet_ml.get_running_experiment()
 
     labels = pred.label_ids
     preds = pred.predictions.argmax(-1)

integrations/model-training/mlflow/mlflow-hello-world/README.md

@@ -20,7 +20,7 @@ python -m pip install -r requirements.txt
 
 ## Run the example
 
-This example is based on the following [MLflow tutorial](https://github.com/mlflow/mlflow/blob/master/examples/keras/train.py).
+This example is based on the following [MLflow tutorial](https://mlflow.org/docs/latest/deep-learning/keras/quickstart/quickstart_keras.html).
 
 ```bash
 python mlflow-hello-world.py
@@ -30,4 +30,4 @@ python mlflow-hello-world.py
 
 If you have previous MLFlow runs that you would like to visualize in Comet.ml, please see:
 
-https://githib.com/comet-ml/comet-for-mlflow
+https://github.com/comet-ml/comet-for-mlflow

integrations/model-training/mlflow/mlflow-hello-world/mlflow-hello-world.py

@@ -1,86 +1,71 @@
 # coding: utf-8
-"""Trains and evaluate a simple MLP
-on the Reuters newswire topic classification task.
-"""
-from __future__ import print_function
+import os
 
-# The following imports are the only additions to code required
-# to automatically log metrics and parameters to Comet.
-import comet_ml  # noqa
+import comet_ml
 
-import mlflow.keras
-import numpy as np
+# You can use 'tensorflow', 'torch' or 'jax' as backend. Make sure to set the
+# environment variable before importing.
+os.environ["KERAS_BACKEND"] = "tensorflow"
 
-import keras
 
-# The following import and function call are the only additions to code required
-# to automatically log metrics and parameters to MLflow.
-import mlflow
-from keras.datasets import reuters
-from keras.layers import Activation, Dense, Dropout
-from keras.models import Sequential
-from keras.preprocessing.text import Tokenizer
+import mlflow.keras  # noqa: E402
+import numpy as np  # noqa: E402
 
-# The sqlite store is needed for the model registry
-mlflow.set_tracking_uri("sqlite:///db.sqlite")
+import keras  # noqa: E402
 
-# We need to create a run before calling keras or MLFlow will end the run by itself
-mlflow.set_experiment("comet-example-mlflow-hello-world")
-mlflow.start_run()
+# Login to Comet if necessary
+comet_ml.login(project_name="comet-example-mlflow-hello-world")
 
-mlflow.keras.autolog()
+# Load dataset
+(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+x_train = np.expand_dims(x_train, axis=3)
+x_test = np.expand_dims(x_test, axis=3)
+x_train[0].shape
 
-max_words = 1000
-batch_size = 32
-epochs = 5
+# Build model
+NUM_CLASSES = 10
+INPUT_SHAPE = (28, 28, 1)
+
+
+def initialize_model():
+    return keras.Sequential(
+        [
+            keras.Input(shape=INPUT_SHAPE),
+            keras.layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
+            keras.layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
+            keras.layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
+            keras.layers.GlobalAveragePooling2D(),
+            keras.layers.Dense(NUM_CLASSES, activation="softmax"),
+        ]
+    )
 
-print("Loading data...")
-(x_train, y_train), (x_test, y_test) = reuters.load_data(
-    num_words=max_words, test_split=0.2
-)
 
-print(len(x_train), "train sequences")
-print(len(x_test), "test sequences")
+model = initialize_model()
+model.summary()
 
-num_classes = np.max(y_train) + 1
-print(num_classes, "classes")
+# Train model
 
-print("Vectorizing sequence data...")
-tokenizer = Tokenizer(num_words=max_words)
-x_train = tokenizer.sequences_to_matrix(x_train, mode="binary")
-x_test = tokenizer.sequences_to_matrix(x_test, mode="binary")
-print("x_train shape:", x_train.shape)
-print("x_test shape:", x_test.shape)
+BATCH_SIZE = 64  # adjust this based on the memory of your machine
+EPOCHS = 3
 
-print(
-    "Convert class vector to binary class matrix "
-    "(for use with categorical_crossentropy)"
+model = initialize_model()
+
+model.compile(
+    loss=keras.losses.SparseCategoricalCrossentropy(),
+    optimizer=keras.optimizers.Adam(),
+    metrics=["accuracy"],
 )
-y_train = keras.utils.to_categorical(y_train, num_classes)
-y_test = keras.utils.to_categorical(y_test, num_classes)
-print("y_train shape:", y_train.shape)
-print("y_test shape:", y_test.shape)
-
-print("Building model...")
-model = Sequential()
-model.add(Dense(512, input_shape=(max_words,)))
-model.add(Activation("relu"))
-model.add(Dropout(0.5))
-model.add(Dense(num_classes))
-model.add(Activation("softmax"))
-
-model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
-
-history = model.fit(
+
+run = mlflow.start_run()
+model.fit(
     x_train,
     y_train,
-    batch_size=batch_size,
-    epochs=epochs,
-    verbose=1,
+    batch_size=BATCH_SIZE,
+    epochs=EPOCHS,
     validation_split=0.1,
+    callbacks=[mlflow.keras.MlflowCallback(run)],
 )
-score = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1)
-print("Test score:", score[0])
-print("Test accuracy:", score[1])
 
 mlflow.keras.log_model(model, "model", registered_model_name="Test Model")
+
+mlflow.end_run()

integrations/model-training/mlflow/mlflow-hello-world/requirements.txt

@@ -1,4 +1,4 @@
-alembic<1.10.0 # workaround ValueError: Constraint must have a name
-comet_ml
+comet_ml>=3.44.0
+keras
 mlflow
 tensorflow