update to latest ray doc

Signed-off-by: Zhi Lin <zhi.lin@intel.com>

Author: Zhi Lin

python/raydp/tests/test_tf.py

@@ -34,12 +34,11 @@ def test_tf_estimator(spark_on_ray_small, use_fs_directory):
     spark = spark_on_ray_small
 
     # ---------------- data process with Spark ------------
-    # calculate z = 3 * x + 4 * y + 5
+    # calculate y = 3 * x + 4
     df: pyspark.sql.DataFrame = spark.range(0, 100000)
     df = df.withColumn("x", rand() * 100)  # add x column
-    df = df.withColumn("y", rand() * 1000)  # ad y column
-    df = df.withColumn("z", df.x * 3 + df.y * 4 + rand() + 5)  # ad z column
-    df = df.select(df.x, df.y, df.z)
+    df = df.withColumn("y", df.x * 3 + rand() + 4)  # add y column
+    df = df.select(df.x, df.y)
 
     train_df, test_df = random_split(df, [0.7, 0.3])
 
@@ -59,8 +58,8 @@ def test_tf_estimator(spark_on_ray_small, use_fs_directory):
                             optimizer=optimizer,
                             loss=loss,
                             metrics=["accuracy", "mse"],
-                            feature_columns=["x", "y"],
-                            label_columns="z",
+                            feature_columns=["x"],
+                            label_columns="y",
                             batch_size=1000,
                             num_epochs=2,
                             use_gpu=False)

python/raydp/tf/estimator.py

@@ -15,13 +15,17 @@
 # limitations under the License.
 #
 
+import json
+import os
+import tempfile
 from typing import Any, List, NoReturn, Optional, Union, Dict
 
 import tensorflow as tf
 import tensorflow.keras as keras
 from tensorflow import DType, TensorShape
 from tensorflow.keras.callbacks import Callback
 
+from ray.train import Checkpoint
 from ray.train.tensorflow import TensorflowTrainer, TensorflowCheckpoint, prepare_dataset_shard
 from ray.air import session
 from ray.air.config import ScalingConfig, RunConfig, FailureConfig
@@ -43,7 +47,7 @@ def __init__(self,
                  metrics: Union[List[keras.metrics.Metric], List[str]] = None,
                  feature_columns: Union[str, List[str]] = None,
                  label_columns: Union[str, List[str]] = None,
-                 merge_feature_columns: bool = True,
+                 merge_feature_columns: bool = False,
                  batch_size: int = 128,
                  drop_last: bool = False,
                  num_epochs: int = 1,
@@ -184,7 +188,14 @@ def train_func(config):
             if config["evaluate"]:
                 test_history = multi_worker_model.evaluate(eval_tf_dataset, callbacks=callbacks)
                 results.append(test_history)
-        session.report({}, checkpoint=TensorflowCheckpoint.from_model(multi_worker_model))
+        with tempfile.TemporaryDirectory() as temp_checkpoint_dir:
+            multi_worker_model.save(os.path.join(temp_checkpoint_dir, "model.keras"))
+            checkpoint_dict = os.path.join(temp_checkpoint_dir, "checkpoint.json")
+            with open(checkpoint_dict, "w") as f:
+                json.dump({"epoch": config["num_epochs"]}, f)
+            checkpoint = Checkpoint.from_directory(temp_checkpoint_dir)
+
+            session.report({}, checkpoint=checkpoint)
 
     def fit(self,
             train_ds: Dataset,

python/raydp/torch/estimator.py

@@ -15,6 +15,8 @@
 # limitations under the License.
 #
 
+import os
+import tempfile
 import inspect
 from typing import Any, Callable, List, NoReturn, Optional, Union, Dict
 
@@ -30,6 +32,7 @@
 
 import ray
 from ray import train
+from ray.train import Checkpoint
 from ray.train.torch import TorchTrainer, TorchCheckpoint
 from ray.air.config import ScalingConfig, RunConfig, FailureConfig
 from ray.air import session
@@ -254,7 +257,18 @@ def train_func(config):
         else:
             # if num_workers = 1, model is not wrapped
             states = model.state_dict()
-        session.report({}, checkpoint=TorchCheckpoint.from_state_dict(states))
+        with tempfile.TemporaryDirectory() as temp_checkpoint_dir:
+            checkpoint = None
+            # In standard DDP training, where the model is the same across all ranks,
+            # only the global rank 0 worker needs to save and report the checkpoint
+            if train.get_context().get_world_rank() == 0:
+                torch.save(
+                    states,
+                    os.path.join(temp_checkpoint_dir, "model.pt"),
+                )
+                checkpoint = Checkpoint.from_directory(temp_checkpoint_dir)
+
+            session.report({}, checkpoint=checkpoint)
 
     @staticmethod
     def train_epoch(dataset, model, criterion, optimizer, metrics, scheduler=None):

python/raydp/xgboost/estimator.py

@@ -23,7 +23,7 @@
 from raydp.spark import spark_dataframe_to_ray_dataset, get_raydp_master_owner
 
 import ray
-from ray.air.config import ScalingConfig, RunConfig, FailureConfig
+from ray.air.config import ScalingConfig, RunConfig, FailureConfig, CheckpointConfig
 from ray.data.dataset import Dataset
 from ray.train.xgboost import XGBoostTrainer, XGBoostCheckpoint
 
@@ -58,7 +58,15 @@ def fit(self,
             max_retries=3) -> NoReturn:
         scaling_config = ScalingConfig(num_workers=self._num_workers,
                                       resources_per_worker=self._resources_per_worker)
-        run_config = RunConfig(failure_config=FailureConfig(max_failures=max_retries))
+        run_config = RunConfig(
+            checkpoint_config=CheckpointConfig(
+                # Checkpoint every iteration.
+                checkpoint_frequency=1,
+                # Only keep the latest checkpoint and delete the others.
+                num_to_keep=1,
+            ),
+            failure_config=FailureConfig(max_failures=max_retries)
+        )
         if self._shuffle:
             train_ds = train_ds.random_shuffle()
             if evaluate_ds:
@@ -109,4 +117,4 @@ def fit_on_spark(self,
             train_ds, evaluate_ds, max_retries)
 
     def get_model(self):
-        return XGBoostCheckpoint(self._results.checkpoint.to_directory()).get_model()
+        return XGBoostTrainer.get_model(self._results.checkpoint)