diff --git a/Training-models-in-SageMaker-notebooks.html b/Training-models-in-SageMaker-notebooks.html
index 503f770..c99bd52 100644
--- a/Training-models-in-SageMaker-notebooks.html
+++ b/Training-models-in-SageMaker-notebooks.html
@@ -1452,8 +1452,7 @@ Cost of distributed computing
Key steps in distributed training with XGBoost
-
1. Data partitioning
-
+
1. Data partitioning
- The dataset is divided among multiple instances. For example, with
two instances, each instance may receive half of the dataset.
- In SageMaker, data partitioning across instances is handled
@@ -1461,8 +1460,7 @@
1. Data partitioning
reducing manual setup.
-
2. Parallel gradient boosting
-
+
2. Parallel gradient boosting
- XGBoost performs gradient boosting by constructing trees iteratively
based on calculated gradients.
- Each instance calculates gradients (first-order derivatives) and
@@ -1473,8 +1471,7 @@
2. Parallel gradient boosting
-
3. Communication between instances
-
+
3. Communication between instances
- After computing gradients and Hessians locally, instances
synchronize to share and combine these values.
- Synchronization keeps the model parameters consistent across
@@ -1485,8 +1482,7 @@
3. Communication between instan
across multiple instances.
-
4. Final model aggregation
-
+
4. Final model aggregation
- Once training completes, XGBoost aggregates the trained trees from
each instance into a single final model.
- This aggregation enables the final model to perform as though it
diff --git a/aio.html b/aio.html
index d86867b..5c5153b 100644
--- a/aio.html
+++ b/aio.html
@@ -3504,8 +3504,7 @@
Cost of distributed computing
Key steps in distributed training with XGBoost
-
1. Data partitioning
-
+1. Data partitioning
- The dataset is divided among multiple instances. For example, with
@@ -3516,8 +3515,7 @@
1. Data partitioning
-
2. Parallel gradient boosting
-
+2. Parallel gradient boosting
- XGBoost performs gradient boosting by constructing trees iteratively
@@ -3531,8 +3529,7 @@
2. Parallel gradient boosting
-
3. Communication between instances
-
+3. Communication between instances
- After computing gradients and Hessians locally, instances
@@ -3546,8 +3543,7 @@
3. Communication between instan
-
4. Final model aggregation
-
+4. Final model aggregation
- Once training completes, XGBoost aggregates the trained trees from
diff --git a/instructor/Training-models-in-SageMaker-notebooks.html b/instructor/Training-models-in-SageMaker-notebooks.html
index b6ded3d..fdc809b 100644
--- a/instructor/Training-models-in-SageMaker-notebooks.html
+++ b/instructor/Training-models-in-SageMaker-notebooks.html
@@ -1454,8 +1454,7 @@
Cost of distributed computing
Key steps in distributed training with XGBoost
-
1. Data partitioning
-
+
1. Data partitioning
- The dataset is divided among multiple instances. For example, with
two instances, each instance may receive half of the dataset.
- In SageMaker, data partitioning across instances is handled
@@ -1463,8 +1462,7 @@
1. Data partitioning
reducing manual setup.
-
2. Parallel gradient boosting
-
+
2. Parallel gradient boosting
- XGBoost performs gradient boosting by constructing trees iteratively
based on calculated gradients.
- Each instance calculates gradients (first-order derivatives) and
@@ -1475,8 +1473,7 @@
2. Parallel gradient boosting
-
3. Communication between instances
-
+
3. Communication between instances
- After computing gradients and Hessians locally, instances
synchronize to share and combine these values.
- Synchronization keeps the model parameters consistent across
@@ -1487,8 +1484,7 @@
3. Communication between instan
across multiple instances.
-
4. Final model aggregation
-
+
4. Final model aggregation
- Once training completes, XGBoost aggregates the trained trees from
each instance into a single final model.
- This aggregation enables the final model to perform as though it
diff --git a/instructor/aio.html b/instructor/aio.html
index 1631ef4..3a37971 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -3512,8 +3512,7 @@
Cost of distributed computing
Key steps in distributed training with XGBoost
-
1. Data partitioning
-
+1. Data partitioning
- The dataset is divided among multiple instances. For example, with
@@ -3524,8 +3523,7 @@
1. Data partitioning
-
2. Parallel gradient boosting
-
+2. Parallel gradient boosting
- XGBoost performs gradient boosting by constructing trees iteratively
@@ -3539,8 +3537,7 @@
2. Parallel gradient boosting
-
3. Communication between instances
-
+3. Communication between instances
- After computing gradients and Hessians locally, instances
@@ -3554,8 +3551,7 @@
3. Communication between instan
-
4. Final model aggregation
-
+4. Final model aggregation
- Once training completes, XGBoost aggregates the trained trees from
diff --git a/md5sum.txt b/md5sum.txt
index 0cb8f66..bde982a 100644
--- a/md5sum.txt
+++ b/md5sum.txt
@@ -9,7 +9,7 @@
"episodes/SageMaker-notebooks-as-controllers.md" "7b44f533d49559aa691b8ab2574b4e81" "site/built/SageMaker-notebooks-as-controllers.md" "2024-11-06"
"episodes/Accessing-S3-via-SageMaker-notebooks.md" "6f7c3a395851fe00f63e7eb44e553830" "site/built/Accessing-S3-via-SageMaker-notebooks.md" "2024-11-06"
"episodes/Interacting-with-code-repo.md" "105dace64e3a1ea6570d314e4b3ccfff" "site/built/Interacting-with-code-repo.md" "2024-11-06"
-"episodes/Training-models-in-SageMaker-notebooks.md" "df102099945f116048ff948364a2ec0a" "site/built/Training-models-in-SageMaker-notebooks.md" "2024-11-07"
+"episodes/Training-models-in-SageMaker-notebooks.md" "513c99991e6d9d5ceb4da7f021af74ec" "site/built/Training-models-in-SageMaker-notebooks.md" "2024-11-07"
"episodes/Training-models-in-SageMaker-notebooks-part2.md" "a508320d07314a39d83b9b4c8114e92b" "site/built/Training-models-in-SageMaker-notebooks-part2.md" "2024-11-07"
"episodes/Hyperparameter-tuning.md" "c9fe9c20d437dc2f88315438ac6460db" "site/built/Hyperparameter-tuning.md" "2024-11-07"
"instructors/instructor-notes.md" "cae72b6712578d74a49fea7513099f8c" "site/built/instructor-notes.md" "2023-03-16"
diff --git a/pkgdown.yml b/pkgdown.yml
index 1dd6e94..839a2d1 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -2,4 +2,4 @@ pandoc: 3.1.11
pkgdown: 2.1.1
pkgdown_sha: ~
articles: {}
-last_built: 2024-11-07T14:39Z
+last_built: 2024-11-07T14:41Z