[Doc] Fix doc lint (#49355)

Signed-off-by: dentiny <[email protected]>
ray-project · Dec 19, 2024 · 95fc8e3 · 95fc8e3
1 parent 53d2145
commit 95fc8e3
Show file tree

Hide file tree

Showing 3 changed files with 29 additions and 29 deletions.
diff --git a/doc/source/ray-core/compiled-graph/quickstart.rst b/doc/source/ray-core/compiled-graph/quickstart.rst
@@ -31,7 +31,7 @@ Create a very simple actor that directly returns a given input using classic Ray
     for _ in range(5):
         msg_ref = a.echo.remote("hello")
         ray.get(msg_ref)
-    
+
     start = time.perf_counter()
     msg_ref = a.echo.remote("hello")
     ray.get(msg_ref)
@@ -68,7 +68,7 @@ Next, execute the DAG and measure the performance.
     for _ in range(5):
         msg_ref = dag.execute("hello")
         ray.get(msg_ref)
-    
+
     start = time.perf_counter()
     # `dag.execute` runs the DAG and returns a future. You can use `ray.get` API.
     msg_ref = dag.execute("hello")
@@ -81,7 +81,7 @@ Next, execute the DAG and measure the performance.
     Execution takes 86.72196418046951 us
 
 The performance of the same DAG improved by 10X. The explanation for this improvement is because the function ``echo`` is cheap and thus highly affected by
-the system overhead. Due to various bookeeping and distributed protocols, the classic Ray Core APIs usually have 1ms+ system overhead. 
+the system overhead. Due to various bookeeping and distributed protocols, the classic Ray Core APIs usually have 1ms+ system overhead.
 Because the DAG is known ahead of time, Compiled Graph can pre-allocate all necessary
 resources ahead of time and greatly reduce the system overhead.
 

diff --git a/doc/source/ray-core/compiled-graph/ray-compiled-graph.rst b/doc/source/ray-core/compiled-graph/ray-compiled-graph.rst
@@ -7,10 +7,10 @@ Ray Compiled Graph
     The API is available from Ray 2.32.
 
 As large language models (LLM) become common, programming distributed systems with multiple GPUs is essential.
-Ray APIs facilitate using multiple GPUs but have limitations such as: 
+Ray APIs facilitate using multiple GPUs but have limitations such as:
 
 * having a high system overhead of over 1ms per task launch, which is unsuitable for high-performance tasks like LLM inference
-* lack direct GPU-to-GPU RDMA communication, requiring external tools like NCCL. 
+* lack direct GPU-to-GPU RDMA communication, requiring external tools like NCCL.
 
 Ray Compiled Graph gives you a classic Ray Core-like API but with:
 

diff --git a/release/release_data_tests.yaml b/release/release_data_tests.yaml
@@ -19,8 +19,8 @@
   run:
     timeout: 3600
     script: >
-      python read_and_consume_benchmark.py 
-      s3://ray-benchmark-data-internal/imagenet/parquet --format parquet 
+      python read_and_consume_benchmark.py
+      s3://ray-benchmark-data-internal/imagenet/parquet --format parquet
       --iter-bundles
 
 - name: read_images
@@ -35,9 +35,9 @@
     timeout: 3600
     script: >
       python read_and_consume_benchmark.py
-      s3://ray-benchmark-data-internal/imagenet/tfrecords --format tfrecords 
+      s3://ray-benchmark-data-internal/imagenet/tfrecords --format tfrecords
       --iter-bundles
-  
+
 - name: read_from_uris
   run:
     timeout: 5400
@@ -62,8 +62,8 @@
 - name: write_parquet
   run:
     timeout: 3600
-    script: > 
-      python read_and_consume_benchmark.py 
+    script: >
+      python read_and_consume_benchmark.py
       s3://ray-benchmark-data/tpch/parquet/sf1000/lineitem --format parquet --write
 
 ###################
@@ -74,7 +74,7 @@
   run:
     timeout: 600
     script: >
-      python read_and_consume_benchmark.py 
+      python read_and_consume_benchmark.py
       s3://ray-benchmark-data/tpch/parquet/sf10000/lineitem --format parquet --count
 
 ###############
@@ -268,38 +268,38 @@
     - __suffix__: numpy
       run:
         script: >
-          python read_and_consume_benchmark.py 
-          s3://ray-benchmark-data/tpch/parquet/sf100/lineitem --format parquet 
+          python read_and_consume_benchmark.py
+          s3://ray-benchmark-data/tpch/parquet/sf100/lineitem --format parquet
           --iter-batches numpy
     - __suffix__: pandas
       run:
         script: >
-          python read_and_consume_benchmark.py 
-          s3://ray-benchmark-data/tpch/parquet/sf100/lineitem --format parquet 
+          python read_and_consume_benchmark.py
+          s3://ray-benchmark-data/tpch/parquet/sf100/lineitem --format parquet
           --iter-batches pandas
     - __suffix__: pyarrow
       run:
         script: >
-          python read_and_consume_benchmark.py 
-          s3://ray-benchmark-data/tpch/parquet/sf100/lineitem --format parquet 
+          python read_and_consume_benchmark.py
+          s3://ray-benchmark-data/tpch/parquet/sf100/lineitem --format parquet
           --iter-batches pyarrow
 
 - name: to_tf
 
   run:
     timeout: 2400
     script: >
-      python read_and_consume_benchmark.py 
-      s3://air-example-data-2/100G-image-data-synthetic-raw/ --format image 
+      python read_and_consume_benchmark.py
+      s3://air-example-data-2/100G-image-data-synthetic-raw/ --format image
       --to-tf image image
 
 - name: iter_torch_batches
 
   run:
     timeout: 2400
     script: >
-      python read_and_consume_benchmark.py 
-      s3://air-example-data-2/100G-image-data-synthetic-raw/ --format image 
+      python read_and_consume_benchmark.py
+      s3://air-example-data-2/100G-image-data-synthetic-raw/ --format image
       --iter-torch-batches
 
 ###########
@@ -339,17 +339,17 @@
   run:
     timeout: 10800
     script: >
-      python dataset/sort_benchmark.py --num-partitions=1000 --partition-size=1e9 
+      python dataset/sort_benchmark.py --num-partitions=1000 --partition-size=1e9
       --shuffle
 
   variations:
     - __suffix__: regular
     - __suffix__: chaos
       run:
-        prepare: > 
-          python setup_chaos.py --chaos TerminateEC2Instance --kill-interval 600 
-          --max-to-kill 2 
-          
+        prepare: >
+          python setup_chaos.py --chaos TerminateEC2Instance --kill-interval 600
+          --max-to-kill 2
+
 - name: sort
   working_dir: nightly_tests
   stable: False
@@ -370,8 +370,8 @@
     - __suffix__: regular
     - __suffix__: chaos
       run:
-        prepare: > 
-          python setup_chaos.py --chaos TerminateEC2Instance --kill-interval 900 
+        prepare: >
+          python setup_chaos.py --chaos TerminateEC2Instance --kill-interval 900
           --max-to-kill 3