update 21.08 gh-pages doc (#3233)

* Update docs for 21.08 release (#3080) * Update docs for 21.08 release Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Modify "or higher" -> "or later" when referring to versions Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Update docs/download.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Move AQE for developers doc into development section of web site Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Add 21.06.1 release information to the download page Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Add information about kryoserialization buffer overflow Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Remove SNAPSHOT from getting-started-on-prem.md Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Update README.md for integration tests to remove SNAPSHOT Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Fix table in supported ops doc issue #2779 Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Fix typo (Abillity -> Ability) Signed-off-by: Sameer Raheja <sraheja@nvidia.com> Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> Signed-off-by: yuali <yuali@nvidia.com> * Update docs for 21.08 release (#3080) * Update docs for 21.08 release Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Modify "or higher" -> "or later" when referring to versions Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Update docs/download.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Move AQE for developers doc into development section of web site Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Add 21.06.1 release information to the download page Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Add information about kryoserialization buffer overflow Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Remove SNAPSHOT from getting-started-on-prem.md Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Update README.md for integration tests to remove SNAPSHOT Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Fix table in supported ops doc issue #2779 Signed-off-by: Sameer Raheja <sraheja@nvidia.com> * Fix typo (Abillity -> Ability) Signed-off-by: Sameer Raheja <sraheja@nvidia.com> Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> Signed-off-by: yuali <yuali@nvidia.com> * update config.md and supported_ops.md Signed-off-by: yuali <yuali@nvidia.com> * update rapids-shuffle.md and download.md Signed-off-by: yuali <yuali@nvidia.com> * update dev/README.md and formated Signed-off-by: yuali <yuali@nvidia.com> * fix typos Signed-off-by: yuali <yuali@nvidia.com> Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com>
NVIDIA · Aug 18, 2021 · 994fc93 · 994fc93
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diff --git a/docs/FAQ.md b/docs/FAQ.md
@@ -64,7 +64,8 @@ Spark driver and executor logs with messages that are similar to the following:
 
 ### What is the right hardware setup to run GPU accelerated Spark?
 
-Reference architectures should be available around Q1 2021.
+GPU accelerated Spark can run on any NVIDIA Pascal or better GPU architecture, including Volta,
+Turing or Ampere. 
 
 ### What parts of Apache Spark are accelerated?
 
@@ -360,6 +361,14 @@ different because of compression. Users can turn on
 the GPU try to replicate more closely what the output ordering would have been if sort were used,
 like on the CPU.
 
+### Why am I getting the error `Failed to open the timezone file` when reading files?
+
+When reading from a file that contains data referring to a particular timezone, e.g.: reading
+timestamps from an ORC file, the system's timezone database at `/usr/share/zoneinfo/` must contain
+the timezone in order to process the data properly. This error often indicates the system is
+missing the timezone database. The timezone database is provided by the `tzdata` package on many
+Linux distributions.
+
 ### Why am I getting an error when trying to use pinned memory?
 
 ```
@@ -377,6 +386,25 @@ for this issue.
 To fix it you can either disable the IOMMU, or you can disable using pinned memory by setting
 [spark.rapids.memory.pinnedPool.size](configs.md#memory.pinnedPool.size) to 0.
 
+### Why am I getting a buffer overflow error when using the KryoSerializer?
+Buffer overflow will happen when trying to serialize an object larger than
+[`spark.kryoserializer.buffer.max`](https://spark.apache.org/docs/latest/configuration.html#compression-and-serialization),
+and may result in an error such as:
+```
+Caused by: com.esotericsoftware.kryo.KryoException: Buffer overflow. Available: 0, required: 636
+    at com.esotericsoftware.kryo.io.Output.require(Output.java:167)
+    at com.esotericsoftware.kryo.io.Output.writeBytes(Output.java:251)
+    at com.esotericsoftware.kryo.io.Output.write(Output.java:219)
+    at java.base/java.io.ObjectOutputStream$BlockDataOutputStream.write(ObjectOutputStream.java:1859)
+    at java.base/java.io.ObjectOutputStream.write(ObjectOutputStream.java:712)
+    at java.base/java.io.BufferedOutputStream.write(BufferedOutputStream.java:123)
+    at java.base/java.io.DataOutputStream.write(DataOutputStream.java:107)
+    ...
+```
+Try increasing the
+[`spark.kryoserializer.buffer.max`](https://spark.apache.org/docs/latest/configuration.html#compression-and-serialization)
+from a default of 64M to something larger, for example 512M.
+
 ### Is speculative execution supported?
 
 Yes, speculative execution in Spark is fine with the RAPIDS Accelerator plugin.

diff --git a/docs/additional-functionality/cache-serializer.md b/docs/additional-functionality/cache-serializer.md
@@ -35,21 +35,12 @@ nav_order: 2
   should not be used.  Using the serializer with negative decimal scales will generate
   an error at runtime.
 
-  Make sure to use the right package corresponding to the spark version you are using. To use
-  this serializer with Spark 3.1.1 please run Spark with the following conf.
+  To use this serializer please run Spark with the following conf.
   ```
   spark-shell --conf spark.sql.cache.serializer=com.nvidia.spark.rapids.shims.spark311.ParquetCachedBatchSerializer"
   ```
-  See the below table for all the names of the serializers corresponding to the Spark
-  versions
+
 
-  | Spark version | Serializer name |
-  | ------ | -----|
-  | 3.1.1 | com.nvidia.spark.rapids.shims.spark311.ParquetCachedBatchSerializer |
-  | 3.1.2 | com.nvidia.spark.rapids.shims.spark312.ParquetCachedBatchSerializer |
-  | 3.1.3 | com.nvidia.spark.rapids.shims.spark313.ParquetCachedBatchSerializer |
-  | 3.2.0 | com.nvidia.spark.rapids.shims.spark320.ParquetCachedBatchSerializer | 
-
 ##          Supported Types                       
 
  All types are supported on the CPU, on the GPU, ArrayType, MapType and BinaryType are not

diff --git a/docs/additional-functionality/qualification-profiling-tools.md b/docs/additional-functionality/qualification-profiling-tools.md
diff --git a/docs/additional-functionality/rapids-shuffle.md b/docs/additional-functionality/rapids-shuffle.md
@@ -36,7 +36,7 @@ be installed on the host and inside Docker containers (if not baremetal). A host
 requirements, like the MLNX_OFED driver and `nv_peer_mem` kernel module. 
 
 The minimum UCX requirement for the RAPIDS Shuffle Manager is 
-[UCX 1.10.1](https://github.com/openucx/ucx/releases/tag/v1.10.1)
+[UCX 1.11.0](https://github.com/openucx/ucx/releases/tag/v1.11.0).
 
 #### Baremetal
 
@@ -65,52 +65,48 @@ The minimum UCX requirement for the RAPIDS Shuffle Manager is
    [file a GitHub issue](https://github.com/NVIDIA/spark-rapids/issues) so we can investigate 
    further.
 
-2. Fetch and install the UCX package for your OS and CUDA version 
-   [UCX 1.10.1](https://github.com/openucx/ucx/releases/tag/v1.10.1).
-
-   RDMA packages have extra requirements that should be satisfied by MLNX_OFED.
-
-   ---
-   **NOTE:**
+2. Fetch and install the UCX package for your OS from:
+   [UCX 1.11.0](https://github.com/openucx/ucx/releases/tag/v1.11.0).
+
+   NOTE: Please install the artifact with the newest CUDA 11.x version (for UCX 1.11.0 please
+   pick CUDA 11.2) as CUDA 11 introduced [CUDA Enhanced Compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#enhanced-compat-minor-releases). 
+   Starting with UCX 1.12, UCX will stop publishing individual artifacts for each minor version of CUDA.
 
-   Please note that the RAPIDS Shuffle Manager is built against 
-   [JUCX 1.11.0](https://search.maven.org/artifact/org.openucx/jucx/1.11.0/jar). This is the JNI
-   component of UCX and was published ahead of the native library (UCX 1.11.0). Please disregard the 
-   startup [compatibility warning](https://github.com/openucx/ucx/issues/6694), 
-   as the JUCX usage within the RAPIDS Shuffle Manager is compatible with UCX 1.10.x.
+   Please refer to our [FAQ](../FAQ.md#what-hardware-is-supported) for caveats with 
+   CUDA Enhanced Compatibility.
 
-   ---
+   RDMA packages have extra requirements that should be satisfied by MLNX_OFED.
 
 ##### CentOS UCX RPM
-The UCX packages for CentOS 7 and 8 are divided into different RPMs. For example, UCX 1.10.1 
+The UCX packages for CentOS 7 and 8 are divided into different RPMs. For example, UCX 1.11.0
 available at
-https://github.com/openucx/ucx/releases/download/v1.10.1/ucx-v1.10.1-centos7-mofed5.x-cuda11.0.tar.bz2
+https://github.com/openucx/ucx/releases/download/v1.11.0/ucx-v1.11.0-centos7-mofed5.x-cuda11.2.tar.bz2
 contains:
 
 ```
-ucx-devel-1.10.1-1.el7.x86_64.rpm   
-ucx-debuginfo-1.10.1-1.el7.x86_64.rpm
-ucx-1.10.1-1.el7.x86_64.rpm         
-ucx-cuda-1.10.1-1.el7.x86_64.rpm
-ucx-rdmacm-1.10.1-1.el7.x86_64.rpm  
-ucx-cma-1.10.1-1.el7.x86_64.rpm
-ucx-ib-1.10.1-1.el7.x86_64.rpm
+ucx-devel-1.11.0-1.el7.x86_64.rpm   
+ucx-debuginfo-1.11.0-1.el7.x86_64.rpm
+ucx-1.11.0-1.el7.x86_64.rpm         
+ucx-cuda-1.11.0-1.el7.x86_64.rpm
+ucx-rdmacm-1.11.0-1.el7.x86_64.rpm  
+ucx-cma-1.11.0-1.el7.x86_64.rpm
+ucx-ib-1.11.0-1.el7.x86_64.rpm
 ```
 
 For a setup without RoCE or Infiniband networking, the only packages required are: 
 
 ```
-ucx-1.10.1-1.el7.x86_64.rpm
-ucx-cuda-1.10.1-1.el7.x86_64.rpm
+ucx-1.11.0-1.el7.x86_64.rpm
+ucx-cuda-1.11.0-1.el7.x86_64.rpm
 ```
 
 If accelerated networking is available, the package list is: 
 
 ```
-ucx-1.10.1-1.el7.x86_64.rpm
-ucx-cuda-1.10.1-1.el7.x86_64.rpm
-ucx-rdmacm-1.10.1-1.el7.x86_64.rpm
-ucx-ib-1.10.1-1.el7.x86_64.rpm
+ucx-1.11.0-1.el7.x86_64.rpm
+ucx-cuda-1.11.0-1.el7.x86_64.rpm
+ucx-rdmacm-1.11.0-1.el7.x86_64.rpm
+ucx-ib-1.11.0-1.el7.x86_64.rpm
 ```
 
 ---
@@ -149,7 +145,7 @@ system if you have RDMA capable hardware.
 Within the Docker container we need to install UCX and its requirements. These are Dockerfile
 examples for Ubuntu 18.04:
 
-The following are examples of Docker containers with UCX 1.10.1 and cuda-11.0 support.
+The following are examples of Docker containers with UCX 1.11.0 and cuda-11.2 support.
 
 | OS Type | RDMA | Dockerfile |
 | ------- | ---- | ---------- |
@@ -288,24 +284,44 @@ In this section, we are using a docker container built using the sample dockerfi
     | 3.0.1 EMR  | com.nvidia.spark.rapids.spark301emr.RapidsShuffleManager |
     | 3.0.2      | com.nvidia.spark.rapids.spark302.RapidsShuffleManager    |
     | 3.0.3      | com.nvidia.spark.rapids.spark303.RapidsShuffleManager    |
+    | 3.0.4      | com.nvidia.spark.rapids.spark304.RapidsShuffleManager    |
     | 3.1.1      | com.nvidia.spark.rapids.spark311.RapidsShuffleManager    |
+    | 3.1.1 CDH  | com.nvidia.spark.rapids.spark311cdh.RapidsShuffleManager |
     | 3.1.2      | com.nvidia.spark.rapids.spark312.RapidsShuffleManager    |
     | 3.1.3      | com.nvidia.spark.rapids.spark313.RapidsShuffleManager    |
-    | 3.2.0      | com.nvidia.spark.rapids.spark320.RapidsShuffleManager    |
-
-2. Recommended settings for UCX 1.10.1+
-```shell
-...
---conf spark.shuffle.manager=com.nvidia.spark.rapids.spark301.RapidsShuffleManager \
---conf spark.shuffle.service.enabled=false \
---conf spark.executorEnv.UCX_TLS=cuda_copy,cuda_ipc,rc,tcp \
---conf spark.executorEnv.UCX_ERROR_SIGNALS= \
---conf spark.executorEnv.UCX_RNDV_SCHEME=put_zcopy \
---conf spark.executorEnv.UCX_MAX_RNDV_RAILS=1 \
---conf spark.executorEnv.UCX_MEMTYPE_CACHE=n \
---conf spark.executorEnv.UCX_IB_RX_QUEUE_LEN=1024 \
---conf spark.executor.extraClassPath=${SPARK_CUDF_JAR}:${SPARK_RAPIDS_PLUGIN_JAR}
-```
+
+2. Settings for UCX 1.11.0+:
+
+    Minimum configuration:
+
+    ```shell
+    ...
+    --conf spark.shuffle.manager=com.nvidia.spark.rapids.[shim package].RapidsShuffleManager \
+    --conf spark.shuffle.service.enabled=false \
+    --conf spark.dynamicAllocation.enabled=false \
+    --conf spark.executor.extraClassPath=${SPARK_CUDF_JAR}:${SPARK_RAPIDS_PLUGIN_JAR} \
+    --conf spark.executorEnv.UCX_ERROR_SIGNALS= \
+    --conf spark.executorEnv.UCX_MEMTYPE_CACHE=n
+    ```
+
+    Recommended configuration:
+
+    ```shell
+    ...
+    --conf spark.shuffle.manager=com.nvidia.spark.rapids.[shim package].RapidsShuffleManager \
+    --conf spark.shuffle.service.enabled=false \
+    --conf spark.dynamicAllocation.enabled=false \
+    --conf spark.executor.extraClassPath=${SPARK_CUDF_JAR}:${SPARK_RAPIDS_PLUGIN_JAR} \
+    --conf spark.executorEnv.UCX_ERROR_SIGNALS= \
+    --conf spark.executorEnv.UCX_MEMTYPE_CACHE=n \
+    --conf spark.executorEnv.UCX_IB_RX_QUEUE_LEN=1024 \
+    --conf spark.executorEnv.UCX_TLS=cuda_copy,cuda_ipc,rc,tcp \
+    --conf spark.executorEnv.UCX_RNDV_SCHEME=put_zcopy \
+    --conf spark.executorEnv.UCX_MAX_RNDV_RAILS=1
+    ```
+
+Please replace `[shim package]` with the appropriate value. For example, the full class name for
+Apache Spark 3.1.3 is: `com.nvidia.spark.rapids.spark313.RapidsShuffleManager`.
 
 Please note `LD_LIBRARY_PATH` should optionally be set if the UCX library is installed in a
 non-standard location.

diff --git a/docs/additional-functionality/rapids-udfs.md b/docs/additional-functionality/rapids-udfs.md
@@ -139,38 +139,38 @@ in the [udf-examples](../../udf-examples) project.
 
 ### Spark Scala UDF Examples
 
-- [URLDecode](https://github.com/NVIDIA/spark-rapids/tree/main/udf-examples/src/main/scala/com/nvidia/spark/rapids/udf/scala/URLDecode.scala)
+- [URLDecode](../../udf-examples/src/main/scala/com/nvidia/spark/rapids/udf/scala/URLDecode.scala)
 decodes URL-encoded strings using the
 [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
-- [URLEncode](https://github.com/NVIDIA/spark-rapids/tree/main/udf-examples/src/main/scala/com/nvidia/spark/rapids/udf/scala/URLEncode.scala)
+- [URLEncode](../../udf-examples/src/main/scala/com/nvidia/spark/rapids/udf/scala/URLEncode.scala)
 URL-encodes strings using the
 [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
 
 ### Spark Java UDF Examples
 
-- [URLDecode](https://github.com/NVIDIA/spark-rapids/tree/main/udf-examples/src/main/java/com/nvidia/spark/rapids/udf/java/URLDecode.java)
+- [URLDecode](../../udf-examples/src/main/java/com/nvidia/spark/rapids/udf/java/URLDecode.java)
 decodes URL-encoded strings using the
 [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
-- [URLEncode](https://github.com/NVIDIA/spark-rapids/tree/main/udf-examples/src/main/java/com/nvidia/spark/rapids/udf/java/URLEncode.java)
+- [URLEncode](../../udf-examples/src/main/java/com/nvidia/spark/rapids/udf/java/URLEncode.java)
 URL-encodes strings using the
 [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
-- [CosineSimilarity](https://github.com/NVIDIA/spark-rapids/tree/main/udf-examples/src/main/java/com/nvidia/spark/rapids/udf/java/CosineSimilarity.java)
+- [CosineSimilarity](../../udf-examples/src/main/java/com/nvidia/spark/rapids/udf/java/CosineSimilarity.java)
 computes the [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity)
-between two float vectors using [native code](https://github.com/NVIDIA/spark-rapids/tree/main/udf-examples/src/main/cpp/src)
+between two float vectors using [native code](../../udf-examples/src/main/cpp/src)
 
 ### Hive UDF Examples
 
-- [URLDecode](https://github.com/NVIDIA/spark-rapids/tree/main/udf-examples/src/main/java/com/nvidia/spark/rapids/udf/hive/URLDecode.java)
+- [URLDecode](../../udf-examples/src/main/java/com/nvidia/spark/rapids/udf/hive/URLDecode.java)
 implements a Hive simple UDF using the
 [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
 to decode URL-encoded strings
-- [URLEncode](https://github.com/NVIDIA/spark-rapids/tree/main/udf-examples/src/main/java/com/nvidia/spark/rapids/udf/hive/URLEncode.java)
+- [URLEncode](../../udf-examples/src/main/java/com/nvidia/spark/rapids/udf/hive/URLEncode.java)
 implements a Hive generic UDF using the
 [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
 to URL-encode strings
-- [StringWordCount](https://github.com/NVIDIA/spark-rapids/tree/main/udf-examples/src/main/java/com/nvidia/spark/rapids/udf/hive/StringWordCount.java)
+- [StringWordCount](../../udf-examples/src/main/java/com/nvidia/spark/rapids/udf/hive/StringWordCount.java)
 implements a Hive simple UDF using
-[native code](https://github.com/NVIDIA/spark-rapids/tree/main/udf-examples/src/main/cpp/src) to count words in strings
+[native code](../../udf-examples/src/main/cpp/src) to count words in strings
 
 
 ## GPU Support for Pandas UDF

diff --git a/docs/additional-functionality/shuffle-docker-examples/Dockerfile.centos_no_rdma b/docs/additional-functionality/shuffle-docker-examples/Dockerfile.centos_no_rdma
@@ -16,14 +16,14 @@
 # Sample Dockerfile to install UCX in a CentosOS 7 image 
 #
 # The parameters are: 
-#   - CUDA_VER: 11.0.3 to pick up the latest 11.x CUDA base layer
-#   - UCX_VER and UCX_CUDA_VER: these are used to pick a package matchin a specific UCX version and 
+#   - CUDA_VER: 11.2.2 to pick up the latest 11.2 CUDA base layer
+#   - UCX_VER and UCX_CUDA_VER: these are used to pick a package matching a specific UCX version and
 #                               CUDA runtime from the UCX github repo.
 #                               See: https://github.com/openucx/ucx/releases/
 
-ARG CUDA_VER=11.0.3
-ARG UCX_VER=v1.10.1
-ARG UCX_CUDA_VER=11.0
+ARG CUDA_VER=11.2.2
+ARG UCX_VER=v1.11.0
+ARG UCX_CUDA_VER=11.2
 
 FROM nvidia/cuda:${CUDA_VER}-runtime-centos7
 ARG UCX_VER
@@ -32,6 +32,6 @@ ARG UCX_CUDA_VER
 RUN yum update -y && yum install -y wget bzip2
 RUN cd /tmp && wget https://github.com/openucx/ucx/releases/download/$UCX_VER/ucx-$UCX_VER-centos7-mofed5.x-cuda$UCX_CUDA_VER.tar.bz2
 RUN cd /tmp && tar -xvf *.bz2 && \
-  yum install -y ucx-1.10.1-1.el7.x86_64.rpm && \
-  yum install -y ucx-cuda-1.10.1-1.el7.x86_64.rpm && \
+  yum install -y ucx-1.11.0-1.el7.x86_64.rpm && \
+  yum install -y ucx-cuda-1.11.0-1.el7.x86_64.rpm && \
   rm -rf /tmp/*.rpm
diff --git a/docs/additional-functionality/shuffle-docker-examples/Dockerfile.centos_rdma b/docs/additional-functionality/shuffle-docker-examples/Dockerfile.centos_rdma
@@ -19,18 +19,18 @@
 # The parameters are: 
 #   - RDMA_CORE_VERSION: Set to 32.1 to match the rdma-core line in the latest 
 #                        released MLNX_OFED 5.x driver
-#   - CUDA_VER: 11.0.3 to pick up the latest 11.x CUDA base layer
-#   - UCX_VER and UCX_CUDA_VER: these are used to pick a package matchin a specific UCX version and 
+#   - CUDA_VER: 11.2.2 to pick up the latest 11.2 CUDA base layer
+#   - UCX_VER and UCX_CUDA_VER: these are used to pick a package matching a specific UCX version and
 #                               CUDA runtime from the UCX github repo.
 #                               See: https://github.com/openucx/ucx/releases/
 #
 # The Dockerfile first fetches and builds `rdma-core` to satisfy requirements for
 # the ucx-ib and ucx-rdma RPMs.
 
 ARG RDMA_CORE_VERSION=32.1
-ARG CUDA_VER=11.0.3
-ARG UCX_VER=v1.10.1
-ARG UCX_CUDA_VER=11.0
+ARG CUDA_VER=11.2.2
+ARG UCX_VER=v1.11.0
+ARG UCX_CUDA_VER=11.2
 
 # Throw away image to build rdma_core
 FROM centos:7 as rdma_core
@@ -63,8 +63,8 @@ RUN cd /tmp && wget https://github.com/openucx/ucx/releases/download/$UCX_VER/uc
 RUN cd /tmp && \
   yum install -y *.rpm && \
   tar -xvf *.bz2 && \
-  yum install -y ucx-1.10.1-1.el7.x86_64.rpm && \
-  yum install -y ucx-cuda-1.10.1-1.el7.x86_64.rpm && \
-  yum install -y ucx-ib-1.10.1-1.el7.x86_64.rpm && \
-  yum install -y ucx-rdmacm-1.10.1-1.el7.x86_64.rpm
+  yum install -y ucx-1.11.0-1.el7.x86_64.rpm && \
+  yum install -y ucx-cuda-1.11.0-1.el7.x86_64.rpm && \
+  yum install -y ucx-ib-1.11.0-1.el7.x86_64.rpm && \
+  yum install -y ucx-rdmacm-1.11.0-1.el7.x86_64.rpm
 RUN rm -rf /tmp/*.rpm && rm /tmp/*.bz2
diff --git a/docs/additional-functionality/shuffle-docker-examples/Dockerfile.ubuntu_no_rdma b/docs/additional-functionality/shuffle-docker-examples/Dockerfile.ubuntu_no_rdma
@@ -16,14 +16,14 @@
 # Sample Dockerfile to install UCX in a Ubuntu 18.04 image
 #
 # The parameters are: 
-#   - CUDA_VER: 11.0.3 to pick up the latest 11.x CUDA base layer
-#   - UCX_VER and UCX_CUDA_VER: these are used to pick a package matchin a specific UCX version and 
+#   - CUDA_VER: 11.2.2 to pick up the latest 11.2 CUDA base layer
+#   - UCX_VER and UCX_CUDA_VER: these are used to pick a package matching a specific UCX version and 
 #                               CUDA runtime from the UCX github repo.
 #                               See: https://github.com/openucx/ucx/releases/
 
-ARG CUDA_VER=11.0
-ARG UCX_VER=v1.10.1
-ARG UCX_CUDA_VER=11.0
+ARG CUDA_VER=11.2.2
+ARG UCX_VER=v1.11.0
+ARG UCX_CUDA_VER=11.2
 
 FROM nvidia/cuda:${CUDA_VER}-runtime-ubuntu18.04
 ARG UCX_VER