Update documentation for Dask cuDF

docs/cudf/source/user_guide/10min.ipynb

@@ -15,7 +15,11 @@
     "\n",
     "[Dask](https://dask.org/) is a flexible library for parallel computing in Python that makes scaling out your workflow smooth and simple. On the CPU, Dask uses Pandas to execute operations in parallel on DataFrame partitions.\n",
     "\n",
-    "[Dask-cuDF](https://github.com/rapidsai/cudf/tree/main/python/dask_cudf) extends Dask where necessary to allow its DataFrame partitions to be processed using cuDF GPU DataFrames instead of Pandas DataFrames. For instance, when you call `dask_cudf.read_csv(...)`, your cluster's GPUs do the work of parsing the CSV file(s) by calling [`cudf.read_csv()`](https://docs.rapids.ai/api/cudf/stable/api_docs/api/cudf.read_csv.html).\n",
+    "[Dask cuDF](https://github.com/rapidsai/cudf/tree/main/python/dask_cudf) extends Dask where necessary to allow its DataFrame partitions to be processed using cuDF GPU DataFrames instead of Pandas DataFrames. For instance, when you call `dask_cudf.read_csv(...)`, your cluster's GPUs do the work of parsing the CSV file(s) by calling [`cudf.read_csv()`](https://docs.rapids.ai/api/cudf/stable/api_docs/api/cudf.read_csv.html).\n",
+    "\n",
+    "\n",
+    "> [!NOTE]  \n",
+    "> This notebook uses the explicit Dask cuDF API (`dask_cudf`) for clarity. However, we strongly recommend that you use Dask's [configuration infrastructure](https://docs.dask.org/en/latest/configuration.html) to set the `\"dataframe.backend\"` to `\"cudf\"`, and work with the `dask.dataframe` API directly. Please see the [Dask cuDF documentation](https://github.com/rapidsai/cudf/tree/main/python/dask_cudf) for more information.\n",
     "\n",
     "\n",
     "## When to use cuDF and Dask-cuDF\n",

python/dask_cudf/README.md

@@ -0,0 +1,135 @@
+# <div align="left"><img src="../../img/rapids_logo.png" width="90px"/>&nbsp;Dask cuDF - A GPU Backend for Dask DataFrame</div>
+
+Dask cuDF (a.k.a. dask-cudf or `dask_cudf`) is an extension library for [Dask DataFrame](https://docs.dask.org/en/stable/dataframe.html). When installed, Dask cuDF is automatically registered as the `"cudf"` [dataframe backend](https://docs.dask.org/en/stable/how-to/selecting-the-collection-backend.html) for Dask DataFrame.
+
+## Using Dask cuDF
+
+### The Dask DataFrame API (Recommended)
+
+Simply set the `"dataframe.backend"` [configuration](https://docs.dask.org/en/stable/configuration.html) to `"cudf"` in Dask, and the public Dask DataFrame API will leverage `cudf` automatically:
+
+```python
+import dask
+dask.config.set({"dataframe.backend": "cudf"})
+
+import dask.dataframe as dd
+# This gives us a cuDF-backed dataframe
+df = dd.read_parquet("data.parquet", ...)
+```
+
+> [!IMPORTANT]
+> The `"dataframe.backend"` configuration will only be used for collection creation when the following APIs are used: `read_parquet`, `read_json`, `read_csv`, `read_orc`, `read_hdf`, and `from_dict`. For example, if `from_map`, `from_pandas`, `from_delayed`, or `from_array` are used, the backend of the new collection will depend on the input to the function:
+
+```python
+import pandas as pd
+import cudf
+
+# This gives us a Pandas-backed dataframe
+dd.from_pandas(pd.DataFrame({"a": range(10)}))
+
+# This gives us a cuDF-backed dataframe
+dd.from_pandas(cudf.DataFrame({"a": range(10)}))
+```
+
+A cuDF-backed DataFrame collection can be moved to the `"pandas"` backend:
+
+```python
+df = df.to_backend("pandas")
+```
+
+Similarly, a Pandas-backed DataFrame collection can be moved to the `"cudf"` backend:
+
+```python
+df = df.to_backend("cudf")
+```
+
+### The Explicit Dask cuDF API
+
+In addition to providing the `"cudf"` backend for Dask DataFrame, Dask cuDF also provides an explicit `dask_cudf` API:
+
+```python
+import dask_cudf
+
+# This always gives us a cuDF-backed dataframe
+df = dask_cudf.read_parquet("data.parquet", ...)
+```
+
+> [!NOTE]
+> This API is used implicitly by the Dask DataFrame API when the `"cudf"` backend is enabled. Therefore, using it directly will not provide any performance benefit over the CPU/GPU-portable `dask.dataframe` API. Also, using some parts of the explicit API are incompatible with automatic query planning (see the next section).
+
+See the [Dask cuDF's API documentation](https://docs.rapids.ai/api/dask-cudf/stable/) for further information.
+
+## Query Planning
+
+Dask cuDF now provides automatic query planning by default (RAPIDS 24.06+). As long as the `"dataframe.query-planning"` configuration is set to `True` (the default) when `dask.dataframe` is first imported, [Dask Expressions](https://github.com/dask/dask-expr) will be used under the hood.
+
+For example, the following user code will automatically benefit from predicate pushdown when the result is computed.
+
+```python
+df = dd.read_parquet("/my/parquet/dataset/")
+result = df.sort_values('B')['A']
+```
+
+Unoptimized expression graph (`df.pprint()`):
+```
+Projection: columns='A'
+  SortValues: by=['B'] shuffle_method='tasks' options={}
+    ReadParquetFSSpec: path='/my/parquet/dataset/' ...
+```
+
+Simplified expression graph (`df.simplify().pprint()`):
+```
+Projection: columns='A'
+  SortValues: by=['B'] shuffle_method='tasks' options={}
+    ReadParquetFSSpec: path='/my/parquet/dataset/' columns=['A', 'B'] ...
+```
+
+> [!NOTE]
+> Dask will automatically simplify the expression graph (within `optimize`) when the result is converted to a task graph (via `compute` or `persist`). The user does not need to call `simplify` themself.
+
+
+## Using Multiple GPUs and Multiple Nodes
+
+Whenever possible, Dask cuDF (i.e. Dask DataFrame) will automatically try to partition your data into small-enough tasks to fit comfortably in the memory of a single GPU. This means the necessary compute tasks needed to compute a query can often be streamed to a single GPU process for out-of-core computing. This also means that the compute tasks can be executed in parallel over a multi-GPU cluster.
+
+> [!IMPORTANT]
+> Neither Dask cuDF nor Dask DataFrame provide support for multi-GPU or multi-node execution on their own. You must deploy a distributed cluster (ideally with [Dask CUDA](https://docs.rapids.ai/api/dask-cuda/stable/)) to leverage multiple GPUs.
+
+In order to execute your Dask workflow on multiple GPUs, you will typically need to use [Dask CUDA](https://docs.rapids.ai/api/dask-cuda/stable/) to deploy distributed Dask cluster, and [Distributed](https://distributed.dask.org/en/stable/client.html) to define a `client` object. For example:
+
+```python
+
+from dask_cuda import LocalCUDACluster
+from distributed import Client
+
+client = Client(
+    LocalCUDACluster(
+        CUDA_VISIBLE_DEVICES="0,1",  # Use two workers (on devices 0 and 1)
+        rmm_pool_size=0.9,  # Use 90% of GPU memory as a pool for faster allocations
+        enable_cudf_spill=True,  # Improve device memory stability
+        local_directory="/fast/scratch/",  # Use fast local storage for spilling
+    )
+)
+
+df = dd.read_parquet("/my/parquet/dataset/")
+agg = df.groupby('B').sum()
+agg.compute()  # This will use the cluster defined above
+```
+
+> [!NOTE]
+> This example uses `compute` to materialize a concrete `cudf.DataFrame` object in local memory. Never call `compute` on a large collection that cannot fit comfortably in the memory of a single GPU! See Dask's [documentation on managing computation](https://distributed.dask.org/en/stable/manage-computation.html) for more details.
+
+Please see the [Dask CUDA](https://docs.rapids.ai/api/dask-cuda/stable/) documentation for more information about deploying GPU-aware clusters (including [best practices](https://docs.rapids.ai/api/dask-cuda/stable/examples/best-practices/)).
+
+## Install
+
+See the [RAPIDS install page](https://docs.rapids.ai/install) for the most up-to-date information and commands for installing Dask cuDF and other RAPIDS packages.
+
+## Resources
+
+- [Dask cuDF API documentation](https://docs.rapids.ai/api/dask-cudf/stable/)
+- [cuDF API documentation](https://docs.rapids.ai/api/cudf/stable/)
+- [10 Minutes to cuDF and Dask cuDF](https://docs.rapids.ai/api/cudf/stable/user_guide/10min/)
+- [Dask CUDA documentation](https://docs.rapids.ai/api/dask-cuda/stable/)
+- [Deployment](https://docs.rapids.ai/deployment/stable/)
+- [RAPIDS Community](https://rapids.ai/learn-more/#get-involved): Get help, contribute, and collaborate.