From 42db4528fc26db490bc947d302dfbadb5edc5611 Mon Sep 17 00:00:00 2001 From: tomvothecoder Date: Tue, 16 Apr 2024 16:09:44 -0700 Subject: [PATCH] Update notebook --- docs/examples/parallel-computing-with-dask.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/examples/parallel-computing-with-dask.ipynb b/docs/examples/parallel-computing-with-dask.ipynb index 3b49bd30..995735c3 100644 --- a/docs/examples/parallel-computing-with-dask.ipynb +++ b/docs/examples/parallel-computing-with-dask.ipynb @@ -105,11 +105,11 @@ " - Data is loaded into memory and **computation** is performed in **streaming fashion**, **block-by-block**\n", "- Computation is controlled by multi-processing or thread pool\n", "\n", + "— https://docs.xarray.dev/en/stable/user-guide/dask.html\n", + "\n", "
\n", " \"Dask\n", - "
\n", - "\n", - "— https://docs.xarray.dev/en/stable/user-guide/dask.html\n" + "\n" ] }, { @@ -275,7 +275,7 @@ "source": [ "## Using a Dask Cluster for Scalable Computations\n", "\n", - "- All of the large-scale Dask collections like Dask Array, Dask DataFrame, and Dask Bag and the fine-grained APIs like delayed and futures generate task graphs where each node in the graph is a normal Python function and edges between nodes are normal Python objects that are created by one task as outputs and used as inputs in another task.\n", + "- All of the large-scale Dask collections like Dask Array, Dask DataFrame, and Dask Bag and the fine-grained APIs like delayed and futures **generate task graphs** where each node in the graph is a normal Python function and edges between nodes are normal Python objects that are created by one task as outputs and used as inputs in another task.\n", "\n", "- After Dask generates these task graphs, it needs to execute them on parallel hardware. This is the job of a **task scheduler**.\n", "\n",