From 95ea273249fb4dec13ffe814fd73d0bc3dad4a06 Mon Sep 17 00:00:00 2001 From: scottyhq Date: Thu, 15 Jun 2023 22:24:19 +0000 Subject: [PATCH] deploy: 05152bee7e8fa681d6b65d61eca22dac1eeb53f0 --- _sources/intermediate/xarray_and_dask.ipynb | 33 ++- intermediate/xarray_and_dask.html | 212 ++++++++++---------- searchindex.js | 2 +- 3 files changed, 122 insertions(+), 125 deletions(-) diff --git a/_sources/intermediate/xarray_and_dask.ipynb b/_sources/intermediate/xarray_and_dask.ipynb index 4de7a1ce..52b7decf 100644 --- a/_sources/intermediate/xarray_and_dask.ipynb +++ b/_sources/intermediate/xarray_and_dask.ipynb @@ -230,9 +230,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "jupyter": { - "outputs_hidden": true - }, "tags": [] }, "outputs": [], @@ -246,16 +243,13 @@ "source": [ "### Exercise\n", "\n", - "Try calling `mean.values` and `mean.data`. Do you understand the difference?" + "Try calling `ds.air.values` and `ds.air.data`. Do you understand the difference?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "jupyter": { - "outputs_hidden": true - }, "tags": [] }, "outputs": [], @@ -331,7 +325,7 @@ "2. `.load()` replaces the dask array in the xarray object with a numpy array.\n", " This is equivalent to `ds = ds.compute()`\n", " \n", - "**Tip:** There is a third option : \"persisting\". `.persist()` loads the values into distributed RAM. The values are computed but remain distributed across workers. So `ds.air.persist()` still returns a dask array. This is useful if you will be repeatedly using a dataset for computation but it is too large to load into local memory. You will see a persistent task on the dashboard. See the [dask user guide](https://docs.dask.org/en/latest/api.html#dask.persist) for more on persisting\n" + "**Tip:** There is a third option : \"persisting\". `.persist()` loads the values into distributed RAM. The values are computed but remain distributed across workers. So `ds.air.persist()` still returns a dask array. This is useful if you will be repeatedly using a dataset for computation but it is too large to load into local memory. You will see a persistent task on the dashboard. See the [dask user guide](https://docs.dask.org/en/latest/api.html#dask.persist) for more on persisting" ] }, { @@ -347,9 +341,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "jupyter": { - "outputs_hidden": true - }, "tags": [] }, "outputs": [], @@ -446,7 +437,10 @@ "\n", "You can use any kind of Dask cluster. This step is completely independent of\n", "xarray. While not strictly necessary, the dashboard provides a nice learning\n", - "tool." + "tool.\n", + "\n", + "By default, Dask uses the current working directory for writing temporary files.\n", + "We choose to use a temporary scratch folder `local_directory='/tmp'` in the example below instead." ] }, { @@ -464,10 +458,17 @@ "# if os.environ.get('JUPYTERHUB_USER'):\n", "# dask.config.set(**{\"distributed.dashboard.link\": \"/user/{JUPYTERHUB_USER}/proxy/{port}/status\"})\n", "\n", - "client = Client(local_directory='/tmp')\n", + "client = Client()\n", "client" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -483,9 +484,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "jupyter": { - "outputs_hidden": true - }, "tags": [] }, "outputs": [], @@ -539,9 +537,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "jupyter": { - "outputs_hidden": true - }, "tags": [] }, "outputs": [], diff --git a/intermediate/xarray_and_dask.html b/intermediate/xarray_and_dask.html index 5818e821..c27f5188 100644 --- a/intermediate/xarray_and_dask.html +++ b/intermediate/xarray_and_dask.html @@ -1143,26 +1143,26 @@

Creating dask-backed Xarray objects
@@ -1566,17 +1566,17 @@

Creating dask-backed Xarray objects @@ -1658,16 +1658,16 @@

Creating dask-backed Xarray objectsair DataArray shows the very nice HTML dask array repr. You can access the underlying chunk sizes using .chunks:

@@ -2077,17 +2077,17 @@

Creating dask-backed Xarray objects @@ -2169,16 +2169,16 @@

Creating dask-backed Xarray objectsDataset need not have the same chunk size along @@ -2393,7 +2393,7 @@

Extracting underlying data

Exercise#

-

Try calling mean.values and mean.data. Do you understand the difference?

+

Try calling ds.air.values and ds.air.data. Do you understand the difference?

ds.air.to_numpy()
@@ -2872,7 +2872,7 @@ 

Lazy computation @@ -2929,23 +2929,23 @@

Lazy computationsingle-machine scheduling. You can visualize dask ‘task graphs’ which represent the requested computation:

@@ -3414,7 +3414,7 @@

Exercise

+ dtype='float32', name='lon'))
  • @@ -3886,7 +3886,7 @@

    Exercise + dtype='float32', name='lon'))

  • @@ -4302,7 +4302,7 @@

    Exercise

    + dtype='float32', name='lon'))
  • @@ -4779,7 +4779,7 @@

    Exercise + dtype='float32', name='lon'))
  • @@ -5207,7 +5207,7 @@

    Exercise + dtype='float32', name='lon'))

  • @@ -5247,6 +5247,8 @@

    Distributed ClustersYou can use any kind of Dask cluster. This step is completely independent of xarray. While not strictly necessary, the dashboard provides a nice learning tool.

    +

    By default, Dask uses the current working directory for writing temporary files. +We choose to use a temporary scratch folder local_directory='/tmp' in the example below instead.

    Client

    -

    Client-9872090d-0ad5-11ee-89bf-000d3a18b39f

    +

    Client-361ed45e-0bcb-11ee-8731-6045bdc9db22

    @@ -5303,7 +5305,7 @@

    Client

    LocalCluster

    -

    a8135e98

    +

    603bf23f

    @@ -5340,11 +5342,11 @@

    Scheduler Info

    Scheduler

    -

    Scheduler-c6ef89db-1857-4b8f-9afb-fc8be78263c0

    +

    Scheduler-45a4921f-ae74-433a-98b8-4f03bd509c71

    - Comm: tcp://127.0.0.1:38805 + Comm: tcp://127.0.0.1:33113 Workers: 2 @@ -5386,7 +5388,7 @@

    Worker: 0

    @@ -5431,7 +5433,7 @@

    Worker: 1

    - Comm: tcp://127.0.0.1:38437 + Comm: tcp://127.0.0.1:35473 Total threads: 1 @@ -5394,7 +5396,7 @@

    Worker: 0

    - Dashboard: http://127.0.0.1:44635/status + Dashboard: http://127.0.0.1:45993/status Memory: 3.39 GiB @@ -5402,13 +5404,13 @@

    Worker: 0

    - Nanny: tcp://127.0.0.1:44395 + Nanny: tcp://127.0.0.1:34533
    - Local directory: /tmp/dask-scratch-space/worker-pk874gve + Local directory: /tmp/dask-scratch-space/worker-bb36zkzb
    @@ -5893,7 +5895,7 @@

    Computation @@ -5948,10 +5950,10 @@

    Computation @@ -6353,7 +6355,7 @@

    Computation @@ -6435,26 +6437,26 @@

    Computation
    @@ -6855,7 +6857,7 @@

    Computation @@ -6910,10 +6912,10 @@

    Computation
    @@ -7354,7 +7356,7 @@

    Computationmean still contains a dask array

    @@ -7814,7 +7816,7 @@

    Computation @@ -7896,26 +7898,26 @@

    Computationchunking and performance

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\ No newline at end of file

    - Comm: tcp://127.0.0.1:43861 + Comm: tcp://127.0.0.1:40117 Total threads: 1 @@ -5439,7 +5441,7 @@

    Worker: 1

    - Dashboard: http://127.0.0.1:42907/status + Dashboard: http://127.0.0.1:42159/status Memory: 3.39 GiB @@ -5447,13 +5449,13 @@

    Worker: 1

    - Nanny: tcp://127.0.0.1:34411 + Nanny: tcp://127.0.0.1:39771
    - Local directory: /tmp/dask-scratch-space/worker-vemgp2hj + Local directory: /tmp/dask-scratch-space/worker-gyv9drz1