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tweaks #236

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186 changes: 89 additions & 97 deletions pgml-dashboard/notebooks/fixtures/notebooks.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -57,36 +57,57 @@
fields:
notebook: 0
cell_type: 1
contents: "## Welcome!\n\nHi there. Welcome to what we hope is the future of machine
learning!\n\nPostgresML is an end-to-end system for training and deploying real
time machine learning models. It handles data versioning, model training, ranking,
\nand safe production release. This dashboard gives an overview of what's happening
in the system and also helps build and deploy experiments. The notebooks,\none
of which you're reading right this moment, are our take on what ML notebooks
could be when used with a real time data store like PostgreSQL.\n\n\n\n\n###
Notebooks\n\nOur Notebooks are similar to Jupyter Notebooks, which you might
be familiar with already. On the bottom of the page, you will find a text editor
which is used to create new cells. Each cell can contain either Markdown which
is just text really, and SQL which can be executed directly on this PostgresML
instance.\n\nEach cell has a little menu in the top right corner, allowing you
to (re)run it (if it's SQL), edit it, and delete it.\n\n\nLet me give you an
example. The next cell (cell #2) will be a SQL cell which will execute a simple
query."
rendering: "<article class=\"markdown-body\"><h2>Welcome!</h2>\n<p>Hi there. Welcome
to what we hope is the future of machine learning!</p>\n<p>PostgresML is an
end-to-end system for training and deploying real time machine learning models.
It handles data versioning, model training, ranking, \nand safe production release.
This dashboard gives an overview of what's happening in the system and also
helps build and deploy experiments. The notebooks,\none of which you're reading
right this moment, are our take on what ML notebooks could be when used with
a real time data store like PostgreSQL.</p>\n<h3>Notebooks</h3>\n<p>Our Notebooks
are similar to Jupyter Notebooks, which you might be familiar with already.
On the bottom of the page, you will find a text editor which is used to create
new cells. Each cell can contain either Markdown which is just text really,
and SQL which can be executed directly on this PostgresML instance.</p>\n<p>Each
cell has a little menu in the top right corner, allowing you to (re)run it (if
it's SQL), edit it, and delete it.</p>\n<p>Let me give you an example. The next
cell (cell #2) will be a SQL cell which will execute a simple query.</p></article>"
contents: '## Welcome!


You''re set up and running on PostgresML! This is an end-to-end system for training
and deploying real time machine learning models. It handles data versioning,
model training and validation, and safe production release. This dashboard web
app will give you an overview of what''s happening in the system and also helps
build and deploy projects. You can use notebooks like this one to interact with
your database in real time and organize your SQL while documenting your code.



### Notebooks


These notebooks are similar to Jupyter Notebooks, which you might be familiar
with already. On the bottom of the page, you will find a text editor which is
used to create new cells. Each cell can contain either Markdown which is just
text really, and SQL which will be executed directly by your Postgres database
server.


Each cell has a little menu in the top right corner, allowing you to (re)run
it (if it''s SQL), edit it, and delete it.



Let me give you an example. The next cell (cell #2) will be a SQL cell which
will execute a simple query.'
rendering: '<article class="markdown-body"><h2>Welcome!</h2>

<p>You''re set up and running on PostgresML! This is an end-to-end system for
training and deploying real time machine learning models. It handles data versioning,
model training and validation, and safe production release. This dashboard web
app will give you an overview of what''s happening in the system and also helps
build and deploy projects. You can use notebooks like this one to interact with
your database in real time and organize your SQL while documenting your code.</p>

<h3>Notebooks</h3>

<p>These notebooks are similar to Jupyter Notebooks, which you might be familiar
with already. On the bottom of the page, you will find a text editor which is
used to create new cells. Each cell can contain either Markdown which is just
text really, and SQL which will be executed directly by your Postgres database
server.</p>

<p>Each cell has a little menu in the top right corner, allowing you to (re)run
it (if it''s SQL), edit it, and delete it.</p>

<p>Let me give you an example. The next cell (cell #2) will be a SQL cell which
will execute a simple query.</p></article>'
execution_time: null
cell_number: 1
version: 1
Expand All @@ -97,11 +118,8 @@
notebook: 0
cell_type: 3
contents: SELECT random();
rendering: "<div class=\"markdown-body\">\n<table>\n <thead>\n <tr>\n \n
\ <td><strong>random</strong></td>\n \n </tr>\n </thead>\n <tbody>\n
\ \n <tr>\n \n <td>0.6822832295608556</td>\n \n </tr>\n
\ \n </tbody>\n</table>\n</div>\n"
execution_time: '00:00:00.000654'
rendering: null
execution_time: null
cell_number: 2
version: 1
deleted_at: null
Expand All @@ -110,62 +128,34 @@
fields:
notebook: 0
cell_type: 1
contents: 'I just asked Postgres to give me a random number. Pretty simple query,
contents: "We just asked Postgres to return a random number. Pretty simple query,
but it demonstrates the notebook functionality pretty well. You can see that
the result of `random()` is currently `0.6822832295608556`. On the bottom right
corner, you can see that it took `0:00:00.000654` or 0 hours, 0 minutes, 0 seconds
and only 00006ns, which I believe is 0.6ms, for Postgres to run this query for
us. This runtime is good to know, because you''ll be able to benchmark some
of PostgresML functionality, including the models we provide, right here in
these notebooks.


Try rerunning the cell again by clicking the "play" button in the top right
corner. You''ll see that the random number will change. Rerunning is a real
time operation and Postgres will give you a different random number every time
(otherwise it wouldn''t be random).


#### Editing a cell


You can edit a cell at any time, including SQL cells which will then run the
new query immediately.


#### Deleting a cell


Deleting a cell is pretty easy: just click on the delete button in the top right
corner. You''ll have 10 seconds to undo the delete if you so desire; we wouldn''t
want you to lose your work because of an accidental click.


#### Shortcuts


The text editor supports the following helpful shortcuts:



| Shortcut | Description |

-----------| --------------------------------------

| `Cmd-/` or `Ctrl-/` | Comment out SQL code. |

| `Cmd-Enter` or `Ctrl-Enter` | Save/create a cell.|


By the way, this was a Markdown table, you can make those here as well.'
rendering: '<article class="markdown-body"><p>I just asked Postgres to give me
the result of `random()` is a float between 0 and 1. On the bottom right corner,
you can see that it took `0:00:00.000654` or 0 hours, 0 minutes, 0 seconds and
only 654ns, or 0.6ms. This run time is good to keep an eye on. It will help
build an intuition for how fast Postgres really is, and how certain operations
scale as the data grows. You'll be able to see how long \n\nTry rerunning the
cell again by clicking the \"play\" button in the top right corner. You'll see
that the random number will change. Rerunning is a real time operation and Postgres
will give you a different random number every time (otherwise it wouldn't be
random).\n\n#### Editing a cell\n\nYou can edit a cell at any time, including
SQL cells which will then run the new query immediately.\n\n#### Deleting a
cell\n\nDeleting a cell is pretty easy: just click on the delete button in the
top right corner. You'll have 10 seconds to undo the delete if you so desire;
we wouldn't want you to lose your work because of an accidental click.\n\n####
Shortcuts\n\nThe text editor supports the following helpful shortcuts:\n\n\n|
Shortcut | Description |\n-----------| --------------------------------------\n|
`Cmd-/` or `Ctrl-/` | Comment out SQL code. |\n| `Cmd-Enter` or `Ctrl-Enter`
| Save/create a cell.|\n\nBy the way, this was a Markdown table, you can make
those here as well."
rendering: '<article class="markdown-body"><p>We just asked Postgres to return
a random number. Pretty simple query, but it demonstrates the notebook functionality
pretty well. You can see that the result of <code>random()</code> is currently
<code>0.6822832295608556</code>. On the bottom right corner, you can see that
it took <code>0:00:00.000654</code> or 0 hours, 0 minutes, 0 seconds and only
00006ns, which I believe is 0.6ms, for Postgres to run this query for us. This
runtime is good to know, because you''ll be able to benchmark some of PostgresML
functionality, including the models we provide, right here in these notebooks.</p>
pretty well. You can see that the result of <code>random()</code> is a float
between 0 and 1. On the bottom right corner, you can see that it took <code>0:00:00.000654</code>
or 0 hours, 0 minutes, 0 seconds and only 654ns, or 0.6ms. This run time is
good to keep an eye on. It will help build an intuition for how fast Postgres
really is, and how certain operations scale as the data grows. You''ll be able
to see how long </p>

<p>Try rerunning the cell again by clicking the "play" button in the top right
corner. You''ll see that the random number will change. Rerunning is a real
Expand Down Expand Up @@ -237,14 +227,19 @@


Thank you for trying out PostgresML! We hope you enjoy your time here and have
fun learning about machine learning, in the comfort of your favorite database.


fun learning about machine learning, in the comfort of your favorite database.'
You may want to check out the [rest of the tutorial''s](/nb/) or dive straight
in with a notebook to test [real time fraud detection](/notebooks/notebook/1/).'
rendering: '<article class="markdown-body"><h3>Thank you</h3>

<p>Thank you for trying out PostgresML! We hope you enjoy your time here and
have
have fun learning about machine learning, in the comfort of your favorite database.</p>

fun learning about machine learning, in the comfort of your favorite database.</p></article>'
<p>You may want to check out the <a href="/nb/">rest of the tutorial''s</a>
or dive straight in with a notebook to test <a href="/notebooks/notebook/1/">real
time fraud detection</a>.</p></article>'
execution_time: null
cell_number: 4
version: 1
Expand All @@ -255,11 +250,8 @@
notebook: 0
cell_type: 3
contents: SELECT 'Have a nice day!' AS greeting;
rendering: "<div class=\"markdown-body\">\n<table>\n <thead>\n <tr>\n \n
\ <td><strong>greeting</strong></td>\n \n </tr>\n </thead>\n <tbody>\n
\ \n <tr>\n \n <td>Have a nice day!</td>\n \n </tr>\n
\ \n </tbody>\n</table>\n</div>\n"
execution_time: '00:00:00.000580'
rendering: null
execution_time: null
cell_number: 5
version: 1
deleted_at: null
Expand Down
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