From 840cec0792998f29e0ea5f0aeaf88db04a659b7d Mon Sep 17 00:00:00 2001 From: Montana Low Date: Mon, 22 Aug 2022 09:20:48 -0700 Subject: [PATCH] tweaks --- .../notebooks/fixtures/notebooks.yml | 186 +++++++++--------- 1 file changed, 89 insertions(+), 97 deletions(-) diff --git a/pgml-dashboard/notebooks/fixtures/notebooks.yml b/pgml-dashboard/notebooks/fixtures/notebooks.yml index 2518e7f0b..66cb5067f 100644 --- a/pgml-dashboard/notebooks/fixtures/notebooks.yml +++ b/pgml-dashboard/notebooks/fixtures/notebooks.yml @@ -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: "

Welcome!

\n

Hi there. Welcome - to what we hope is the future of machine learning!

\n

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.

\n

Notebooks

\n

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.

\n

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.

\n

Let me give you an example. The next - cell (cell #2) will be a SQL cell which will execute a simple query.

" + 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: '

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.

' execution_time: null cell_number: 1 version: 1 @@ -97,11 +118,8 @@ notebook: 0 cell_type: 3 contents: SELECT random(); - rendering: "
\n\n \n \n \n - \ \n \n \n \n \n - \ \n \n \n \n \n \n - \ \n \n
random
0.6822832295608556
\n
\n" - execution_time: '00:00:00.000654' + rendering: null + execution_time: null cell_number: 2 version: 1 deleted_at: null @@ -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: '

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: '

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.

+ pretty well. You can see that 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

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 @@ -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: '

Thank you

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.

- fun learning about machine learning, in the comfort of your favorite database.

' +

You may want to check out the rest of the tutorial''s + or dive straight in with a notebook to test real + time fraud detection.

' execution_time: null cell_number: 4 version: 1 @@ -255,11 +250,8 @@ notebook: 0 cell_type: 3 contents: SELECT 'Have a nice day!' AS greeting; - rendering: "
\n\n \n \n \n - \ \n \n \n \n \n - \ \n \n \n \n \n \n - \ \n \n
greeting
Have a nice day!
\n
\n" - execution_time: '00:00:00.000580' + rendering: null + execution_time: null cell_number: 5 version: 1 deleted_at: null