Add colab links

examples/LLM_Workflows/RAG_document_extract_chunk_embed/README.md

@@ -14,3 +14,8 @@ Open it in google collab:
 * simple_pipeline.ipynb - this contains documentation and code. Read this.
 * pipeline.py - what the code in simple_pipeline.ipynb creates for easy reference
 * requirements.txt - python dependencies required (outside of jupyter lab)
+
+To exercise this example you can run it in Google Colab:
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)
+](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/LLM_Workflows/simple_pipeline.ipynb)

examples/caching_nodes/caching_graph_adapter/README.md

@@ -16,3 +16,8 @@ For iterating during development, the general process would be:
        its name to the adapter in the `force_compute` argument. Then, this node and its downstream
        nodes will be computed instead of loaded from cache.
 4. When no longer required, you can just skip (3) and any caching behavior will be skipped.
+
+To exercise this example you can run it in Google Colab:
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)
+](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/caching_nodes/caching_graph_adapter/caching_nodes.ipynb)

examples/caching_nodes/caching_graph_adapter/caching_nodes.ipynb

@@ -7,6 +7,13 @@
     "# Caching Nodes with Hamilton"
    ]
   },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "#! pip isntall pandas pyarrow sf-hamilton"
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

examples/contrib/README.md

@@ -9,6 +9,10 @@ For the purpose of this example, we will create a virtual environment with hamil
 2. `. venv/bin/activate` (on MacOS / Linux) or `. venv/bin/Scripts` (Windows)
 3. `pip install -r requirements.txt`
 
+Or run it in Google Colab:
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)
+](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/contrib/notebook.ipynb)
+
 
 # 3 ways to import
 There are 3 main ways to use community dataflows: static installation, dynamic installation, and local copy (see [documentation](https://github.com/DAGWorks-Inc/hamilton/tree/main/contrib)). We present each of them in this example:

examples/contrib/notebook.ipynb

@@ -20,6 +20,13 @@
     "collapsed": false
    }
   },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "# !pip install sf-hamilton-contrib"
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

examples/dagster/hamilton_code/README.md

@@ -18,6 +18,12 @@ The directory also include `mock_api.py` which simulates a `ressource` in the Da
     python run.py
     ```
 
+Or run it in Google Colab:
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)
+](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/dagster/hamilton_code/notebook.ipynb)
+
+
+
 ## Going further
 - Learn the basics of Hamilton via the `Concepts/` [documentation section](https://hamilton.dagworks.io/en/latest/concepts/node/)
 - Visit [tryhamilton.dev](tryhamilton.dev) for an interactive tutorial in your browser

examples/dagster/hamilton_code/notebook.ipynb

@@ -9,6 +9,13 @@
     "[Tips on Hamilton + notebooks in the docs](https://hamilton.dagworks.io/en/latest/how-tos/use-in-jupyter-notebook/)"
    ]
   },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": ""
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

examples/dask/community_demo/README.md

@@ -19,6 +19,12 @@ pip install -r requirements.txt
 jupyter notebook
 ```
 
+Or run it in Google Colab:
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)
+](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/dask/community_demo/demo_day_notebook.ipynb)
+
+
+
 If you have questions, or need help with this example,
 join us on [slack](https://join.slack.com/t/hamilton-opensource/shared_invite/zt-1bjs72asx-wcUTgH7q7QX1igiQ5bbdcg), and we'll try to help!
 

examples/dask/community_demo/demo_day_notebook.ipynb

@@ -1,18 +1,17 @@
 {
  "cells": [
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-22T22:27:16.952106Z",
-     "start_time": "2023-05-22T22:27:15.116241Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
    "outputs": [],
+   "execution_count": null,
+   "source": "#!pip install pandas \"sf-hamilton[dask,visualization]\""
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": 1,
    "source": [
     "# Cell 1 - import the things you need\n",
     "import logging\n",

examples/dask/hello_world/README.md

@@ -19,6 +19,12 @@ idea is that you'd swap this module out for other ways of loading data or use @c
 *  `run_with_delayed_and_dask_objects.py` shows the combination of the above. It is slightly non-sensical, since we're
 entirely operating on what are dask objects effectively. But otherwise shows the code pattern to use both.
 
+Or run it in Google Colab:
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)
+](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/dask/hello_world/notebook.ipynb)
+
+
+
 # Visualization of execution
 Here is the graph of execution:
 

examples/dlt/README.md

@@ -29,6 +29,12 @@ It includes a pipeline to ingest messages from Slack channels and generate threa
     python run.py general dlt
     ```
 
+Or run it in Google Colab:
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)
+](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/dlt/notebook.ipynb)
+
+
+
 # References
 - dlt to ingest [Slack data](https://dlthub.com/docs/dlt-ecosystem/verified-sources/slack)
 - Reconstructing Slack threads [docs](https://api.slack.com/messaging/retrieving#finding_threads)