diff --git a/README.md b/README.md index eecc60a..2d91cdd 100644 --- a/README.md +++ b/README.md @@ -35,7 +35,7 @@ That's it. Your GreatAI service is _nearly_ ready for production use. Many of th Before production, replace the default local TinyDB tracing database with a production-suitable backend, configure storage and deployment infrastructure, and review the remaining best practices for your use case. -[Check out the full documentation here](https://great-ai.scoutinscience.com). +> **Published documentation:** [Read the rendered guides, API reference, and notebooks](https://great-ai.scoutinscience.com). The files under `docs/` use MkDocs features that repository previews may not render. ## Why is this GREAT? diff --git a/docs/examples/scibert/data.ipynb b/docs/examples/scibert/data.ipynb index 97a7fb8..e3368d3 100644 --- a/docs/examples/scibert/data.ipynb +++ b/docs/examples/scibert/data.ipynb @@ -6,7 +6,7 @@ "source": [ "# Explore data and feasibility of approach\n", "\n", - "![screenshot fo the annotator tool](/media/annotator.png)\n", + "![screenshot fo the annotator tool](https://great-ai.scoutinscience.com/media/annotator.png)\n", "\n", "We had asked our clients and in-house experts to annotate sentences using a rigorous guideline. The aim is to decide on which sentences they would like to see in a summary for a paper.\n", "\n", @@ -276,7 +276,7 @@ "source": [ "We get an F1-score of 0.78 without any hyperparameter-optimisation; this task may be feasible to solve with AI.\n", "\n", - "Next: [Part 2](/examples/scibert/train)" + "Next: [Part 2](https://great-ai.scoutinscience.com/examples/scibert/train)" ] } ], diff --git a/docs/examples/scibert/deploy.ipynb b/docs/examples/scibert/deploy.ipynb index b35e6b2..5b7d803 100644 --- a/docs/examples/scibert/deploy.ipynb +++ b/docs/examples/scibert/deploy.ipynb @@ -290,7 +290,7 @@ "metadata": {}, "source": [ "In this case, the service is built as a docker image, pushed to our image registry and subsequent rolling update is performed in the production cluster.\n", - "To check out the Dockerimage, go to [the additional files page](/examples/scibert/additional-files)." + "To check out the Dockerimage, go to [the additional files page](https://great-ai.scoutinscience.com/examples/scibert/additional-files)." ] } ], diff --git a/docs/examples/scibert/index.md b/docs/examples/scibert/index.md index 9332607..9878914 100644 --- a/docs/examples/scibert/index.md +++ b/docs/examples/scibert/index.md @@ -7,8 +7,8 @@ This is a simplified example illustrating how `great-ai` is used in practice at ## Objectives -1. You will see how the [great_ai.utilities](/reference/utilities) can integrate into your Data Science workflow. -2. You will see how [great_ai.large_file](/reference/large-file) can be used to version and store your trained model. +1. You will see how the [great_ai.utilities](https://great-ai.scoutinscience.com/reference/utilities) can integrate into your Data Science workflow. +2. You will see how [great_ai.large_file](https://great-ai.scoutinscience.com/reference/large-file) can be used to version and store your trained model. 3. You will see how [GreatAI][great_ai.GreatAI] should be used to prepare your model for a robust and responsible deployment. 4. You will see multiple ways of customising your deployment. @@ -37,13 +37,13 @@ We load and analyse the data by calculating inter-rater reliability and checking We simply fine-tune SciBERT. -After training and evaluating a model, it is exported using [great_ai.save_model][]. For more info, check out [the configuration how-to page](/how-to-guides/configure-service). +After training and evaluating a model, it is exported using [great_ai.save_model][]. For more info, check out [the configuration how-to page](https://great-ai.scoutinscience.com/how-to-guides/configure-service). ### [Deployment notebook](deploy.ipynb) We customise the GreatAI configuration, create custom caching for the model and implement an inference function that can be hardened by wrapping it in a [GreatAI][great_ai.GreatAI] instance. We also extract the attention weights as a quasi-explanation. -Finally, we test the model's inference function through the GreatAI dashboard. [The only thing left is to deploy the hardened service properly.](/how-to-guides/use-service) +Finally, we test the model's inference function through the GreatAI dashboard. [The only thing left is to deploy the hardened service properly.](https://great-ai.scoutinscience.com/how-to-guides/use-service) #### [Additional files](additional-files.md) diff --git a/docs/examples/scibert/train.ipynb b/docs/examples/scibert/train.ipynb index 6e96889..b9a02fa 100644 --- a/docs/examples/scibert/train.ipynb +++ b/docs/examples/scibert/train.ipynb @@ -341,7 +341,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next: [Part 3](/examples/scibert/deploy)" + "Next: [Part 3](https://great-ai.scoutinscience.com/examples/scibert/deploy)" ] } ], diff --git a/docs/examples/simple/data.ipynb b/docs/examples/simple/data.ipynb index 31dc3fc..2ad9193 100644 --- a/docs/examples/simple/data.ipynb +++ b/docs/examples/simple/data.ipynb @@ -8,10 +8,10 @@ "\n", "Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best practices. In short, we train a domain classifier on the [semantic scholar dataset](https://api.semanticscholar.org/corpus) by taking full advantage of `great-ai`. Subsequently, we create a production-ready deployment.\n", "\n", - "![position of this step in the lifecycle](/media/scope-data.svg)\n", + "![position of this step in the lifecycle](https://great-ai.scoutinscience.com/media/scope-data.svg)\n", "> The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook.\n", "\n", - "Since the true scope of `great-ai` is the phase between proof-of-concept code and production-ready service, it is predominantly used in the [deployment notebook](/examples/simple/deploy). Feel free to skip there, or continue reading if you'd like to see the full picture.\n", + "Since the true scope of `great-ai` is the phase between proof-of-concept code and production-ready service, it is predominantly used in the [deployment notebook](https://great-ai.scoutinscience.com/examples/simple/deploy). Feel free to skip there, or continue reading if you'd like to see the full picture.\n", "\n", "### Extract\n", "\n", @@ -199,7 +199,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Next: [Part 2](/examples/simple/train)" + "### Next: [Part 2](https://great-ai.scoutinscience.com/examples/simple/train)" ] } ], diff --git a/docs/examples/simple/deploy.ipynb b/docs/examples/simple/deploy.ipynb index cd9fade..61abb02 100644 --- a/docs/examples/simple/deploy.ipynb +++ b/docs/examples/simple/deploy.ipynb @@ -8,10 +8,10 @@ "\n", "> Part 3: Create production inference function\n", "\n", - "![position of this step in the lifecycle](/media/scope-deploy.svg)\n", + "![position of this step in the lifecycle](https://great-ai.scoutinscience.com/media/scope-deploy.svg)\n", "> The blue boxes show the steps implemented in this notebook.\n", "\n", - "In [Part 2](/examples/simple/train), we trained our AI model. Now, it's time to create **G**eneral **R**obust **E**nd-to-end **A**utomated **T**rustworthy deployment.\n", + "In [Part 2](https://great-ai.scoutinscience.com/examples/simple/train), we trained our AI model. Now, it's time to create **G**eneral **R**obust **E**nd-to-end **A**utomated **T**rustworthy deployment.\n", "\n", "## Create the inference function\n", "\n", diff --git a/docs/examples/simple/train.ipynb b/docs/examples/simple/train.ipynb index 1ba1e87..6e82e50 100644 --- a/docs/examples/simple/train.ipynb +++ b/docs/examples/simple/train.ipynb @@ -6,14 +6,14 @@ "source": [ "# Optimise and train a model\n", "\n", - "![position of this step in the lifecycle](/media/scope-train.svg)\n", + "![position of this step in the lifecycle](https://great-ai.scoutinscience.com/media/scope-train.svg)\n", "> The blue boxes show the steps implemented in this notebook.\n", "\n", "In the first part, we have cleaned and transformed our training data. We can now access this data using `great_ai.LargeFile`. Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. \n", "\n", "In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using `great_ai.save_model`.\n", "\n", - "## Load data that has been extracted in [part 1](/examples/simple/data)" + "## Load data that has been extracted in [part 1](https://great-ai.scoutinscience.com/examples/simple/data)" ] }, { @@ -861,7 +861,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Next: [Part 3](/examples/simple/deploy)" + "## Next: [Part 3](https://great-ai.scoutinscience.com/examples/simple/deploy)" ] } ], diff --git a/docs/how-to-guides/call-remote.md b/docs/how-to-guides/call-remote.md index 7ba80e6..325175b 100644 --- a/docs/how-to-guides/call-remote.md +++ b/docs/how-to-guides/call-remote.md @@ -22,7 +22,7 @@ async def slow_greeter(your_name): await sleep(2) return f'Hi {your_name}!' ``` -> Run this in development mode by executing `great-ai server.py` or `python3 -m great_ai server.py` if you're on Windows and [`great-ai` is not in your `PATH`](/how-to-guides/install). +> Run this in development mode by executing `great-ai server.py` or `python3 -m great_ai server.py` if you're on Windows and [`great-ai` is not in your `PATH`](https://great-ai.scoutinscience.com/how-to-guides/install). ### Client @@ -48,7 +48,7 @@ print(results) > Run this script as a regular Python script by executing `python3 client.py`. -![screenshot of result](/media/remote-sync.png){ loading=lazy } +![screenshot of result](https://great-ai.scoutinscience.com/media/remote-sync.png){ loading=lazy } As you can see, everything worked as expected. There is one way to improve it, though. ## An `async` example @@ -86,6 +86,6 @@ asyncio.run(main()) > Replace `client.py` with this async client. Note that although async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart. -![screenshot of result](/media/remote-async.png){ loading=lazy } +![screenshot of result](https://great-ai.scoutinscience.com/media/remote-async.png){ loading=lazy } This also works and might be considerably quicker in some use cases. diff --git a/docs/how-to-guides/create-service.md b/docs/how-to-guides/create-service.md index 0a8734a..05f0f92 100644 --- a/docs/how-to-guides/create-service.md +++ b/docs/how-to-guides/create-service.md @@ -38,7 +38,7 @@ This not only allows you to statically type-check your code, but by default, Gre ## With async -Asynchronous code can result in immense performance gains in some instances. For example, you might rely on a third-party service, do database access, or [call a remote GreatAI instance](/how-to-guides/call-remote). In these cases, you can make your function `async` without any other changes. +Asynchronous code can result in immense performance gains in some instances. For example, you might rely on a third-party service, do database access, or [call a remote GreatAI instance](https://great-ai.scoutinscience.com/how-to-guides/call-remote). In these cases, you can make your function `async` without any other changes. ```python title="async_greeter.py" from great_ai import GreatAI @@ -117,4 +117,4 @@ def add_number(positive_number: int, secret: int) -> int: assert add_number(1).output == 5 ``` -1. Refer to [the configuration page](/how-to-guides/configure-service) for specifying where to store your models. +1. Refer to [the configuration page](https://great-ai.scoutinscience.com/how-to-guides/configure-service) for specifying where to store your models. diff --git a/docs/how-to-guides/handle-training-data.md b/docs/how-to-guides/handle-training-data.md index 8400227..76db2a6 100644 --- a/docs/how-to-guides/handle-training-data.md +++ b/docs/how-to-guides/handle-training-data.md @@ -60,7 +60,7 @@ After the initial data gathering, end-to-end feedback can also be integrated int The scaffolded REST API contains endpoints for managing traces and their feedbacks. -![screenshot of swagger](/media/feedback.png){ loading=lazy } +![screenshot of swagger](https://great-ai.scoutinscience.com/media/feedback.png){ loading=lazy } When [great_ai.query_ground_truth][] is executed, it implicitly filters for traces that have feedback. Therefore, both the `ground_truth` and the `online` traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API. diff --git a/docs/how-to-guides/large-file.md b/docs/how-to-guides/large-file.md index c0209ef..57ec672 100644 --- a/docs/how-to-guides/large-file.md +++ b/docs/how-to-guides/large-file.md @@ -1,6 +1,6 @@ # How to use LargeFiles -The functions [save_model][great_ai.use_model] and [@use_model][great_ai.use_model] wrap LargeFile instances. Hence, besides configuring [LargeFile](/reference/large-file), users have few reasons to use LargeFiles directly. +The functions [save_model][great_ai.use_model] and [@use_model][great_ai.use_model] wrap LargeFile instances. Hence, besides configuring [LargeFile](https://great-ai.scoutinscience.com/reference/large-file), users have few reasons to use LargeFiles directly. ## Motivation diff --git a/docs/how-to-guides/use-service.md b/docs/how-to-guides/use-service.md index cd8272d..fef170b 100644 --- a/docs/how-to-guides/use-service.md +++ b/docs/how-to-guides/use-service.md @@ -1,6 +1,6 @@ # How to perform prediction with GreatAI -After [creating a GreatAI service](/how-to-guides/create-service) by wrapping your prediction function, and optionally [configuring it](/how-to-guides/configure-service), it's time to do some prediction. +After [creating a GreatAI service](https://great-ai.scoutinscience.com/how-to-guides/create-service) by wrapping your prediction function, and optionally [configuring it](https://great-ai.scoutinscience.com/how-to-guides/configure-service), it's time to do some prediction. Let's take the following example: diff --git a/docs/index.md b/docs/index.md index ecfbf60..e032d49 100644 --- a/docs/index.md +++ b/docs/index.md @@ -110,7 +110,7 @@ However, research indicates that professionals rarely use them. This may be due [:fontawesome-brands-docker: Find it on DockerHub](https://hub.docker.com/repository/docker/schmelczera/great-ai){ .md-button .md-button--primary } -[:fontawesome-solid-laptop-code: Check out the tutorial](/tutorial){ .md-button .md-button--primary } +[:fontawesome-solid-laptop-code: Check out the tutorial](https://great-ai.scoutinscience.com/tutorial){ .md-button .md-button--primary } ## Production use diff --git a/docs/reference/large-file.md b/docs/reference/large-file.md index 37b5706..ea97621 100644 --- a/docs/reference/large-file.md +++ b/docs/reference/large-file.md @@ -4,7 +4,7 @@ from great_ai.large_file import * ``` -For more details about using LargeFiles, check out the [how to guide](/how-to-guides/large-file/). +For more details about using LargeFiles, check out the [how to guide](https://great-ai.scoutinscience.com/how-to-guides/large-file/). ::: great_ai.large_file.LargeFileLocal options: diff --git a/docs/tutorial/deploy.ipynb b/docs/tutorial/deploy.ipynb index 72f49da..7a96491 100644 --- a/docs/tutorial/deploy.ipynb +++ b/docs/tutorial/deploy.ipynb @@ -141,11 +141,11 @@ "\n", "Your service now has tracing and API scaffolding. Before deploying, replace the default local TinyDB tracing backend, configure storage and infrastructure, evaluate the model for its intended use, and review the remaining best practices. Model trustworthiness requires evidence and ongoing oversight beyond wrapping the inference function. You (or your organisation) remain responsible for deploying and operating the service.\n", "\n", - "There are three main approaches to deploy a GreatAI service: For more info about them, check out [the deployment how-to](/how-to-guides/use-service).\n", + "There are three main approaches to deploy a GreatAI service: For more info about them, check out [the deployment how-to](https://great-ai.scoutinscience.com/how-to-guides/use-service).\n", "\n", - "For more thorough examples, see the [examples page](/examples/simple/data).\n", + "For more thorough examples, see the [examples page](https://great-ai.scoutinscience.com/examples/simple/data).\n", "\n", - "### [Go back to the summary](/tutorial/#summary)" + "### [Go back to the summary](https://great-ai.scoutinscience.com/tutorial/#summary)" ] } ], diff --git a/docs/tutorial/index.md b/docs/tutorial/index.md index b75e95b..a94f27c 100644 --- a/docs/tutorial/index.md +++ b/docs/tutorial/index.md @@ -4,8 +4,8 @@ Let's see `great-ai` in action by going over the lifecycle of a simple service. ## Objectives -1. You will see how the [great_ai.utilities](/reference/utilities) can integrate into your Data Science workflow. -2. You will use [great_ai.large_file](/reference/large-file) to version and store your trained model. +1. You will see how the [great_ai.utilities](https://great-ai.scoutinscience.com/reference/utilities) can integrate into your Data Science workflow. +2. You will use [great_ai.large_file](https://great-ai.scoutinscience.com/reference/large-file) to version and store your trained model. 3. You will use [GreatAI][great_ai.GreatAI] to add tracing and API scaffolding to your inference function, then review the remaining deployment requirements. ## Overview @@ -52,7 +52,7 @@ After training and evaluating a model, it is exported using [great_ai.save_model save_model(model, key='my-domain-predictor') ``` - For more info, check out [the configuration how-to page](/how-to-guides/configure-service). + For more info, check out [the configuration how-to page](https://great-ai.scoutinscience.com/how-to-guides/configure-service). ### [Deployment notebook](deploy.ipynb) @@ -72,10 +72,10 @@ def predict_domain(sentence, model): 1. [@use_model][great_ai.use_model] loads and injects your model into the `predict_domain` function's `model` argument. You can freely reference it, knowing that the function is always provided with it. -Finally, we test the model's inference function through the GreatAI dashboard. Before production, configure a suitable tracing database and storage, evaluate the model for its intended use, review the remaining best practices, and [deploy the service](/how-to-guides/use-service). +Finally, we test the model's inference function through the GreatAI dashboard. Before production, configure a suitable tracing database and storage, evaluate the model for its intended use, review the remaining best practices, and [deploy the service](https://great-ai.scoutinscience.com/how-to-guides/use-service).
-[:material-book: Learn about all the features](/how-to-guides/create-service){ .md-button .md-button--primary } +[:material-book: Learn about all the features](https://great-ai.scoutinscience.com/how-to-guides/create-service){ .md-button .md-button--primary } -[:material-test-tube: Look at more examples](/examples/simple/data){ .md-button .md-button--secondary } +[:material-test-tube: Look at more examples](https://great-ai.scoutinscience.com/examples/simple/data){ .md-button .md-button--secondary }
diff --git a/docs/tutorial/train.ipynb b/docs/tutorial/train.ipynb index 26c0b21..7333ec2 100644 --- a/docs/tutorial/train.ipynb +++ b/docs/tutorial/train.ipynb @@ -32,7 +32,7 @@ "source": [ "First, we have to get some data. After downloading it [from here](https://github.com/allenai/scibert/tree/master/data/text_classification/mag), we might notice that the dataset is in [JSON Lines](https://jsonlines.org/) format (each line is a separate JSON document). \n", "\n", - "Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also [cleaned](/reference/utilities/#great_ai.utilities.clean.clean) to remove any LaTeX, XML, unicode, PDF-extraction artifacts." + "Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also [cleaned](https://great-ai.scoutinscience.com/reference/utilities/#great_ai.utilities.clean.clean) to remove any LaTeX, XML, unicode, PDF-extraction artifacts." ] }, { @@ -58,7 +58,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, we can load the dataset and extract the *training* samples from it. Since we're impatient, we can do it in parallel using the [`simple_parallel_map`](/reference/utilities/#great_ai.utilities.simple_parallel_map) function.\n", + "Now, we can load the dataset and extract the *training* samples from it. Since we're impatient, we can do it in parallel using the [`simple_parallel_map`](https://great-ai.scoutinscience.com/reference/utilities/#great_ai.utilities.simple_parallel_map) function.\n", "\n", "> Open files in Python are iterable: in text mode, each iteration returns the next line." ] @@ -292,7 +292,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### [Let's continue by finishing the deployment in the next notebook.](/tutorial/deploy)" + "### [Let's continue by finishing the deployment in the next notebook.](https://great-ai.scoutinscience.com/tutorial/deploy)" ] } ],