Minor fixes

docs/examples/simple/data.ipynb

@@ -92,7 +92,13 @@
     "from typing import List, Tuple\n",
     "import json\n",
     "import gzip\n",
-    "from great_ai.utilities import simple_parallel_map, clean, is_english, predict_language, unchunk\n",
+    "from great_ai.utilities import (\n",
+    "    simple_parallel_map,\n",
+    "    clean,\n",
+    "    is_english,\n",
+    "    predict_language,\n",
+    "    unchunk,\n",
+    ")\n",
     "\n",
     "\n",
     "def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]:\n",
@@ -119,7 +125,9 @@
     "    ]\n",
     "\n",
     "\n",
-    "preprocessed_data = unchunk(simple_parallel_map(preprocess_chunk, chunks, concurrency=4))"
+    "preprocessed_data = unchunk(\n",
+    "    simple_parallel_map(preprocess_chunk, chunks, concurrency=4)\n",
+    ")"
    ]
   },
   {

docs/examples/simple/deploy.ipynb

@@ -59,6 +59,7 @@
     "    parameter,\n",
     ")\n",
     "\n",
+    "\n",
     "@GreatAI.create\n",
     "@use_model(\"small-domain-prediction\", version=\"latest\")\n",
     "@parameter(\"target_confidence\", validator=lambda c: 0 <= c <= 100)\n",

docs/how-to-guides/configure-service.md

@@ -35,9 +35,9 @@ configure(
 
 The only aspect that cannot be automated is choosing the backing storage for the database and file storage.
 
-Right now, you have 3 options for storing the models and large datasets: [great_ai.large_file.LargeFileLocal][], [great_ai.large_file.LargeFileMongo][], and [great_ai.large_file.LargeFileS3][].
+Right now, you have 3 options for storing the models and large datasets: [LargeFileLocal][great_ai.large_file.LargeFileLocal], [LargeFileMongo][great_ai.large_file.LargeFileMongo], and [LargeFileS3][great_ai.large_file.LargeFileS3].
 
-Without explicit configuration, [great_ai.large_file.LargeFileLocal][] is selected by default. This one still version-controls your files but it only stores them in a local path.
+Without explicit configuration, [LargeFileLocal][great_ai.large_file.LargeFileLocal] is selected by default. This one still version-controls your files but it only stores them in a local path.
 
 !!! important
     If your working directory contains a `mongo.ini` or `s3.ini` file, an attempt is made to auto-configure [LargeFileMongo][great_ai.large_file.LargeFileMongo] or [LargeFileS3][great_ai.large_file.LargeFileS3] respectively.
@@ -76,6 +76,7 @@ save_model(model, 'my-model')
 MONGO_CONNECTION_STRING=mongodb://localhost:27017  # this is the default value
 # if `MONGO_CONNECTION_STRING` is specified, this default is overridden
 MONGO_CONNECTION_STRING=ENV:MONGO_CONNECTION_STRING
+
 MONGO_DATABASE=my-database  # it is automatically created if doesn't exist
 ```
 
@@ -98,4 +99,4 @@ By default, a thread-safe version of [TinyDB](https://tinydb.readthedocs.io/en/l
 
 ### MongoDB
 
-At the moment, only MongoDB is supported as a production-ready `TracingDatabase`. In order to use it, you have to either place a file named `mongo.ini` in your working directory, or explicitly call [MongoDbDriver.configure_credentials_from_file][great_ai.MongoDbDriver.configure_credentials_from_file] or [MongoDbDriver.configure_credentials][great_ai.MongoDbDriver.configure_credentials].
+At the moment, only MongoDB is supported as a production-ready `TracingDatabase`. In order to use it, you have to either place a file named `mongo.ini` in your working directory, or explicitly call [MongoDbDriver.configure_credentials_from_file][great_ai.MongoDbDriver] or [MongoDbDriver.configure_credentials][great_ai.MongoDbDriver.configure_credentials].

docs/how-to-guides/create-service.md

@@ -24,7 +24,7 @@ def greeter(your_name):
 
 ## With types
 
-[Type annotating your codebase](https://realpython.com/python-type-checking/){ target=_blank } can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature.
+Even though it's not required by GreatAI, [type annotating your codebase](https://realpython.com/python-type-checking/){ target=_blank } can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature.
 
 ```python title="type_safe_greeter.py"
 from great_ai import GreatAI
@@ -100,7 +100,7 @@ assert type_safe_greeter('Andras').output == 'Hi Andras'
 Refer to the following example summarising the options you have when instantiating a GreatAI service.
 
 ```python title="complex.py"
-from great_ai import save_model, GreatAI, parameter, use_model
+from great_ai import save_model, GreatAI, parameter, use_model, log_metric
 
 save_model(4, 'secret-number')  #(1)
 
@@ -108,6 +108,10 @@ save_model(4, 'secret-number')  #(1)
 @parameter('positive_number', validator=lambda n: n > 0, disable_logging=True)
 @use_model('secret-number', version='latest', model_kwarg_name='secret')
 def add_number(positive_number: int, secret: int) -> int:
+    log_metric(
+        'log directly into the returned Trace', 
+        positive_number * 2
+    )
     return positive_number + secret
 
 assert add_number(1).output == 5

docs/how-to-guides/use-service.md

@@ -51,7 +51,8 @@ Some configuration options are also supported.
 ```sh
 great-ai greeter.py --port 8000 --host 127.0.0.1 --timeout_keep_alive 10
 ```
-> For more options (but no Notebook support, use [uvicorn](https://www.uvicorn.org/){ target=_blank })
+??? note "More options"
+    For more options (but no Notebook support), simply use [uvicorn](https://www.uvicorn.org/){ target=_blank } for starting your app (available at `greeter.app`).
 
 ### In production
 
@@ -80,7 +81,7 @@ docker run -p 6060:6060 --volume `pwd`:/app --rm \
 
 #### Use a Platform-as-a-Service
 
-Similarly to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as [DO App platform](https://www.digitalocean.com/products/app-platform){ target=_blank } or [MLEM](https://mlem.ai/){ target=_blank }) that take a Docker image as a source and handle the rest of the deployment.
+Similarly to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as [AWS ECS](https://aws.amazon.com/ecs/){ target=_blank }, [DO App platform](https://www.digitalocean.com/products/app-platform){ target=_blank }, [MLEM](https://mlem.ai/){ target=_blank }) that take a Docker image as a source and handle the rest of the deployment.
 
 To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as [PyTorch](https://pytorch.org/){ target=_blank } or [TensorFlow](https://www.tensorflow.org/){ target=_blank }.
 

docs/tutorial/deploy.ipynb

@@ -143,7 +143,7 @@
     "\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",
     "\n",
-    "For more thorough examples, see the [examples page](/examples).\n",
+    "For more thorough examples, see the [examples page](/examples/simple/data).\n",
     "\n",
     "### [Go back to the summary](/tutorial/#summary)"
    ]

docs/tutorial/index.md

@@ -25,7 +25,7 @@ We use the same synthetic dataset derived from the [Microsoft Academic Graph](ht
 
 ## Summary
 
-### The [training notebook](train.ipynb)
+### [Training notebook](train.ipynb)
 
 We load and preprocess the dataset while relying on [great_ai.utilities.clean][great_ai.utilities.clean.clean] for doing the heavy-lifting. Additionally, the preprocessing is parallelised using [great_ai.utilities.simple_parallel_map][]
 
@@ -52,7 +52,7 @@ After training and evaluating a model, it is exported using [great_ai.save_model
 
     For more info, checkout [the configuration how-to page](/how-to-guides/configure-service).
 
-### The [deployment notebook](deploy.ipynb)
+### [Deployment notebook](deploy.ipynb)
 
 We create an inference function that can be hardened by wrapping it in a [GreatAI][great_ai.GreatAI] instance.
 
@@ -73,5 +73,7 @@ def predict_domain(sentence, model):
 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)
 
 <div style="display: flex; justify-content: center;" markdown>  
-[:material-book: Learn about more features](/how-to-guides/create-service){ .md-button .md-button--primary }
+[:material-book: Learn about all the features](/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 }
 </div>