refactor: add docs and remove connect function

CHANGELOG.md

@@ -10,14 +10,20 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
-- Add support for CSV files to the `fit` function. [#592](https://github.com/jina-ai/finetuner/pull/592)
+- Add support for CSV files to the `fit` function. ([#592](https://github.com/jina-ai/finetuner/pull/592))
 
 ### Removed
 
+- Remove `connect` function. ([#596](https://github.com/jina-ai/finetuner/pull/596))
+
 ### Changed
 
+- Enhance documentation of login functionalities. ([#596](https://github.com/jina-ai/finetuner/pull/596))
+
 ### Fixed
 
+- Fix links to functions in the documentation. ([#596](https://github.com/jina-ai/finetuner/pull/596))
+
 ### Docs
 
 

docs/get-started/how-it-works.md

@@ -36,7 +36,7 @@ From an engineering perspective,
 we have hidden all the complexity of machine learning algorithms and resource configuration (such as GPUs).
 All you need to do is decide on your backbone model and prepare your training data.
 
-Once you have logged in to the Jina Ecosystem with {meth}`~finetuner.login()` or `~finetuner.notebook_login()`, 
+Once you have logged in to the Jina Ecosystem with {meth}`~finetuner.login()` or {func}`~finetuner.notebook_login()`,
 Finetuner will push your training data into our *Cloud Artifact Storage* (only visible to you).
 At the same time, we will spin-up an isolated computational resource
 with proper memory, CPU, GPU dedicated to your fine-tuning job.

docs/notebooks/image_to_image.ipynb

@@ -171,7 +171,7 @@
         "but recommended in order to retrieve your run easily and have some context about it.\n",
         "* Furthermore, we had to provide names of the `train_data`.\n",
         "* We set `TripletMarginLoss`.\n",
-        "* Additionally, we use {class}`~finetuner.callback.EvaluationCallback` for evaluation.\n",
+        "* Additionally, we use `finetuner.callback.EvaluationCallback` for evaluation.\n",
         "* Lastly, we set the number of `epochs` and provide a `learning_rate`."
       ],
       "metadata": {
@@ -224,8 +224,8 @@
       "cell_type": "markdown",
       "source": [
         "## Evaluating\n",
-        "Currently, we don't have a user-friendly way to get evaluation metrics from the `~finetuner.callback.EvaluationCallback` we initialized previously.\n",
-        "What you can do for now is to call `~finetuner.run.Run.logs()` in the end of the run and see evaluation results:\n",
+        "Currently, we don't have a user-friendly way to get evaluation metrics from the `finetuner.callback.EvaluationCallback` we initialized previously.\n",
+        "What you can do for now is to call `run.logs()` in the end of the run and see evaluation results:\n",
         "\n",
         "```bash\n",
         "  Training [5/5] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 76/76 0:00:00 0:03:15 • loss: 0.003\n",

docs/notebooks/image_to_image.md

@@ -111,7 +111,7 @@ Let's understand what this piece of code does:
 but recommended in order to retrieve your run easily and have some context about it.
 * Furthermore, we had to provide names of the `train_data`.
 * We set `TripletMarginLoss`.
-* Additionally, we use {class}`~finetuner.callback.EvaluationCallback` for evaluation.
+* Additionally, we use `finetuner.callback.EvaluationCallback` for evaluation.
 * Lastly, we set the number of `epochs` and provide a `learning_rate`.
 <!-- #endregion -->
 
@@ -142,8 +142,8 @@ You can continue monitoring the runs by checking the status - `finetuner.run.Run
 
 <!-- #region id="BMpQxydypeZ3" -->
 ## Evaluating
-Currently, we don't have a user-friendly way to get evaluation metrics from the `~finetuner.callback.EvaluationCallback` we initialized previously.
-What you can do for now is to call `~finetuner.run.Run.logs()` in the end of the run and see evaluation results:
+Currently, we don't have a user-friendly way to get evaluation metrics from the `finetuner.callback.EvaluationCallback` we initialized previously.
+What you can do for now is to call `run.logs()` in the end of the run and see evaluation results:
 
 ```bash
   Training [5/5] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 76/76 0:00:00 0:03:15 • loss: 0.003

docs/notebooks/text_to_image.ipynb

@@ -12,7 +12,8 @@
     },
     "language_info": {
       "name": "python"
-    }
+    },
+    "accelerator": "GPU"
   },
   "cells": [
     {
@@ -280,7 +281,7 @@
       "cell_type": "code",
       "source": [
         "text_da = DocumentArray([Document(text='some text to encode')])\n",
-        "image_da = DocumentArray([Document(uri='my-image.png')])\n",
+        "image_da = DocumentArray([Document(uri='https://www.collinsdictionary.com/images/full/apple_158989157.jpg')])\n",
         "\n",
         "clip_text_encoder = finetuner.get_model(artifact=artifact, select_model='clip-text')\n",
         "clip_image_encoder = finetuner.get_model(artifact=artifact, select_model='clip-vision')\n",

docs/notebooks/text_to_image.md

@@ -177,7 +177,7 @@ let's use the fine-tuned model to encode a new `Document`:
 
 ```python id="v95QsuEyzE-B"
 text_da = DocumentArray([Document(text='some text to encode')])
-image_da = DocumentArray([Document(uri='my-image.png')])
+image_da = DocumentArray([Document(uri='https://www.collinsdictionary.com/images/full/apple_158989157.jpg')])
 
 clip_text_encoder = finetuner.get_model(artifact=artifact, select_model='clip-text')
 clip_image_encoder = finetuner.get_model(artifact=artifact, select_model='clip-vision')

docs/walkthrough/integrate-with-jina.md

@@ -7,7 +7,7 @@ It is worth noting that, while training data can be provided as a csv, data for
 (integrate-with-docarray)=
 ## Embed DocumentArray
 
-To embed a [DocumentArray](https://docarray.jina.ai/) with a fine-tuned model, you can get the model of your Run via the `get_model` function and embed it via the `encode` function:
+To embed a [DocumentArray](https://docarray.jina.ai/) with a fine-tuned model, you can get the model of your Run via the {func}`~finetuner.get_model` function and embed it via the {func}`finetuner.encode` function:
 
 ````{tab} Artifact id and token
 ```python
@@ -121,7 +121,7 @@ executors:
 ````
 
 As you can see, it's super easy! 
-If you did not call `save_artifact`,
+If you did not call {func}`~finetuner.run.Run.save_artifact`,
 you need to provide the `artifact_id` and `token`.
 `FinetunerExecutor` will automatically pull your model from the Jina AI Cloud to the container.
 
@@ -169,7 +169,7 @@ models: `clip-vision` and `clip-text`.
 The vision model allows you to embed images and the text model can encode text passages
 into the same vector space.
 To use those models, you have to provide the name of the model via an additional
-`select_model` parameter to the `get_model` function.
+`select_model` parameter to the {func}`~finetuner.get_model` function.
 
 
 ````{tab} CLIP text model

docs/walkthrough/login.md

@@ -2,7 +2,7 @@
 # Login
 
 Since Finetuner leverages cloud resources for fine-tuning,
-you are required to {meth}`~finetuner.login()` (or `~finetuner.notebook_login()`) and obtain a token from Jina before starting a fine-tuning job.
+you are required to {meth}`~finetuner.login()` (or {func}`~finetuner.notebook_login()`) and obtain a token from Jina before starting a fine-tuning job.
 It is as simple as:
 
 ```python
@@ -12,10 +12,17 @@ finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Googl
 ```
 
 A browser window should pop up with different login options.
-After {meth}`~finetuner.login()` or `~finetuner.notebook_login()` you will see this in your terminal:
+After {meth}`~finetuner.login()` or {func}`~finetuner.notebook_login()` you will see this in your terminal:
 
 ```bash
 🔐 Successfully logged in to Jina AI as [USER NAME]!
+```
+
+ Now, an authentication token is generated which can be read with the {func}`~finetuner.get_token` function.
+If you have been logged in before, the existing token will not be overwritten, however, if you want this to be happen, you can set the `force` attribute in the login function to true.
+
+```
+finetuner.login(force=True)
 ```
 
 ```{admonition} Why do I need to login?

finetuner/__init__.py

@@ -34,18 +34,21 @@
 ft = Finetuner()
 
 
-def login(force: bool = False):
+def login(force: bool = False) -> None:
+    """
+    Login to Jina AI to use cloud-based fine-tuning. Thereby, a authentication token is
+    generated which can be read with the :func:`~finetuner.get_token` function.
+
+    :param force: If set to true, an existing token will be overwritten. Otherwise,
+        you will not login again, if a valid token already exists.
+    """
     ft.login(force=force)
 
 
 def notebook_login(force: bool = False):
     ft.notebook_login(force=force)
 
 
-def connect():
-    ft.connect()
-
-
 def list_callbacks() -> Dict[str, callback.CallbackStubType]:
     """List available callbacks."""
     return {

finetuner/finetuner.py

@@ -40,12 +40,6 @@ def notebook_login(self, force: bool = False):
         """
         hubble.notebook_login(force=force, post_success=self._init_state)
 
-    def connect(self):
-        """Connects finetuner to Hubble without logging in again.
-        Use this function, if you are already logged in.
-        """
-        self._init_state()
-
     @staticmethod
     def _get_cwd() -> str:
         """Returns current working directory."""