feat: adopt hubble sdk on notebook login

CHANGELOG.md

@@ -31,6 +31,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Change CLIP fine-tuning example in the documentation ([#569](https://github.com/jina-ai/finetuner/pull/569))
 
+- Use latest Hubble with `notebook_login` support ([#576](https://github.com/jina-ai/finetuner/pull/576))
+
 ### Fixed
 
 - Bump flake8 to `5.0.4`. ([#568](https://github.com/jina-ai/finetuner/pull/568))

README.md

@@ -129,7 +129,7 @@ The following code snippet describes how to fine-tune ResNet50 on [Totally Looks
 import finetuner
 from finetuner.callback import EvaluationCallback
 
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook/Google Colab
 
 run = finetuner.fit(
     model='resnet50',
@@ -149,7 +149,7 @@ Fine-tuning might take 5 minute to finish. You can later re-connect your run wit
 ```python
 import finetuner
 
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 
 run = finetuner.get_run('resnet50-tll-run')
 

docs/_templates/template_ft_in_action.md

@@ -56,7 +56,7 @@ print(run.status())
 "Since some runs might take up to several hours/days, you can reconnect to your run very easily to monitor its status and logs."
 ```python
 import finetuner
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 run = finetuner.get_run('my_run')
 ```
 

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 logged into the Jina Ecosystem with {meth}`~finetuner.login()`, 
+Once you have logged in to the Jina Ecosystem with {meth}`~finetuner.login()` or `~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/tasks/image-to-image.md

@@ -40,10 +40,10 @@ For this example, we're gonna go with `resnet50`.
 ## Fine-tuning
 From now on, all the action happens in the cloud! 
 
-First you need to {ref}`login to Jina ecosystem <login-to-jina-ecosystem>`:
+First, {ref}`log in to the Jina ecosystem <login-to-jina-ecosystem>`:
 ```python
 import finetuner
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 ```
 
 Now, you can easily start a fine-tuning job with {meth}`~finetuner.fit`:
@@ -95,7 +95,7 @@ Since some runs might take up to several hours, it's important to know how to re
 
 ```python
 import finetuner
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 
 run = finetuner.get_run('resnet-tll')
 ```

docs/tasks/text-to-image.md

@@ -29,7 +29,7 @@ From now on, all the action happens in the cloud!
 First you need to {ref}`login to Jina ecosystem <login-to-jina-ecosystem>`:
 ```python
 import finetuner
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 ```
 
 Now that everything's ready, let's create a fine-tuning run!
@@ -73,7 +73,7 @@ Since some runs might take up to several hours/days, it's important to know how
 
 ```python
 import finetuner
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login in jupyter notebook/google colab
 run = finetuner.get_run('clip-fashion')
 ```
 

docs/tasks/text-to-text.md

@@ -103,7 +103,7 @@ import finetuner
 from finetuner.callback import EvaluationCallback
 
 # Make sure to login to Jina Cloud
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 
 # Start fine-tuning as a run within an experiment
 run = finetuner.fit(
@@ -156,7 +156,7 @@ Since some runs might take up to several hours, you can reconnect to your run ve
 ```python
 import finetuner
 
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 run = finetuner.get_run('finetune-quora-dataset-bert-base-cased')
 print(f'Run status: {run.status()}')
 ```
@@ -168,7 +168,7 @@ Our `EvaluationCallback` during fine-tuning ensures that after each epoch, an ev
 ```python
 import finetuner
 
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 run = finetuner.get_run('finetune-quora-dataset-bert-base-cased')
 print(f'Run logs: {run.logs()}')
 ```

docs/walkthrough/index.md

@@ -22,7 +22,7 @@ import finetuner
 from docarray import DocumentArray
 
 # Login to Jina ecosystem
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 
 # Prepare training data
 train_data = DocumentArray(...)
@@ -43,7 +43,7 @@ run.save_artifact(directory='experiment')
 You should see this in your terminal:
 
 ```bash
-🔐 Successfully login to Jina ecosystem!
+🔐 Successfully logged in to Jina AI as [USER NAME]!
 Run name: vigilant-tereshkova
 Run logs:
 

docs/walkthrough/integrate-with-jina.md

@@ -13,7 +13,7 @@ To embed a [DocumentArray](https://docarray.jina.ai/) with a fine-tuned model, y
 from docarray import DocumentArray, Document
 import finetuner
 
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 
 token = finetuner.get_token()
 run = finetuner.get_run(
@@ -76,7 +76,7 @@ You have three options:
 import finetuner
 from jina import Flow
 
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab()
 
 token = finetuner.get_token()
 run = finetuner.get_run(
@@ -172,7 +172,7 @@ To use those models, you have to provide the name of the model via an additional
 from docarray import DocumentArray, Document
 import finetuner
 
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 
 token = finetuner.get_token()
 run = finetuner.get_run(
@@ -192,7 +192,7 @@ finetuner.encode(model=model, data=da)
 from docarray import DocumentArray, Document
 import finetuner
 
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 
 token = finetuner.get_token()
 run = finetuner.get_run(
@@ -215,7 +215,7 @@ If you want to host the CLIP models, you also have to provide the name of the mo
 import finetuner
 from jina import Flow
 
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 
 token = finetuner.get_token()
 run = finetuner.get_run(

docs/walkthrough/login.md

@@ -2,20 +2,20 @@
 # Login
 
 Since Finetuner leverages cloud resources for fine-tuning,
-you are required to {meth}`~finetuner.login()` and obtain a token from Jina before starting a fine-tuning job.
+you are required to {meth}`~finetuner.login()` (or `~finetuner.notebook_login()`) and obtain a token from Jina before starting a fine-tuning job.
 It is as simple as:
 
 ```python
 import finetuner
 
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 ```
 
 A browser window should pop up with different login options.
-After {meth}`~finetuner.login()` you will see this in your terminal:
+After {meth}`~finetuner.login()` or `~finetuner.notebook_login()` you will see this in your terminal:
 
 ```bash
-🔐 Successfully login to Jina ecosystem!
+🔐 Successfully logged in to Jina AI as [USER NAME]!
 ```
 
 ```{admonition} Why do I need to login?

docs/walkthrough/save-model.md

@@ -21,7 +21,7 @@ In the example below, we show how to connect to an existing run and download a t
 ```python
 import finetuner
 
-finetuner.login()
+finetuner.login()  # use finetuner.notebook_login() in Jupyter notebook or Google colab
 
 # connect to the experiment we created previously.
 experiment = finetuner.get_experiment('finetune-flickr-dataset')
@@ -40,7 +40,7 @@ If the fine-tuning finished,
 you can see this in the terminal:
 
 ```bash
-🔐 Successfully login to Jina Ecosystem!
+🔐 Successfully logged in to Jina AI as [USER NAME]!
 Run status: FINISHED
 Run Artifact id: 62972acb5de25a53fdbfcecc
 Run logs:

finetuner/__init__.py

@@ -34,6 +34,10 @@ def login():
     ft.login()
 
 
+def notebook_login():
+    ft.notebook_login()
+
+
 def connect():
     ft.connect()
 

finetuner/finetuner.py

@@ -27,6 +27,17 @@ def login(self):
         hubble.login()
         self._init_state()
 
+    def notebook_login(self, force: bool = False):
+        """Log in to Hubble account, initialize a client object
+        and create a default experiment.
+
+        :param force: If set to true, overwrite token and re-login.
+
+        Note: This works for Jupyter notebook, Google Colab..
+        """
+        hubble.notebook_login(force=force)
+        self._init_state()
+
     def connect(self):
         """Connects finetuner to Hubble without logging in again.
         Use this function, if you are already logged in.

setup.py

@@ -64,7 +64,7 @@
         install_requires=[
             'docarray[common]>=0.13.31',
             'finetuner-stubs==0.10.4',
-            'jina-hubble-sdk>=0.19.0',
+            'jina-hubble-sdk>=0.20.2',
         ],
         extras_require={
             'full': [