Rename UI launch command; auto-open browser; update Colab links to wl-*

README.md

@@ -53,7 +53,11 @@ pip install weightslab
 ```
 **2. Launch Studio**
 ```bash
+<<<<<<< HEAD
+weightslab launch  # then open https://localhost:5173 🚀
+=======
 weightslab start  # preferred localhost:50051; falls back to a free port if busy 🚀
+>>>>>>> a6f0c1acbac49221358a21b0bde87b348c09204b
 ```
 **3. Wrap your training script**
 ```python
@@ -116,7 +120,11 @@ For a detailed installation guide and advanced configuration &rarr; [Installatio
 3. **Run your script, then launch the UI in a separate terminal:**
 ```bash
    python train.py
+<<<<<<< HEAD
+   weightslab launch
+=======
    weightslab start
+>>>>>>> a6f0c1acbac49221358a21b0bde87b348c09204b
 ```
 
 4. **Open your browser** at the URL printed by `weightslab start` and inspect your training in real time.

docs/_static/examples-gallery.js

@@ -20,39 +20,39 @@
       desc: 'CNN digit classifier on MNIST. Register hyperparameters, monitor per-sample loss, and use the deny-aware sampler to focus on hard examples.',
       tags: ['classification', 'supervised', 'mnist', 'cnn'],
       url: 'examples/pytorch/classification.html',
-      colab: COLAB + 'PyTorch/ws-classification.ipynb'
+      colab: COLAB + 'PyTorch/wl-classification.ipynb'
     },
     {
       badge: 'PyTorch', color: 'pytorch',
       title: 'Segmentation — BDD100k',
       desc: 'Per-pixel semantic segmentation with a UNet. Track per-sample IoU and visualise mask overlays directly in the studio.',
       tags: ['segmentation', 'semantic', 'bdd100k', 'masks', 'dense prediction'],
       url: 'examples/pytorch/segmentation.html',
-      colab: COLAB + 'PyTorch/ws-segmentation.ipynb'
+      colab: COLAB + 'PyTorch/wl-segmentation.ipynb'
     },
     {
       badge: 'PyTorch', color: 'pytorch',
       title: 'Detection — Penn-Fudan',
       desc: 'Bounding-box detection on Penn-Fudan pedestrians. Per-instance multi-index dataframe with (sample_id, annotation_id) keys.',
       tags: ['detection', 'object detection', 'bounding boxes', 'penn-fudan'],
       url: 'examples/pytorch/detection.html',
-      colab: COLAB + 'PyTorch/ws-detection.ipynb'
+      colab: COLAB + 'PyTorch/wl-detection.ipynb'
     },
     {
       badge: 'PyTorch', color: 'pytorch',
       title: 'Clustering — Face Recognition',
       desc: 'Metric learning with triplet loss on face datasets. Store and explore high-dimensional embeddings per sample in the studio.',
       tags: ['clustering', 'unsupervised', 'embeddings', 'face recognition', 'metric learning'],
       url: 'examples/pytorch/clustering.html',
-      colab: COLAB + 'PyTorch/ws-clustering.ipynb'
+      colab: COLAB + 'PyTorch/wl-clustering.ipynb'
     },
     {
       badge: 'PyTorch', color: 'pytorch',
       title: 'Generation / Anomaly Detection',
       desc: 'Unsupervised anomaly detection on MVTec with a multi-task UNet. Monitor reconstruction quality and per-sample anomaly scores.',
       tags: ['anomaly detection', 'generation', 'unsupervised', 'mvtec', 'reconstruction'],
       url: 'examples/pytorch/generation.html',
-      colab: COLAB + 'PyTorch/ws-generation.ipynb'
+      colab: COLAB + 'PyTorch/wl-generation.ipynb'
     },
     {
       badge: 'Lightning', color: 'lightning',
@@ -81,7 +81,7 @@
       desc: 'Dynamic subscribed signal that classifies each sample\'s loss trajectory (monotonic, U-shape, spiked, …) and auto-tags it.',
       tags: ['loss analysis', 'signal', 'categorical tag', 'per-sample', 'trajectory'],
       url: 'examples/usecases/loss_shape_classification.html',
-      colab: COLAB + 'Usecases/ws-segmentation-loss-shapes.ipynb'
+      colab: COLAB + 'Usecases/wl-segmentation-loss-shapes-classification.ipynb'
     }
   ];
 

docs/_static/wl-ribbon.js

@@ -10,7 +10,11 @@
     'The studio streams signals in real-time — no need to wait for an epoch to end to see results.',
     '<code>weightslab start example --cls</code> launches a full MNIST classification demo in one command.',
     'Use <code>subscribe_to=</code> on a signal to build reactive per-sample analytics derived from other signals.',
+<<<<<<< HEAD
+    'Run <code>weightslab launch --certs</code> to enable HTTPS + mTLS for secure remote studio access.',
+=======
     'Run <code>weightslab start --certs</code> to enable HTTPS + mTLS for secure remote studio access.',
+>>>>>>> a6f0c1acbac49221358a21b0bde87b348c09204b
     'Set <code>preload_labels=False</code> for large datasets to speed up startup; labels are loaded lazily.',
     'Use <code>array_return_proxies=True</code> (default) to avoid loading the full dataset array into RAM.',
     'Set <code>WEIGHTSLAB_LOG_LEVEL=DEBUG</code> to see full gRPC logs when debugging connectivity issues.',

docs/configuration.rst

@@ -354,15 +354,23 @@ Deploying the Studio
 ~~~~~~~~~~~~~~~~~~~~~
 
 All Weights Studio configuration variables are passed to the UI at launch time
+<<<<<<< HEAD
+via ``weightslab launch``.  There are two ways to supply them.
+=======
 via ``weightslab start``.  There are two ways to supply them.
+>>>>>>> a6f0c1acbac49221358a21b0bde87b348c09204b
 
 **Option 1 — shell exports (quick, per-session)**
 
 .. code-block:: bash
 
    export ENABLE_AGENT=0
    export BB_THUMB_RENDER=50
+<<<<<<< HEAD
+   weightslab launch
+=======
   weightslab start
+>>>>>>> a6f0c1acbac49221358a21b0bde87b348c09204b
 
 **Option 2 — ``.env`` file (persistent, version-controllable)**
 
@@ -380,7 +388,11 @@ Then launch normally:
 
 .. code-block:: bash
 
+<<<<<<< HEAD
+   weightslab launch
+=======
   weightslab start
+>>>>>>> a6f0c1acbac49221358a21b0bde87b348c09204b
 
 WeightsLab loads the ``.env`` file automatically.  Shell exports take precedence
 over ``.env`` values.

docs/examples/pytorch/classification.rst

@@ -157,14 +157,18 @@ debug values, custom distances, etc.
 
    .. code-block:: bash
 
+<<<<<<< HEAD
+      weightslab launch           # 1. deploy the studio
+=======
       weightslab start               # 1. deploy the studio
+>>>>>>> a6f0c1acbac49221358a21b0bde87b348c09204b
       weightslab start example --cls # 2. start the classification demo
 
 
 .. raw:: html
 
    <div style="text-align:right; margin-top:2rem;">
-     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/ws-classification.ipynb" target="_blank" rel="noopener noreferrer">
+     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-classification.ipynb" target="_blank" rel="noopener noreferrer">
        <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
      </a>
    </div>

docs/examples/pytorch/clustering.rst

@@ -93,14 +93,18 @@ silhouette score to identify consistently confused identities.
 
    .. code-block:: bash
 
+<<<<<<< HEAD
+      weightslab launch            # 1. deploy the studio
+=======
       weightslab start                # 1. deploy the studio
+>>>>>>> a6f0c1acbac49221358a21b0bde87b348c09204b
       weightslab start example --clus # 2. start the clustering demo
 
 
 .. raw:: html
 
    <div style="text-align:right; margin-top:2rem;">
-     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/ws-clustering.ipynb" target="_blank" rel="noopener noreferrer">
+     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-clustering.ipynb" target="_blank" rel="noopener noreferrer">
        <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
      </a>
    </div>

docs/examples/pytorch/detection.rst

@@ -125,14 +125,18 @@ pipeline. See :ref:`good-practice-get-items` for the recommended signature.
 
    .. code-block:: bash
 
+<<<<<<< HEAD
+      weightslab launch           # 1. deploy the studio
+=======
     weightslab start               # 1. deploy the studio
+>>>>>>> a6f0c1acbac49221358a21b0bde87b348c09204b
       weightslab start example --det # 2. start the detection demo
 
 
 .. raw:: html
 
    <div style="text-align:right; margin-top:2rem;">
-     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/ws-detection.ipynb" target="_blank" rel="noopener noreferrer">
+     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-detection.ipynb" target="_blank" rel="noopener noreferrer">
        <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
      </a>
    </div>

docs/examples/pytorch/generation.rst

@@ -99,5 +99,9 @@ filter by pair distance to find the hardest negatives.
 
    .. code-block:: bash
 
+<<<<<<< HEAD
+      weightslab launch           # 1. deploy the studio
+=======
       weightslab start               # 1. deploy the studio
+>>>>>>> a6f0c1acbac49221358a21b0bde87b348c09204b
       weightslab start example --gen # 2. start the generation / anomaly demo

docs/examples/pytorch/segmentation.rst

@@ -112,14 +112,18 @@ these signals over training steps and lets you sort samples by them.
 
    .. code-block:: bash
 
+<<<<<<< HEAD
+      weightslab launch           # 1. deploy the studio
+=======
       weightslab start               # 1. deploy the studio
+>>>>>>> a6f0c1acbac49221358a21b0bde87b348c09204b
       weightslab start example --seg # 2. start the segmentation demo
 
 
 .. raw:: html
 
    <div style="text-align:right; margin-top:2rem;">
-     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/ws-segmentation.ipynb" target="_blank" rel="noopener noreferrer">
+     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/PyTorch/wl-segmentation.ipynb" target="_blank" rel="noopener noreferrer">
        <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
      </a>
    </div>

docs/examples/usecases/lidar_detection.rst

@@ -120,7 +120,11 @@ and override ``load_points`` and optionally ``render_thumbnail_2d``:
 
    .. code-block:: bash
 
+<<<<<<< HEAD
+      weightslab launch               # 1. deploy the studio
+=======
     weightslab start                   # 1. deploy the studio
+>>>>>>> a6f0c1acbac49221358a21b0bde87b348c09204b
       weightslab start example --2d_det  # 2a. 2D pillar-grid detection
       # or
       weightslab start example --3d_det  # 2b. 3D bounding-box detection

docs/examples/usecases/loss_shape_classification.rst

@@ -213,7 +213,7 @@ Workflow in the studio
 .. raw:: html
 
    <div style="text-align:right; margin-top:2rem;">
-     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Usecases/ws-segmentation-loss-shapes.ipynb" target="_blank" rel="noopener noreferrer">
+     <a href="https://colab.research.google.com/github/GrayboxTech/weightslab/blob/main/weightslab/examples/Notebooks/Usecases/wl-segmentation-loss-shapes-classification.ipynb" target="_blank" rel="noopener noreferrer">
        <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
      </a>
    </div>

docs/four_way_approach.rst

@@ -53,6 +53,6 @@ Typical integration flow to custom Python script
   .. code-block:: bash
 
    weightslab start
-     python train.py
+   python train.py
 
 - Resume training from the UI and use tags/discards/signals to iteratively improve data and model behavior.

docs/quickstart.rst

@@ -139,7 +139,7 @@ Pass ``--certs`` to generate (if missing) and use TLS certificates + a gRPC auth
 .. code-block:: bash
 
    weightslab start              # unsecured HTTP (default)
-   weightslab start --certs    # secured HTTPS + gRPC auth (run `weightslab se` first)
+   weightslab start --certs      # secured HTTPS + gRPC auth (run `weightslab se` first)
 
 .. important::