feat: Migrate to HeliaEdge.

.devcontainer/devcontainer.json

@@ -46,6 +46,7 @@
     "customizations": {
         "vscode": {
             "extensions": [
+                "openai.chatgpt",
                 "GitHub.copilot",
                 "GitHub.copilot-chat",
                 "ms-toolsai.jupyter",

.github/workflows/release-please-lock.yaml

@@ -0,0 +1,39 @@
+name: Sync uv.lock for release PRs
+
+on:
+  pull_request:
+    types:
+      - opened
+      - synchronize
+      - ready_for_review
+      - reopened
+
+jobs:
+  update-uv-lock:
+    if: startsWith(github.head_ref, 'release-please--')
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - name: Checkout release PR branch
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.head_ref }}
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+      - name: Regenerate uv.lock
+        run: uv lock
+      - name: Commit updated lockfile
+        if: ${{ !cancelled() }}
+        run: |
+          if git diff --quiet -- uv.lock; then
+            echo "uv.lock already up to date."
+            exit 0
+          fi
+          git config user.name "github-actions[bot]"
+          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
+          git add uv.lock
+          git commit -m "chore: sync uv.lock after version bump"
+          git push

.github/workflows/release-please.yaml

@@ -0,0 +1,20 @@
+name: Release Please
+
+on:
+  push:
+    branches:
+      - main
+  workflow_dispatch:
+
+jobs:
+  release-please:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+      pull-requests: write
+      issues: write
+    steps:
+      - uses: googleapis/release-please-action@v4
+        with:
+          config-file: release-please-config.json
+          manifest-file: .release-please-manifest.json

.github/workflows/release.yaml

@@ -1,9 +1,11 @@
-name: Release to PyPi
+name: Release to PyPI
 
 on:
   release:
-    types:
-      - created
+    types: [published]
+  push:
+    tags:
+      - "v*"
   workflow_dispatch:
 
 jobs:

.release-please-manifest.json

@@ -0,0 +1,3 @@
+{
+  ".": "1.6.0"
+}

README.md

@@ -100,15 +100,15 @@ __HeartKit__ exposes several open-source datasets for training each of the Heart
 
 __HeartKit__ provides a __model factory__ that allows you to easily create and train customized models. The model factory includes a number of modern networks well suited for efficient, real-time edge applications. Each model architecture exposes a number of high-level parameters that can be used to customize the network for a given application. These parameters can be set as part of the configuration accessible via the CLI and Python package.
 
-- **[TCN](https://ambiqai.github.io/neuralspot-edge/models/tcn)**: A CNN leveraging dilated convolutions (key=`tcn`)
-- **[U-Net](https://ambiqai.github.io/neuralspot-edge/models/unet)**: A CNN with encoder-decoder architecture for segmentation tasks (key=`unet`)
-- **[U-NeXt](https://ambiqai.github.io/neuralspot-edge/models/unext)**: A U-Net variant leveraging MBConv blocks (key=`unext`)
-- **[EfficientNetV2](https://ambiqai.github.io/neuralspot-edge/models/efficientnet)**: A CNN leveraging MBConv blocks (key=`efficientnet`)
-- **[MobileOne](https://ambiqai.github.io/neuralspot-edge/models/mobileone)**: A CNN aimed at sub-1ms inference (key=`mobileone`)
-- **[ResNet](https://ambiqai.github.io/neuralspot-edge/models/resnet)**: A popular CNN often used for vision tasks (key=`resnet`)
-- **[Conformer](https://ambiqai.github.io/neuralspot-edge/models/conformer)**: A transformer composed of both convolutional and self-attention blocks (key=`conformer`)
-- **[MetaFormer](https://ambiqai.github.io/neuralspot-edge/models/metaformer)**: A transformer composed of both spatial mixing and channel mixing blocks (key=`metaformer`)
-- **[TSMixer](https://ambiqai.github.io/neuralspot-edge/models/tsmixer)**: An All-MLP Architecture for Time Series Classification (key=`tsmixer`)
+- **[TCN](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tcn)**: A CNN leveraging dilated convolutions (key=`tcn`)
+- **[U-Net](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unet)**: A CNN with encoder-decoder architecture for segmentation tasks (key=`unet`)
+- **[U-NeXt](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unext)**: A U-Net variant leveraging MBConv blocks (key=`unext`)
+- **[EfficientNetV2](https://ambiqai.github.io/helia-edge/api/helia_edge/models/efficientnet)**: A CNN leveraging MBConv blocks (key=`efficientnet`)
+- **[MobileOne](https://ambiqai.github.io/helia-edge/api/helia_edge/models/mobileone)**: A CNN aimed at sub-1ms inference (key=`mobileone`)
+- **[ResNet](https://ambiqai.github.io/helia-edge/api/helia_edge/models/resnet)**: A popular CNN often used for vision tasks (key=`resnet`)
+- **[Conformer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/conformer)**: A transformer composed of both convolutional and self-attention blocks (key=`conformer`)
+- **[MetaFormer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/metaformer)**: A transformer composed of both spatial mixing and channel mixing blocks (key=`metaformer`)
+- **[TSMixer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tsmixer)**: An All-MLP Architecture for Time Series Classification (key=`tsmixer`)
 - **[Bring-Your-Own-Model (BYOM)](https://ambiqai.github.io/heartkit/models/byom)**: Register new SoTA model architectures w/ custom configurations
 
 ---

docs/css/custom.css

@@ -38,7 +38,7 @@ a.internal-link::after {
 }
 
 .md-grid {
-  max-width: 1440px;
+  max-width: 1840px;
 }
 
 /* Give space to lower icons so Gitter chat doesn't get on top of them */

docs/datasets/icentia11k.md

@@ -12,7 +12,7 @@ More info available on [PhysioNet website](https://physionet.org/content/icentia
 
     ```py linenums="1"
     from pathlib import Path
-    import neuralspot_edge as nse
+    import helia_edge as helia
     import heartkit as hk
 
     ds = hk.DatasetFactory.get('icentia11k')(
@@ -24,7 +24,7 @@ More info available on [PhysioNet website](https://physionet.org/content/icentia
 
     # Create signal generator
     data_gen = self.ds.signal_generator(
-        patient_generator=nse.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
+        patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
         frame_size=256,
         samples_per_patient=5,
         target_rate=100,

docs/datasets/lsad.md

@@ -12,7 +12,7 @@ Please visit [Physionet](https://physionet.org/content/ecg-arrhythmia/1.0.0/) fo
 
     ```py linenums="1"
     from pathlib import Path
-    import neuralspot_edge as nse
+    import helia_edge as helia
     import heartkit as hk
 
     ds = hk.DatasetFactory.get('lsad')(
@@ -24,7 +24,7 @@ Please visit [Physionet](https://physionet.org/content/ecg-arrhythmia/1.0.0/) fo
 
     # Create signal generator
     data_gen = self.ds.signal_generator(
-        patient_generator=nse.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
+        patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
         frame_size=256,
         samples_per_patient=5,
         target_rate=100,

docs/datasets/ludb.md

@@ -12,7 +12,7 @@ Please visit [Physionet](https://physionet.org/content/ludb/1.0.1/) for more det
 
     ```py linenums="1"
     from pathlib import Path
-    import neuralspot_edge as nse
+    import helia_edge as helia
     import heartkit as hk
 
     ds = hk.DatasetFactory.get('ludb')(
@@ -24,7 +24,7 @@ Please visit [Physionet](https://physionet.org/content/ludb/1.0.1/) for more det
 
     # Create signal generator
     data_gen = self.ds.signal_generator(
-        patient_generator=nse.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
+        patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
         frame_size=256,
         samples_per_patient=5,
         target_rate=100,

docs/datasets/ptbxl.md

@@ -12,7 +12,7 @@ Please visit [Physionet](https://physionet.org/content/ptb-xl/1.0.3/) for more d
 
     ```py linenums="1"
     from pathlib import Path
-    import neuralspot_edge as nse
+    import helia_edge as helia
     import heartkit as hk
 
     ds = hk.DatasetFactory.get('ptbxl')(
@@ -24,7 +24,7 @@ Please visit [Physionet](https://physionet.org/content/ptb-xl/1.0.3/) for more d
 
     # Create signal generator
     data_gen = self.ds.signal_generator(
-        patient_generator=nse.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
+        patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
         frame_size=256,
         samples_per_patient=5,
         target_rate=100,

docs/datasets/qtdb.md

@@ -10,7 +10,7 @@ Please visit [Physionet](https://doi.org/10.13026/C24K53) for more details.
 
     ```py linenums="1"
     from pathlib import Path
-    import neuralspot_edge as nse
+    import helia_edge as helia
     import heartkit as hk
 
     ds = hk.DatasetFactory.get('qtdb')(
@@ -22,7 +22,7 @@ Please visit [Physionet](https://doi.org/10.13026/C24K53) for more details.
 
     # Create signal generator
     data_gen = self.ds.signal_generator(
-        patient_generator=nse.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
+        patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
         frame_size=256,
         samples_per_patient=5,
         target_rate=100,

docs/guides/byot.ipynb

@@ -76,7 +76,7 @@
     "import tensorflow as tf\n",
     "import numpy as np\n",
     "import numpy.typing as npt\n",
-    "import neuralspot_edge as nse\n",
+    "import helia_edge as helia\n",
     "import matplotlib.pyplot as plt"
    ]
   },
@@ -90,7 +90,7 @@
     "os.environ[\"HK_DATASET_PATH\"] = os.getenv(\"HK_DATASET_PATH\", \"./datasets\")\n",
     "\n",
     "plot_theme = hk.utils.dark_theme\n",
-    "nse.utils.silence_tensorflow()\n",
+    "helia.utils.silence_tensorflow()\n",
     "_ = hk.utils.setup_plotting(plot_theme)"
    ]
   },
@@ -163,7 +163,7 @@
     "    ) -> Generator[tuple[npt.NDArray, npt.NDArray], None, None]:\n",
     "        if isinstance(samples_per_patient, Iterable):\n",
     "            samples_per_patient = samples_per_patient[0]\n",
-    "        for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle):\n",
+    "        for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle):\n",
     "            for x, y in self.patient_data_generator(pt_id, samples_per_patient):\n",
     "                yield x, y\n",
     "            # END FOR\n",
@@ -242,7 +242,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "DataloaderFactory = nse.utils.create_factory(factory=\"BYOT.DataloaderFactory\", type=hk.HKDataloader)\n",
+    "DataloaderFactory = helia.utils.create_factory(factory=\"BYOT.DataloaderFactory\", type=hk.HKDataloader)\n",
     "DataloaderFactory.register(\"ptbxl\", PtbxlDataloader)"
    ]
   },
@@ -326,14 +326,14 @@
     "\n",
     "def load_train_datasets(\n",
     "    datasets: list[hk.HKDataset],\n",
-    "    dataloaderFactory: nse.utils.ItemFactory[hk.HKDataloader],\n",
+    "    dataloaderFactory: helia.utils.ItemFactory[hk.HKDataloader],\n",
     "    params: hk.HKTaskParams,\n",
     ") -> tuple[tf.data.Dataset, tf.data.Dataset]:\n",
     "    \"\"\"Loads training and validation datasets.\n",
     "\n",
     "    Args:\n",
     "        datasets(list[hk.HKDataset]): List of datasets to load.\n",
-    "        dataloaderFactory(nse.utils.ItemFactory[hk.HKDataloader]): Factory to create dataloaders.\n",
+    "        dataloaderFactory(helia.utils.ItemFactory[hk.HKDataloader]): Factory to create dataloaders.\n",
     "        params(hk.HKTaskParams): Task parameters.\n",
     "\n",
     "    Returns:\n",
@@ -392,10 +392,10 @@
     "    \"\"\"\n",
     "    os.makedirs(params.job_dir, exist_ok=True)\n",
     "\n",
-    "    logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"train.log\")\n",
+    "    logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"train.log\")\n",
     "    logger.debug(f\"Creating working directory in {params.job_dir}\")\n",
     "\n",
-    "    params.seed = nse.utils.set_random_seed(params.seed)\n",
+    "    params.seed = helia.utils.set_random_seed(params.seed)\n",
     "    logger.debug(f\"Random seed {params.seed}\")\n",
     "\n",
     "    with open(params.job_dir / \"train_config.json\", \"w\", encoding=\"utf-8\") as fp:\n",
@@ -416,7 +416,7 @@
     "    # Load existing model\n",
     "    if params.resume and params.model_file:\n",
     "        logger.debug(f\"Loading model from file {params.model_file}\")\n",
-    "        model = nse.models.load_model(params.model_file)\n",
+    "        model = helia.models.load_model(params.model_file)\n",
     "        params.model_file = None\n",
     "    else:\n",
     "        logger.debug(\"Creating model from scratch\")\n",
@@ -429,7 +429,7 @@
     "        )\n",
     "    # END IF\n",
     "\n",
-    "    flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / \"model_flops.log\")\n",
+    "    flops = helia.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / \"model_flops.log\")\n",
     "\n",
     "    t_mul = 1\n",
     "    first_steps = (params.steps_per_epoch * params.epochs) / (np.power(params.lr_cycles, t_mul) - t_mul + 1)\n",
@@ -482,7 +482,7 @@
     "    )\n",
     "    logger.debug(f\"Model saved to {params.model_file}\")\n",
     "\n",
-    "    nse.plotting.plot_history_metrics(\n",
+    "    helia.plotting.plot_history_metrics(\n",
     "        history.history,\n",
     "        metrics=[\"loss\", metrics[0].name],\n",
     "        save_path=params.job_dir / \"history.png\",\n",
@@ -509,10 +509,10 @@
     "        params (HKTaskParams): Evaluation parameters\n",
     "    \"\"\"\n",
     "    os.makedirs(params.job_dir, exist_ok=True)\n",
-    "    logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"test.log\")\n",
+    "    logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"test.log\")\n",
     "    logger.debug(f\"Creating working directory in {params.job_dir}\")\n",
     "\n",
-    "    params.seed = nse.utils.set_random_seed(params.seed)\n",
+    "    params.seed = helia.utils.set_random_seed(params.seed)\n",
     "    logger.debug(f\"Random seed {params.seed}\")\n",
     "\n",
     "    datasets = [hk.DatasetFactory.get(ds.name)(**ds.params) for ds in params.datasets]\n",
@@ -522,7 +522,7 @@
     "    test_y = np.concatenate([y for _, y in test_ds.as_numpy_iterator()])\n",
     "\n",
     "    logger.debug(\"Loading model\")\n",
-    "    model = nse.models.load_model(params.model_file)\n",
+    "    model = helia.models.load_model(params.model_file)\n",
     "\n",
     "    logger.debug(\"Performing inference\")\n",
     "    rst = model.evaluate(test_ds, verbose=params.verbose, return_dict=True)\n",