notebooks/setup/register_azureml_models.ipynb

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   "cell_type": "code",
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   "source": [
    "import azureml\n",
    "from azureml.core import Workspace\n",
    "from model_drift import settings, helpers\n",
    "# check core SDK version number\n",
    "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to workspace\n",
    "ws = Workspace.from_config(settings.AZUREML_CONFIG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.model import Model\n",
    "\n",
    "model_path = \"pretrained-chexpert/epoch=4-step=29844.ckpt\"\n",
    "model = Model.register(model_path=settings.MODEL_DIR.joinpath(model_path),\n",
    "                        model_name=helpers.modelpath2name(model_path),\n",
    "                        tags={\"data\": \"chexpert\", \"model\": \"classification\", \"frontal_only\": True},\n",
    "                        description=\"OSS chexpert from stanford, trained on frontal images only\",\n",
    "                        workspace=ws)\n"
   ]
  },
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   "source": [
    "from azureml.core.model import Model\n",
    "\n",
    "model_path = \"classifier/padchest-trained/loyal_rail_r71fmpjd-023.ckpt\"\n",
    "model = Model.register(model_path=settings.MODEL_DIR.joinpath(model_path),\n",
    "                        model_name=helpers.modelpath2name(model_path),\n",
    "                        tags={\"data\": \"padchest\", \"model\": \"classifier\"},\n",
    "                        description=\"trained classifier finetuned on padchest data from chexpert pretrained model\",\n",
    "                        workspace=ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = \"vae/padchest-trained/lime_night_wlbcf6py-049.ckpt\"\n",
    "model = Model.register(model_path=settings.MODEL_DIR.joinpath(model_path),\n",
    "                       model_name=helpers.modelpath2name(model_path),\n",
    "                       tags={\"data\": \"padchest\", \"model\": \"vae\", \"frontal_only\": False},\n",
    "                       description=\"vae on trained padchest data, frontal and non-frontal images\",\n",
    "                       workspace=ws)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using your own models trained on AzureML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you want to register your own models, you can do so directly from the run\n",
    "\n",
    "# Example:\n",
    "from azureml.core import Experiment, Run\n",
    "from model_drift.azure_utils import get_run\n",
    "run_id = \"<your run id>\"\n",
    "experiment = \"<experiment name>\"\n",
    "exp = Experiment(workspace=ws, name=experiment)\n",
    "run = Run(exp, run_id)\n",
    "model_name = \"<model name>\" #inert model name\n",
    "run.register_model(\n",
    "            model_path=\"outputs/checkpoints/epoch=049.ckpt\", #example path\n",
    "             model_name=model_name,\n",
    "             tags={}, ## Add tags here\n",
    "             description=\"\" # Whatever description you want\n",
    "             )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  }
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