From bb5d8bf7fa9ca3a801f4dc8f5d57c90f81089897 Mon Sep 17 00:00:00 2001 From: Cassandre Notton <94611630+CassNot@users.noreply.github.com> Date: Fri, 24 Apr 2026 12:04:35 -0400 Subject: [PATCH] Update: trackio support instead of wandb --- QML_with_MerLin/Webinar/QORC-demo-MNIST.ipynb | 671 ++++++++++-------- QML_with_MerLin/Webinar/wandb_utils.py | 215 ------ requirements.txt | 1 + 3 files changed, 380 insertions(+), 507 deletions(-) delete mode 100644 QML_with_MerLin/Webinar/wandb_utils.py diff --git a/QML_with_MerLin/Webinar/QORC-demo-MNIST.ipynb b/QML_with_MerLin/Webinar/QORC-demo-MNIST.ipynb index b2de2c9..1642b62 100644 --- a/QML_with_MerLin/Webinar/QORC-demo-MNIST.ipynb +++ b/QML_with_MerLin/Webinar/QORC-demo-MNIST.ipynb @@ -11,7 +11,7 @@ "\n", "> **Rambach *et al.*** (2025) — *Photonic Quantum-Accelerated Machine Learning*, [arXiv:2512.08318](https://arxiv.org/abs/2512.08318)\n", "\n", - "> **Check out our live experiment !** You can find our live experiment from March, 18th, 2026 on our [Weights and Biases project](https://wandb.ai/merlinquantum/webinar-merlin-for-hpc)\n", + "> **Check out our live experiment !** You can track a similar workflow locally or on a Hugging Face Space with [Trackio](https://huggingface.co/docs/trackio).\n", "\n", "---\n", "\n", @@ -84,7 +84,7 @@ "- **`perceval`** — Quandela's photonic quantum computing framework.\n", "- **`torch`** / **`torchvision`** — data loading and the linear readout model.\n", "- **`scikit-learn`** — PCA, SVC, and metrics.\n", - "- **`wandb`** — experiment tracking (optional but recommended).\n", + "- **`trackio`** — experiment tracking (optional but recommended).\n", "\n", "### Reproducibility\n", "\n", @@ -104,21 +104,19 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "1d0fba1d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All seeds fixed. Ready to go!\n" - ] + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T19:12:16.070718Z", + "start_time": "2026-04-02T19:12:15.995638Z" } - ], + }, + "outputs": [], "source": [ "from __future__ import annotations\n", "\n", + "import os\n", "import random\n", "from datetime import datetime\n", "from pathlib import Path\n", @@ -126,7 +124,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import torch\n", - "import wandb\n", + "import trackio\n", "import merlin as ML\n", "import perceval as pcvl\n", "\n", @@ -135,9 +133,10 @@ "from sklearn.decomposition import PCA\n", "from sklearn.svm import LinearSVC\n", "from torchvision.datasets import MNIST\n", + "from dotenv import load_dotenv\n", "\n", "from token_utils import load_cloud_token\n", - "from wandb_utils import (\n", + "from trackio_utils import (\n", " log_figure,\n", " make_mnist_samples_figure,\n", " make_pca_variance_figure,\n", @@ -145,8 +144,9 @@ " make_pca_components_figure,\n", " make_confusion_matrix_figure,\n", " export_and_log_circuit,\n", - " log_features_as_csv_artifact,\n", + " save_feature_csvs,\n", " save_embeddings,\n", + " write_summary_file,\n", ")\n", "\n", "# ---------------------------------------------------------------------------\n", @@ -158,8 +158,8 @@ "VAL_FRACTION = 0.2 # held-out validation fraction\n", "READOUT_EPOCHS = 300 # linear-readout training epochs\n", "READOUT_LR = 1e-2 # Adam learning-rate\n", - "CONFUSION_LOG_EVERY = 25 # how often to log confusion matrices to W&B\n", - "WANDB_PROJECT = \"webinar-test\"\n", + "CONFUSION_LOG_EVERY = 25 # how often to log confusion matrices to Trackio\n", + "TRACKIO_PROJECT = \"webinar-demo\"\n", "\n", "# ---------------------------------------------------------------------------\n", "# Reproducibility helpers\n", @@ -182,114 +182,46 @@ "id": "348370e2", "metadata": {}, "source": [ - "### Initialise Weights & Biases\n", + "### Initialise Trackio\n", "\n", - "[Weights & Biases](https://wandb.ai) (W&B) is an experiment-tracking platform. \n", + "[Trackio](https://huggingface.co/docs/trackio) is an experiment-tracking platform. \n", "Running the cell below opens a new *run* that will record every metric, figure, and artifact we produce throughout the notebook.\n", "\n", - "> **Tip:** You can skip W&B by replacing `wandb.init(...)` with a lightweight mock, but all `run.log(...)` calls will then silently no-op.\n" + "> **Tip:** This notebook now uses pure `trackio`, so images, tables, and saved files all go through the Trackio API.\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "95183a0b", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T19:12:19.730099Z", + "start_time": "2026-04-02T19:12:16.082953Z" + } + }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[34m\u001B[1mwandb\u001B[0m: [wandb.login()] Loaded credentials for https://api.wandb.ai from /Users/cassandrenotton/.netrc.\n", - "\u001B[34m\u001B[1mwandb\u001B[0m: Currently logged in as: \u001B[33mcassandre-notton\u001B[0m (\u001B[33mmerlinquantum\u001B[0m) to \u001B[32mhttps://api.wandb.ai\u001B[0m. Use \u001B[1m`wandb login --relogin`\u001B[0m to force relogin\n" - ] - }, - { - "data": { - "text/html": [], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Tracking run with wandb version 0.25.1" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Run data is saved locally in /Users/cassandrenotton/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/wandb/run-20260317_085138-lah64qd2" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Syncing run qorc-mnist-20260317-085136 to Weights & Biases (docs)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - " View project at https://wandb.ai/merlinquantum/webinar-test" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - " View run at https://wandb.ai/merlinquantum/webinar-test/runs/lah64qd2" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "name": "stdout", "output_type": "stream", "text": [ - "W&B run initialised: qorc-mnist-20260317-085136 → https://wandb.ai/merlinquantum/webinar-test/runs/lah64qd2\n" + "* Trackio project initialized: webinar-demo\n", + "* Trackio metrics logged to: /Users/cassandrenotton/.cache/huggingface/trackio\n", + "* View dashboard by running in your terminal:\n", + "\u001b[1m\u001b[38;5;208mtrackio show --project \"webinar-demo\"\u001b[0m\n", + "* or by running in Python: trackio.show(project=\"webinar-demo\")\n", + "* Apple Silicon detected, enabling automatic system metrics logging\n", + "* Created new run: qorc-mnist-20260424-112826\n", + "Trackio run initialised: qorc-mnist-20260424-112826\n", + "Local outputs directory: trackio_outputs/qorc-mnist-20260424-112826\n" ] } ], "source": [ - "# Output directory for local artifact copies (CSV features, circuit text, …)\n", - "output_dir = Path(\"./wandb_outputs\")\n", - "output_dir.mkdir(parents=True, exist_ok=True)\n", - "\n", - "run = wandb.init(\n", - " project=WANDB_PROJECT,\n", - " name=f\"qorc-mnist-{datetime.now().strftime('%Y%m%d-%H%M%S')}\",\n", + "run_name = f\"qorc-mnist-{datetime.now().strftime('%Y%m%d-%H%M%S')}\"\n", + "run = trackio.init(\n", + " project=TRACKIO_PROJECT,\n", + " name=run_name,\n", " config={\n", " \"per_class_train\": PER_CLASS_TRAIN,\n", " \"per_class_test\": PER_CLASS_TEST,\n", @@ -300,8 +232,25 @@ " \"confusion_log_every\": CONFUSION_LOG_EVERY,\n", " \"seed\": 123,\n", " },\n", + " embed=False,\n", ")\n", - "print(f\"W&B run initialised: {run.name} → {run.url}\")\n" + "\n", + "output_dir = Path(\"./trackio_outputs\") / run_name\n", + "image_output_dir = output_dir / \"images\"\n", + "output_dir.mkdir(parents=True, exist_ok=True)\n", + "image_output_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "summary_data = {\n", + " \"project\": TRACKIO_PROJECT,\n", + " \"run_name\": run_name,\n", + " \"seed\": 123,\n", + "}\n", + "\n", + "run_url = getattr(run, \"url\", None)\n", + "print(f\"Trackio run initialised: {run_name}\")\n", + "if run_url:\n", + " print(f\"Trackio dashboard URL: {run_url}\")\n", + "print(f\"Local outputs directory: {output_dir}\")\n" ] }, { @@ -331,7 +280,12 @@ "cell_type": "code", "execution_count": 3, "id": "fcb6c677", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T19:12:20.064831Z", + "start_time": "2026-04-02T19:12:19.891565Z" + } + }, "outputs": [ { "name": "stdout", @@ -386,7 +340,12 @@ "cell_type": "code", "execution_count": 4, "id": "35dd0db3", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T19:12:21.938404Z", + "start_time": "2026-04-02T19:12:20.073465Z" + } + }, "outputs": [ { "data": { @@ -404,17 +363,10 @@ "text": [ "Sample images logged.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[34m\u001B[1mwandb\u001B[0m: \u001B[33mWARNING\u001B[0m Artifact \"mnist-selected-images\" already exists with the same content. No new version will be created.\n" - ] } ], "source": [ - "# ── Visualise a few training images ──\n", + "# -- Visualise a few training images --\n", "image_fig = make_mnist_samples_figure(\n", " X_train_pixels[:16],\n", " y_train_np[:16],\n", @@ -422,11 +374,9 @@ ")\n", "plt.show()\n", "\n", - "# Log to W&B and save as a CSV artifact\n", - "log_figure(run, \"data/sample_images\", image_fig)\n", - "log_features_as_csv_artifact(\n", - " run,\n", - " artifact_name=\"mnist-selected-images\",\n", + "# Log the figure and save the selected samples as Trackio project files\n", + "log_figure(\"data/sample_images\", image_fig, image_output_dir)\n", + "save_feature_csvs(\n", " output_dir=output_dir / \"images\",\n", " arrays={\n", " \"X_train_pixels\": (X_train_pixels, y_train_np),\n", @@ -467,7 +417,12 @@ "cell_type": "code", "execution_count": 5, "id": "7a1fc864", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T19:12:22.058344Z", + "start_time": "2026-04-02T19:12:22.002772Z" + } + }, "outputs": [ { "name": "stdout", @@ -504,7 +459,7 @@ "print(f\"PCA global min/max : {pca_global_min:.4f} / {pca_global_max:.4f}\")\n", "print(f\"X_train_q range : [{X_train_q.min():.4f}, {X_train_q.max():.4f}]\")\n", "\n", - "run.log({\n", + "trackio.log({\n", " \"pca/global_min\": float(pca_global_min),\n", " \"pca/global_max\": float(pca_global_max),\n", " \"pca/explained_variance_sum\": float(pca.explained_variance_ratio_.sum()),\n", @@ -515,7 +470,12 @@ "cell_type": "code", "execution_count": 6, "id": "2ab0f727", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T19:12:22.832591Z", + "start_time": "2026-04-02T19:12:22.074699Z" + } + }, "outputs": [ { "data": { @@ -529,7 +489,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "PosixPath('trackio_outputs/qorc-mnist-20260424-112826/images/pca/components.png')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -554,17 +524,17 @@ "# 1. Scree plot — share of variance per component\n", "fig_var = make_pca_variance_figure(pca)\n", "plt.show()\n", - "log_figure(run, \"pca/explained_variance\", fig_var)\n", + "log_figure(\"pca/explained_variance\", fig_var, image_output_dir)\n", "\n", "# 2. Scatter plot of the first two principal components (colour = class)\n", "fig_proj = make_pca_projection_figure(X_train_pca, y_train_np, \"Train PCA projection (PC1 vs PC2)\")\n", "plt.show()\n", - "log_figure(run, \"pca/train_projection_pc1_pc2\", fig_proj)\n", + "log_figure(\"pca/train_projection_pc1_pc2\", fig_proj, image_output_dir)\n", "\n", "# 3. Spatial layout of each principal component (reshape to 28×28)\n", "fig_comp = make_pca_components_figure(pca, n_components=N_QFEATURES)\n", "plt.show()\n", - "log_figure(run, \"pca/components\", fig_comp)\n" + "log_figure(\"pca/components\", fig_comp, image_output_dir)\n" ] }, { @@ -594,7 +564,12 @@ "cell_type": "code", "execution_count": 7, "id": "69885314", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T19:12:23.035789Z", + "start_time": "2026-04-02T19:12:22.850418Z" + } + }, "outputs": [ { "name": "stdout", @@ -612,6 +587,16 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "PosixPath('trackio_outputs/qorc-mnist-20260424-112826/images/baseline/test_confusion_matrix.png')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -623,13 +608,13 @@ "\n", "print(f\"L-SVC baseline accuracy : {baseline_acc:.4f} ({int(baseline_acc * len(y_test_np))}/{len(y_test_np)} correct)\")\n", "\n", - "# ── Log to W&B ──\n", - "run.log({\"baseline/accuracy\": float(baseline_acc)})\n", + "# ── Log to Trackio ──\n", + "trackio.log({\"baseline/accuracy\": float(baseline_acc)})\n", "fig_cm_baseline = make_confusion_matrix_figure(\n", " y_test_np, baseline_preds, \"Baseline L-SVC — test confusion matrix\"\n", ")\n", "plt.show()\n", - "log_figure(run, \"baseline/test_confusion_matrix\", fig_cm_baseline)\n" + "log_figure(\"baseline/test_confusion_matrix\", fig_cm_baseline, image_output_dir)\n" ] }, { @@ -674,7 +659,12 @@ "cell_type": "code", "execution_count": 8, "id": "d0e62204", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T19:12:23.245148Z", + "start_time": "2026-04-02T19:12:23.049595Z" + } + }, "outputs": [ { "data": { @@ -799,7 +789,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -836,15 +826,13 @@ "cell_type": "code", "execution_count": 9, "id": "6ebc7165", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T19:12:23.615295Z", + "start_time": "2026-04-02T19:12:23.267113Z" + } + }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[34m\u001B[1mwandb\u001B[0m: \u001B[33mWARNING\u001B[0m A graphql request initiated by the public wandb API timed out (timeout=19 sec). Create a new API with an integer timeout larger than 19, e.g., `api = wandb.Api(timeout=29)` to increase the graphql timeout.\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -854,8 +842,8 @@ } ], "source": [ - "# ── Export circuit description and log to W&B ──\n", - "export_and_log_circuit(reservoir, run, output_dir / \"circuit\")\n", + "# -- Export circuit description and log to Trackio --\n", + "export_and_log_circuit(reservoir, output_dir / \"circuit\")\n", "print(\"Circuit exported and logged.\")\n" ] }, @@ -891,7 +879,12 @@ "cell_type": "code", "execution_count": 10, "id": "0a9828c2", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T19:12:24.080118Z", + "start_time": "2026-04-02T19:12:23.638099Z" + } + }, "outputs": [ { "name": "stdout", @@ -935,10 +928,8 @@ "print(f\"Z_train_ideal shape : {Z_train_ideal.shape}\")\n", "print(f\"Z_test_ideal shape : {Z_test_ideal.shape}\")\n", "\n", - "# ── Log features as CSV artifacts ──\n", - "log_features_as_csv_artifact(\n", - " run,\n", - " artifact_name=\"qorc-ideal-features\",\n", + "# -- Save feature CSV files through Trackio --\n", + "save_feature_csvs(\n", " output_dir=output_dir / \"features\",\n", " arrays={\n", " \"Z_train_ideal\": (Z_train_ideal, y_train_np),\n", @@ -946,7 +937,7 @@ " },\n", ")\n", "\n", - "# ── Standardise and concatenate with raw pixels ──\n", + "# -- Standardise and concatenate with raw pixels --\n", "qorc_mean_ideal = Z_train_ideal.mean(axis=0)\n", "qorc_std_ideal = Z_train_ideal.std(axis=0)\n", "\n", @@ -985,14 +976,19 @@ "| Learning rate | `READOUT_LR` = 1e-2 |\n", "\n", "A validation split (`VAL_FRACTION = 0.2`) is carved out of the training set to monitor over-fitting. \n", - "Training/validation/test curves and confusion matrices are logged to W&B every `CONFUSION_LOG_EVERY` epochs.\n" + "Training/validation/test curves and confusion matrices are logged to Trackio every `CONFUSION_LOG_EVERY` epochs.\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "290d1e63", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T19:12:24.129588Z", + "start_time": "2026-04-02T19:12:24.118273Z" + } + }, "outputs": [ { "name": "stdout", @@ -1000,13 +996,6 @@ "text": [ "train_linear_readout() defined.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[34m\u001B[1mwandb\u001B[0m: \u001B[33mWARNING\u001B[0m Artifact \"qorc-ideal-features\" already exists with the same content. No new version will be created.\n" - ] } ], "source": [ @@ -1017,8 +1006,9 @@ " y_val: np.ndarray,\n", " X_test: np.ndarray,\n", " y_test: np.ndarray,\n", - " run: \"wandb.sdk.wandb_run.Run\",\n", " run_prefix: str,\n", + " output_dir: Path,\n", + " summary_data: dict[str, object],\n", " n_epochs: int = READOUT_EPOCHS,\n", " lr: float = READOUT_LR,\n", " seed: int = 123,\n", @@ -1031,8 +1021,9 @@ " ----------\n", " X_train / X_val / X_test : feature matrices\n", " y_train / y_val / y_test : integer class labels\n", - " run : active W&B run for logging\n", - " run_prefix : string prefix for W&B metric names (e.g. 'ideal_readout')\n", + " run_prefix : string prefix for tracked metric names (e.g. 'ideal_readout')\n", + " output_dir : directory used for locally saved Trackio image files\n", + " summary_data : dictionary of final run metadata written to JSON at the end\n", " n_epochs : number of gradient-descent steps\n", " lr : Adam learning rate\n", " seed : for weight initialisation reproducibility\n", @@ -1057,14 +1048,11 @@ " y_val_t = torch.tensor(y_val, dtype=torch.long)\n", " X_test_t = torch.tensor(X_test, dtype=torch.float32)\n", "\n", - " run.summary[f\"{run_prefix}_model\"] = str(model)\n", - " try:\n", - " run.watch(model, log=\"all\", log_freq=max(1, n_epochs // 10))\n", - " except Exception:\n", - " pass # harmless if called more than once\n", + " summary_data[f\"{run_prefix}_model\"] = str(model)\n", + " epoch_metric = f\"{run_prefix}/epoch\"\n", "\n", " for epoch in range(1, n_epochs + 1):\n", - " # ── forward + backward pass ──\n", + " # -- forward + backward pass --\n", " model.train()\n", " optimiser.zero_grad()\n", " train_logits = model(X_train_t)\n", @@ -1072,7 +1060,7 @@ " train_loss.backward()\n", " optimiser.step()\n", "\n", - " # ── evaluation (no gradients needed) ──\n", + " # -- evaluation (no gradients needed) --\n", " model.eval()\n", " with torch.no_grad():\n", " val_logits = model(X_val_t)\n", @@ -1087,34 +1075,40 @@ " val_acc = accuracy_score(y_val, val_preds)\n", " test_acc = accuracy_score(y_test, test_preds)\n", "\n", - " run.log(\n", + " trackio.log(\n", " {\n", " f\"{run_prefix}/train_loss\": float(train_loss.item()),\n", " f\"{run_prefix}/val_loss\": float(val_loss.item()),\n", " f\"{run_prefix}/train_acc\": float(train_acc),\n", " f\"{run_prefix}/val_acc\": float(val_acc),\n", " f\"{run_prefix}/test_acc\": float(test_acc),\n", - " f\"{run_prefix}/epoch\": epoch,\n", + " epoch_metric: epoch,\n", " },\n", " step=epoch,\n", " )\n", "\n", - " # ── periodic confusion-matrix logging ──\n", + " # -- periodic confusion-matrix logging --\n", " if epoch % confusion_every == 0 or epoch == 1 or epoch == n_epochs:\n", " cm_fig = make_confusion_matrix_figure(\n", " y_true=y_test,\n", " y_pred=test_preds,\n", " title=f\"{run_prefix} — test confusion matrix (epoch {epoch})\",\n", " )\n", - " run.log({f\"{run_prefix}/test_confusion_matrix\": wandb.Image(cm_fig)}, step=epoch)\n", - " plt.close(cm_fig)\n", + " log_figure(\n", + " f\"{run_prefix}/test_confusion_matrix\",\n", + " cm_fig,\n", + " output_dir,\n", + " suffix=f\"epoch-{epoch:03d}\",\n", + " )\n", "\n", - " # ── final predictions ──\n", + " # -- final predictions --\n", " model.eval()\n", " with torch.no_grad():\n", " final_preds = model(X_test_t).argmax(dim=1).cpu().numpy()\n", "\n", - " run.summary[f\"{run_prefix}_final_test_acc\"] = float(accuracy_score(y_test, final_preds))\n", + " final_acc = float(accuracy_score(y_test, final_preds))\n", + " trackio.log({f\"{run_prefix}_final_test_acc\": final_acc})\n", + " summary_data[f\"{run_prefix}_final_test_acc\"] = final_acc\n", " return final_preds, model\n", "\n", "\n", @@ -1125,7 +1119,12 @@ "cell_type": "code", "execution_count": 12, "id": "615d1798", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T19:12:25.791168Z", + "start_time": "2026-04-02T19:12:24.138155Z" + } + }, "outputs": [ { "name": "stdout", @@ -1139,19 +1138,6 @@ "\n", "MerLin ideal accuracy : 0.8300 (83/100 correct)\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[34m\u001B[1mwandb\u001B[0m: \u001B[33mWARNING\u001B[0m Tried to log to step 1 that is less than the current step 8. Steps must be monotonically increasing, so this data will be ignored. See https://wandb.me/define-metric to log data out of order.\n", - "\u001B[34m\u001B[1mwandb\u001B[0m: \u001B[33mWARNING\u001B[0m Tried to log to step 2 that is less than the current step 8. Steps must be monotonically increasing, so this data will be ignored. See https://wandb.me/define-metric to log data out of order.\n", - "\u001B[34m\u001B[1mwandb\u001B[0m: \u001B[33mWARNING\u001B[0m Tried to log to step 3 that is less than the current step 8. Steps must be monotonically increasing, so this data will be ignored. See https://wandb.me/define-metric to log data out of order.\n", - "\u001B[34m\u001B[1mwandb\u001B[0m: \u001B[33mWARNING\u001B[0m Tried to log to step 4 that is less than the current step 8. Steps must be monotonically increasing, so this data will be ignored. See https://wandb.me/define-metric to log data out of order.\n", - "\u001B[34m\u001B[1mwandb\u001B[0m: \u001B[33mWARNING\u001B[0m Tried to log to step 5 that is less than the current step 8. Steps must be monotonically increasing, so this data will be ignored. See https://wandb.me/define-metric to log data out of order.\n", - "\u001B[34m\u001B[1mwandb\u001B[0m: \u001B[33mWARNING\u001B[0m Tried to log to step 6 that is less than the current step 8. Steps must be monotonically increasing, so this data will be ignored. See https://wandb.me/define-metric to log data out of order.\n", - "\u001B[34m\u001B[1mwandb\u001B[0m: \u001B[33mWARNING\u001B[0m Tried to log to step 7 that is less than the current step 8. Steps must be monotonically increasing, so this data will be ignored. See https://wandb.me/define-metric to log data out of order.\n" - ] } ], "source": [ @@ -1176,14 +1162,16 @@ " y_val=y_val_ideal,\n", " X_test=X_test_ideal,\n", " y_test=y_test_np,\n", - " run=run,\n", " run_prefix=\"ideal_readout\",\n", + " output_dir=image_output_dir,\n", + " summary_data=summary_data,\n", " seed=123,\n", ")\n", "\n", "ideal_acc = accuracy_score(y_test_np, ideal_preds)\n", - "run.log({\"ideal/accuracy\": float(ideal_acc)})\n", - "run.summary[\"ideal_model_parameters\"] = int(sum(p.numel() for p in ideal_model.parameters()))\n", + "trackio.log({\"ideal/accuracy\": float(ideal_acc)})\n", + "summary_data[\"ideal_model_parameters\"] = int(sum(p.numel() for p in ideal_model.parameters()))\n", + "trackio.log({\"ideal_model_parameters\": summary_data[\"ideal_model_parameters\"]})\n", "\n", "print(f\"\\nMerLin ideal accuracy : {ideal_acc:.4f} ({int(ideal_acc * len(y_test_np))}/{len(y_test_np)} correct)\")\n" ] @@ -1220,15 +1208,20 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "f95919fc", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T20:12:30.116986Z", + "start_time": "2026-04-02T19:12:25.839396Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Connecting to remote quantum processor (sim:belenos) …\n", + "Connecting to remote quantum processor (sim:ascella) …\n", "Connection established.\n", "Sending training data (500 samples) to the quantum processor …\n", "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", @@ -1239,66 +1232,24 @@ "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", - "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Chunk attempt 1/3 failed: HTTPSConnectionPool(host='api.cloud.quandela.com', port=443): Max retries exceeded with url: /api/job/status/73c7511e-c144-4a05-b074-74ee6554abde (Caused by NameResolutionError(\"HTTPSConnection(host='api.cloud.quandela.com', port=443): Failed to resolve 'api.cloud.quandela.com' ([Errno 8] nodename nor servname provided, or not known)\"))\n" - ] - }, - { - "ename": "ConnectionError", - "evalue": "HTTPSConnectionPool(host='api.cloud.quandela.com', port=443): Max retries exceeded with url: /api/platforms/sim%3Abelenos/processor (Caused by NameResolutionError(\"HTTPSConnection(host='api.cloud.quandela.com', port=443): Failed to resolve 'api.cloud.quandela.com' ([Errno 8] nodename nor servname provided, or not known)\"))", - "output_type": "error", - "traceback": [ - "\u001B[31m---------------------------------------------------------------------------\u001B[39m", - "\u001B[31mgaierror\u001B[39m Traceback (most recent call last)", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/urllib3/connection.py:204\u001B[39m, in \u001B[36mHTTPConnection._new_conn\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 203\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m204\u001B[39m sock = \u001B[43mconnection\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcreate_connection\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 205\u001B[39m \u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_dns_host\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mport\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 206\u001B[39m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mtimeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 207\u001B[39m \u001B[43m \u001B[49m\u001B[43msource_address\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43msource_address\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 208\u001B[39m \u001B[43m \u001B[49m\u001B[43msocket_options\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43msocket_options\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 209\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 210\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m socket.gaierror \u001B[38;5;28;01mas\u001B[39;00m e:\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/urllib3/util/connection.py:60\u001B[39m, in \u001B[36mcreate_connection\u001B[39m\u001B[34m(address, timeout, source_address, socket_options)\u001B[39m\n\u001B[32m 58\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m LocationParseError(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mhost\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m'\u001B[39m\u001B[33m, label empty or too long\u001B[39m\u001B[33m\"\u001B[39m) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m60\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m res \u001B[38;5;129;01min\u001B[39;00m \u001B[43msocket\u001B[49m\u001B[43m.\u001B[49m\u001B[43mgetaddrinfo\u001B[49m\u001B[43m(\u001B[49m\u001B[43mhost\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mport\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfamily\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msocket\u001B[49m\u001B[43m.\u001B[49m\u001B[43mSOCK_STREAM\u001B[49m\u001B[43m)\u001B[49m:\n\u001B[32m 61\u001B[39m af, socktype, proto, canonname, sa = res\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/miniconda3/lib/python3.12/socket.py:976\u001B[39m, in \u001B[36mgetaddrinfo\u001B[39m\u001B[34m(host, port, family, type, proto, flags)\u001B[39m\n\u001B[32m 975\u001B[39m addrlist = []\n\u001B[32m--> \u001B[39m\u001B[32m976\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m res \u001B[38;5;129;01min\u001B[39;00m \u001B[43m_socket\u001B[49m\u001B[43m.\u001B[49m\u001B[43mgetaddrinfo\u001B[49m\u001B[43m(\u001B[49m\u001B[43mhost\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mport\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfamily\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mtype\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mproto\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mflags\u001B[49m\u001B[43m)\u001B[49m:\n\u001B[32m 977\u001B[39m af, socktype, proto, canonname, sa = res\n", - "\u001B[31mgaierror\u001B[39m: [Errno 8] nodename nor servname provided, or not known", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001B[31mNameResolutionError\u001B[39m Traceback (most recent call last)", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/urllib3/connectionpool.py:787\u001B[39m, in \u001B[36mHTTPConnectionPool.urlopen\u001B[39m\u001B[34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001B[39m\n\u001B[32m 786\u001B[39m \u001B[38;5;66;03m# Make the request on the HTTPConnection object\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m787\u001B[39m response = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_make_request\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 788\u001B[39m \u001B[43m \u001B[49m\u001B[43mconn\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 789\u001B[39m \u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 790\u001B[39m \u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 791\u001B[39m \u001B[43m \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43mtimeout_obj\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 792\u001B[39m \u001B[43m \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 793\u001B[39m \u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 794\u001B[39m \u001B[43m \u001B[49m\u001B[43mchunked\u001B[49m\u001B[43m=\u001B[49m\u001B[43mchunked\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 795\u001B[39m \u001B[43m \u001B[49m\u001B[43mretries\u001B[49m\u001B[43m=\u001B[49m\u001B[43mretries\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 796\u001B[39m \u001B[43m \u001B[49m\u001B[43mresponse_conn\u001B[49m\u001B[43m=\u001B[49m\u001B[43mresponse_conn\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 797\u001B[39m \u001B[43m \u001B[49m\u001B[43mpreload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpreload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 798\u001B[39m \u001B[43m \u001B[49m\u001B[43mdecode_content\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdecode_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 799\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mresponse_kw\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 800\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 802\u001B[39m \u001B[38;5;66;03m# Everything went great!\u001B[39;00m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/urllib3/connectionpool.py:488\u001B[39m, in \u001B[36mHTTPConnectionPool._make_request\u001B[39m\u001B[34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001B[39m\n\u001B[32m 487\u001B[39m new_e = _wrap_proxy_error(new_e, conn.proxy.scheme)\n\u001B[32m--> \u001B[39m\u001B[32m488\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m new_e\n\u001B[32m 490\u001B[39m \u001B[38;5;66;03m# conn.request() calls http.client.*.request, not the method in\u001B[39;00m\n\u001B[32m 491\u001B[39m \u001B[38;5;66;03m# urllib3.request. It also calls makefile (recv) on the socket.\u001B[39;00m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/urllib3/connectionpool.py:464\u001B[39m, in \u001B[36mHTTPConnectionPool._make_request\u001B[39m\u001B[34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001B[39m\n\u001B[32m 463\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m464\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_validate_conn\u001B[49m\u001B[43m(\u001B[49m\u001B[43mconn\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 465\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m (SocketTimeout, BaseSSLError) \u001B[38;5;28;01mas\u001B[39;00m e:\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/urllib3/connectionpool.py:1093\u001B[39m, in \u001B[36mHTTPSConnectionPool._validate_conn\u001B[39m\u001B[34m(self, conn)\u001B[39m\n\u001B[32m 1092\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m conn.is_closed:\n\u001B[32m-> \u001B[39m\u001B[32m1093\u001B[39m \u001B[43mconn\u001B[49m\u001B[43m.\u001B[49m\u001B[43mconnect\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1095\u001B[39m \u001B[38;5;66;03m# TODO revise this, see https://github.com/urllib3/urllib3/issues/2791\u001B[39;00m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/urllib3/connection.py:759\u001B[39m, in \u001B[36mHTTPSConnection.connect\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 758\u001B[39m sock: socket.socket | ssl.SSLSocket\n\u001B[32m--> \u001B[39m\u001B[32m759\u001B[39m \u001B[38;5;28mself\u001B[39m.sock = sock = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_new_conn\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 760\u001B[39m server_hostname: \u001B[38;5;28mstr\u001B[39m = \u001B[38;5;28mself\u001B[39m.host\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/urllib3/connection.py:211\u001B[39m, in \u001B[36mHTTPConnection._new_conn\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 210\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m socket.gaierror \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[32m--> \u001B[39m\u001B[32m211\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m NameResolutionError(\u001B[38;5;28mself\u001B[39m.host, \u001B[38;5;28mself\u001B[39m, e) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01me\u001B[39;00m\n\u001B[32m 212\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m SocketTimeout \u001B[38;5;28;01mas\u001B[39;00m e:\n", - "\u001B[31mNameResolutionError\u001B[39m: HTTPSConnection(host='api.cloud.quandela.com', port=443): Failed to resolve 'api.cloud.quandela.com' ([Errno 8] nodename nor servname provided, or not known)", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001B[31mMaxRetryError\u001B[39m Traceback (most recent call last)", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/requests/adapters.py:644\u001B[39m, in \u001B[36mHTTPAdapter.send\u001B[39m\u001B[34m(self, request, stream, timeout, verify, cert, proxies)\u001B[39m\n\u001B[32m 643\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m644\u001B[39m resp = \u001B[43mconn\u001B[49m\u001B[43m.\u001B[49m\u001B[43murlopen\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 645\u001B[39m \u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m=\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m.\u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 646\u001B[39m \u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m=\u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 647\u001B[39m \u001B[43m \u001B[49m\u001B[43mbody\u001B[49m\u001B[43m=\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m.\u001B[49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 648\u001B[39m \u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m.\u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 649\u001B[39m \u001B[43m \u001B[49m\u001B[43mredirect\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m 650\u001B[39m \u001B[43m \u001B[49m\u001B[43massert_same_host\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m 651\u001B[39m \u001B[43m \u001B[49m\u001B[43mpreload_content\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m 652\u001B[39m \u001B[43m \u001B[49m\u001B[43mdecode_content\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m 653\u001B[39m \u001B[43m \u001B[49m\u001B[43mretries\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mmax_retries\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 654\u001B[39m \u001B[43m \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[43m=\u001B[49m\u001B[43mtimeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 655\u001B[39m \u001B[43m \u001B[49m\u001B[43mchunked\u001B[49m\u001B[43m=\u001B[49m\u001B[43mchunked\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 656\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 658\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m (ProtocolError, \u001B[38;5;167;01mOSError\u001B[39;00m) \u001B[38;5;28;01mas\u001B[39;00m err:\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/urllib3/connectionpool.py:841\u001B[39m, in \u001B[36mHTTPConnectionPool.urlopen\u001B[39m\u001B[34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001B[39m\n\u001B[32m 839\u001B[39m new_e = ProtocolError(\u001B[33m\"\u001B[39m\u001B[33mConnection aborted.\u001B[39m\u001B[33m\"\u001B[39m, new_e)\n\u001B[32m--> \u001B[39m\u001B[32m841\u001B[39m retries = \u001B[43mretries\u001B[49m\u001B[43m.\u001B[49m\u001B[43mincrement\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 842\u001B[39m \u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merror\u001B[49m\u001B[43m=\u001B[49m\u001B[43mnew_e\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_pool\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_stacktrace\u001B[49m\u001B[43m=\u001B[49m\u001B[43msys\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexc_info\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m2\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[32m 843\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 844\u001B[39m retries.sleep()\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/urllib3/util/retry.py:535\u001B[39m, in \u001B[36mRetry.increment\u001B[39m\u001B[34m(self, method, url, response, error, _pool, _stacktrace)\u001B[39m\n\u001B[32m 534\u001B[39m reason = error \u001B[38;5;129;01mor\u001B[39;00m ResponseError(cause)\n\u001B[32m--> \u001B[39m\u001B[32m535\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m MaxRetryError(_pool, url, reason) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mreason\u001B[39;00m \u001B[38;5;66;03m# type: ignore[arg-type]\u001B[39;00m\n\u001B[32m 537\u001B[39m log.debug(\u001B[33m\"\u001B[39m\u001B[33mIncremented Retry for (url=\u001B[39m\u001B[33m'\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[33m'\u001B[39m\u001B[33m): \u001B[39m\u001B[38;5;132;01m%r\u001B[39;00m\u001B[33m\"\u001B[39m, url, new_retry)\n", - "\u001B[31mMaxRetryError\u001B[39m: HTTPSConnectionPool(host='api.cloud.quandela.com', port=443): Max retries exceeded with url: /api/platforms/sim%3Abelenos/processor (Caused by NameResolutionError(\"HTTPSConnection(host='api.cloud.quandela.com', port=443): Failed to resolve 'api.cloud.quandela.com' ([Errno 8] nodename nor servname provided, or not known)\"))", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001B[31mConnectionError\u001B[39m Traceback (most recent call last)", - "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[14]\u001B[39m\u001B[32m, line 43\u001B[39m\n\u001B[32m 40\u001B[39m x_test_q_t = torch.tensor(X_test_q, dtype=torch.float32)\n\u001B[32m 42\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mSending training data (\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mX_train_q.shape[\u001B[32m0\u001B[39m]\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m samples) to the quantum processor …\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m---> \u001B[39m\u001B[32m43\u001B[39m Z_train_proc = as_numpy(\u001B[43mproc\u001B[49m\u001B[43m.\u001B[49m\u001B[43mforward\u001B[49m\u001B[43m(\u001B[49m\u001B[43mreservoir\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mx_train_q_t\u001B[49m\u001B[43m)\u001B[49m)\n\u001B[32m 44\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[33mTraining data retrieved.\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m 46\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mSending test data (\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mX_test_q.shape[\u001B[32m0\u001B[39m]\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m samples) to the quantum processor …\u001B[39m\u001B[33m\"\u001B[39m)\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/merlin/core/merlin_processor.py:198\u001B[39m, in \u001B[36mMerlinProcessor.forward\u001B[39m\u001B[34m(self, module, input, nsample, timeout)\u001B[39m\n\u001B[32m 196\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Synchronous convenience wrapper around forward_async().\"\"\"\u001B[39;00m\n\u001B[32m 197\u001B[39m fut = \u001B[38;5;28mself\u001B[39m.forward_async(module, \u001B[38;5;28minput\u001B[39m, nsample=nsample, timeout=timeout)\n\u001B[32m--> \u001B[39m\u001B[32m198\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfut\u001B[49m\u001B[43m.\u001B[49m\u001B[43mwait\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/torch/futures/__init__.py:82\u001B[39m, in \u001B[36mFuture.wait\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 63\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mwait\u001B[39m(\u001B[38;5;28mself\u001B[39m) -> T:\n\u001B[32m 64\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33mr\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 65\u001B[39m \u001B[33;03m Block until the value of this ``Future`` is ready.\u001B[39;00m\n\u001B[32m 66\u001B[39m \n\u001B[32m (...)\u001B[39m\u001B[32m 80\u001B[39m \u001B[33;03m also throw an error.\u001B[39;00m\n\u001B[32m 81\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m82\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mwait\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/torch/futures/__init__.py:280\u001B[39m, in \u001B[36mFuture.set_exception..raise_error\u001B[39m\u001B[34m(fut_result)\u001B[39m\n\u001B[32m 279\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mraise_error\u001B[39m(fut_result):\n\u001B[32m--> \u001B[39m\u001B[32m280\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m fut_result\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/merlin/core/merlin_processor.py:295\u001B[39m, in \u001B[36mMerlinProcessor.forward_async.._run_pipeline\u001B[39m\u001B[34m()\u001B[39m\n\u001B[32m 292\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m._cancelled_error()\n\u001B[32m 294\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m should_offload:\n\u001B[32m--> \u001B[39m\u001B[32m295\u001B[39m x = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_offload_quantum_layer_with_chunking\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 296\u001B[39m \u001B[43m \u001B[49m\u001B[43mlayer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnsample\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdeadline\u001B[49m\n\u001B[32m 297\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 298\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 299\u001B[39m \u001B[38;5;28;01mwith\u001B[39;00m torch.no_grad():\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/merlin/core/merlin_processor.py:341\u001B[39m, in \u001B[36mMerlinProcessor._offload_quantum_layer_with_chunking\u001B[39m\u001B[34m(self, layer, input_tensor, nsample, state, deadline)\u001B[39m\n\u001B[32m 339\u001B[39m chunks.append((start, end))\n\u001B[32m 340\u001B[39m start = end\n\u001B[32m--> \u001B[39m\u001B[32m341\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_run_chunks_pooled\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 342\u001B[39m \u001B[43m \u001B[49m\u001B[43mlayer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mconfig\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minput_tensor\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mchunks\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnsample\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdeadline\u001B[49m\n\u001B[32m 343\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/merlin/core/merlin_processor.py:410\u001B[39m, in \u001B[36mMerlinProcessor._run_chunks_pooled\u001B[39m\u001B[34m(self, layer, config, input_tensor, chunks, nsample, state, deadline)\u001B[39m\n\u001B[32m 407\u001B[39m time.sleep(\u001B[32m0.01\u001B[39m)\n\u001B[32m 409\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m errors:\n\u001B[32m--> \u001B[39m\u001B[32m410\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m errors[\u001B[32m0\u001B[39m]\n\u001B[32m 412\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m torch.cat(outputs, dim=\u001B[32m0\u001B[39m)\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/merlin/core/merlin_processor.py:368\u001B[39m, in \u001B[36mMerlinProcessor._run_chunks_pooled.._call\u001B[39m\u001B[34m(s, e, idx)\u001B[39m\n\u001B[32m 364\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m 365\u001B[39m base_label = (\n\u001B[32m 366\u001B[39m \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mmer:\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mlayer_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m:\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mstate[\u001B[33m'\u001B[39m\u001B[33mcall_id\u001B[39m\u001B[33m'\u001B[39m]\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m:\u001B[39m\u001B[38;5;132;01m{\u001B[39;00midx\u001B[38;5;250m \u001B[39m+\u001B[38;5;250m \u001B[39m\u001B[32m1\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtotal_chunks\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m\n\u001B[32m 367\u001B[39m )\n\u001B[32m--> \u001B[39m\u001B[32m368\u001B[39m t = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_run_chunk\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 369\u001B[39m \u001B[43m \u001B[49m\u001B[43mlayer\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 370\u001B[39m \u001B[43m \u001B[49m\u001B[43mconfig\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 371\u001B[39m \u001B[43m \u001B[49m\u001B[43minput_tensor\u001B[49m\u001B[43m[\u001B[49m\u001B[43ms\u001B[49m\u001B[43m:\u001B[49m\u001B[43me\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 372\u001B[39m \u001B[43m \u001B[49m\u001B[43mnsample\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 373\u001B[39m \u001B[43m \u001B[49m\u001B[43mstate\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 374\u001B[39m \u001B[43m \u001B[49m\u001B[43mdeadline\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 375\u001B[39m \u001B[43m \u001B[49m\u001B[43mjob_base_label\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbase_label\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 376\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 377\u001B[39m outputs[idx] = t\n\u001B[32m 378\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m ex:\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/merlin/core/merlin_processor.py:467\u001B[39m, in \u001B[36mMerlinProcessor._run_chunk\u001B[39m\u001B[34m(self, layer, config, input_chunk, nsample, state, deadline, job_base_label)\u001B[39m\n\u001B[32m 463\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTimeoutError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33mRemote call timed out (remote cancel issued)\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m 465\u001B[39m \u001B[38;5;66;03m# Build a fresh RemoteProcessor and Sampler on each attempt so that\u001B[39;00m\n\u001B[32m 466\u001B[39m \u001B[38;5;66;03m# a corrupted RP doesn't poison retries.\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m467\u001B[39m rp = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_create_fresh_rp\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 468\u001B[39m rp.set_circuit(config[\u001B[33m\"\u001B[39m\u001B[33mcircuit\u001B[39m\u001B[33m\"\u001B[39m])\n\u001B[32m 469\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m config.get(\u001B[33m\"\u001B[39m\u001B[33minput_state\u001B[39m\u001B[33m\"\u001B[39m):\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/merlin/core/merlin_processor.py:642\u001B[39m, in \u001B[36mMerlinProcessor._create_fresh_rp\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 640\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.session.build_remote_processor()\n\u001B[32m 641\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m642\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_clone_remote_processor\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mremote_processor\u001B[49m\u001B[43m)\u001B[49m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/merlin/core/merlin_processor.py:652\u001B[39m, in \u001B[36mMerlinProcessor._clone_remote_processor\u001B[39m\u001B[34m(self, rp)\u001B[39m\n\u001B[32m 646\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_clone_remote_processor\u001B[39m(\u001B[38;5;28mself\u001B[39m, rp: RemoteProcessor) -> RemoteProcessor:\n\u001B[32m 647\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"Create a sibling RemoteProcessor with its own RPC handler (thread-safe).\u001B[39;00m\n\u001B[32m 648\u001B[39m \n\u001B[32m 649\u001B[39m \u001B[33;03m Forwards the token extracted at init time so that inline-token\u001B[39;00m\n\u001B[32m 650\u001B[39m \u001B[33;03m RemoteProcessors are cloned correctly.\u001B[39;00m\n\u001B[32m 651\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m652\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mRemoteProcessor\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 653\u001B[39m \u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[43m=\u001B[49m\u001B[43mrp\u001B[49m\u001B[43m.\u001B[49m\u001B[43mname\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 654\u001B[39m \u001B[43m \u001B[49m\u001B[43mtoken\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_token\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 655\u001B[39m \u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m=\u001B[49m\u001B[43mrp\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget_rpc_handler\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43murl\u001B[49m\n\u001B[32m 656\u001B[39m \u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43mhasattr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mrp\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget_rpc_handler\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43murl\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 657\u001B[39m \u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m 658\u001B[39m \u001B[43m \u001B[49m\u001B[43mproxies\u001B[49m\u001B[43m=\u001B[49m\u001B[43mrp\u001B[49m\u001B[43m.\u001B[49m\u001B[43mproxies\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 659\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/perceval/runtime/remote_processor.py:119\u001B[39m, in \u001B[36mRemoteProcessor.__init__\u001B[39m\u001B[34m(self, name, token, url, proxies, rpc_handler, m, noise)\u001B[39m\n\u001B[32m 117\u001B[39m \u001B[38;5;28mself\u001B[39m._type = ProcessorType.SIMULATOR\n\u001B[32m 118\u001B[39m \u001B[38;5;28mself\u001B[39m._available_circuit_parameters = {}\n\u001B[32m--> \u001B[39m\u001B[32m119\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mfetch_data\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 120\u001B[39m get_logger().info(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mConnected to Cloud platform \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m.name\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m, channel.general)\n\u001B[32m 122\u001B[39m \u001B[38;5;28mself\u001B[39m._thresholded_output = \u001B[33m\"\u001B[39m\u001B[33mdetector\u001B[39m\u001B[33m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m._specs \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m._specs[\u001B[33m\"\u001B[39m\u001B[33mdetector\u001B[39m\u001B[33m\"\u001B[39m] == \u001B[33m\"\u001B[39m\u001B[33mthreshold\u001B[39m\u001B[33m\"\u001B[39m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/perceval/runtime/remote_processor.py:149\u001B[39m, in \u001B[36mRemoteProcessor.fetch_data\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 147\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mfetch_data\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[32m 148\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m149\u001B[39m platform_details = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_rpc_handler\u001B[49m\u001B[43m.\u001B[49m\u001B[43mfetch_platform_details\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 150\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m HTTPError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[32m 151\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m HTTPError(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mError while fetching platform details: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00me\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/perceval/runtime/rpc_handler.py:128\u001B[39m, in \u001B[36mRPCHandler.fetch_platform_details\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 126\u001B[39m endpoint = \u001B[38;5;28mself\u001B[39m.build_endpoint(_ENDPOINT_PLATFORM_DETAILS_NEW, quote_name, _PROCESSOR_SECTION)\n\u001B[32m 127\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m128\u001B[39m response = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mget_request\u001B[49m\u001B[43m(\u001B[49m\u001B[43mendpoint\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 129\u001B[39m \u001B[38;5;66;03m# TEMPORARY CODE\u001B[39;00m\n\u001B[32m 130\u001B[39m \u001B[38;5;66;03m# TODO: to be removed in version 1.2\u001B[39;00m\n\u001B[32m 131\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[33m'\u001B[39m\u001B[33marchitecture\u001B[39m\u001B[33m'\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m response.get(\u001B[33m'\u001B[39m\u001B[33mspecs\u001B[39m\u001B[33m'\u001B[39m, {}):\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/perceval/runtime/rpc_handler.py:89\u001B[39m, in \u001B[36mRPCHandler.get_request\u001B[39m\u001B[34m(self, endpoint)\u001B[39m\n\u001B[32m 87\u001B[39m error_info = \u001B[33m'\u001B[39m\u001B[33m'\u001B[39m.join(traceback.format_stack()[:-\u001B[32m1\u001B[39m])\n\u001B[32m 88\u001B[39m get_logger().debug(error_info, channel.general)\n\u001B[32m---> \u001B[39m\u001B[32m89\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m e\n\u001B[32m 91\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m 92\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m resp.json()\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/perceval/runtime/rpc_handler.py:84\u001B[39m, in \u001B[36mRPCHandler.get_request\u001B[39m\u001B[34m(self, endpoint)\u001B[39m\n\u001B[32m 81\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mget_request\u001B[39m(\u001B[38;5;28mself\u001B[39m, endpoint: \u001B[38;5;28mstr\u001B[39m) -> \u001B[38;5;28;01mNone\u001B[39;00m | \u001B[38;5;28mdict\u001B[39m:\n\u001B[32m 82\u001B[39m \u001B[38;5;66;03m# requests may throw an IO Exception, let the user deal with it\u001B[39;00m\n\u001B[32m 83\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m---> \u001B[39m\u001B[32m84\u001B[39m resp = \u001B[43mrequests\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[43mendpoint\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mrequest_timeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mproxies\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mproxies\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 85\u001B[39m resp.raise_for_status()\n\u001B[32m 86\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/requests/api.py:73\u001B[39m, in \u001B[36mget\u001B[39m\u001B[34m(url, params, **kwargs)\u001B[39m\n\u001B[32m 62\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mget\u001B[39m(url, params=\u001B[38;5;28;01mNone\u001B[39;00m, **kwargs):\n\u001B[32m 63\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33mr\u001B[39m\u001B[33;03m\"\"\"Sends a GET request.\u001B[39;00m\n\u001B[32m 64\u001B[39m \n\u001B[32m 65\u001B[39m \u001B[33;03m :param url: URL for the new :class:`Request` object.\u001B[39;00m\n\u001B[32m (...)\u001B[39m\u001B[32m 70\u001B[39m \u001B[33;03m :rtype: requests.Response\u001B[39;00m\n\u001B[32m 71\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m73\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mget\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m=\u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/requests/api.py:59\u001B[39m, in \u001B[36mrequest\u001B[39m\u001B[34m(method, url, **kwargs)\u001B[39m\n\u001B[32m 55\u001B[39m \u001B[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001B[39;00m\n\u001B[32m 56\u001B[39m \u001B[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001B[39;00m\n\u001B[32m 57\u001B[39m \u001B[38;5;66;03m# cases, and look like a memory leak in others.\u001B[39;00m\n\u001B[32m 58\u001B[39m \u001B[38;5;28;01mwith\u001B[39;00m sessions.Session() \u001B[38;5;28;01mas\u001B[39;00m session:\n\u001B[32m---> \u001B[39m\u001B[32m59\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43msession\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m=\u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m=\u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/requests/sessions.py:589\u001B[39m, in \u001B[36mSession.request\u001B[39m\u001B[34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001B[39m\n\u001B[32m 584\u001B[39m send_kwargs = {\n\u001B[32m 585\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mtimeout\u001B[39m\u001B[33m\"\u001B[39m: timeout,\n\u001B[32m 586\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mallow_redirects\u001B[39m\u001B[33m\"\u001B[39m: allow_redirects,\n\u001B[32m 587\u001B[39m }\n\u001B[32m 588\u001B[39m send_kwargs.update(settings)\n\u001B[32m--> \u001B[39m\u001B[32m589\u001B[39m resp = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43msend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mprep\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43msend_kwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 591\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m resp\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/requests/sessions.py:703\u001B[39m, in \u001B[36mSession.send\u001B[39m\u001B[34m(self, request, **kwargs)\u001B[39m\n\u001B[32m 700\u001B[39m start = preferred_clock()\n\u001B[32m 702\u001B[39m \u001B[38;5;66;03m# Send the request\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m703\u001B[39m r = \u001B[43madapter\u001B[49m\u001B[43m.\u001B[49m\u001B[43msend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 705\u001B[39m \u001B[38;5;66;03m# Total elapsed time of the request (approximately)\u001B[39;00m\n\u001B[32m 706\u001B[39m elapsed = preferred_clock() - start\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Documents/projects/QML_project/tutorials/Photonic-Quantum-Computing-Tutorials/Quantum_Machine_Learning-MerLin/venv/lib/python3.12/site-packages/requests/adapters.py:677\u001B[39m, in \u001B[36mHTTPAdapter.send\u001B[39m\u001B[34m(self, request, stream, timeout, verify, cert, proxies)\u001B[39m\n\u001B[32m 673\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(e.reason, _SSLError):\n\u001B[32m 674\u001B[39m \u001B[38;5;66;03m# This branch is for urllib3 v1.22 and later.\u001B[39;00m\n\u001B[32m 675\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m SSLError(e, request=request)\n\u001B[32m--> \u001B[39m\u001B[32m677\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mConnectionError\u001B[39;00m(e, request=request)\n\u001B[32m 679\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m ClosedPoolError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[32m 680\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mConnectionError\u001B[39;00m(e, request=request)\n", - "\u001B[31mConnectionError\u001B[39m: HTTPSConnectionPool(host='api.cloud.quandela.com', port=443): Max retries exceeded with url: /api/platforms/sim%3Abelenos/processor (Caused by NameResolutionError(\"HTTPSConnection(host='api.cloud.quandela.com', port=443): Failed to resolve 'api.cloud.quandela.com' ([Errno 8] nodename nor servname provided, or not known)\"))" + "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", + "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", + "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", + "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", + "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", + "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", + "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", + "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", + "Training data retrieved.\n", + "Sending test data (100 samples) to the quantum processor …\n", + "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", + "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", + "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", + "[warning] Lowered 'max_samples' from user defined value (10000) to 'max_shots' value (100) for consistency.\n", + "Test data retrieved.\n", + "Normalising processor quantum features …\n", + "Training linear readout on processor features …\n", + "MerLinProcessor accuracy : 0.8400\n" ] } ], @@ -1324,15 +1275,15 @@ "\n", "if not CLOUD_TOKEN:\n", " print(\"MerLinProcessor run skipped – set CLOUD_TOKEN in your .env file.\")\n", - " run.summary[\"processor_status\"] = \"skipped_no_cloud_token\"\n", + " summary_data[\"processor_status\"] = \"skipped_no_cloud_token\"\n", "\n", "else:\n", - " print(\"Connecting to remote quantum processor (sim:belenos) …\")\n", + " print(\"Connecting to remote quantum processor (sim:ascella) …\")\n", " pcvl.RemoteConfig.set_token(CLOUD_TOKEN)\n", - " remote_processor = pcvl.RemoteProcessor(\"sim:belenos\")\n", + " remote_processor = pcvl.RemoteProcessor(\"sim:ascella\")\n", " print(\"Connection established.\")\n", "\n", - " # ── MerlinProcessor wraps the remote backend ──\n", + " # -- MerlinProcessor wraps the remote backend --\n", " proc = ML.MerlinProcessor(\n", " remote_processor,\n", " microbatch_size=32, # samples per API call\n", @@ -1352,9 +1303,7 @@ " Z_test_proc = as_numpy(proc.forward(reservoir, x_test_q_t))\n", " print(\"Test data retrieved.\")\n", "\n", - " log_features_as_csv_artifact(\n", - " run,\n", - " artifact_name=\"qorc-processor-features\",\n", + " save_feature_csvs(\n", " output_dir=output_dir / \"features\",\n", " arrays={\n", " \"Z_train_proc\": (Z_train_proc, y_train_np),\n", @@ -1362,7 +1311,7 @@ " },\n", " )\n", "\n", - " # ── Standardise and concatenate with pixels ──\n", + " # -- Standardise and concatenate with pixels --\n", " print(\"Normalising processor quantum features …\")\n", " qorc_mean_proc = Z_train_proc.mean(axis=0)\n", " qorc_std_proc = Z_train_proc.std(axis=0)\n", @@ -1386,15 +1335,17 @@ " y_val=y_val_proc,\n", " X_test=X_test_proc,\n", " y_test=y_test_np,\n", - " run=run,\n", " run_prefix=\"processor_readout\",\n", + " output_dir=image_output_dir,\n", + " summary_data=summary_data,\n", " seed=123,\n", " )\n", " processor_acc = accuracy_score(y_test_np, proc_preds)\n", " print(f\"MerLinProcessor accuracy : {processor_acc:.4f}\")\n", "\n", - " run.log({\"processor/accuracy\": float(processor_acc)})\n", - " run.summary[\"processor_model_parameters\"] = int(sum(p.numel() for p in proc_model.parameters()))\n", + " trackio.log({\"processor/accuracy\": float(processor_acc)})\n", + " summary_data[\"processor_model_parameters\"] = int(sum(p.numel() for p in proc_model.parameters()))\n", + " trackio.log({\"processor_model_parameters\": summary_data[\"processor_model_parameters\"]})\n", "\n", " embeddings_payload.update({\n", " \"Z_train_processor\": Z_train_proc,\n", @@ -1413,11 +1364,11 @@ "\n", "## 8 · Results & Comparison\n", "\n", - "We collect all three accuracy scores into a summary table and log it to W&B.\n", + "We collect all three accuracy scores into a summary table and log it to Trackio.\n", "\n", "After this cell:\n", - "- All intermediate embeddings (pixel features, PCA projections, quantum features) are saved locally as an `.npz` file and uploaded to W&B as an artifact.\n", - "- The W&B run is cleanly finished with `wandb.finish()`.\n", + "- All intermediate embeddings (pixel features, PCA projections, quantum features) are saved locally as an `.npz` file and uploaded through Trackio.\n", + "- The Trackio run is cleanly finished with `trackio.finish()`.\n", "\n", "### Interpreting the results\n", "\n", @@ -1433,24 +1384,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "7e91a434", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T20:29:29.891002Z", + "start_time": "2026-04-02T20:29:18.930957Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=======================================================\n", + " Accuracy comparison\n", + "=======================================================\n", + " L-SVC baseline : 0.8300\n", + " MerLin ideal (local sim) : 0.8300\n", + " MerLinProcessor (hardware) : 0.8400\n", + "=======================================================\n", + "* Run finished. Uploading logs to Trackio (please wait...)\n", + "\n", + "Trackio run finished locally. Ready to sync to Hugging Face.\n" + ] + } + ], "source": [ - "# ── Save all embeddings as a compressed .npz artifact ──\n", - "save_embeddings(output_dir=output_dir, run=run, embedding_dict=embeddings_payload)\n", + "# -- Save all embeddings as a compressed Trackio project file --\n", + "save_embeddings(output_dir=output_dir, embedding_dict=embeddings_payload)\n", "\n", - "# ── Build results table and log to W&B ──\n", + "# -- Build results table and log to Trackio --\n", "rows = [\n", " [\"L-SVC baseline\", float(baseline_acc)],\n", " [\"MerLin ideal (local sim)\", float(ideal_acc)],\n", " [\"MerLinProcessor (remote)\", None if processor_acc is None else float(processor_acc)],\n", "]\n", - "results_table = wandb.Table(columns=[\"run\", \"accuracy\"], data=rows)\n", - "run.log({\"results/accuracy_table\": results_table})\n", + "results_table = trackio.Table(columns=[\"run\", \"accuracy\"], data=rows, allow_mixed_types=True)\n", + "trackio.log({\"results/accuracy_table\": results_table})\n", + "\n", + "# -- Persist final summary metadata --\n", + "write_summary_file(summary_data, output_dir)\n", "\n", - "# ── Print summary ──\n", + "# -- Print summary --\n", "print(\"=\" * 55)\n", "print(\" Accuracy comparison\")\n", "print(\"=\" * 55)\n", @@ -1462,9 +1438,101 @@ " print(f\" MerLinProcessor (hardware) : {processor_acc:.4f}\")\n", "print(\"=\" * 55)\n", "\n", - "# ── Finish W&B run ──\n", - "wandb.finish()\n", - "print(\"\\nW&B run finished. All artifacts have been saved.\")\n" + "# -- Finish the local Trackio run --\n", + "trackio.finish()\n", + "print(\"\\nTrackio run finished locally. Ready to sync to Hugging Face.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "97e29a65", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## 9 · Publish to Hugging Face\n", + "\n", + "After the local Trackio run is finished, you can push the full project to a Hugging Face Space.\n", + "\n", + "By default, this cell syncs to the Hugging Face Space `Quandela/webinar-demo`.\n", + "\n", + "If you want to override that target, add these optional entries to `.env`:\n", + "\n", + "```\n", + "HF_SPACE_ID=Quandela/webinar-demo\n", + "TRACKIO_SYNC_PRIVATE=true\n", + "```\n", + "\n", + "If `HF_SPACE_ID` is not set, the cell below will use `Quandela/webinar-demo`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86ae4c17", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Syncing Trackio project 'webinar-demo' to Hugging Face Space 'Quandela/webinar-demo' ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing Files (118 / 118): 100%|██████████| 40.3MB / 40.3MB, 0.00B/s \n", + "New Data Upload: | | 0.00B / 0.00B, 0.00B/s \n", + "No files have been modified since last commit. Skipping to prevent empty commit.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Project data uploaded to bucket: https://huggingface.co/buckets/Quandela/webinar-demo-bucket-4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No files have been modified since last commit. Skipping to prevent empty commit.\n", + "No files have been modified since last commit. Skipping to prevent empty commit.\n", + "Processing Files (2 / 2): 100%|██████████| 657kB / 657kB, 0.00B/s \n", + "New Data Upload: | | 0.00B / 0.00B, 0.00B/s \n", + "No files have been modified since last commit. Skipping to prevent empty commit.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Static Space deployed: \u001b[1m\u001b[38;5;208mhttps://huggingface.co/spaces/Quandela/webinar-demo\u001b[0m\n", + "Trackio project synced to Hugging Face Space: Quandela/webinar-demo\n" + ] + } + ], + "source": [ + "load_dotenv(Path('.env'), override=False)\n", + "# First, we created a space webinar-demo on the Quandela Hugging Face account.\n", + "default_hf_space_id = 'Quandela/webinar-demo' # You can change it to fit your own namespace !\n", + "hf_space_id = os.getenv('HF_SPACE_ID', default_hf_space_id).strip()\n", + "sync_private = os.getenv('TRACKIO_SYNC_PRIVATE', 'true').strip().lower() in {'1', 'true', 'yes', 'on'}\n", + "\n", + "print(f'Syncing Trackio project {TRACKIO_PROJECT!r} to Hugging Face Space {hf_space_id!r} ...')\n", + "synced_space = trackio.sync(\n", + " project=TRACKIO_PROJECT,\n", + " space_id=default_hf_space_id,\n", + " private=False,\n", + " sdk=\"static\",\n", + " force=True,\n", + " run_in_background=False,\n", + ")\n", + "print(f\"Trackio project synced to Hugging Face Space: {synced_space}\")\n", + "\n" ] }, { @@ -1474,7 +1542,7 @@ "source": [ "---\n", "\n", - "## 9 · Going Further\n", + "## 10 · Going Further\n", "\n", "### Exercises\n", "\n", @@ -1493,21 +1561,40 @@ "- Mujal *et al.* (2021) — *Opportunities in Quantum Reservoir Computing and Extreme Learning Machines*, Adv. Quantum Technol.\n", "- Perceval documentation: [perceval.quandela.net](https://perceval.quandela.net)\n", "- MerLin documentation: [merlinquantum.ai](https://merlinquantum.ai)\n", - "- Weights & Biases documentation: [docs.wandb.ai](https://docs.wandb.ai)" + "- Trackio documentation: [huggingface.co/docs/trackio](https://huggingface.co/docs/trackio)" ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, - "source": "", - "id": "d0d3edcdff221761" + "id": "d0d3edcdff221761", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-02T20:12:30.652286Z", + "start_time": "2026-04-02T19:09:33.303371Z" + } + }, + "outputs": [], + "source": [] } ], "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" } }, "nbformat": 4, diff --git a/QML_with_MerLin/Webinar/wandb_utils.py b/QML_with_MerLin/Webinar/wandb_utils.py deleted file mode 100644 index 97562d6..0000000 --- a/QML_with_MerLin/Webinar/wandb_utils.py +++ /dev/null @@ -1,215 +0,0 @@ -""" -Weights & Biases utility helpers for the QORC-MNIST demo. - -All figure-creation and W&B logging functions live here so that the main -script stays focused on the ML/quantum workflow. -""" - -from __future__ import annotations - -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -import wandb -import perceval as pcvl - -from sklearn.decomposition import PCA -from sklearn.metrics import confusion_matrix - - -# --------------------------------------------------------------------------- -# Core logging helper -# --------------------------------------------------------------------------- - -def log_figure(run: "wandb.sdk.wandb_run.Run", key: str, fig: plt.Figure) -> None: - """Log a Matplotlib figure to W&B and close it.""" - run.log({key: wandb.Image(fig)}) - plt.close(fig) - - -# --------------------------------------------------------------------------- -# Figure factories -# --------------------------------------------------------------------------- - -def make_mnist_samples_figure( - flat_images: np.ndarray, - labels: np.ndarray, - title: str, - n_rows: int = 2, - n_cols: int = 8, -) -> plt.Figure: - """Grid of MNIST examples for pedagogical logging.""" - fig, axes = plt.subplots(n_rows, n_cols, figsize=(1.5 * n_cols, 1.5 * n_rows)) - axes = axes.flatten() - for idx, ax in enumerate(axes): - if idx >= len(flat_images): - ax.axis("off") - continue - ax.imshow(flat_images[idx].reshape(28, 28), cmap="gray") - ax.set_title(f"label={int(labels[idx])}", fontsize=9) - ax.axis("off") - fig.suptitle(title) - fig.tight_layout() - return fig - - -def make_pca_variance_figure(pca: PCA) -> plt.Figure: - """PCA explained variance ratio bar + cumulative line.""" - comp_idx = np.arange(1, len(pca.explained_variance_ratio_) + 1) - fig, ax = plt.subplots(figsize=(8, 4)) - ax.bar(comp_idx, pca.explained_variance_ratio_, color="#2a9d8f") - ax.plot(comp_idx, np.cumsum(pca.explained_variance_ratio_), marker="o", color="#e76f51") - ax.set_xlabel("PCA component") - ax.set_ylabel("Explained variance ratio") - ax.set_title("PCA variance profile") - ax.grid(alpha=0.2) - fig.tight_layout() - return fig - - -def make_pca_projection_figure(X_pca: np.ndarray, y: np.ndarray, title: str) -> plt.Figure: - """First two PCA dimensions colored by class.""" - fig, ax = plt.subplots(figsize=(6, 5)) - scatter = ax.scatter(X_pca[:, 0], X_pca[:, 1], c=y, cmap="tab10", s=18, alpha=0.8) - ax.set_xlabel("PC1") - ax.set_ylabel("PC2") - ax.set_title(title) - legend = ax.legend(*scatter.legend_elements(), title="Digit", loc="best") - ax.add_artist(legend) - ax.grid(alpha=0.2) - fig.tight_layout() - return fig - - -def make_pca_components_figure(pca: PCA, n_components: int) -> plt.Figure: - """PCA component vectors visualized as 28×28 pseudo-images.""" - n_components = min(n_components, pca.components_.shape[0]) - n_cols = 4 - n_rows = int(np.ceil(n_components / n_cols)) - fig, axes = plt.subplots(n_rows, n_cols, figsize=(3 * n_cols, 3 * n_rows)) - axes = np.array(axes).reshape(-1) - for idx, ax in enumerate(axes): - if idx >= n_components: - ax.axis("off") - continue - comp = pca.components_[idx].reshape(28, 28) - im = ax.imshow(comp, cmap="coolwarm") - ax.set_title(f"PC{idx + 1}") - ax.axis("off") - fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04) - fig.suptitle("PCA components (reshaped to image space)") - fig.tight_layout() - return fig - - -def make_confusion_matrix_figure(y_true: np.ndarray, y_pred: np.ndarray, title: str) -> plt.Figure: - """Confusion matrix figure from predictions.""" - cm = confusion_matrix(y_true, y_pred, labels=np.arange(10)) - fig, ax = plt.subplots(figsize=(7, 6)) - im = ax.imshow(cm, cmap="Blues") - ax.set_xlabel("Predicted") - ax.set_ylabel("True") - ax.set_title(title) - ax.set_xticks(np.arange(10)) - ax.set_yticks(np.arange(10)) - fig.colorbar(im, ax=ax) - fig.tight_layout() - return fig - - -# --------------------------------------------------------------------------- -# Circuit export -# --------------------------------------------------------------------------- - -def export_and_log_circuit( - reservoir, - run: "wandb.sdk.wandb_run.Run", - output_dir: Path, -) -> None: - """Export Perceval circuit visuals and log them to W&B.""" - output_dir.mkdir(parents=True, exist_ok=True) - circuit_artifact = wandb.Artifact("perceval-circuit", type="visualization") - - circuit_text_path = output_dir / "reservoir_circuit.txt" - circuit_text_path.write_text(str(reservoir.circuit), encoding="utf-8") - circuit_artifact.add_file(str(circuit_text_path)) - - if hasattr(pcvl, "pdisplay_to_file"): - exported = False - for ext in ("png", "svg", "pdf"): - candidate = output_dir / f"reservoir_circuit.{ext}" - try: - pcvl.pdisplay_to_file(reservoir.circuit, str(candidate)) - if candidate.exists(): - circuit_artifact.add_file(str(candidate)) - if ext == "png": - run.log({"reservoir/circuit_diagram": wandb.Image(str(candidate))}) - exported = True - break - except Exception: - continue - if not exported: - _log_circuit_text(run, reservoir) - else: - _log_circuit_text(run, reservoir) - - run.log_artifact(circuit_artifact) - - -def _log_circuit_text(run: "wandb.sdk.wandb_run.Run", reservoir) -> None: - fig, ax = plt.subplots(figsize=(12, 2)) - ax.text(0.01, 0.5, str(reservoir.circuit), family="monospace", fontsize=9) - ax.axis("off") - fig.tight_layout() - log_figure(run, "reservoir/circuit_text_render", fig) - - -# --------------------------------------------------------------------------- -# Feature CSV artifacts -# --------------------------------------------------------------------------- - -def log_features_as_csv_artifact( - run: "wandb.sdk.wandb_run.Run", - artifact_name: str, - output_dir: Path, - arrays: dict[str, tuple[np.ndarray, np.ndarray]], -) -> None: - """ - Save feature arrays as CSV files and upload them as a W&B artifact. - - Each entry in `arrays` maps a file stem to a (features, labels) pair. - The resulting CSV has columns feature_0, feature_1, …, feature_N, label - and is immediately downloadable from the W&B Artifacts browser. - """ - output_dir.mkdir(parents=True, exist_ok=True) - artifact = wandb.Artifact(artifact_name, type="dataset", - description="Quantum reservoir feature embeddings (CSV)") - - for name, (features, labels) in arrays.items(): - n_features = features.shape[1] - header = ",".join([f"feature_{i}" for i in range(n_features)] + ["label"]) - data = np.concatenate([features, labels.reshape(-1, 1)], axis=1) - csv_path = output_dir / f"{name}.csv" - np.savetxt(csv_path, data, delimiter=",", header=header, comments="") - artifact.add_file(str(csv_path)) - - run.log_artifact(artifact) - - -# --------------------------------------------------------------------------- -# Embedding artifact -# --------------------------------------------------------------------------- - -def save_embeddings( - output_dir: Path, - run: "wandb.sdk.wandb_run.Run", - embedding_dict: dict[str, np.ndarray], -) -> None: - """Save embeddings/features as a W&B artifact.""" - output_dir.mkdir(parents=True, exist_ok=True) - embedding_path = output_dir / "embeddings.npz" - np.savez(embedding_path, **embedding_dict) - artifact = wandb.Artifact("qorc-embeddings", type="dataset") - artifact.add_file(str(embedding_path)) - run.log_artifact(artifact) diff --git a/requirements.txt b/requirements.txt index f74bfae..a78d6c4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,3 +5,4 @@ dotenv==0.9.9 python-dotenv==1.2.2 ipywidgets +trackio \ No newline at end of file