diff --git a/tuning/autotune_scvi.ipynb b/tuning/autotune_scvi.ipynb index 9b9bc62..7d7a3f0 100644 --- a/tuning/autotune_scvi.ipynb +++ b/tuning/autotune_scvi.ipynb @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-12-07T19:29:24.822153Z", @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-12-07T19:29:24.826130Z", @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-12-07T19:29:28.346115Z", @@ -107,22 +107,7 @@ "shell.execute_reply": "2023-12-07T19:29:28.349520Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Seed set to 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Last run with scvi-tools version: 1.1.0\n" - ] - } - ], + "outputs": [], "source": [ "scvi.settings.seed = 0\n", "print(\"Last run with scvi-tools version:\", scvi.__version__)" @@ -139,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-12-07T19:29:28.368700Z", @@ -154,6 +139,7 @@ "sns.set_theme()\n", "torch.set_float32_matmul_precision(\"high\")\n", "save_dir = tempfile.TemporaryDirectory()\n", + "scvi.settings.logging_dir = save_dir.name\n", "\n", "%config InlineBackend.print_figure_kwargs={\"facecolor\": \"w\"}\n", "%config InlineBackend.figure_format=\"retina\"" @@ -169,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-12-07T19:29:28.379095Z", @@ -178,29 +164,7 @@ "shell.execute_reply": "2023-12-07T19:29:30.397616Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[34mINFO \u001b[0m Downloading file at \u001b[35m/tmp/tmpafthtjrs/\u001b[0m\u001b[95mhca_subsampled_20k.h5ad\u001b[0m \n", - "Downloading...: 100%|██████████| 65714/65714.0 [00:00<00:00, 97942.71it/s] \n" - ] - }, - { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 18641 × 26662\n", - " obs: 'NRP', 'age_group', 'cell_source', 'cell_type', 'donor', 'gender', 'n_counts', 'n_genes', 'percent_mito', 'percent_ribo', 'region', 'sample', 'scrublet_score', 'source', 'type', 'version', 'cell_states', 'Used'\n", - " var: 'gene_ids-Harvard-Nuclei', 'feature_types-Harvard-Nuclei', 'gene_ids-Sanger-Nuclei', 'feature_types-Sanger-Nuclei', 'gene_ids-Sanger-Cells', 'feature_types-Sanger-Cells', 'gene_ids-Sanger-CD45', 'feature_types-Sanger-CD45', 'n_counts'\n", - " uns: 'cell_type_colors'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "adata = scvi.data.heart_cell_atlas_subsampled(save_path=save_dir.name)\n", "adata" @@ -216,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-12-07T19:29:30.399694Z", @@ -225,21 +189,7 @@ "shell.execute_reply": "2023-12-07T19:29:31.758275Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 18641 × 2000\n", - " obs: 'NRP', 'age_group', 'cell_source', 'cell_type', 'donor', 'gender', 'n_counts', 'n_genes', 'percent_mito', 'percent_ribo', 'region', 'sample', 'scrublet_score', 'source', 'type', 'version', 'cell_states', 'Used'\n", - " var: 'gene_ids-Harvard-Nuclei', 'feature_types-Harvard-Nuclei', 'gene_ids-Sanger-Nuclei', 'feature_types-Sanger-Nuclei', 'gene_ids-Sanger-Cells', 'feature_types-Sanger-Cells', 'gene_ids-Sanger-CD45', 'feature_types-Sanger-CD45', 'n_counts', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm'\n", - " uns: 'cell_type_colors', 'hvg'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sc.pp.highly_variable_genes(adata, n_top_genes=2000, flavor=\"seurat_v3\", subset=True)\n", "adata" @@ -263,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-12-07T19:29:31.760288Z", @@ -272,20 +222,7 @@ "shell.execute_reply": "2023-12-07T19:29:31.916065Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":119: FutureWarning: SparseDataset is deprecated and will be removed in late 2024. It has been replaced by the public classes CSRDataset and CSCDataset.\n", - "\n", - "For instance checks, use `isinstance(X, (anndata.experimental.CSRDataset, anndata.experimental.CSCDataset))` instead.\n", - "\n", - "For creation, use `anndata.experimental.sparse_dataset(X)` instead.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "model_cls = scvi.model.SCVI\n", "model_cls.setup_anndata(adata)" @@ -301,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-12-07T19:29:31.918113Z", @@ -329,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-12-07T19:29:32.071461Z", @@ -338,144 +275,7 @@ "shell.execute_reply": "2023-12-07T19:29:32.084124Z" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
ModelTuner registry for SCVI\n",
-       "
\n" - ], - "text/plain": [ - "ModelTuner registry for SCVI\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
                  Tunable hyperparameters                  \n",
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓\n",
-       "┃      Hyperparameter      ┃ Default value ┃    Source    ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩\n",
-       "│         n_hidden         │      128      │     VAE      │\n",
-       "│         n_latent         │      10       │     VAE      │\n",
-       "│         n_layers         │       1       │     VAE      │\n",
-       "│       dropout_rate       │      0.1      │     VAE      │\n",
-       "│        dispersion        │     gene      │     VAE      │\n",
-       "│     log_variational      │     True      │     VAE      │\n",
-       "│     gene_likelihood      │     zinb      │     VAE      │\n",
-       "│   latent_distribution    │    normal     │     VAE      │\n",
-       "│    encode_covariates     │     False     │     VAE      │\n",
-       "│ deeply_inject_covariates │     True      │     VAE      │\n",
-       "│      use_batch_norm      │     both      │     VAE      │\n",
-       "│      use_layer_norm      │     none      │     VAE      │\n",
-       "│  use_observed_lib_size   │     True      │     VAE      │\n",
-       "│      var_activation      │     None      │     VAE      │\n",
-       "│        optimizer         │     Adam      │ TrainingPlan │\n",
-       "│            lr            │     0.001     │ TrainingPlan │\n",
-       "│       weight_decay       │     1e-06     │ TrainingPlan │\n",
-       "│           eps            │     0.01      │ TrainingPlan │\n",
-       "│    n_steps_kl_warmup     │     None      │ TrainingPlan │\n",
-       "│    n_epochs_kl_warmup    │      400      │ TrainingPlan │\n",
-       "│   reduce_lr_on_plateau   │     False     │ TrainingPlan │\n",
-       "│        lr_factor         │      0.6      │ TrainingPlan │\n",
-       "│       lr_patience        │      30       │ TrainingPlan │\n",
-       "│       lr_threshold       │      0.0      │ TrainingPlan │\n",
-       "│          lr_min          │       0       │ TrainingPlan │\n",
-       "│      max_kl_weight       │      1.0      │ TrainingPlan │\n",
-       "│      min_kl_weight       │      0.0      │ TrainingPlan │\n",
-       "│        batch_size        │      128      │     SCVI     │\n",
-       "└──────────────────────────┴───────────────┴──────────────┘\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[3m Tunable hyperparameters \u001b[0m\n", - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1m Hyperparameter \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mDefault value\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Source \u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m n_hidden \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 128 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m n_latent \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 10 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m n_layers \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 1 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m dropout_rate \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 0.1 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m dispersion \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m gene \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m log_variational \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m True \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m gene_likelihood \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m zinb \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m latent_distribution \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m normal \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m encode_covariates \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m False \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33mdeeply_inject_covariates\u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m True \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m use_batch_norm \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m both \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m use_layer_norm \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m none \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m use_observed_lib_size \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m True \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m var_activation \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m None \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m VAE \u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m optimizer \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m Adam \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mTrainingPlan\u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m lr \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 0.001 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mTrainingPlan\u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m weight_decay \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 1e-06 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mTrainingPlan\u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m eps \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 0.01 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mTrainingPlan\u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m n_steps_kl_warmup \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m None \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mTrainingPlan\u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m n_epochs_kl_warmup \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 400 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mTrainingPlan\u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m reduce_lr_on_plateau \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m False \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mTrainingPlan\u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m lr_factor \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 0.6 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mTrainingPlan\u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m lr_patience \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 30 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mTrainingPlan\u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m lr_threshold \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 0.0 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mTrainingPlan\u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m lr_min \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 0 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mTrainingPlan\u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m max_kl_weight \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 1.0 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mTrainingPlan\u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m min_kl_weight \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 0.0 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32mTrainingPlan\u001b[0m\u001b[32m \u001b[0m│\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m batch_size \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 128 \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m SCVI \u001b[0m\u001b[32m \u001b[0m│\n", - "└──────────────────────────┴───────────────┴──────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
       Available metrics        \n",
-       "┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
-       "┃     Metric      ┃    Mode    ┃\n",
-       "┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
-       "│ validation_loss │    min     │\n",
-       "└─────────────────┴────────────┘\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[3m Available metrics \u001b[0m\n", - "┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1m Metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Mode \u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33mvalidation_loss\u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m min \u001b[0m\u001b[38;5;128m \u001b[0m│\n", - "└─────────────────┴────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
                         Default search space                         \n",
-       "┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃ Hyperparameter ┃ Sample function ┃  Arguments  ┃ Keyword arguments ┃\n",
-       "┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
-       "│    n_hidden    │     choice      │ [[64, 128]] │        {}         │\n",
-       "└────────────────┴─────────────────┴─────────────┴───────────────────┘\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[3m Default search space \u001b[0m\n", - "┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mHyperparameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mSample function\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Arguments \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mKeyword arguments\u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n", - "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m n_hidden \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m choice \u001b[0m\u001b[38;5;128m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m[[64, 128]]\u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;208m \u001b[0m\u001b[38;5;208m {} \u001b[0m\u001b[38;5;208m \u001b[0m│\n", - "└────────────────┴─────────────────┴─────────────┴───────────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "scvi_tuner.info()" ] @@ -498,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-12-07T19:29:32.085956Z", @@ -551,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-12-07T19:29:32.089406Z", @@ -560,93 +360,7 @@ "shell.execute_reply": "2023-12-07T19:32:33.391108Z" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "
\n", - "
\n", - "

Tune Status

\n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
Current time:2023-12-07 19:32:33
Running for: 00:02:59.06
Memory: 9.6/125.7 GiB
\n", - "
\n", - "
\n", - "
\n", - "

System Info

\n", - " Using AsyncHyperBand: num_stopped=5
Bracket: Iter 64.000: -456.2224578857422 | Iter 32.000: -457.0051574707031 | Iter 16.000: -460.7762145996094 | Iter 8.000: -468.9873046875 | Iter 4.000: -479.00633239746094 | Iter 2.000: -495.6261749267578 | Iter 1.000: -533.16845703125
Logical resource usage: 10.0/20 CPUs, 1.0/1 GPUs (0.0/1.0 accelerator_type:G)\n", - "
\n", - " \n", - "
\n", - "
\n", - "
\n", - "

Trial Status

\n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
Trial name status loc n_hidden n_layers lr validation_loss
_trainable_43635405TERMINATED192.168.80.2:1032 64 20.00169 456.297
_trainable_b8c4083fTERMINATED192.168.80.2:1032 256 10.000978826 457.779
_trainable_8b17d361TERMINATED192.168.80.2:1032 128 20.0015655 552.937
_trainable_8d3749ebTERMINATED192.168.80.2:1032 256 30.00327036 456.801
_trainable_ca2deba8TERMINATED192.168.80.2:1032 128 20.0030879 457.043
\n", - "
\n", - "
\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-12-07 19:29:34,307\tINFO tune.py:645 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n", - "2023-12-07 19:32:33,383\tINFO tune.py:1143 -- Total run time: 179.08 seconds (179.06 seconds for the tuning loop).\n" - ] - } - ], + "outputs": [], "source": [ "ray.init(log_to_driver=False)\n", "results = scvi_tuner.fit(\n", @@ -661,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-12-07T19:32:33.393199Z", @@ -670,16 +384,7 @@ "shell.execute_reply": "2023-12-07T19:32:33.394995Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'n_hidden': 64, 'n_layers': 2}\n", - "{'plan_kwargs': {'lr': 0.0016900002795988184}}\n" - ] - } - ], + "outputs": [], "source": [ "print(results.model_kwargs)\n", "print(results.train_kwargs)" @@ -750,7 +455,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.11.7" }, "vscode": { "interpreter": {