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Merge pull request #307 from scverse/update-tuning/autotune_scvi.ipynb
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automated update for tuning/autotune_scvi.ipynb
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@ori-kron-wis
ori-kron-wis authored Aug 27, 2024
2 parents 981e0ee + 8738c82 commit b968714
Showing 1 changed file with 81 additions and 93 deletions.
174 changes: 81 additions & 93 deletions tuning/autotune_scvi.ipynb
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"\u001b[0m"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\r\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n"
]
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"/env/lib/python3.11/site-packages/scvi_colab/_core.py:41: UserWarning: \n",
"/usr/local/lib/python3.11/site-packages/scvi_colab/_core.py:41: UserWarning: \n",
" Not currently in Google Colab environment.\n",
"\n",
" Please run with `run_outside_colab=True` to override.\n",
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"execution_count": 3,
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"outputs": [
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"name": "stdout",
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"text": [
"Last run with scvi-tools version: 1.1.0\n"
"Last run with scvi-tools version: 1.1.6\n"
]
}
],
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"\u001b[34mINFO \u001b[0m Downloading file at \u001b[35m/tmp/tmp6xxk_1x6/\u001b[0m\u001b[95mhca_subsampled_20k.h5ad\u001b[0m \n"
"\u001b[34mINFO \u001b[0m Downloading file at \u001b[35m/tmp/tmp5sb6wnfn/\u001b[0m\u001b[95mhca_subsampled_20k.h5ad\u001b[0m \n"
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"execution_count": 7,
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"text": [
"<frozen abc>: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 It has been replaced by the public classes CSRDataset and CSCDataset.\n",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)"
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"execution_count": 10,
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"execution_count": 11,
"metadata": {
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" <h3>Tune Status</h3>\n",
" <table>\n",
"<tbody>\n",
"<tr><td>Current time:</td><td>2024-02-12 22:37:57</td></tr>\n",
"<tr><td>Running for: </td><td>00:02:21.96 </td></tr>\n",
"<tr><td>Memory: </td><td>8.0/125.7 GiB </td></tr>\n",
"<tr><td>Current time:</td><td>2024-08-19 13:32:34</td></tr>\n",
"<tr><td>Running for: </td><td>00:01:32.13 </td></tr>\n",
"<tr><td>Memory: </td><td>5.9/125.7 GiB </td></tr>\n",
"</tbody>\n",
"</table>\n",
" </div>\n",
" <div class=\"vDivider\"></div>\n",
" <div class=\"systemInfo\">\n",
" <h3>System Info</h3>\n",
" Using AsyncHyperBand: num_stopped=5<br>Bracket: Iter 64.000: -457.03416442871094 | Iter 32.000: -457.7937927246094 | Iter 16.000: -462.5426483154297 | Iter 8.000: -470.41017150878906 | Iter 4.000: -484.6981658935547 | Iter 2.000: -516.056640625 | Iter 1.000: -592.379638671875<br>Logical resource usage: 10.0/20 CPUs, 1.0/1 GPUs (0.0/1.0 accelerator_type:G)\n",
" Using AsyncHyperBand: num_stopped=5<br>Bracket: Iter 64.000: -456.6855773925781 | Iter 32.000: -458.8435363769531 | Iter 16.000: -464.8856201171875 | Iter 8.000: -474.24481201171875 | Iter 4.000: -490.88726806640625 | Iter 2.000: -507.64378356933594 | Iter 1.000: -611.3925170898438<br>Logical resource usage: 10.0/20 CPUs, 1.0/1 GPUs (0.0/1.0 accelerator_type:G)\n",
" </div>\n",
" \n",
" </div>\n",
Expand All @@ -685,11 +673,11 @@
"<tr><th>Trial name </th><th>status </th><th>loc </th><th style=\"text-align: right;\"> n_hidden</th><th style=\"text-align: right;\"> n_layers</th><th style=\"text-align: right;\"> lr</th><th style=\"text-align: right;\"> validation_loss</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td>_trainable_8f41734d</td><td>TERMINATED</td><td>172.29.0.2:1469</td><td style=\"text-align: right;\"> 128</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\">0.00148765 </td><td style=\"text-align: right;\"> 457.597</td></tr>\n",
"<tr><td>_trainable_f42dd38f</td><td>TERMINATED</td><td>172.29.0.2:1469</td><td style=\"text-align: right;\"> 64</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\">0.000455217</td><td style=\"text-align: right;\"> 748.298</td></tr>\n",
"<tr><td>_trainable_a19ea641</td><td>TERMINATED</td><td>172.29.0.2:1469</td><td style=\"text-align: right;\"> 256</td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\">0.000388237</td><td style=\"text-align: right;\"> 525.478</td></tr>\n",
"<tr><td>_trainable_619aa78d</td><td>TERMINATED</td><td>172.29.0.2:1469</td><td style=\"text-align: right;\"> 64</td><td style=\"text-align: right;\"> 2</td><td style=\"text-align: right;\">0.000326625</td><td style=\"text-align: right;\"> 758.515</td></tr>\n",
"<tr><td>_trainable_681ab3ba</td><td>TERMINATED</td><td>172.29.0.2:1469</td><td style=\"text-align: right;\"> 256</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\">0.00392304 </td><td style=\"text-align: right;\"> 457.508</td></tr>\n",
"<tr><td>_trainable_3afda194</td><td>TERMINATED</td><td>172.29.0.2:1509</td><td style=\"text-align: right;\"> 256</td><td style=\"text-align: right;\"> 2</td><td style=\"text-align: right;\">0.000639646</td><td style=\"text-align: right;\"> 459.067</td></tr>\n",
"<tr><td>_trainable_a74e6eda</td><td>TERMINATED</td><td>172.29.0.2:1509</td><td style=\"text-align: right;\"> 256</td><td style=\"text-align: right;\"> 2</td><td style=\"text-align: right;\">0.000551579</td><td style=\"text-align: right;\"> 612.665</td></tr>\n",
"<tr><td>_trainable_92bc17ed</td><td>TERMINATED</td><td>172.29.0.2:1509</td><td style=\"text-align: right;\"> 64</td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\">0.000642845</td><td style=\"text-align: right;\"> 611.393</td></tr>\n",
"<tr><td>_trainable_1cc85ac8</td><td>TERMINATED</td><td>172.29.0.2:1509</td><td style=\"text-align: right;\"> 64</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\">0.00985192 </td><td style=\"text-align: right;\"> 494.813</td></tr>\n",
"<tr><td>_trainable_21481557</td><td>TERMINATED</td><td>172.29.0.2:1509</td><td style=\"text-align: right;\"> 128</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\">0.000347918</td><td style=\"text-align: right;\"> 708.115</td></tr>\n",
"</tbody>\n",
"</table>\n",
" </div>\n",
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"name": "stderr",
"output_type": "stream",
"text": [
"2024-02-12 22:35:35,776\tINFO tune.py:583 -- [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"
"2024-08-19 13:31:01,903\tINFO tune.py:583 -- [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"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-02-12 22:37:57,784\tINFO tune.py:1042 -- Total run time: 142.01 seconds (141.96 seconds for the tuning loop).\n"
"2024-08-19 13:32:34,211\tINFO tune.py:1042 -- Total run time: 92.31 seconds (92.12 seconds for the tuning loop).\n"
]
}
],
Expand All @@ -764,19 +752,19 @@
"execution_count": 12,
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{
"name": "stdout",
"output_type": "stream",
"text": [
"{'n_hidden': 256, 'n_layers': 3}\n",
"{'plan_kwargs': {'lr': 0.003923035750529307}}\n"
"{'n_hidden': 256, 'n_layers': 2}\n",
"{'plan_kwargs': {'lr': 0.0006396456621967837}}\n"
]
}
],
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"name": "python",
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"pygments_lexer": "ipython3",
"version": "3.11.7"
"version": "3.11.9"
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"vscode": {
"interpreter": {
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