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Add warning and clear cells
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@martin-steinegger
martin-steinegger committed Nov 17, 2024
1 parent b339885 commit 1a941a8
Showing 1 changed file with 27 additions and 147 deletions.
174 changes: 27 additions & 147 deletions boltz1.ipynb
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"provenance": [],
"machine_shape": "hm",
"gpuType": "A100",
"authorship_tag": "ABX9TyMG+xMrC8CD3APVXWbRvd84",
"authorship_tag": "ABX9TyP6Tde8GuCpqZ+80nF7ZR3o",
"include_colab_link": true
},
"kernelspec": {
Expand All @@ -29,6 +29,19 @@
"<a href=\"https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/boltz1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# This is a work-in-progress notebook for [Boltz](https://github.com/jwohlwend/boltz)\n",
"\n",
"⚠️ **Warning to Users:**\n",
"- **Alpha Version:** This notebook is currently under active development and is considered a beta version.\n",
"- **Usage at Your Own Risk:** Use this notebook at your own discretion and risk."
],
"metadata": {
"id": "KNOrLaJFdiA-"
}
},
{
"cell_type": "code",
"source": [
Expand Down Expand Up @@ -132,30 +145,17 @@
"cellView": "form",
"id": "AcYvVeDESi2a"
},
"execution_count": 35,
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"cellView": "form",
"id": "4eXNO1JJHYrB",
"outputId": "c9b764cb-37e7-4ec0-8519-b488163d3b40"
"id": "4eXNO1JJHYrB"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"CPU times: user 30 µs, sys: 6 µs, total: 36 µs\n",
"Wall time: 39.1 µs\n"
]
}
],
"outputs": [],
"source": [
"#@title Install dependencies\n",
"%%time\n",
Expand All @@ -182,36 +182,11 @@
"!colabfold_batch \"{queries_path}\" \"{jobname}\" --msa-only"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"cellView": "form",
"id": "4aFDR4IhRe6y",
"outputId": "e32be2a0-2192-4385-9948-fafe3180dea4"
"id": "4aFDR4IhRe6y"
},
"execution_count": 37,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-11-17 23:21:37,398 Running colabfold 1.5.5 (c21e1768d18e3608e6e6d99c97134317e7e41c75)\n",
"\n",
"WARNING: You are welcome to use the default MSA server, however keep in mind that it's a\n",
"limited shared resource only capable of processing a few thousand MSAs per day. Please\n",
"submit jobs only from a single IP address. We reserve the right to limit access to the\n",
"server case-by-case when usage exceeds fair use. If you require more MSAs: You can \n",
"precompute all MSAs with `colabfold_search` or host your own API and pass it to `--host-url`\n",
"\n",
"2024-11-17 23:21:39,576 Running on GPU\n",
"2024-11-17 23:21:40,309 Found 4 citations for tools or databases\n",
"2024-11-17 23:21:40,310 Query 1/1: test_a5e17_1_0 (length 59)\n",
"COMPLETE: 100% 150/150 [00:22<00:00, 6.53it/s] \n",
"2024-11-17 23:22:03,313 Saved test_a5e17_1/test_a5e17_1_0.pickle\n",
"2024-11-17 23:22:04,055 Done\n"
]
}
]
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
Expand All @@ -220,36 +195,11 @@
"!boltz predict --out_dir \"{jobname}\" \"{jobname}/{jobname}.fasta\""
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"cellView": "form",
"id": "bgaBXxXtIAu9",
"outputId": "7cb18790-83f7-42db-d3f1-1043025520b0"
"id": "bgaBXxXtIAu9"
},
"execution_count": 38,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading data and model to /root/.boltz. You may change this by setting the --cache flag.\n",
"Checking input data.\n",
"Processing input data.\n",
"100% 1/1 [00:00<00:00, 16.14it/s]\n",
"GPU available: True (cuda), used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"HPU available: False, using: 0 HPUs\n",
"You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
"2024-11-17 23:22:42.568213: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
"2024-11-17 23:22:42.619224: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
"Predicting DataLoader 0: 100% 1/1 [00:10<00:00, 10.87s/it]Number of failed examples: 0\n",
"Predicting DataLoader 0: 100% 1/1 [00:10<00:00, 10.87s/it]\n"
]
}
]
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
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"files.download(zip_filename)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"cellView": "form",
"id": "jdSBSTOpaULF",
"outputId": "921ee9cb-9d73-48b7-de0b-0ec29d73d2bf"
"id": "jdSBSTOpaULF"
},
"execution_count": 42,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_bb74d9f7-a559-405a-b95e-4444e730cef3\", \"results_test_a5e17_1.zip\", 1119339)"
]
},
"metadata": {}
}
]
"execution_count": null,
"outputs": []
}
]
}

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