"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"from jupytercards import display_flashcards\n",
"cards='/home/jupyter/flashcards/'\n",
@@ -659,31 +13,7 @@
]
}
],
- "metadata": {
- "environment": {
- "kernel": "python3",
- "name": "tf2-gpu.2-8.m102",
- "type": "gcloud",
- "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-8:m102"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.12"
- }
- },
+ "metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
diff --git a/GoogleCloud/submodule01_Intro_to_terminal.ipynb b/GoogleCloud/submodule01_Intro_to_terminal.ipynb
index 948eea1..a0602cd 100644
--- a/GoogleCloud/submodule01_Intro_to_terminal.ipynb
+++ b/GoogleCloud/submodule01_Intro_to_terminal.ipynb
@@ -29,9 +29,7 @@
{
"cell_type": "markdown",
"id": "bf672094-38a4-4e24-87de-598a0befd5d1",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"\n",
"## Jupyter Notebook Basics\n",
@@ -57,9 +55,7 @@
"cell_type": "code",
"execution_count": null,
"id": "37a3c9e6-9725-41eb-a284-774efe841bcf",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -71,9 +67,7 @@
{
"cell_type": "markdown",
"id": "54959383-4605-42ad-bd55-fe2c09d4d498",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"\n",
" \n",
@@ -88,9 +82,7 @@
"cell_type": "code",
"execution_count": null,
"id": "b94461dd-996b-4419-9822-127a46967918",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -106,33 +98,10 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "dd071114-5301-4650-8f0a-15b99e65b230",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " "
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# FLASHCARDS\n",
"\n",
@@ -143,10 +112,7 @@
{
"cell_type": "markdown",
"id": "a26ac6a7-05e0-4c99-ad3a-32f2829d2b14",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
+ "metadata": {},
"source": [
"\n",
"You may notice that each cell has a vertical line to the left of it when the cell has been selected, you can click on the vertical line and collapse the cell. The same is true for the output of the code cell. Collapsing cells comes in pretty handy when the results of the code cell are long! Another way to manage commands with lengthy results is by enabling scrolling. To do this right click anywhere within the notebook and select the option for `Enable Scrolling for Outputs`.\n",
@@ -207,9 +173,7 @@
{
"cell_type": "markdown",
"id": "ffdf8adf-ce9e-462d-94d4-3d44b850c0ee",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"\n",
"### Getting Started in the Terminal Environment\n",
@@ -241,9 +205,7 @@
"cell_type": "code",
"execution_count": null,
"id": "054614c2-3eb0-46bc-91a1-2578593f3687",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -255,9 +217,7 @@
{
"cell_type": "markdown",
"id": "7353d25e-9eed-4ff7-9176-502a0b3b9773",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"Notice that in the terminal window to exit out of the manual we had to use the `q` to get the prompt to return. Another way of looking at a summary of the information in the manual page is with the `--help` flag. Try typing `mkdir --help` in the terminal window. The information in the manual pages tends to be more clearly laid out and better organized, but the `--help` flag is a quick and easy way to remind yourself of the flags available.\n",
"\n",
@@ -339,9 +299,7 @@
"cell_type": "code",
"execution_count": null,
"id": "f49506c2-6c72-449e-9197-542f50d14678",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -359,9 +317,7 @@
{
"cell_type": "markdown",
"id": "2cda9c8c-d2d0-4380-b332-4ff1a644ea51",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"## Paths, Where Is Your Data Stored?\n",
"-------------------\n",
@@ -383,9 +339,7 @@
"cell_type": "code",
"execution_count": null,
"id": "f4dc07da-3f1f-4c3e-bc97-b61c25451c72",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -410,9 +364,7 @@
{
"cell_type": "markdown",
"id": "e71c89cd-0c6a-4da2-915c-46ec1f975ae6",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"You can see that we are in the `jupyter` directory and this directory is inside a directory called `home`. \n",
"\n",
@@ -423,9 +375,7 @@
"cell_type": "code",
"execution_count": null,
"id": "e520676d-3e75-4d54-b488-2944e5478b1f",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -548,33 +498,10 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "bfccc937-62e7-48c8-9745-1e03236f3e2c",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " "
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# FLASHCARD\n",
"from IPython.display import IFrame\n",
@@ -584,9 +511,7 @@
{
"cell_type": "markdown",
"id": "3ae4b9a0-4eff-427e-bc7e-a292dc42f76d",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"## File Naming Considerations\n",
"---------\n",
@@ -661,31 +586,7 @@
]
}
],
- "metadata": {
- "environment": {
- "kernel": "python3",
- "name": "common-cpu.m104",
- "type": "gcloud",
- "uri": "gcr.io/deeplearning-platform-release/base-cpu:m104"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.4"
- }
- },
+ "metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
diff --git a/GoogleCloud/submodule02_Intro_to_cloud_computing.ipynb b/GoogleCloud/submodule02_Intro_to_cloud_computing.ipynb
index 87bff20..dc88f3d 100644
--- a/GoogleCloud/submodule02_Intro_to_cloud_computing.ipynb
+++ b/GoogleCloud/submodule02_Intro_to_cloud_computing.ipynb
@@ -3,13 +3,7 @@
{
"cell_type": "markdown",
"id": "40ee29a5-94d5-4a06-9e45-40f95df8a650",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "metadata": {},
"source": [
"# Cloud computing\n",
"\n",
@@ -113,9 +107,7 @@
{
"cell_type": "markdown",
"id": "415ae2bc-59f1-4a01-afc1-cc4b1c9f3f4c",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"## Google Cloud Storage Buckets, One Option for Long Term Data Storage\n",
"--------\n",
@@ -152,9 +144,7 @@
"cell_type": "code",
"execution_count": null,
"id": "ffcb317d-d2b7-4d11-b08c-da6840ef24f0",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -166,31 +156,7 @@
]
}
],
- "metadata": {
- "environment": {
- "kernel": "python3",
- "name": "common-cpu.m104",
- "type": "gcloud",
- "uri": "gcr.io/deeplearning-platform-release/base-cpu:m104"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.4"
- }
- },
+ "metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
diff --git a/GoogleCloud/submodule03_genomics_file_format.ipynb b/GoogleCloud/submodule03_genomics_file_format.ipynb
index bd790a3..21426f3 100644
--- a/GoogleCloud/submodule03_genomics_file_format.ipynb
+++ b/GoogleCloud/submodule03_genomics_file_format.ipynb
@@ -23,9 +23,7 @@
{
"cell_type": "markdown",
"id": "1cbeaea5-8155-4ed4-b951-e641f533be7b",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"## Genomic Annotation Data, GFF/GTF file Format\n",
"------\n",
@@ -322,9 +320,7 @@
"cell_type": "code",
"execution_count": null,
"id": "9fc00331-2d69-49ba-8e49-047cb81566d2",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -341,9 +337,7 @@
{
"cell_type": "markdown",
"id": "ed33c67a-1ef0-4491-b59d-776dfff623a6",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"## Genomic Sequence Data, Fasta Files\n",
"----------------\n",
@@ -396,9 +390,7 @@
"cell_type": "code",
"execution_count": null,
"id": "a5754551-bf07-4025-8d8e-ac5cbbfd95a9",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -452,9 +444,7 @@
"cell_type": "code",
"execution_count": null,
"id": "0991d53a-cefb-40b9-8807-f819434d7afd",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -507,9 +497,7 @@
"cell_type": "code",
"execution_count": null,
"id": "fbde4a80-e24b-46e0-ae0e-e003315d1256",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -536,9 +524,7 @@
{
"cell_type": "markdown",
"id": "55c6610f-52db-4d00-a354-81c2fddada07",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"The sequence alphabet of this file indicates that these are protein sequences and you'll notice that each sequences is about the same length. There isn't a lot of information in the headers, but we can see that each sequence comes from a different species and we know that there are the same number of headers and sequence lines. Together this information indicates that these sequences are from a protein alignment. To be sure we can use NCBI's [BLAST tool](https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastp&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome) to check the identity of some of these sequences. \n",
"\n",
@@ -659,9 +645,7 @@
{
"cell_type": "markdown",
"id": "49b4c02a-b675-4f96-85c8-fc54c3329480",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"## Raw Sequencing Data, FASTQ Files\n",
"------------------------------\n",
@@ -908,31 +892,7 @@
"source": []
}
],
- "metadata": {
- "environment": {
- "kernel": "python3",
- "name": "common-cpu.m104",
- "type": "gcloud",
- "uri": "gcr.io/deeplearning-platform-release/base-cpu:m104"
- },
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.12"
- }
- },
+ "metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
diff --git a/GoogleCloud/submodule04_beyond_basic_bash.ipynb b/GoogleCloud/submodule04_beyond_basic_bash.ipynb
index d0f1871..5455952 100644
--- a/GoogleCloud/submodule04_beyond_basic_bash.ipynb
+++ b/GoogleCloud/submodule04_beyond_basic_bash.ipynb
@@ -3,9 +3,7 @@
{
"cell_type": "markdown",
"id": "6963dbcc-0794-4f51-b589-5c3bf1ff4103",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"# Beyond Basic BASH Coding\n",
"------\n",
@@ -104,9 +102,7 @@
{
"cell_type": "markdown",
"id": "f1c37b72-36be-4141-a692-caa954d9e8ad",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"## For & While Loops\n",
"-------\n",
@@ -128,9 +124,7 @@
"cell_type": "code",
"execution_count": null,
"id": "cbcae554-f686-4114-b129-b1f636988495",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -184,9 +178,7 @@
{
"cell_type": "markdown",
"id": "32debf32-ce7c-4b4b-88e8-0d55fb7d5810",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"### For Loop\n",
"\n",
@@ -197,9 +189,7 @@
"cell_type": "code",
"execution_count": null,
"id": "a9046c97-b8dc-4b55-af3b-e875fade21b2",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -238,9 +228,7 @@
"cell_type": "code",
"execution_count": null,
"id": "474b7da7-b213-4874-ab65-3b06ea3562ad",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -256,9 +244,7 @@
{
"cell_type": "markdown",
"id": "e58ab4a4-c787-4939-adb3-146b95585eb8",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"Now let's use variations on some of the complex code we wrote in the earlier lesson **Genomic File Formats** to write a loop to check how many reads contain the start codon `ATG`. We can do this by searching for matches with `grep` and counting how many times it was found with `wc -l` (literally how many lines are returned), and repeating this process for each sample using a while loop.\n"
]
@@ -267,9 +253,7 @@
"cell_type": "code",
"execution_count": null,
"id": "6705dc4f-a7d3-4fb4-9189-bc4af0e78891",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -347,9 +331,7 @@
"cell_type": "code",
"execution_count": null,
"id": "100a2f16-c778-4b08-a6b7-c2460b4ab219",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -370,9 +352,7 @@
"cell_type": "code",
"execution_count": null,
"id": "65fa9ddd-5c35-4c9f-8728-e19b7ad4b41e",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -395,9 +375,7 @@
"cell_type": "code",
"execution_count": null,
"id": "2f8185c6-b059-4023-b654-182199dc625f",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -482,9 +460,7 @@
"cell_type": "code",
"execution_count": null,
"id": "729b493c-8971-4d2c-a74e-4ffd930506cb",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -502,31 +478,7 @@
"source": []
}
],
- "metadata": {
- "environment": {
- "kernel": "python3",
- "name": "common-cpu.m104",
- "type": "gcloud",
- "uri": "gcr.io/deeplearning-platform-release/base-cpu:m104"
- },
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.12"
- }
- },
+ "metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
diff --git a/GoogleCloud/submodule05_software_management.ipynb b/GoogleCloud/submodule05_software_management.ipynb
index 8defd84..383217f 100644
--- a/GoogleCloud/submodule05_software_management.ipynb
+++ b/GoogleCloud/submodule05_software_management.ipynb
@@ -3,9 +3,7 @@
{
"cell_type": "markdown",
"id": "1553273d-c130-4398-8256-2d3d7b2aa66a",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"# Installing and Managing Bioinformatic Software\n",
"\n",
@@ -189,9 +187,7 @@
{
"cell_type": "markdown",
"id": "21e21a1b-c662-4d9c-856b-a800bbf1f853",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"## Creating a Conda Environment\n",
"--------\n",
@@ -279,9 +275,7 @@
"cell_type": "code",
"execution_count": null,
"id": "b9642a1e-5154-44b9-990f-5dc5209ae0f3",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -304,9 +298,7 @@
"cell_type": "code",
"execution_count": null,
"id": "61c54ef1-6cf6-4d53-af7d-8d37cbe1bd20",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -357,9 +349,7 @@
"cell_type": "code",
"execution_count": null,
"id": "c149e564-2bfc-4b2e-99f9-ac3f2b6a499c",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -459,9 +449,7 @@
"cell_type": "code",
"execution_count": null,
"id": "39a3979a-2147-4237-973b-7ddfd40e7585",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -486,9 +474,7 @@
"cell_type": "code",
"execution_count": null,
"id": "8f1b018a-9538-4cce-885f-aeaf4add3a2a",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -521,9 +507,7 @@
"cell_type": "code",
"execution_count": null,
"id": "195d98c4-5cf0-4c78-8f78-443dd4524e43",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -535,9 +519,7 @@
{
"cell_type": "markdown",
"id": "9e0a5c00-f5fe-49cf-8331-931d469685af",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"You will notice that in the first run where we only used one thread the output indicated that the analysis analyzed each file sequentially, whereas when we used two threads the files were analyzed in parallel and finished at almost the same time. The parellelization in this case uses one thread per file so using more than two threads would not have sped up the analysis time.\n",
"\n",
@@ -562,9 +544,7 @@
"cell_type": "code",
"execution_count": null,
"id": "86432f8c-0cb7-4807-ae09-a69d4c6293f2",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -604,9 +584,7 @@
"cell_type": "code",
"execution_count": null,
"id": "0d1e9ef6-f108-486b-9dab-926a6f5be466",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -632,9 +610,7 @@
"cell_type": "code",
"execution_count": null,
"id": "48625ab6-243c-4491-ba5d-66d258a86bcd",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -674,31 +650,7 @@
]
}
],
- "metadata": {
- "environment": {
- "kernel": "python3",
- "name": "common-cpu.m104",
- "type": "gcloud",
- "uri": "gcr.io/deeplearning-platform-release/base-cpu:m104"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.4"
- }
- },
+ "metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
diff --git a/GoogleCloud/submodule06_putting_it_all_together.ipynb b/GoogleCloud/submodule06_putting_it_all_together.ipynb
index 3181ed0..4d21928 100644
--- a/GoogleCloud/submodule06_putting_it_all_together.ipynb
+++ b/GoogleCloud/submodule06_putting_it_all_together.ipynb
@@ -23,9 +23,7 @@
"cell_type": "code",
"execution_count": null,
"id": "ac12ca19-ef6a-4554-92b2-9b4489a4b107",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -116,9 +114,7 @@
"cell_type": "code",
"execution_count": null,
"id": "9199a45e-5711-464f-8ff5-0dc9708c1f3b",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -185,9 +181,7 @@
{
"cell_type": "markdown",
"id": "dd70fe01-c2a7-4978-a655-8b94c41c76a3",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"## Assembling the Genome (Spades)\n",
"----------\n",
@@ -219,10 +213,7 @@
"cell_type": "code",
"execution_count": null,
"id": "fcf66e46-7861-43d5-b2d5-3105229c7dd3",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -478,31 +469,7 @@
"source": []
}
],
- "metadata": {
- "environment": {
- "kernel": "conda-env-annotation-py",
- "name": "common-cpu.m104",
- "type": "gcloud",
- "uri": "gcr.io/deeplearning-platform-release/base-cpu:m104"
- },
- "kernelspec": {
- "display_name": "Python [conda env:annotation]",
- "language": "python",
- "name": "conda-env-annotation-py"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
+ "metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
diff --git a/GoogleCloud/submodule07_error_mitigation.ipynb b/GoogleCloud/submodule07_error_mitigation.ipynb
index a5a1335..2fee556 100644
--- a/GoogleCloud/submodule07_error_mitigation.ipynb
+++ b/GoogleCloud/submodule07_error_mitigation.ipynb
@@ -34,9 +34,7 @@
"cell_type": "code",
"execution_count": null,
"id": "0fdcc3f2-47f0-48f1-91c4-7a04992e96fc",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -62,9 +60,7 @@
"cell_type": "code",
"execution_count": null,
"id": "fc6dfb9c-9426-4c93-954f-06fdb3a85c37",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -77,9 +73,7 @@
"cell_type": "code",
"execution_count": null,
"id": "548c4ed9-8c75-4020-a69a-d376a42f6ec0",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -93,9 +87,7 @@
"cell_type": "code",
"execution_count": null,
"id": "aa1eb263-207e-4645-8c5c-f0138ba9c5da",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -109,9 +101,7 @@
"cell_type": "code",
"execution_count": null,
"id": "61e2398c-17b7-4211-9c82-588f745b94e4",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -147,9 +137,7 @@
"cell_type": "code",
"execution_count": null,
"id": "9e42dd93-8400-46bf-bca0-6a9ee18136d0",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -175,9 +163,7 @@
"cell_type": "code",
"execution_count": null,
"id": "58f4370f-27c5-4dc9-b17b-26528029e35f",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -223,9 +209,7 @@
{
"cell_type": "markdown",
"id": "f925eb00-3958-40d4-9ef6-0bd704b8347e",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"source": [
"We started with typos because these are the most common mistakes we diagnose in our workshops, especially with regard to case. \n",
"\n",
@@ -244,9 +228,7 @@
"cell_type": "code",
"execution_count": null,
"id": "455892b0-5d8c-4b62-ae95-231fff74909d",
- "metadata": {
- "tags": []
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%bash\n",
@@ -338,31 +320,7 @@
]
}
],
- "metadata": {
- "environment": {
- "kernel": "conda-env-annotation-py",
- "name": "common-cpu.m104",
- "type": "gcloud",
- "uri": "gcr.io/deeplearning-platform-release/base-cpu:m104"
- },
- "kernelspec": {
- "display_name": "Python [conda env:annotation]",
- "language": "python",
- "name": "conda-env-annotation-py"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
+ "metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
diff --git a/reusable-workflow-repo b/reusable-workflow-repo
deleted file mode 160000
index 3237900..0000000
--- a/reusable-workflow-repo
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 3237900c16e854cf76b24e58b3543dbb63e1a2eb