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Merge pull request #949 from Labelbox/ovalle15-patch-15
Style updates - cleaned up outputs
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‎examples/basics/custom_embeddings.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {
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"id": "wRIdzkYf7T18"
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"id": "wRIdzkYf7T18",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "13ee42f0-4206-493f-f402-ac7c84916e5e"
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},
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"outputs": [],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m189.2/189.2 KB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m52.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Building wheel for pygeotile (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
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]
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}
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],
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"source": [
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"# labelbox\n",
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"!pip3 install -q labelbox[data]\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "9k82ueIu8Dy1",
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"outputId": "cc728790-fc62-4d8d-a3e7-94739ffec809"
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"outputId": "5323157e-872b-4bf2-a65c-0fa9662cdbe8"
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},
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"outputs": [
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{
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"text": [
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" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
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" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.8/62.8 KB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Building wheel for advlib (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 32,
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"metadata": {
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"id": "tOIyo5pC7PTz"
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},
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"outputs": [],
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"source": [
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"# get images from a Labelbox dataset\n",
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"# Our systems start to process data after 1000 embeddings of each type, for this demo make sure your dataset is over 1000 data rows",
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"dataset = client.get_dataset(\"<ADD YOUR DATASET ID>\")\n",
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"drs = list(dataset.export_data_rows(timeout_seconds=9999))\n",
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"data_row_ids = [dr.uid for dr in drs]"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 33,
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"metadata": {
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"id": "iJFGf0w7swnW"
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},
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 35,
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"metadata": {
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"id": "u0ZgybLK67n0",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "d644f81a-014e-4de9-913a-74211972e9b2"
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"outputId": "7e1991a0-8e0a-4e63-e8d8-dfdcf095a625"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Nb of custom embedding vectors in sanity_check_payload: 1000\n",
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"sanity_check_payload: "
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]
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},
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"IOPub data rate exceeded.\n",
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"The notebook server will temporarily stop sending output\n",
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"to the client in order to avoid crashing it.\n",
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"To change this limit, set the config variable\n",
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"`--NotebookApp.iopub_data_rate_limit`.\n",
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"\n",
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"Current values:\n",
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"NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
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"NotebookApp.rate_limit_window=3.0 (secs)\n",
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"\n"
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"Nb of custom embedding vectors in sanity_check_payload: 1000\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 46,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "YQeCS_U98BD2",
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"outputId": "178dc3be-6e89-4df8-ec3d-2fa6dacc0be0"
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"outputId": "cdeb0bc7-9ea1-4864-88ff-122f6dab8af4"
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},
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"outputs": [
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{
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"text": [
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"00000000-0000-0000-0000-000000000000 - Image Embedding (CLIP ViT-B/32) - dims: 512 \n",
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"00000000-0000-0000-0000-000000000001 - Text embedding (All-MPNet-base-v2) - dims: 768 \n",
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"521eadfe-f8e9-4135-9ead-fef8e9713546 - my_custom_embedding_2048_dimensions - dims: 2048 \n",
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"a03948c1-151a-4a1a-b948-c1151a6a1a1d - ResNet50_2048_dimensions - dims: 2048 \n",
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"baf8856a-e5f7-4781-b885-6ae5f7b78192 - my_custom_embedding - dims: 8 \n"
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"45cafc7a-5314-462a-8afc-7a5314062a3b - my_custom_embedding_2048_dimensions - dims: 2048 \n",
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"7d3a6118-589d-4b6c-ba61-18589dbb6ccf - ResNet50_2048_dimensions - dims: 2048 \n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 47,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "spyHzkLP67dI",
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"outputId": "21b6fda1-7a38-4bd5-d244-dfc90b8a5090"
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"outputId": "8a046562-5eb1-4fb1-8f23-6958f8b58e1f"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Embedding type created id=521eadfe-f8e9-4135-9ead-fef8e9713546\n"
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"Embedding type created id=da5d4b0f-e440-4e2e-9d4b-0fe4400e2e8d\n"
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]
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}
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],
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"source": [
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"# # Create a new custom embedding\n",
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"!advtool embeddings create my_custom_embedding_2048_dimensions 2048\n",
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"!advtool embeddings create my_custom_embedding_2048_dimensions_v2 2048\n",
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"# will return the ID of the newly created embedding, e.g. 0ddc5d5c-0963-41ad-9c5d-5c0963a1ad98"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"# # Delete a custom embedding\n",
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"# !advtool embeddings delete 521eadfe-f8e9-4135-9ead-fef8e9713546"
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"# !advtool embeddings delete 2e122b85-7def-44fb-922b-857defe4fb8a"
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],
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"metadata": {
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"id": "MafxKyncxyvR"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 49,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "twDd5XNM67Zo",
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"outputId": "a7715fe7-3fc3-43d0-8316-bbc45a7dee60"
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"outputId": "cb4573de-5417-449b-b560-9f99cfe6eda4"
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},
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"outputs": [
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{
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}
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],
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"source": [
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"# Upload the payload to Labelbox\n",
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"!advtool embeddings import 521eadfe-f8e9-4135-9ead-fef8e9713546 ./payload.ndjson"
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"# Upload the payload to Labelbox \n",
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"!advtool embeddings import da5d4b0f-e440-4e2e-9d4b-0fe4400e2e8d ./payload.ndjson"
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]
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},
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 63,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "wC0eeEPM9aAM",
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"outputId": "5889b2f8-1a07-4748-b3bf-efab545f1417"
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"outputId": "55932ddf-2dde-48c1-fd90-29532b8cfdf2"
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"0\n"
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"1000\n"
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]
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}
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],
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"source": [
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"# count how many data rows have a specific custom embedding\n",
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"!advtool embeddings count 521eadfe-f8e9-4135-9ead-fef8e9713546"
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"# count how many data rows have a specific custom embedding (This can take a couple of minutes)\n",
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"!advtool embeddings count da5d4b0f-e440-4e2e-9d4b-0fe4400e2e8d"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 64,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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"id": "5AKDMJfO9Z51",
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"outputId": "b3b6e7ca-1e99-4563-d8fe-038375008b69"
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"outputId": "207f251f-0350-451e-ffcb-661cafb0529f"
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},
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"outputs": [
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{

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