这是indexloc提供的服务，不要输入任何密码
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1,377 changes: 689 additions & 688 deletions notebooks/community/model_garden/model_garden_camp_zipnerf.ipynb
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89 changes: 42 additions & 47 deletions notebooks/community/model_garden/model_garden_camp_zipnerf_gradio.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -30,18 +30,18 @@
},
"source": [
"# Vertex AI Model Garden - CamP ZipNeRF (Jax) Gradio Notebook\n",
"<table><tbody><tr>\n",
" <td style=\"text-align: center\">\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_camp_zipnerf_gradio.ipynb\">\n",
" <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
" </a>\n",
" </td>\n",
" <td style=\"text-align: center\">\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_camp_zipnerf_gradio.ipynb\">\n",
" <img alt=\"GitHub logo\" src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" width=\"32px\"><br> View on GitHub\n",
" </a>\n",
" </td>\n",
"</tr></tbody></table>"
"\u003ctable\u003e\u003ctbody\u003e\u003ctr\u003e\n",
" \u003ctd style=\"text-align: center\"\u003e\n",
" \u003ca href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_camp_zipnerf_gradio.ipynb\"\u003e\n",
" \u003cimg alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"\u003e\u003cbr\u003e Run in Colab Enterprise\n",
" \u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd style=\"text-align: center\"\u003e\n",
" \u003ca href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_camp_zipnerf_gradio.ipynb\"\u003e\n",
" \u003cimg alt=\"GitHub logo\" src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" width=\"32px\"\u003e\u003cbr\u003e View on GitHub\n",
" \u003c/a\u003e\n",
" \u003c/td\u003e\n",
"\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e"
]
},
{
Expand Down Expand Up @@ -421,7 +421,6 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "j1evctm2h34g"
},
"outputs": [],
Expand All @@ -431,8 +430,8 @@
"\n",
"import concurrent.futures\n",
"import glob\n",
"import hashlib\n",
"import logging\n",
"import hashlib\n",
"import mimetypes\n",
"import os\n",
"import re\n",
Expand Down Expand Up @@ -480,11 +479,9 @@
"GCS_API_ENDPOINT = \"https://storage.cloud.google.com/\"\n",
"\n",
"# Configure logging\n",
"logging.basicConfig(\n",
" level=logging.INFO, format=\"%(asctime)s - %(levelname)s - %(message)s\"\n",
")\n",
"logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
"\n",
"# track unique experiments\n",
"# Track unique experiments\n",
"unique_experiments = set()\n",
"\n",
"# Define dataset and table IDs\n",
Expand Down Expand Up @@ -525,11 +522,11 @@
" return None\n",
"\n",
"\n",
"def get_job_name_with_datetime(prefix: str) -> str:\n",
"def get_job_name_with_datetime(prefix: str) -\u003e str:\n",
" return prefix + datetime.now().strftime(\"_%Y%m%d_%H%M%S\")\n",
"\n",
"\n",
"def get_vertex_ai_job_status(job_id: str) -> str:\n",
"def get_vertex_ai_job_status(job_id: str) -\u003e str:\n",
" job = aiplatform.CustomJob.get(job_id)\n",
" return job.state\n",
"\n",
Expand All @@ -548,17 +545,17 @@
" return link\n",
"\n",
"\n",
"def get_vertex_ai_pipeline_job_status(job_id: str) -> str:\n",
"def get_vertex_ai_pipeline_job_status(job_id: str) -\u003e str:\n",
" job = aiplatform.PipelineJob.get(job_id)\n",
" return job.state\n",
"\n",
"\n",
"def get_bucket_and_blob_name(filepath: str) -> tuple:\n",
"def get_bucket_and_blob_name(filepath: str) -\u003e tuple:\n",
" gs_suffix = filepath.split(\"gs://\", 1)[1]\n",
" return tuple(gs_suffix.split(\"/\", 1))\n",
"\n",
"\n",
"def get_bucket_and_blob_name_https(filepath: str) -> tuple:\n",
"def get_bucket_and_blob_name_https(filepath: str) -\u003e tuple:\n",
" gs_suffix = filepath.split(\"https://\", 1)[1]\n",
" return tuple(gs_suffix.split(\"/\", 1))\n",
"\n",
Expand All @@ -567,7 +564,7 @@
" return bigquery.Client()\n",
"\n",
"\n",
"def is_gcs_path(input_path: str) -> bool:\n",
"def is_gcs_path(input_path: str) -\u003e bool:\n",
" \"\"\"Checks if the input path is a Google Cloud Storage (GCS) path.\n",
"\n",
" Args:\n",
Expand Down Expand Up @@ -685,7 +682,7 @@
" client.download_blob_to_file(gcs_uri, f)\n",
"\n",
"\n",
"def list_gcs_bucket_contents() -> dict:\n",
"def list_gcs_bucket_contents() -\u003e dict:\n",
" client = storage.Client()\n",
" bucket_name = get_bucket_and_blob_name(BUCKET_NAME)[0]\n",
" bucket = client.get_bucket(bucket_name)\n",
Expand Down Expand Up @@ -747,12 +744,12 @@
" return f\"Images uploaded successfully to {gcs_dir_path}.\"\n",
"\n",
"\n",
"def fetch_job_times(job_id: str) -> tuple:\n",
"def fetch_job_times(job_id: str) -\u003e tuple:\n",
" job = aiplatform.CustomJob.get(job_id)\n",
" return job.create_time, job.start_time, job.end_time\n",
"\n",
"\n",
"def fetch_pipeline_job_times(job_id: str) -> tuple:\n",
"def fetch_pipeline_job_times(job_id: str) -\u003e tuple:\n",
" pipeline_job = aiplatform.PipelineJob.get(job_id)\n",
" while True:\n",
" try:\n",
Expand All @@ -768,7 +765,7 @@
" return create_time, start_time, update_time\n",
"\n",
"\n",
"def list_bq_folder_contents_colmap(table_id: str) -> dict:\n",
"def list_bq_folder_contents_colmap(table_id: str) -\u003e dict:\n",
" client = get_bigquery_client()\n",
" query = f\"\"\"\n",
" SELECT Experiment_ID, Scene_Name, Job_Status, Image_Count, Colmap_Job_ID, Created_Time, Start_Time, End_Time,\n",
Expand Down Expand Up @@ -823,7 +820,7 @@
" return folder_counts\n",
"\n",
"\n",
"def list_bq_folder_contents_training(table_id: str) -> dict:\n",
"def list_bq_folder_contents_training(table_id: str) -\u003e dict:\n",
" client = get_bigquery_client()\n",
" query = f\"\"\"\n",
" SELECT Experiment_ID, Scene_Name, Job_Status, Image_Count, Colmap_Job_ID, Training_Job_ID, Training_Job_Name, Created_Time, Start_Time, End_Time,\n",
Expand Down Expand Up @@ -875,7 +872,7 @@
" return folder_counts\n",
"\n",
"\n",
"def list_bq_folder_contents_rendering(table_id: str) -> dict:\n",
"def list_bq_folder_contents_rendering(table_id: str) -\u003e dict:\n",
" client = get_bigquery_client()\n",
" query = f\"\"\"\n",
" SELECT Experiment_ID, Scene_Name, Job_Status, Image_Count, Colmap_Job_ID, Training_Job_ID, Training_Job_Name, Rendering_Job_ID, Rendering_Job_Name, Created_Time, Start_Time, End_Time,\n",
Expand Down Expand Up @@ -937,7 +934,7 @@
" return folder_counts\n",
"\n",
"\n",
"def get_bq_folders_dataframe_colmap(table_id: str) -> pd.DataFrame:\n",
"def get_bq_folders_dataframe_colmap(table_id: str) -\u003e pd.DataFrame:\n",
" try:\n",
" folder_counts = list_bq_folder_contents_colmap(table_id)\n",
" data = [\n",
Expand All @@ -959,11 +956,11 @@
" ]\n",
" return pd.DataFrame(data).sort_values(by=\"Experiment ID\").reset_index(drop=True)\n",
" except Exception as e:\n",
" logging.info(f\"Please upload a dataset to obtain an {e}.\", exc_info=True)\n",
" logging.info(f\"Exception encountered in {e}.\", exc_info=True)\n",
" return pd.DataFrame()\n",
"\n",
"\n",
"def get_bq_folders_dataframe_training(table_id: str) -> pd.DataFrame:\n",
"def get_bq_folders_dataframe_training(table_id: str) -\u003e pd.DataFrame:\n",
" try:\n",
" folder_counts = list_bq_folder_contents_training(table_id)\n",
" data = [\n",
Expand All @@ -985,11 +982,11 @@
" ]\n",
" return pd.DataFrame(data).sort_values(by=\"Experiment ID\").reset_index(drop=True)\n",
" except Exception as e:\n",
" logging.info(f\"Please upload a dataset to obtain an {e}.\", exc_info=True)\n",
" logging.info(f\"Exception encountered in {e}.\", exc_info=True)\n",
" return pd.DataFrame()\n",
"\n",
"\n",
"def get_bq_folders_dataframe_rendering(table_id: str) -> pd.DataFrame:\n",
"def get_bq_folders_dataframe_rendering(table_id: str) -\u003e pd.DataFrame:\n",
" try:\n",
" folder_counts = list_bq_folder_contents_rendering(table_id)\n",
" data = [\n",
Expand Down Expand Up @@ -1020,7 +1017,7 @@
" ]\n",
" return pd.DataFrame(data).sort_values(by=\"Experiment ID\").reset_index(drop=True)\n",
" except Exception as e:\n",
" logging.info(f\"Please upload a dataset to obtain an {e}.\", exc_info=True)\n",
" logging.info(f\"Exception encountered in {e}.\", exc_info=True)\n",
" return pd.DataFrame()\n",
"\n",
"\n",
Expand Down Expand Up @@ -1444,10 +1441,6 @@
" frame_rate=1,\n",
" progress=gr.Progress(),\n",
" ):\n",
" if not scene_name:\n",
" gr.Warning(\"Please provide a name for the scene. This field is required.\")\n",
" return get_bq_folders_dataframe_colmap(colmap_table_id)\n",
"\n",
" if gcs_folder:\n",
" prepare_instance_images_from_gcs(\n",
" scene_name, experiment_name, gcs_folder, progress\n",
Expand Down Expand Up @@ -1650,7 +1643,7 @@
"\n",
" def on_row_select(folders_df, evt: gr.SelectData):\n",
" row_index = evt.index[0]\n",
" if 0 <= row_index < len(folders_df):\n",
" if 0 \u003c= row_index \u003c len(folders_df):\n",
" selected_value = folders_df.iloc[row_index, 0]\n",
" colmap_job_id = folders_df[\n",
" folders_df[\"Experiment ID\"] == selected_value\n",
Expand Down Expand Up @@ -1901,9 +1894,9 @@
" data_training_job_name = get_job_name_with_datetime(\"cloudnerf_gradio_training\")\n",
" unique_experiments.add(selected_folder)\n",
"\n",
" colmap_job_status = colmap_df[colmap_df[\"Experiment ID\"] == selected_folder][\n",
" \"Job Status\"\n",
" ].iloc[0]\n",
" colmap_job_status = colmap_df[\n",
" colmap_df[\"Experiment ID\"] == selected_folder\n",
" ][\"Job Status\"].iloc[0]\n",
" if colmap_job_status != \"SUCCEEDED\":\n",
" gr.Warning(\"Please wait until the colmap job is finished.\")\n",
" return get_bq_folders_dataframe_training(training_table_id)\n",
Expand Down Expand Up @@ -2077,7 +2070,7 @@
"\n",
" def on_row_select(training_df, evt: gr.SelectData):\n",
" row_index = evt.index[0]\n",
" if 0 <= row_index < len(training_df):\n",
" if 0 \u003c= row_index \u003c len(training_df):\n",
" selected_value = training_df.iloc[row_index, 0]\n",
" training_job_id = training_df[\n",
" training_df[\"Experiment ID\"] == selected_value\n",
Expand Down Expand Up @@ -2229,6 +2222,7 @@
" rendering_df = get_bq_folders_dataframe_rendering(rendering_table_id)\n",
" training_df = get_bq_folders_dataframe_training(training_table_id)\n",
"\n",
"\n",
" training_job_status = training_df[\n",
" training_df[\"Experiment ID\"] == selected_folder\n",
" ][\"Job Status\"].iloc[0]\n",
Expand Down Expand Up @@ -2460,7 +2454,7 @@
"\n",
" def on_row_select(rendering_df, evt: gr.SelectData):\n",
" row_index = evt.index[0]\n",
" if 0 <= row_index < len(rendering_df):\n",
" if 0 \u003c= row_index \u003c len(rendering_df):\n",
" selected_value = rendering_df.iloc[row_index, 0]\n",
" rendering_job_status = rendering_df[\n",
" rendering_df[\"Experiment ID\"] == selected_value\n",
Expand Down Expand Up @@ -2902,7 +2896,7 @@
"\n",
" def on_row_select(folders_df, evt: gr.SelectData):\n",
" row_index = evt.index[0]\n",
" if 0 <= row_index < len(folders_df):\n",
" if 0 \u003c= row_index \u003c len(folders_df):\n",
" selected_value = folders_df.iloc[row_index, 0]\n",
" colmap_job_id = folders_df[\n",
" folders_df[\"Experiment ID\"] == selected_value\n",
Expand Down Expand Up @@ -3175,6 +3169,7 @@
"metadata": {
"colab": {
"name": "model_garden_camp_zipnerf_gradio.ipynb",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
Expand Down
2 changes: 1 addition & 1 deletion notebooks/community/model_garden/model_garden_jax_owl_vit_v2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -651,7 +651,7 @@
"jax_owl2_endpoint = jax_owl2_model.deploy(\n",
" deployed_model_display_name=\"jax_owl2_deployed\",\n",
" traffic_split={\"0\": 100},\n",
" machine_type=\"n1-highmem-16\",\n",
" machine_type=\"n1-highmem-64\",\n",
" min_replica_count=1,\n",
" max_replica_count=1,\n",
")"
Expand Down
39 changes: 18 additions & 21 deletions notebooks/community/model_garden/model_garden_jax_vision_transformer.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -72,9 +72,7 @@
"source": [
"## Overview\n",
"\n",
"This notebook demonstrates finetuning a [JAX ViT-B16 model](https://github.com/google-research/vision_transformer#available-vit-models) for image classification task on GPU and deploying them on Vertex AI for online prediction.\n",
"\n",
"Learn more about [Generative AI Support in Vertex AI](https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-model-garden-and-generative-ai-studio)."
"This notebook demonstrates finetuning a [JAX ViT-B16 model](https://github.com/google-research/vision_transformer#available-vit-models) for image classification task on GPU and deploying them on Vertex AI for online prediction."
]
},
{
Expand All @@ -85,21 +83,18 @@
"source": [
"### Objective\n",
"\n",
"In this tutorial, you learn how fine-tune, deploy and predict with a Vertex AI pretrained JAX Vision Transformer based model.\n",
"In this tutorial, you learn how to:\n",
"\n",
"- Finetune a JAX Vision Transformer based model.\n",
"- Upload the model to [Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).\n",
"- Deploy the model on [Endpoint](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints).\n",
"- Run online predictions for image classification.\n",
"\n",
"This tutorial uses the following Google Cloud ML services and resources:\n",
"\n",
"- Vertex AI Model Garden\n",
"- Vertex AI Training\n",
"- Vertex AI Model Registry\n",
"- Vertex AI Online Prediction\n",
"\n",
"The steps performed are:\n",
"\n",
"- Finetune a JAX Vision Transformer based model.\n",
"- Upload the model to [Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).\n",
"- Deploy the model on [Endpoint](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints).\n",
"- Run online predictions for image classification.\n"
"- Vertex AI Online Prediction"
]
},
{
Expand Down Expand Up @@ -170,10 +165,10 @@
"outputs": [],
"source": [
"# Automatically restart kernel after installs so that your environment can access the new packages.\n",
"# import IPython\n",
"import IPython\n",
"\n",
"# app = IPython.Application.instance()\n",
"# app.kernel.do_shutdown(True)"
"app = IPython.Application.instance()\n",
"app.kernel.do_shutdown(True)"
]
},
{
Expand Down Expand Up @@ -219,7 +214,7 @@
},
"outputs": [],
"source": [
"PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}\n",
"PROJECT_ID = \"your-project-id\" # @param {type:\"string\"}\n",
"\n",
"# Set the project id\n",
"! gcloud config set project {PROJECT_ID}"
Expand Down Expand Up @@ -434,9 +429,11 @@
"outputs": [],
"source": [
"# The pre-built training docker image.\n",
"TRAIN_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/jax-vit-train-gpu\"\n",
"TRAIN_DOCKER_URI = (\n",
" \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/jax-vit-train-gpu\"\n",
")\n",
"# The pre-built TF SavedModel conversion docker image.\n",
"MODEL_CONVERSION_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/jax-vit-model-conversion\"\n",
"MODEL_CONVERSION_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/jax-vit-model-conversion\"\n",
"# The pre-built prediction docker image.\n",
"OPTIMIZED_TF_RUNTIME_IMAGE_URI = (\n",
" \"us-docker.pkg.dev/vertex-ai-restricted/prediction/tf_opt-gpu.nightly:latest\"\n",
Expand Down Expand Up @@ -516,7 +513,7 @@
"### Prepare dataset\n",
"\n",
"If you are not using [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview#all_datasets), then you need to prepare your dataset and store it on Cloud Storage. The following example shows\n",
"how to do this for the [tf_flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers). If using TensorFlow Datasets, you pass\n",
"how to do this for the [tf_flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers). If using TensorFlow Datasets, you can just pass\n",
"the dataset name such as `tf_flowers` to the `--config.dataset` flag and bypass this section."
]
},
Expand Down Expand Up @@ -864,7 +861,7 @@
"jax_vit_model.delete()\n",
"\n",
"# Delete Cloud Storage objects that were created.\n",
"delete_bucket = True\n",
"delete_bucket = False\n",
"if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
" ! gsutil -m rm -r $BUCKET_URI"
]
Expand Down
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