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denisj3030andGitHub 49dfbf7f14 Merge branch 'main' into ocr 2025-05-23 13:31:37 -04:00
denisj3030 f0d75e59e1 small fixed 2025-05-23 17:27:59 +00:00
denisj3030 9dea76a52a fix codestral ver 2025-05-23 15:42:46 +00:00
denisj3030 2e56b498ec fix missing ] 2025-05-23 15:24:06 +00:00
denisj3030 d66ae4cb78 adding mistral ocr 2025-05-23 14:10:50 +00:00
denisj3030 5001259780 adding mistral ocr 2025-05-23 14:02:11 +00:00
680 changed files with 32642 additions and 48835 deletions
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@@ -7,7 +7,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Set up Python
uses: actions/setup-python@v6
uses: actions/setup-python@v5
with:
python-version: '3.x'
- name: Fetch pull request branch
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@@ -4,7 +4,7 @@
# 2. To lint specific notebooks:
# docker run -v ${PWD}:/setup/app gcr.io/python-docs-samples-tests/notebook_linter:latest notebooks/1.ipynb notebooks/2.ipynb
FROM python:3.14
FROM python:3.13
WORKDIR setup
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@@ -3,8 +3,8 @@ ipython
jupyter
nbconvert
black==25.1.0
pyupgrade==3.21.0
pyupgrade==3.19.1
isort==6.0.1
flake8==7.3.0
flake8==7.2.0
nbqa==1.9.1
-1
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@@ -29,5 +29,4 @@
/vertex_model_garden/model_oss/vllm @kathyyu-google
/vertex_model_garden/benchmarking_reports @lavraicse
/vertex_model_garden/model_oss/autogluon @lavraicse
/vertex_distributed_training/a3mega/llama-3-8b-nemo-pretraining @mstyer-google @erwinh85 @mchrestkha
@@ -1,3 +1,3 @@
torch==2.8.0
torch==2.2.0
torchvision==0.9.1
tensorboard==2.5.0
@@ -1,3 +1,3 @@
torch==2.7.0
torch==2.2.0
torchvision==0.9.1
tensorboard==2.5.0
@@ -1,126 +0,0 @@
# Vertex AI Training: Llama 3.1 8B pre-training using Nvidia A3 Mega VMs (H100)
This document provides a step-by-step guide for pre-training a Llama 3.1 8B model on the `en-wiki` dataset using multiple [Vertex AI Custom Training](https://cloud.google.com/vertex-ai/docs/training/overview) `a3-megagpu-8g` nodes.
We will use a custom container based on NVIDIA's [NeMo Framework](https://docs.nvidia.com/nemo-framework/user-guide/24.07/overview.html) to demonstrate a scalable, multi-node training workflow. All required artifacts and commands are included.
## 1. Prerequisites
### 1.1. Google Cloud Project setup
- **Enable APIs:** Ensure the Vertex AI API is [enabled for your project](http://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).
- **H100 Mega Quota:** A3 Mega VMs are powered by H100 GPUs. Request quota for `custom_model_training_nvidia_h100_mega_gpus` in one of the [supported regions](https://cloud.google.com/vertex-ai/docs/general/locations#accelerator_support). If using Spot VMs, request `custom_model_training_preemptible_nvidia_h100_mega_gpus` quota instead.
- **Reservations (Optional but recommended):** For guaranteed capacity, [create a reservation](https://cloud.google.com/compute/docs/instances/reservations-shared) and ensure the reservation is shared with the Vertex AI service account. This guide requires a minimum of **16 H100 GPUs** (2 full A3 Mega nodes).
### 1.2. GCS bucket
Create a [Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) in the same region where you have quota. If you're using Hierarchical Namespace for your bucket, you may need to update permissions of the Vertex AI Custom Code Service Agent .
This bucket is used for:
- Staging the training application.
- Storing model checkpoints and logs.
- Storing data if you use your own data.
## 2. Setup & configuration
### 2.1. Clone the repo
First clone the repo into your development environment.
```bash
git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git
```
Navigate to the root folder for this sample.
### 2.2. Environment Setup
First, configure your local environment. These variables are used in subsequent commands.
```bash
# Required: Update with your values
export PROJECT_ID="<your-project-id>"
export REPOSITORY="<your-artifact-registry-repo-name>" # e.g., "my-containers"
export BUCKET="<your-gcs-bucket-name>"
# Optional: Change if needed
export REGION="us-central1"
# --- Do not change the lines below ---
export ARTIFACT_REGISTRY="${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPOSITORY}"
export REPO_ROOT=$(git rev-parse --show-toplevel)
```
## 3. Build and push a docker container image to Artifact Registry
Normally, you can use any custom training container on Vertex AI Training. In this example you build a NeMo Docker image that is based on the [Nvidia’s NeMo 24.09](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags) image. Use Cloud Build to build and push the container image.
This document picked NeMo as the demonstrating container since it’s a widely adopted GPU LLM training framework providing high performance and versatile training functionalities.
In addition to the base image, some customizations are included to form the final prebuilt image:
- Some dependencies are installed to integrate with Vertex AI Training.
- An entrypoint script that sets up required environments and calls the training job.
- Some patches are applied to the NeMo code to let it load the dataset from a GCS bucket.
Run this command to build the container and push the container into the Google Artifact Registry.
```bash
cd "${REPO_ROOT}/community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining"
export IMAGE_NAME="vertex-nemo-llama"
gcloud builds submit . \
--project="${PROJECT_ID}" \
--region="${REGION}" \
--config=docker/cloudbuild.yml \
--substitutions="_ARTIFACT_REGISTRY=${ARTIFACT_REGISTRY},_IMAGE_NAME=${IMAGE_NAME}" \
--timeout="2h" \
--machine-type="e2-highcpu-32"
```
## 4. Launch the Training Job
### 4.1. Job Configuration File
Once the container is built, update the job_config.json to set up the training job.
File: job_config.json
```json
{
"project_id": "<project-id>",
"region": "<region>",
"zone": "<zone if using reservation>",
"bucket": "<bucket>",
"dataset_bucket": "github-repo/data/third-party/enwiki-latest-pages-articles",
"image_uri": "<docker image uri from artifact registry>",
"strategy": "spot",
"nodes": "2",
"machine_type": "a3-megagpu-8g",
"gpu_type": "NVIDIA_H100_MEGA_80GB",
"gpus_per_node": "8",
"recipe_name": "llama3_1_8b_pretrain_a3mega",
"job_prefix": "vertex-spot-",
"reservation_name": ""
}
```
### 4.2 Launch the Training Job
First, create a Python virtual environment using your tool of choice, then install
the requirements specified in `requirements.txt`. Using `pip`, the command would be:
```bash
pip install -r requirements.txt
```
Now launch the Vertex AI training job using the provided Python script.
```bash
python3 scripts/launch.py --config_file=job_config.json
```
This script reads job_config.json, defines the cluster specification (2 nodes, 8 GPUs each), and submits the custom training job to Vertex AI.
## 5. Monitor and Clean Up
### 5.1. Monitoring
Vertex AI Console: Track the job's status in the Google Cloud Console under Vertex AI > Training > Custom Jobs.
Logs: View detailed logs in Cloud Logging by filtering for your job name.
Checkpoints: Model checkpoints are saved to your GCS bucket at the path specified in your training script's configuration.
### 5.2. Cleaning Up
To avoid ongoing charges, delete the resources you created:
- The Artifact Registry image.
- The contents of the GCS bucket (checkpoints, logs).
- The Vertex AI Custom Job will eventually complete or fail, incurring no further cost.
@@ -1,265 +0,0 @@
# Reference:
# https://github.com/NVIDIA/NeMo-Framework-Launcher/blob/24.07/launcher_scripts/conf/training/llama/llama3_1_8b.yaml
name: llama3_1_8b_pretrain_a3mega
restore_from_path: null # used when starting from a .nemo file
trainer:
devices: 8
num_nodes: 1
accelerator: gpu
precision: bf16
logger: false # logger provided by exp_manager
enable_checkpointing: false
use_distributed_sampler: false
max_epochs: -1 # PTL default. In practice, max_steps will be reached first.
max_steps: 30 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches
log_every_n_steps: 1
val_check_interval: null
limit_val_batches: 1
limit_test_batches: 1
accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models
gradient_clip_val: 1.0
benchmark: false
enable_model_summary: false # default PTL callback for this does not support model parallelism, instead we log manually
exp_manager:
explicit_log_dir: null
exp_dir: /data
name: ${name}
create_dllogger_logger: true
dllogger_logger_kwargs:
verbose: true
stdout: true
json_file: "/data/dllogger.json"
create_wandb_logger: false
wandb_logger_kwargs:
project: null
name: null
resume_if_exists: true
resume_ignore_no_checkpoint: true
create_checkpoint_callback: false
checkpoint_callback_params:
monitor: val_loss
save_top_k: 3
mode: min
always_save_nemo: false # saves nemo file during validation, not implemented for model parallel
save_nemo_on_train_end: false # not recommended when training large models on clusters with short time limits
filename: 'megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples}'
model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}}
seconds_to_sleep: 5 # Allows node_rank!=0 to sleep and let node0 to init, like preparing data
model:
mcore_gpt: true
# specify micro_batch_size, global_batch_size, and model parallelism
# gradient accumulation will be done automatically based on data_parallel_size
micro_batch_size: 1 # limited by GPU memory
global_batch_size: 1024 # will use more micro batches to reach global batch size
tensor_model_parallel_size: 1 # intra-layer model parallelism
pipeline_model_parallel_size: 2 # inter-layer model parallelism
context_parallel_size: 1
virtual_pipeline_model_parallel_size: null # interleaved pipeline
## Sequence Parallelism
# Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially
# See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details.
sequence_parallel: false
fsdp: false
fsdp_cpu_offload: true
fsdp_sharding_strategy: "full" # Method to shard model states. Available options are 'full', 'hybrid', and 'grad'.
fsdp_grad_reduce_dtype: "16" # Gradient reduction data type.
fsdp_sharded_checkpoint: false # Store and load FSDP shared checkpoint.
fsdp_use_orig_params: false # Set to True to use FSDP for specific peft scheme.
# Distributed checkpoint setup
dist_ckpt_format: "torch_dist" # Set to 'torch_dist' to use PyTorch distributed checkpoint format.
dist_ckpt_load_on_device: true # whether to load checkpoint weights directly on GPU or to CPU
dist_ckpt_parallel_save: true # if true, each worker will write its own part of the dist checkpoint
dist_ckpt_parallel_save_within_dp: false # if true, save will be parallelized only within a DP group (whole world otherwise), which might slightly reduce the save overhead
dist_ckpt_parallel_load: false # if true, each worker will load part of the dist checkpoint and exchange with NCCL. Might use some extra GPU memory
dist_ckpt_torch_dist_multiproc: 2 # number of extra processes per rank used during ckpt save with PyTorch distributed format
dist_ckpt_assume_constant_structure: false # set to True only if the state dict structure doesn't change within a single job. Allows caching some computation across checkpoint saves.
dist_ckpt_parallel_dist_opt: true # parallel save/load of a DistributedOptimizer. 'True' allows performant save and reshardable checkpoints. Set to 'False' only in order to minimize the number of checkpoint files.
dist_ckpt_load_strictness: null # defines checkpoint keys mismatch behavior (only during dist-ckpt load). Choices: assume_ok_unexpected (default - try loading without any check), log_all (log mismatches), raise_all (raise mismatches)
# model architecture
encoder_seq_length: 8192
max_position_embeddings: ${.encoder_seq_length}
num_layers: 32 # 8b: 32 | 70b: 80 | 405b: 126
hidden_size: 4096 # 8b: 4096 | 70b: 8192 | 405b: 16384
ffn_hidden_size: 14336 # 8b: 14336 | 70b: 28672 | 405b: 53248
num_attention_heads: 32 # 8b: 32 | 70b: 64 | 405b: 128
num_query_groups: 8 # Number of query groups for group query attention. If None, normal attention is used. 8b: 8 | 70b: 8 | 405b: 16
init_method_std: 0.01 # Standard deviation of the zero mean normal distribution used for weight initialization. 8b: 0.01 | 70b: 0.008944 | 405b: 0.02
use_scaled_init_method: true # use scaled residuals initialization
hidden_dropout: 0.0 # Dropout probability for hidden state transformer.
attention_dropout: 0.0 # Dropout probability for attention
ffn_dropout: 0.0 # Dropout probability in the feed-forward layer.
kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null
apply_query_key_layer_scaling: true # scale Q * K^T by 1 / layer-number.
normalization: 'rmsnorm' # Normalization layer to use. Options are 'layernorm', 'rmsnorm'
layernorm_epsilon: 1e-5
do_layer_norm_weight_decay: false # True means weight decay on all params
make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency.
pre_process: true # add embedding
post_process: true # add pooler
persist_layer_norm: true # Use of persistent fused layer norm kernel.
bias: false # Whether to use bias terms in all weight matrices.
activation: 'fast-swiglu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu']
headscale: false # Whether to learn extra parameters that scale the output of the each self-attention head.
transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer']
openai_gelu: false # Use OpenAI's GELU instead of the default GeLU
normalize_attention_scores: true # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True.
position_embedding_type: 'rope' # Position embedding type. Options ['learned_absolute', 'rope']
rotary_percentage: 1.0 # If using position_embedding_type=rope, then the per head dim is multiplied by this.
attention_type: 'multihead' # Attention type. Options ['multihead']
share_embeddings_and_output_weights: false # Share embedding and output layer weights.
scale_positional_embedding: true # This is false for llama3 models. Only used for >= llama3.1.
# Use GPT2BPETokenizer for test, because the testing dataset is tokenized by this tokenizer.
# https://docs.nvidia.com/nemo-framework/user-guide/24.07/playbooks/singlenodepretrain.html#data-download-and-pre-processing
tokenizer:
library: megatron
type: GPT2BPETokenizer
model: null # /path/to/tokenizer.model
vocab_file: null
merge_file: null
delimiter: null # only used for tabular tokenizer
sentencepiece_legacy: false # Legacy=True allows you to add special tokens to sentencepiece tokenizers.
# Mixed precision
native_amp_init_scale: 4294967296 # 2 ** 32
native_amp_growth_interval: 1000
hysteresis: 2 # Gradient scale hysteresis
fp32_residual_connection: false # Move residual connections to fp32
fp16_lm_cross_entropy: false # Move the cross entropy unreduced loss calculation for lm head to fp16
# Megatron O2-style half-precision
megatron_amp_O2: true # Enable O2-level automatic mixed precision using main parameters
grad_allreduce_chunk_size_mb: 125
# Fusion
grad_div_ar_fusion: true # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism..
gradient_accumulation_fusion: true # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2.
bias_activation_fusion: true # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function.
bias_dropout_add_fusion: true # Use a kernel that fuses the bias addition, dropout and residual connection addition.
masked_softmax_fusion: true # Use a kernel that fuses the attention softmax with it's mask.
apply_rope_fusion: true # Use a kernel to add rotary positional embeddings. Only used if position_embedding_type=rope
cross_entropy_loss_fusion: true
# Miscellaneous
seed: 1234
resume_from_checkpoint: null # manually set the checkpoint file to load from
use_cpu_initialization: false # Init weights on the CPU (slow for large models)
onnx_safe: false # Use work-arounds for known problems with Torch ONNX exporter.
apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this
gradient_as_bucket_view: true # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory)
sync_batch_comm: false # Enable stream synchronization after each p2p communication between pipeline stages
## Activation Checkpointing
# NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed.
# These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+).
# See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details.
# 'full' will checkpoint the entire transformer layer.
activations_checkpoint_granularity: null # 'selective' or 'full'
activations_checkpoint_method: null # 'uniform', 'block'
# 'uniform' divides the total number of transformer layers and checkpoints the input activation
# of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model.
# 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity
activations_checkpoint_num_layers: null
# when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory.
# when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage.
num_micro_batches_with_partial_activation_checkpoints: null
# This feature is valid only when used with pipeline-model-parallelism.
# When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed
# and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is
# set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint
# per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'.
# This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage.
activations_checkpoint_layers_per_pipeline: null
# This feature is valid only when used with pipeline-model-parallelism.
# When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later
# pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than
# stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage
# uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints',
# this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path.
## Transformer Engine
transformer_engine: true
fp8: false # enables fp8 in TransformerLayer forward
fp8_e4m3: false # sets fp8_format = recipe.Format.E4M3
fp8_hybrid: false # sets fp8_format = recipe.Format.HYBRID
fp8_margin: 0 # scaling margin
fp8_interval: 1 # scaling update interval
fp8_amax_history_len: 1024 # Number of steps for which amax history is recorded per tensor
fp8_amax_compute_algo: 'max' # 'most_recent' or 'max'. Algorithm for computing amax from history
ub_tp_comm_overlap: false # do not turn on because of b/397797926
use_flash_attention: true
gc_interval: 100
## Offloading Activations/Weights to CPU
cpu_offloading: false
cpu_offloading_num_layers: ${sum:${.num_layers},-1} # This value should be between [1,num_layers-1] as we don't want to offload the final layer's activations and expose any offloading duration for the final layer
cpu_offloading_activations: true
cpu_offloading_weights: true
data:
# Path to data must be specified by the user.
# Supports List, String and Dictionary
# List : can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]",
# Or see example below:
# data_prefix:
# - .5
# - /raid/data/pile/my-gpt3_00_text_document
# - .5
# - /raid/data/pile/my-gpt3_01_text_document
# Dictionary: can override from CLI "model.data.data_prefix"={"train":[1.0, /path/to/data], "validation":/path/to/data, "test":/path/to/test}
# Or see example below:
# "model.data.data_prefix: {train:[1.0,/path/to/data], validation:[/path/to/data], test:[/path/to/test]}"
data_prefix: [1.0, /data/hfbpe_gpt_training_data_text_document]
index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix
data_impl: mmap
splits_string: 900,50,50
seq_length: ${model.encoder_seq_length}
skip_warmup: true
num_workers: 2
dataloader_type: single # cyclic
reset_position_ids: false # Reset position ids after end-of-document token
reset_attention_mask: false # Reset attention mask after end-of-document token
eod_mask_loss: false # Mask loss for the end of document tokens
validation_drop_last: true # Set to false if the last partial validation samples is to be consumed
no_seqlen_plus_one_input_tokens: false # Set to True to disable fetching (sequence length + 1) input tokens, instead get (sequence length) input tokens and mask the last token
pad_samples_to_global_batch_size: false # Set to True if you want to pad the last partial batch with -1's to equal global batch size
shuffle_documents: true # Set to False to disable documents shuffling. Sample index will still be shuffled
# Nsys profiling options
nsys_profile:
enabled: false
start_step: 0 # Global batch to start profiling
end_step: 1 # Global batch to end profiling
ranks: [0] # Global rank IDs to profile
gen_shape: false # Generate model and kernel details including input shapes
memory_profile:
enabled: false
start_step: 0
end_step: 1
ranks: [0]
output_path: /data # Must be a dir
optim:
name: distributed_fused_adam # E.g., fused_adam or set _target_: torch.optim.AdamW field
lr: 2e-5
weight_decay: 0.01
betas:
- 0.9
- 0.98
bucket_cap_mb: 125
overlap_grad_sync: true
overlap_param_sync: true
contiguous_grad_buffer: true
contiguous_param_buffer: true
sched:
name: CosineAnnealing
warmup_steps: 400
constant_steps: 0
min_lr: 2e-6
@@ -1,26 +0,0 @@
# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
steps:
- name: 'gcr.io/cloud-builders/docker'
args:
- 'build'
- '--tag=${_ARTIFACT_REGISTRY}/${_IMAGE_NAME}'
- '--file=docker/vertex-dist-recipes.Dockerfile'
- '.'
automapSubstitutions: true
env:
- 'DOCKER_BUILDKIT=1'
images:
- '${_ARTIFACT_REGISTRY}/${_IMAGE_NAME}'
@@ -1,41 +0,0 @@
diff --git a/nemo/collections/nlp/parts/megatron_trainer_builder.py b/nemo/collections/nlp/parts/megatron_trainer_builder.py
index b2c85cde4..a3a9670c3 100644
--- a/nemo/collections/nlp/parts/megatron_trainer_builder.py
+++ b/nemo/collections/nlp/parts/megatron_trainer_builder.py
@@ -19,6 +19,7 @@ from lightning_fabric.utilities.exceptions import MisconfigurationException
from omegaconf import DictConfig
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelSummary
+from pytorch_lightning.callbacks import Callback
from pytorch_lightning.plugins.environments import TorchElasticEnvironment
from nemo.collections.common.metrics.perf_metrics import FLOPsMeasurementCallback
@@ -38,6 +39,23 @@ from nemo.utils.callbacks.dist_ckpt_io import (
AsyncFinalizerCallback,
DistributedCheckpointIO,
)
+from vmg.util.device_stats import gpu_stats_str
+
+class GpuStatsMon(Callback):
+ def on_train_start(self, trainer, pl_module) -> None:
+ rank=pl_module.global_rank
+ print(f'train_start: {rank=} {gpu_stats_str()}', flush=True)
+
+ def on_train_batch_start(self, trainer, pl_module, batch, batch_idx) -> None:
+ rank=pl_module.global_rank
+ print(f'batch_start: {rank=} {gpu_stats_str()}', flush=True)
+
+ def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx) -> None:
+ rank=pl_module.global_rank
+ print(f'batch_end: {rank=} {gpu_stats_str()}', flush=True)
class MegatronTrainerBuilder:
@@ -178,6 +196,7 @@ class MegatronTrainerBuilder:
if self.cfg.get('exp_manager', {}).get('log_tflops_per_sec_per_gpu', True):
callbacks.append(FLOPsMeasurementCallback(self.cfg))
+ callbacks.append(GpuStatsMon())
return callbacks
def create_trainer(self, callbacks=None) -> Trainer:
@@ -1,41 +0,0 @@
diff -ruN old-datasets/blended_megatron_dataset_builder.py datasets/blended_megatron_dataset_builder.py
--- old-datasets/blended_megatron_dataset_builder.py 2025-05-02 04:08:45.369199665 +0000
+++ datasets/blended_megatron_dataset_builder.py 2025-05-02 04:10:47.369119891 +0000
@@ -2,6 +2,7 @@
import logging
import math
+import os
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Callable, Iterable, List, Optional, Type, Union
@@ -353,7 +354,7 @@
num_dataset_builder_threads = self.config.num_dataset_builder_threads
if torch.distributed.is_initialized():
- rank = torch.distributed.get_rank()
+ rank = int(os.getenv("LOCAL_RANK", "0"))
# First, build on rank 0
if rank == 0:
num_workers = num_dataset_builder_threads
@@ -475,7 +476,7 @@
Optional[Union[DistributedDataset, Iterable]]: The DistributedDataset instantion, the Iterable instantiation, or None
"""
if torch.distributed.is_initialized():
- rank = torch.distributed.get_rank()
+ rank = int(os.getenv("LOCAL_RANK", "0"))
dataset = None
diff -ruN old-datasets/gpt_dataset.py datasets/gpt_dataset.py
--- old-datasets/gpt_dataset.py 2025-05-02 04:08:45.369199665 +0000
+++ datasets/gpt_dataset.py 2025-05-02 04:09:30.309170278 +0000
@@ -351,7 +351,7 @@
if not path_to_cache or (
not cache_hit
- and (not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0)
+ and (not torch.distributed.is_initialized() or int(os.getenv("LOCAL_RANK", "0")) == 0)
):
log_single_rank(
@@ -1,13 +0,0 @@
diff --git a/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py b/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py
index 8da15148d..005cae6c9 100644
--- a/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py
+++ b/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py
@@ -104,6 +104,8 @@ def convert(input_nemo_file, output_hf_file, precision=None, cpu_only=False) ->
dummy_trainer = Trainer(devices=1, accelerator='cpu', strategy=NLPDDPStrategy())
model_config = MegatronGPTModel.restore_from(input_nemo_file, trainer=dummy_trainer, return_config=True)
model_config.tensor_model_parallel_size = 1
+ model_config.virtual_pipeline_model_parallel_size = None
+ model_config.sequence_parallel = False
model_config.pipeline_model_parallel_size = 1
if cpu_only:
map_location = torch.device('cpu')
@@ -1,24 +0,0 @@
diff --git a/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py b/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py
index bfe8ea359..dfeaf93b5 100644
--- a/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py
+++ b/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py
@@ -13,6 +13,8 @@
# limitations under the License.
import torch.multiprocessing as mp
+import torch.distributed as dist
+
from omegaconf.omegaconf import OmegaConf
from nemo.collections.nlp.models.language_modeling.megatron_gpt_sft_model import MegatronGPTSFTModel
@@ -76,6 +78,10 @@ def main(cfg) -> None:
trainer.fit(model)
+ if dist.is_available() and dist.is_initialized():
+ dist.barrier()
+ dist.destroy_process_group()
+
if __name__ == '__main__':
main()
@@ -1,13 +0,0 @@
diff --git a/src/utils/training_metrics/process_training_results.py b/src/utils/training_metrics/process_training_results.py
index 3e82a66..e61e1d8 100644
--- a/src/utils/training_metrics/process_training_results.py
+++ b/src/utils/training_metrics/process_training_results.py
@@ -134,7 +134,7 @@ def get_average_step_time(file: str, start_step: int, end_step: int) -> float:
for line in datajson:
if line.get("step") != "PARAMETER":
step = line.get("step")
- if step >= start_step and step <= end_step:
+ if step >= start_step and step <= end_step and "train_step_timing in s" in line["data"]:
time_step_accumulator += line["data"].get("train_step_timing in s")
num_steps += 1
if num_steps == 0:
@@ -1,10 +0,0 @@
dllogger@git+https://github.com/NVIDIA/dllogger@v1.0.0
# Fixing these libraries versions to avoid conflicting or broken packages.
immutabledict==4.2.1
protobuf==4.25.8
opencv-python-headless==4.11.0.86
docutils==0.16
urllib3==2.5.0
google-cloud-storage==3.0.0
retrying
@@ -1,18 +0,0 @@
# cuml-cu12==24.8.0 was installed in nemo:24.09
# Removing cuml=24.4.0 to avoid conflicting packages.
cudf==24.4.0
cugraph==24.4.0
cugraph-service-server==24.4.0
cuml==24.4.0
dask-cudf==24.4.0
raft-dask==24.4.0
cugraph-dgl==24.4.0
cugraph-pyg==24.4.0
# The following packages are removed temporarily to avoid conflicting packages
# and can be brought back if needed.
tensorrt-llm==0.12.0
img2dataset==1.45.0
Sphinx==8.1.3
sphinxcontrib-bibtex==2.6.3
torchx==0.7.0
nemo-run
@@ -1,66 +0,0 @@
# Dockerfile wrapping NeMo.
#
# To workaround base nemo docker image using too many layers, we use Multi-stage
# build to first collect the additional files we'll need.
FROM alpine:latest AS prep_files
WORKDIR /workspace
RUN mkdir -p configs vdt vdt/util
COPY scripts/*.py vdt/
COPY scripts/util/*.py vdt/util/
COPY configs/* configs/
COPY docker/patches/24.09/* vdt/patches/
RUN chmod a+rwX -R vdt
# Copy license.
RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
# Available tags
# https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags
# It installs NeMo source code in /opt/NeMo folder, with tag=r2.0.0
FROM nvcr.io/nvidia/nemo:24.09
RUN apt-get update && apt-get install -y sudo zsh tmux && \
rm -rf /var/lib/apt/lists*
RUN echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] http://packages.cloud.google.com/apt cloud-sdk main" | \
tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && \
curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | \
apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - && \
apt-get update -y && apt-get install google-cloud-sdk -y && \
rm -rf /var/lib/apt/lists*
# Install libraries with pip
ENV PIP_ROOT_USER_ACTION=ignore
# We expect this will be run in the root directory of the vertex-dist-recipes repo
ARG HOST_SRC_DIR="."
# The pre-installed NeMo introduces a lot of deps conflicts.
# We uninstall the confilicting libs and reinstall some of them as needed.
COPY ${HOST_SRC_DIR}/docker/uninstall.txt /tmp/uninstall.txt
RUN cat /tmp/uninstall.txt | grep -v '#' | xargs pip uninstall -y
COPY ${HOST_SRC_DIR}/docker/requirements.txt /tmp/requirements.txt
RUN pip install -r /tmp/requirements.txt
# Make sure there's no inconsistent pip libraries.
RUN pip check
WORKDIR /workspace
# Copy configs
COPY ${HOST_SRC_DIR}/configs/* /opt/NeMo/examples/nlp/language_modeling/conf/
# Copy all additional files we need from `prep_files` image.
COPY --from=prep_files /workspace/ .
# Install for `src/utils/training_metrics/process_training_results.py` to report
# throughput and MFU numbers.
RUN git clone https://github.com/AI-Hypercomputer/gpu-recipes.git
# This hack is needed for multi-node training while not using a sharing file system.
RUN patch --verbose -l -d /opt/megatron-lm/megatron/core/datasets -p1 -i /workspace/vdt/patches/local_rank.patch; \
git -C /workspace/gpu-recipes apply /workspace/vdt/patches/throughput_calc.patch; \
git -C /opt/NeMo apply /workspace/vdt/patches/nemo2hf.patch; \
git -C /opt/NeMo apply /workspace/vdt/patches/sigabort.patch;
# git -C /opt/NeMo apply /workspace/vdt/patches/gpu_stats.patch;
# Do not put an entrypoint here. Specify the entrypoint in the docker run script.
@@ -1,16 +0,0 @@
{
"project_id": "<your_project_id>",
"region": "us-central1",
"zone": "us-central1-c",
"bucket": "<your_bucket",
"dataset_bucket": "github-repo/data/third-party/enwiki-latest-pages-articles",
"image_uri": "<your_image_uri>",
"strategy": "spot",
"nodes": "2",
"machine_type": "a3-megagpu-8g",
"gpu_type": "NVIDIA_H100_MEGA_80GB",
"gpus_per_node": "8",
"recipe_name": "llama3_1_8b_pretrain_a3mega",
"job_prefix": "vertex-ai",
"reservation_name": ""
}
@@ -1,49 +0,0 @@
absl-py==2.2.2
annotated-types==0.7.0
anyio==4.9.0
black==25.1.0
cachetools==5.5.2
certifi==2025.4.26
charset-normalizer==3.4.2
click==8.1.8
docstring_parser==0.16
google-api-core==2.24.2
google-auth==2.40.1
google-cloud-aiplatform==1.92.0
google-cloud-bigquery==3.31.0
google-cloud-core==2.4.3
google-cloud-resource-manager==1.14.2
google-cloud-storage==2.19.0
google-crc32c==1.7.1
google-genai==1.14.0
google-resumable-media==2.7.2
googleapis-common-protos==1.70.0
grpc-google-iam-v1==0.14.2
grpcio==1.71.0
grpcio-status==1.71.0
h11==0.16.0
httpcore==1.0.9
httpx==0.28.1
idna==3.10
mypy_extensions==1.1.0
numpy==2.2.5
packaging==25.0
pathspec==0.12.1
platformdirs==4.3.8
proto-plus==1.26.1
protobuf==5.29.4
pyasn1==0.6.1
pyasn1_modules==0.4.2
pydantic==2.11.4
pydantic_core==2.33.2
python-dateutil==2.9.0.post0
pytz==2025.2
requests==2.32.4
rsa==4.9.1
shapely==2.1.0
six==1.17.0
sniffio==1.3.1
typing-inspection==0.4.0
typing_extensions==4.13.2
urllib3==2.5.0
websockets==15.0.1
@@ -1,173 +0,0 @@
"""Launch script for Vertex distributed training"""
# Copy the sample_job_config.json file to job_config.json
# to define the job parameters.
#
# Run like this:
#
# python3 vertex_dist_train/launch.py --config_file=job_config.json
#
import datetime
import json
import os
import pprint
from collections.abc import Sequence
from typing import Any, List
from absl import app, flags
from google.cloud import aiplatform
from google.cloud.aiplatform_v1.types.custom_job import Scheduling
from pytz import timezone
FLAGS = flags.FLAGS
flags.DEFINE_string("config_file", None, "Path to JSON config file")
flags.DEFINE_boolean(
"debug", False, "Debug mode: just print the command, don't run it."
)
def launch_job(
job_name: str,
project: str,
region: str,
gcs_bucket: str,
image_uri: str,
entrypoint_cmd: List[str],
trainer_args: List[Any],
num_nodes: int,
machine_type: str,
num_gpus_per_node: int,
gpu_type: str,
strategy: str,
reservation_name: str = "",
):
assert strategy in ("dws", "spot", "reservation")
aiplatform.init(
project=project, location=region, staging_bucket=gcs_bucket
)
train_job = aiplatform.CustomContainerTrainingJob(
display_name=job_name,
container_uri=image_uri,
command=entrypoint_cmd,
)
job_args = dict(
args=trainer_args,
enable_web_access=True,
replica_count=num_nodes,
machine_type=machine_type,
accelerator_type=gpu_type,
accelerator_count=num_gpus_per_node,
boot_disk_size_gb=1000,
restart_job_on_worker_restart=True,
#restart_job_on_worker_restart=False,
)
if strategy == "spot":
job_args.update({"scheduling_strategy": Scheduling.Strategy.SPOT.name})
elif strategy == "dws":
job_args.update(
{"scheduling_strategy": Scheduling.Strategy.FLEX_START.name}
)
elif strategy == "reservation":
assert reservation_name != "", (
"If using a reservation, provide the reservation_name in the "
"format `projects/{project_id_or_number}/zones/{zone}/"
"reservations/{reservation_name}`"
)
job_args.update(
{
"reservation_affinity_type": "SPECIFIC_RESERVATION",
"reservation_affinity_key": "compute.googleapis.com/reservation-name",
"reservation_affinity_values": [reservation_name],
}
)
pprint.pprint(job_args)
if not FLAGS.debug:
train_job.submit(**job_args)
def main(argv: Sequence[str]) -> None:
config_file_path = FLAGS.config_file
print(f"Reading job config from {config_file_path}")
with open(config_file_path, encoding="utf-8") as config_file:
config = json.load(config_file)
project_id = config["project_id"]
region = config["region"]
zone = config["zone"]
bucket = config["bucket"]
dataset_bucket = config["dataset_bucket"]
n_nodes = int(config["nodes"])
machine_type = config["machine_type"]
num_gpus_per_node = int(config["gpus_per_node"])
gpu_type = config["gpu_type"]
reservation_name = config.get("reservation_name")
reservation_full_name = (
f"projects/{project_id}/zones/{zone}/reservations/{reservation_name}"
if "reservation_name" in config
else ""
)
strategy = config["strategy"]
recipe_name = config["recipe_name"]
job_prefix = config["job_prefix"]
image_uri = config["image_uri"]
# Job name
timestamp = (
datetime.datetime.now()
.astimezone(timezone("US/Pacific"))
.strftime("%Y%m%d_%H%M%S")
)
job_name = f"{recipe_name}-{timestamp}"
if job_prefix:
job_name = f"{job_prefix}-{job_name}"
base_output_dir = os.path.join("/gcs", bucket, job_name)
# Training command and args
entrypoint_cmd = ["python3", "vdt/run.py"]
dataset_bucket = f"gs://{config['dataset_bucket']}"
trainer_args = [
f"--train_data_gcs={dataset_bucket}",
"/opt/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py",
"--config-path=conf/",
f"--config-name={recipe_name}.yaml",
f"exp_manager.explicit_log_dir={base_output_dir}",
f"exp_manager.dllogger_logger_kwargs.json_file={base_output_dir}/dllogger.json",
"+exp_manager.create_tensorboard_logger=true",
"exp_manager.create_checkpoint_callback=false",
f"trainer.num_nodes={n_nodes}",
f"trainer.devices={num_gpus_per_node}",
"trainer.max_steps=10",
"trainer.log_every_n_steps=1",
"model.tokenizer.vocab_file=/data/gpt2-vocab.json",
"model.tokenizer.merge_file=/data/gpt2-merges.txt",
"model.data.data_prefix=[1.0,/data/hfbpe_gpt_training_data_text_document]",
]
launch_job(
job_name=job_name,
project=project_id,
region=region,
gcs_bucket=bucket,
image_uri=image_uri,
entrypoint_cmd=entrypoint_cmd,
trainer_args=trainer_args,
num_nodes=n_nodes,
machine_type=machine_type,
num_gpus_per_node=num_gpus_per_node,
gpu_type=gpu_type,
strategy=strategy,
reservation_name=reservation_full_name,
)
if __name__ == "__main__":
app.run(main)
@@ -1,85 +0,0 @@
"""Entrypoint for Vertex Distributed Training container."""
import argparse
import os
import sys
from collections.abc import Sequence
from subprocess import STDOUT, check_output, run
from absl import app, flags, logging
from util import cluster_spec
from retrying import retry
# PyTorch barrier call which synchronizes all of the nodes before launching the training process.
# This makes sure that processes will block until all processes are ready.
# Improves the reliability of spot VM usage for multi-node training jobs
@retry(stop_max_attempt_number=100, wait_exponential_multiplier=1000)
def barrier_with_retry() -> None:
import torch
logging.info("Starting barrier on RANK {}".format(os.environ["RANK"]))
torch.distributed.init_process_group()
torch.distributed.barrier()
torch.distributed.destroy_process_group()
logging.info("Finished barrier on RANK {}".format(os.environ["RANK"]))
def main(unused_argv: Sequence[str]) -> None:
parser = argparse.ArgumentParser()
parser.add_argument(
"--train_data_gcs",
type=str,
help="Download training data from gcs path",
)
args, unknown = parser.parse_known_args()
for key, val in os.environ.items():
logging.info("ENV %s=%s", key, val)
if args.train_data_gcs:
local_dir = "/data"
if not os.path.exists(local_dir):
os.mkdir(local_dir)
logging.info("downloading %s to %s...", args.train_data_gcs, local_dir)
check_output(
[
"gcloud",
"storage",
"cp",
"-r",
f"{args.train_data_gcs}/*",
local_dir,
],
stderr=STDOUT,
)
logging.info("%s downloaded.", args.train_data_gcs)
primary_node_addr, primary_node_port, node_rank, num_nodes = (
cluster_spec.get_cluster_spec()
)
cmd = [
"torchrun",
"--nproc-per-node=8",
f"--nnodes={num_nodes}",
f"--node_rank={node_rank}",
]
if num_nodes > 1:
cmd += [
"--max-restarts=3",
"--rdzv-backend=static",
f'--rdzv_id={os.getenv("CLOUD_ML_JOB_ID", primary_node_port)}',
f"--rdzv-endpoint={primary_node_addr}:{primary_node_port}",
]
cmd += unknown
logging.info("launching with cmd: \n%s", " \\\n".join(cmd))
barrier_with_retry()
run(cmd, stdout=sys.stdout, stderr=sys.stdout, check=True)
if __name__ == "__main__":
logging.get_absl_handler().python_handler.stream = sys.stdout
app.run(
main, flags_parser=lambda _args: flags.FLAGS(_args, known_only=True)
)
@@ -1,81 +0,0 @@
"""Get cluster info from environment variables."""
import dataclasses
import json
import os
from absl import logging
@dataclasses.dataclass
class ClusterInfo:
"""Contains information about the cluster.
Attributes:
primary_node_addr: The address of the primary node.
primary_node_port: The port of the primary node.
node_rank: The rank of the node.
num_nodes: The number of nodes in the cluster.
"""
primary_node_addr: str | None = None
primary_node_port: str | None = None
node_rank: int = 0
num_nodes: int = 1
# Allows unpacking operation like
# primary_node_addr, primary_node_port, _, _ = ClusterInfo()
# See https://stackoverflow.com/a/70753113
def __iter__(self):
return iter(dataclasses.astuple(self))
def get_cluster_spec() -> ClusterInfo:
"""Parses CLUSTER_SPEC environment variable and returns the cluster info.
Returns:
A ClusterInfo object.
"""
cluster_spec = os.getenv("CLUSTER_SPEC", None)
# If CLUSTER_SPEC is not set, use individual vars to construct cluster info.
if not cluster_spec:
cluster_info = ClusterInfo(
primary_node_addr=os.getenv("MASTER_ADDR", None),
primary_node_port=os.getenv("MASTER_PORT", None),
node_rank=int(os.getenv("RANK", "0")),
num_nodes=int(os.getenv("NNODES", "1")),
)
return cluster_info
cluster_data = json.loads(cluster_spec)
# Get primary node info
primary_node = cluster_data["cluster"]["workerpool0"][0]
logging.info("primary node: %s", primary_node)
primary_node_addr, primary_node_port = primary_node.split(":")
logging.info("primary node address: %s", primary_node_addr)
logging.info("primary node port: %s", primary_node_port)
# Determine node rank of this machine
workerpool = cluster_data["task"]["type"]
if workerpool == "workerpool0":
node_rank = 0
elif workerpool == "workerpool1":
# Add 1 for the primary node, since `index` is the index of workerpool1.
node_rank = cluster_data["task"]["index"] + 1
else:
raise ValueError(
"Only workerpool0 and workerpool1 are supported. Unknown workerpool:"
f" {workerpool}"
)
logging.info("node rank: %s", node_rank)
# Calculate total nodes.
num_nodes = 1 # For the primary node.
if "workerpool1" in cluster_data["cluster"]:
num_nodes += len(cluster_data["cluster"]["workerpool1"])
logging.info("num nodes: %s", num_nodes)
return ClusterInfo(
primary_node_addr, primary_node_port, node_rank, num_nodes
)
@@ -1,59 +0,0 @@
"""Add tests for cluster_spec.py."""
import os
from . import cluster_spec
# TODO(styer): Use pytest instead
class ClusterSpecTest(googletest.TestCase):
def setUp(self):
super().setUp()
self.curr_env_var = os.environ.copy()
def tearDown(self):
super().tearDown()
os.environ = self.curr_env_var
def test_get_cluster_spec_from_env_vars(self):
os.environ["CLUSTER_SPEC"] = ""
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = "8080"
os.environ["RANK"] = "0"
os.environ["NNODES"] = "2"
cluster_info = cluster_spec.get_cluster_spec()
self.assertEqual(cluster_info.primary_node_addr, "127.0.0.1")
self.assertEqual(cluster_info.primary_node_port, "8080")
self.assertEqual(cluster_info.node_rank, 0)
self.assertEqual(cluster_info.num_nodes, 2)
def test_get_cluster_spec_from_cluster_spec(self):
os.environ[
"CLUSTER_SPEC"
] = """
{
"cluster": {
"workerpool0": [
"127.0.0.1:8080"
],
"workerpool1": [
"127.0.0.2:8080",
"127.0.0.3:8080"
]
},
"task": {
"type": "workerpool1",
"index": 0
}
}
"""
cluster_info = cluster_spec.get_cluster_spec()
self.assertEqual(cluster_info.primary_node_addr, "127.0.0.1")
self.assertEqual(cluster_info.primary_node_port, "8080")
self.assertEqual(cluster_info.node_rank, 1)
self.assertEqual(cluster_info.num_nodes, 3)
if __name__ == "__main__":
googletest.main()
@@ -1,14 +1,13 @@
"""Common util functions for notebook."""
import base64
from collections.abc import Sequence
import datetime
import io
import json
import os
import subprocess
import time
from typing import Any
from typing import Any, Dict, Sequence
from google import auth
from google.cloud import storage
@@ -284,7 +283,7 @@ def decode_image(
return image
def get_label_map(label_map_yaml_filepath: str) -> dict[int, str]:
def get_label_map(label_map_yaml_filepath: str) -> Dict[int, str]:
"""Returns class id to label mapping given a filepath to the label map.
Args:
@@ -510,17 +509,6 @@ def get_quota(project_id: str, region: str, resource_id: str) -> int:
):
return -1
all_regions_data = quota_data[0]["consumerQuotaLimits"][0]["quotaBuckets"]
# If the quota data does not have dimensions, it is global quota. However,
# global quota may be overridden by regional quota. So we need to check the
# global quota first.
global_quota = -1
if (
all_regions_data
and "dimensions" not in all_regions_data[0]
and "effectiveLimit" in all_regions_data[0]
):
global_quota = int(all_regions_data[0]["effectiveLimit"])
for region_data in all_regions_data:
if (
region_data.get("dimensions")
@@ -530,13 +518,12 @@ def get_quota(project_id: str, region: str, resource_id: str) -> int:
return int(region_data["effectiveLimit"])
else:
return 0
return global_quota
return -1
def get_resource_id(
accelerator_type: str,
is_for_training: bool,
is_spot: bool = False,
is_restricted_image: bool = False,
is_dynamic_workload_scheduler: bool = False,
) -> str:
@@ -546,7 +533,6 @@ def get_resource_id(
accelerator_type: The accelerator type.
is_for_training: Whether the resource is used for training. Set false for
serving use case.
is_spot: Whether the resource is used with Spot.
is_restricted_image: Whether the image is hosted in `vertex-ai-restricted`.
is_dynamic_workload_scheduler: Whether the resource is used with Dynamic
Workload Scheduler.
@@ -562,9 +548,7 @@ def get_resource_id(
"NVIDIA_A100_80GB": "nvidia_a100_80gb_gpus",
"NVIDIA_H100_80GB": "nvidia_h100_gpus",
"NVIDIA_H100_MEGA_80GB": "nvidia_h100_mega_gpus",
"NVIDIA_H200_141GB": "nvidia_h200_gpus",
"NVIDIA_TESLA_T4": "nvidia_t4_gpus",
"TPU_V6e": "tpu_v6e",
"TPU_V5e": "tpu_v5e",
"TPU_V3": "tpu_v3",
}
@@ -579,10 +563,6 @@ def get_resource_id(
restricted_image_training_accelerator_map = {
"NVIDIA_A100_80GB": "restricted_image_training_nvidia_a100_80gb_gpus",
}
spot_serving_accelerator_map = {
key: f"custom_model_serving_preemptible_{accelerator_suffix_map[key]}"
for key in accelerator_suffix_map
}
serving_accelerator_map = {
key: f"custom_model_serving_{accelerator_suffix_map[key]}"
for key in accelerator_suffix_map
@@ -611,11 +591,8 @@ def get_resource_id(
else:
if is_dynamic_workload_scheduler:
raise ValueError("Dynamic Workload Scheduler does not work for serving.")
accelerator_map = (
spot_serving_accelerator_map if is_spot else serving_accelerator_map
)
if accelerator_type in accelerator_map:
return accelerator_map[accelerator_type]
if accelerator_type in serving_accelerator_map:
return serving_accelerator_map[accelerator_type]
else:
raise ValueError(
f"Could not find accelerator type: {accelerator_type} for serving."
@@ -628,28 +605,13 @@ def check_quota(
accelerator_type: str,
accelerator_count: int,
is_for_training: bool,
is_spot: bool = False,
is_restricted_image: bool = False,
is_dynamic_workload_scheduler: bool = False,
) -> None:
"""Checks if the project and the region has the required quota.
Args:
project_id: The project id.
region: The region.
accelerator_type: The accelerator type.
accelerator_count: The number of accelerators to check quota for.
is_for_training: Whether the resource is used for training. Set false for
serving use case.
is_spot: Whether the resource is used with Spot.
is_restricted_image: Whether the image is hosted in `vertex-ai-restricted`.
is_dynamic_workload_scheduler: Whether the resource is used with Dynamic
Workload Scheduler.
"""
):
"""Checks if the project and the region has the required quota."""
resource_id = get_resource_id(
accelerator_type,
is_for_training=is_for_training,
is_spot=is_spot,
is_restricted_image=is_restricted_image,
is_dynamic_workload_scheduler=is_dynamic_workload_scheduler,
)
-1
View File
@@ -26,7 +26,6 @@
/vertex_endpoints/find_ideal_machine_type/find_ideal_machine_type/find_ideal_machine_type.ipynb @entrpn
/vertex_endpoints/nvidia-triton/nvidia-triton-custom-container-prediction.ipynb @RajeshThallam
/vertex_endpoints/optimized_tensorflow_runtime @vlasenkoalexey
/notebooks/community/alphagenome/cloudai_alphagenome_vai_quickstart.ipynb @dpanigra
/notebooks/community/ml_ops/stage2/get_started_with_visionapi_and_automl.ipynb @mansari
/notebooks/community/neo4j/graph_paysim.ipynb @benofben @laeg
/notebooks/community/ml_ops/stage1/get_started_with_visionapi_and_vertex_datasets.ipynb @mansari
-93
View File
@@ -1,93 +0,0 @@
![AlphaGenome header image](https://raw.githubusercontent.com/google-deepmind/alphagenome/refs/heads/main/docs/source/_static/header.png)
# AlphaGenome
[**Overview**](#overview) | [**Use Cases**](#use-cases) | [**Documentation**](#documentation) | [**Pricing**](#pricing) | [**Quick start**](#quick-start)
## Overview
**Disclaimer:** *Experimental*.
*The AlphaGenome Private Preview is a "Pre-GA Offering" subject to the "Pre-GA
Offerings Terms" in the General Service Terms section of the Google Cloud
[Service Specific Terms](https://cloud.google.com/terms/service-terms). It is
also a “Generative AI Preview Product” as defined in and subject to the
[Additional Terms for Generative AI Preview Products](https://cloud.google.com/trustedtester/aitos?e=48754805&hl=en).
Pre-GA products are available "as is" and might have limited support. For more
information, see the [launch stage](https://cloud.google.com/products?e=48754805#product-launch-stages)
descriptions.*
Access to the AlphaGenome model capabilities requires application and approval.
Users must be added to an allowlist to use the service.
If you are interested in applying to the program, **Request Access** above.
&nbsp;
AlphaGenome is Google DeepMind’s unifying model for deciphering the regulatory
code within DNA sequences.
AlphaGenome offers multimodal predictions, encompassing diverse functional
outputs such as gene expression, splicing patterns, chromatin features, and
contact maps (see diagram below). The model analyzes DNA sequences of up to 1
million base pairs in length and can deliver predictions at single base-pair
resolution for most outputs.
Training data was sourced from large public consortia including
[ENCODE](http://encodeproject.org/), [GTEx](https://www.gtexportal.org/),
[4D Nucleome](https://4dnucleome.org/) and
[FANTOM5](https://fantom.gsc.riken.jp/5/), which experimentally measured these
properties covering important modalities of gene regulation across hundreds of
human and mouse cell types and tissues.
![Diagram showing an overview of the AlphaGenome model architecture and its inputs/outputs](https://www.alphagenomedocs.com/_images/model_overview.png)
## Use Cases
* **Sequence-to-function predictions:** Predict multiple functional tracks (such as gene expression, splicing) from DNA sequences across a wide variety of tissues and cell types.
* **Variant effect scoring:** Assess the impact of genetic variants by comparing predictions for the reference and alternative alleles and summarising the differences between them.
* **Identify functional regions:** Use in silico mutagenesis (ISM) to identify functionally important regions in the DNA sequence.
* **Human and mouse capability:** Generate predictions for both human and mouse genomes.
## Documentation
This API provides access to AlphaGenome, Google DeepMind's unifying model for
deciphering the regulatory code within DNA sequences. AlphaGenome offers
multimodal predictions, encompassing diverse functional outputs including gene
expression, splicing patterns, chromatin features, and contact maps (see diagram
below). The model analyzes up to 1 million base pairs of DNA sequence and can
deliver predictions at single base-pair resolution for most modalities.
AlphaGenome achieves state-of-the-art performance across a range of genomic
prediction benchmarks, including diverse variant effect prediction tasks.
The Google Cloud API for AlphaGenome provides a way for Google Cloud customers
to explore the AlphaGenome API for commercial use cases. This API is in private
preview (Request Access above). Once allowlisted, customers can access the API
directly or use the [colab](cloudai_alphagenome_vai_quickstart.ipynb).
### Acknowledgements
*Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., ... Kohli, P. (2025). AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model. bioRxiv.* [https://doi.org/10.1101/2025.06.25.661532](https://doi.org/10.1101/2025.06.25.661532)
### Contact
If you have any questions on using these models on Google Cloud please contact:
[alphagenome-cloud-external@google.com](mailto:alphagenome-cloud-external@google.com) or join the community [Discourse](https://www.alphagenomecommunity.com/) for more generic questions on AlphaGenome.
### Links
* Read our [paper](https://doi.org/10.1101/2025.06.25.661532)
* Read our [blog post](https://deepmind.google/discover/blog/alphagenome-ai-for-better-understanding-the-genome)
* Join the [community](https://www.alphagenomecommunity.com/)
* Check out the [AlphaGenome 101 Video](https://youtu.be/Xbvloe13nak)
## Pricing
Access to AlphaGenome on Vertex AI is currently restricted.
To utilize these models via this service:
* You must **Request Access** using your Google contact.
* Your application will be reviewed, and if approved, you will be **added to
an allowlist**.
* Only allowlisted users can access the API
* **Pricing information** will be shared directly with users upon approval
and placement on the allowlist.
## Quick start
The quickest way to get started with the AlphaGenome in Google Cloud Platform is to run [our example notebook](cloudai_alphagenome_vai_quickstart.ipynb) in [Google Colab](https://colab.research.google.com/).
File diff suppressed because one or more lines are too long
@@ -37,18 +37,18 @@
"\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/bigquery_ml/Anomaly_detection_in_Cloud_Audit_logs_with_BQML.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/bigquery_ml/Anomaly_detection_in_Cloud_Audit_logs_with_BQML.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/bigquery_ml/Anomaly_detection_in_Cloud_Audit_logs_with_BQML.ipynb\">\n",
" <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
" Open in Vertex AI Workbench\n",
" </a>\n",
" </td> \n",
@@ -47,18 +47,18 @@
"\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/bigquery_ml/bq_ml_with_vision_translation_nlp.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/bigquery_ml/bq_ml_with_vision_translation_nlp.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/bigquery_ml/bq_ml_with_vision_translation_nlp.ipynb\">\n",
" <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
" Open in Vertex AI Workbench\n",
" </a>\n",
" </td>\n",
@@ -35,18 +35,18 @@
"\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/bigquery_ml/bqml-online-prediction.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/bigquery_ml/bqml-online-prediction.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/bigquery_ml/bqml-online-prediction.ipynb\">\n",
" <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
" Open in Vertex AI Workbench\n",
" </a>\n",
" </td> \n",
@@ -38,7 +38,7 @@
" </td>\n",
" <td>\n",
" <a href=\"github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/cohere/cohere_embedding_with_matching_engine.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -36,18 +36,18 @@
"\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/vertex_ai_experiments_classification.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/vertex_ai_experiments_classification.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/vertex_ai_experiments_classification.ipynb\">\n",
" <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
" Open in Vertex AI Workbench\n",
" </a>\n",
" </td> \n",
@@ -33,18 +33,18 @@
"\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/explainable_ai/SDK_Custom_Container_XAI.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/explainable_ai/SDK_Custom_Container_XAI.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/explainable_ai/SDK_Custom_Container_XAI.ipynb\">\n",
" <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
" Open in Vertex AI Workbench\n",
" </a>\n",
" </td> \n",
@@ -33,12 +33,12 @@
"\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/feature_store/gapic-feature-store.ipynb\"\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/feature_store/gapic-feature-store.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,20 +34,20 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_vertex_feature_store_serving.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
" \n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_vertex_feature_store_serving.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" \n",
" <td>\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/stage6/get_started_vertex_feature_store_serving.ipynb\">\n",
" <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
" Open in Vertex AI Workbench\n",
" </a>\n",
" </td>\n",
@@ -33,18 +33,18 @@
"\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/vertex-ai-samples/blob/main/notebooks/community/feature_store/mobile_gaming/mobile_gaming_feature_store.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/feature_store/mobile_gaming/mobile_gaming_feature_store.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/feature_store/mobile_gaming/mobile_gaming_feature_store.ipynb\">\n",
" <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
" Open in Vertex AI Workbench\n",
" </a>\n",
" </td> \n",
@@ -32,12 +32,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/feature_store/sdk-feature-store.ipynb\"\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/feature_store/sdk-feature-store.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_export_edge.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_export_edge.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_online_proxy.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_online_proxy.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_object_detection_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_object_detection_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_object_detection_export_edge.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_object_detection_export_edge.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_object_detection_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_object_detection_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_segmentation_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_segmentation_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_segmentation_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_segmentation_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_binary_classification_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_binary_classification_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_binary_classification_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_binary_classification_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_batch_explain.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_batch_explain.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_export_cloud.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_export_cloud.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_online_explain.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_online_explain.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_forecasting_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_forecasting_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_regression_online_bq.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_regression_online_bq.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_classification_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_classification_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_classification_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_classification_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_entity_extraction_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_entity_extraction_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_entity_extraction_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_entity_extraction_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_multi-label_classification_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_multi-label_classification_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_multi-label_classification_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_multi-label_classification_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_sentiment_analysis_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_sentiment_analysis_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_sentiment_analysis_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_sentiment_analysis_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_video_action_recognition_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_video_classification_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_video_classification_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_video_object_tracking_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_video_object_tracking_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_batch_explain.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_batch_explain.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_ab_testing.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_ab_testing.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_container.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_container.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_explain.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_explain.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -35,18 +35,18 @@
"\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/gapic/custom/showcase_custom_image_classification_online_explain_example_based_api.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/gapic/custom/showcase_custom_image_classification_online_explain_example_based_api.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/notebooks/community/gapic/custom/showcase_custom_image_classification_online_explain_example_based_api.ipynb\">\n",
" <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
" Open in Vertex AI Workbench\n",
" </a>\n",
" </td> \n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_exported_ds.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_exported_ds.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_pipeline.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_pipeline.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_raw.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_raw.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_tfserving.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_classification_online_tfserving.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_super_resolution_online_post.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_image_super_resolution_online_post.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_classification_online_exported_ds.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_classification_online_exported_ds.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_batch_explain.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_batch_explain.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_online_container.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_online_container.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_online_explain.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_online_explain.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_online_pipeline.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_online_pipeline.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_online_tfserving.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_tabular_regression_online_tfserving.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_text_binary_classification_batch.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_text_binary_classification_batch.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_text_binary_classification_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_text_binary_classification_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_text_binary_classification_online_container.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_text_binary_classification_online_container.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_text_binary_classification_online_pipeline.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_text_binary_classification_online_pipeline.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_text_binary_classification_online_tfserving.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_text_binary_classification_online_tfserving.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_text_classification_online_exported_ds.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_custom_text_classification_online_exported_ds.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_hyperparmeter_tuning_image_classification.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_hyperparmeter_tuning_image_classification.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_hyperparmeter_tuning_tabular_regression.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_hyperparmeter_tuning_tabular_regression.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_hyperparmeter_tuning_text_binary_classification.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_hyperparmeter_tuning_text_binary_classification.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_local_image_classification_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_local_image_classification_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_local_tabular_regression_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_local_tabular_regression_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_local_text_binary_classification_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_local_text_binary_classification_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
@@ -34,12 +34,12 @@
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_tfhub_image_classification_online.ipynb\">\n",
" <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/custom/showcase_tfhub_image_classification_online.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",

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