Update CODEOWNERS

2026-09-27 15:42:05 +00:00 · 2025-04-07 14:50:50 -04:00
688 changed files with 22157 additions and 42279 deletions
@@ -238,7 +238,7 @@ def _get_notebook_python_version(notebook_path: str) -> str:

            # Look for the python version specification pattern
            re_match = re.search(
-                r"python version = (\d+\.\d+)", markdown, flags=re.IGNORECASE
+                "python version = (\d+\.\d+)", markdown, flags=re.IGNORECASE
            )
            if re_match:
                # get the version number
@@ -1,3 +1,5 @@
+notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb
+notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb
 notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb
 notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb
 .cloud-build/tests/python_version_test.ipynb
@@ -3,8 +3,8 @@ ipython
 jupyter
 nbconvert
 black==25.1.0
-pyupgrade==3.20.0
+pyupgrade==3.19.1
 isort==6.0.1
-flake8==7.3.0
+flake8==7.2.0
 nbqa==1.9.1

@@ -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==2.7.0
+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==2.4.0
-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:
@@ -334,7 +333,6 @@ def vqa_predict(
    image: Any,
    language_code: str = "en",
    new_width: int = 1000,
-    use_dedicated_endpoint: bool = False,
 ) -> Sequence[str]:
  """Predicts the answer to a question about an image using an Endpoint."""
  # Resize and convert image to base64 string.
@@ -358,9 +356,7 @@ def vqa_predict(
        "image": resized_image_base64,
    })

-  response = endpoint.predict(
-      instances=instances, use_dedicated_endpoint=use_dedicated_endpoint
-  )
+  response = endpoint.predict(instances=instances)
  return [pred.get("response") for pred in response.predictions]


@@ -370,7 +366,6 @@ def caption_predict(
    image: Any,
    caption_prompt: bool = False,
    new_width: int = 1000,
-    use_dedicated_endpoint: bool = False,
 ) -> str:
  """Predicts a caption for a given image using an Endpoint."""
  # Resize and convert image to base64 string.
@@ -385,9 +380,7 @@ def caption_predict(
    instance["prompt"] = caption_prompt_format.format(language_code)

  instances = [instance]
-  response = endpoint.predict(
-      instances=instances, use_dedicated_endpoint=use_dedicated_endpoint
-  )
+  response = endpoint.predict(instances=instances)
  return response.predictions[0].get("response")


@@ -396,7 +389,6 @@ def ocr_predict(
    ocr_prompt: str,
    image: Any,
    new_width: int = 1000,
-    use_dedicated_endpoint: bool = False,
 ) -> str:
  """Extracts text from a given image using an Endpoint."""
  # Resize and convert image to base64 string.
@@ -408,9 +400,7 @@ def ocr_predict(
    instance["prompt"] = ocr_prompt
  instances = [instance]

-  response = endpoint.predict(
-      instances=instances, use_dedicated_endpoint=use_dedicated_endpoint
-  )
+  response = endpoint.predict(instances=instances)
  return response.predictions[0].get("response")


@@ -419,7 +409,6 @@ def detect_predict(
    detect_prompt: str,
    image: Any,
    new_width: int = 1000,
-    use_dedicated_endpoint: bool = False,
 ) -> str:
  """Predicts the answer to a question about an image using an Endpoint."""
  # Resize and convert image to base64 string.
@@ -431,9 +420,7 @@ def detect_predict(
    instance["prompt"] = detect_prompt
  instances = [instance]

-  response = endpoint.predict(
-      instances=instances, use_dedicated_endpoint=use_dedicated_endpoint
-  )
+  response = endpoint.predict(instances=instances)
  return response.predictions[0].get("response")


@@ -510,17 +497,6 @@ 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 +506,12 @@ 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 +521,6 @@ 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 +536,7 @@ 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 +551,6 @@ 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 +579,8 @@ 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 +593,13 @@ 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,
  )
@@ -7,7 +7,7 @@ import json
 import multiprocessing
 import os
 import subprocess
-from typing import Any, Callable, Dict, Tuple, Union
+from typing import Any, Callable, Dict, Union
 from absl import logging
 import accelerate
 import datasets
@@ -70,9 +70,7 @@ def force_gcs_fuse_path(gcs_uri: str) -> str:


 def download_gcs_uri_to_local(
-    gcs_uri: str,
-    destination_dir: str = LOCAL_BASE_MODEL_DIR,
-    check_path_exists: bool = True,
+    gcs_uri: str, destination_dir: str = LOCAL_BASE_MODEL_DIR
 ) -> str:
  """Downloads GCS URI to local.

@@ -83,7 +81,6 @@ def download_gcs_uri_to_local(
  Args:
    gcs_uri: GCS URI to download.
    destination_dir: Local directory directory.
-    check_path_exists: Whether to check if the path exists.

  Returns:
    Local path to target folder/file.
@@ -92,7 +89,7 @@ def download_gcs_uri_to_local(
      destination_dir,
      os.path.basename(os.path.normpath(gcs_uri)),
  )
-  if check_path_exists and os.path.exists(target):
+  if os.path.exists(target):
    logging.info("File %s already exists.", target)
    return target
  if accelerate.PartialState().is_local_main_process:
@@ -418,42 +415,13 @@ def get_filtered_dataset(
  return filtered_dataset


-def format_dataset(
-    dataset: datasets.Dataset,
-    input_column: str,
-    template: str = None,
-    tokenizer: transformers.PreTrainedTokenizer | None = None,
-) -> datasets.Dataset:
-  """Takes a raw dataset and formats it using a template and tokenizer.
-
-  Args:
-    dataset: The raw (unprocessed) dataset to format.
-    input_column: The input column in the dataset to be used or updaded by the
-      template. If it does not exist, the template's `prompt_no_input` will be
-      used, and the input_column will be created.
-    template: Name of the JSON template file under `templates/` or GCS path to
-      the template file.
-    tokenizer: The tokenizer to use for chat_template templates.
-
-  Returns:
-    A dataset compatible with the template.
-  """
-  return dataset.map(
-      _format_template_fn(
-          template,
-          input_column=input_column,
-          tokenizer=tokenizer,
-      )
-  )
-
-
 def load_dataset_with_template(
    dataset_name: str,
    split: str,
    input_column: str,
    template: str = None,
    tokenizer: transformers.PreTrainedTokenizer | None = None,
-) -> Tuple[Any, Any]:
+) -> Any:
  """Loads dataset with templates.

  Args:
@@ -467,15 +435,19 @@ def load_dataset_with_template(
    tokenizer: The tokenizer to use for chat_template templates.

  Returns:
-    The raw dataset and the dataset compatible with the template.
+    A dataset compatible with the template.
  """
-  raw = _get_dataset(dataset_name, split=split)
+  dataset = _get_dataset(dataset_name, split=split)
  if template:
-    templated = format_dataset(raw, input_column, template, tokenizer)
-  else:
-    templated = None
+    dataset = dataset.map(
+        _format_template_fn(
+            template,
+            input_column=input_column,
+            tokenizer=tokenizer,
+        )
+    )

-  return raw, templated
+  return dataset


 def validate_dataset_with_template(
@@ -549,11 +521,12 @@ def validate_dataset_with_template(
        f" https://github.com/GoogleCloudPlatform/{_VERTEX_AI_SAMPLES_GITHUB_REPO_NAME}/tree/main/{_VERTEX_AI_SAMPLES_GITHUB_TEMPLATE_DIR}."
    )

-  dataset = format_dataset(
-      _get_dataset(dataset_name, split, num_proc),
-      input_column,
-      template_path,
-      tokenizer,
+  dataset = _get_dataset(dataset_name, split, num_proc).map(
+      _format_template_fn(
+          template_path,
+          input_column=input_column,
+          tokenizer=tokenizer,
+      )
  )

  if tokenizer is not None:
@@ -3,7 +3,6 @@
 import copy
 import dataclasses
 import datetime
-import inspect
 import os
 import signal
 import subprocess
@@ -12,7 +11,7 @@ from absl import flags
 from absl import logging
 from absl.testing import parameterized
 import command_builder
-import immutabledict
+import frozendict
 import torch

 _DOCKER_URI = flags.DEFINE_string('docker_uri', None, 'docker image uri')
@@ -34,19 +33,19 @@ _LOCAL_OUTPUT_DIR = flags.DEFINE_string(

 _GCS_INPUT_DIR = flags.DEFINE_string(
    'gcs_input_dir',
-    'gs://vmg-tuning-docker-test',
+    'gs://peft-docker-test',
    'GCS directory that stores model checkpoint, dataset and etc.',
 )

 _GCS_OUTPUT_DIR = flags.DEFINE_string(
    'gcs_output_dir',
-    'gs://vmg-tuning-docker-test/output',
+    'gs://peft-docker-test/output',
    'GCS directory that stores test output.',
 )

 _GCS_TESTDATA_DIR = 'peft-train-image-test'

-_THROUGHPUT_TEST_EXCEPTIONS = immutabledict.immutabledict({
+_THROUGHPUT_TEST_EXCEPTIONS = frozendict.frozendict({
    ('bm_deepspeed_zero3_8gpu_gemma-2-9b-it_4bit.txt', '12.0'): float('inf'),
    ('bm_fsdp_8gpu_llama3.1-70b-hf_4bit.txt', '20.0'): float('inf'),
    ('bm_deepspeed_zero2_8gpu_gemma-2-2b-it_bfloat16.txt', '12.0'): 20.0,
@@ -102,25 +101,26 @@ class TestBase(parameterized.TestCase):
    return self.command_builder.build_cmd() + self.task_cmd_builder.build_cmd()

  def run_cmd(self) -> int:
-    return run_cmd(self.cmd(), output_file=None)
+    logging.info('running command: \n%s', ' \\\n'.join(self.cmd()))
+    if _DRY_RUN.value:
+      return 0
+
+    p = subprocess.Popen(self.cmd(), stdout=sys.stdout, stderr=sys.stderr)
+    try:
+      unused_output, unused_error = p.communicate()
+      return p.returncode
+    except KeyboardInterrupt:
+      p.send_signal(signal.SIGINT)
+      return 0

  def gcs_output_dir(self):
    return _GCS_OUTPUT_DIR.value

-  def local_input_dir(self):
-    """Returns local input dir in host/docker."""
-    return _LOCAL_INPUT_DIR.value
-
  def local_output_dir(self):
-    """Returns local output dir in host/docker."""
    return _LOCAL_OUTPUT_DIR.value

-  def get_testcase_name(self):
-    """Returns the function name at the calling site."""
-    # https://docs.python.org/3/library/inspect.html#inspect.FrameInfo
-    cur_frame = inspect.currentframe()
-    # https://stackoverflow.com/a/17366561
-    return cur_frame.f_back.f_code.co_name
+  def local_input_dir(self):
+    return _LOCAL_INPUT_DIR.value


 def get_timestamp():
@@ -157,44 +157,13 @@ def get_test_data_path(name: str, download: bool = True) -> str:

  local_data = os.path.join(_LOCAL_INPUT_DIR.value, name)
  if not os.path.exists(local_data):
-    # If `name` is a file in sub-folders, then create the sub-folders under
-    # `_LOCAL_INPUT_DIR`.
-    local_data_dir = os.path.dirname(local_data)
-    if not os.path.exists(local_data_dir):
-      os.makedirs(local_data_dir)
-
-    download_from_gcs(os.path.join(_GCS_INPUT_DIR.value, name), local_data_dir)
+    download_from_gcs(
+        os.path.join(_GCS_INPUT_DIR.value, name), _LOCAL_INPUT_DIR.value
+    )

  return local_data


-def run_cmd(cmd: list[str], output_file: str = None) -> int:
-  """Runs the command and returns the return code.
-
-  Args:
-    cmd: The command to run.
-    output_file: The file to write the output to.
-
-  Returns:
-    The return code of the command.
-  """
-  logging.info('running command: \n%s', ' \\\n'.join(cmd))
-  if _DRY_RUN.value:
-    return 0
-  stdout = sys.stdout if output_file is None else open(output_file, 'w')
-  p = subprocess.Popen(cmd, stdout=stdout, stderr=sys.stderr)
-  try:
-    unused_output, unused_error = p.communicate()
-    return_code = p.returncode
-  except KeyboardInterrupt:
-    p.send_signal(signal.SIGINT)
-    return_code = 0
-  finally:
-    if output_file is not None:
-      stdout.close()
-  return return_code
-
-
 def get_pretrained_model_name_or_path(model_id: str) -> str:
  # If `model_id` contains `/`, it is assumed to be HF model or model from GCS.
  if '/' in model_id:
@@ -1,79 +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)
@@ -1,24 +0,0 @@
-"""Utility functions."""
-
-import logging
-import subprocess
-import sys
-import time
-
-
-def run_cmd(cmd: list[str]) -> float:
-  """Runs the command and logs the output.
-
-  Args:
-    cmd: The command to run.
-
-  Returns:
-    The time it took to run the command.
-  """
-  cmd_str = ' \\\n'.join(cmd)
-  logging.info('launching cmd: \n%s', cmd_str)
-  start_time = time.time()
-  subprocess.run(cmd, stdout=sys.stdout, stderr=sys.stdout, check=True)
-  elapsed_time = round(time.time() - start_time, 2)
-  logging.info('Command %s finished in %0.2f seconds.', cmd_str, elapsed_time)
-  return elapsed_time
@@ -1,197 +0,0 @@
-"""Calculate dataset statistics like token, example and character counts."""
-
-from collections.abc import Mapping, Sequence
-import dataclasses
-import json
-from typing import Any
-import datasets
-import numpy as np
-import transformers
-from util import dataset_validation_util
-
-_MAX_NUM_DATASET_SAMPLES = 6
-
-
-@dataclasses.dataclass
-class SupervisedTuningDatasetBucket:
-  """Represents a histogram bucket for tuning dataset distribution stats."""
-
-  count: float = 0
-  left: float = 0
-  right: float = 0
-
-
-@dataclasses.dataclass
-class SupervisedTuningDatasetDistribution:
-  """Represents a histogram with summary statistics for tuning dataset distribution stats."""
-
-  sum: int = 0
-  billable_sum: int = 0
-  min: float = 0
-  max: float = 0
-  mean: float = 0
-  median: float = 0
-  p5: float = 0
-  p95: float = 0
-  buckets: list[SupervisedTuningDatasetBucket] = dataclasses.field(
-      default_factory=list
-  )
-
-
-# Represents detailed tuning dataset statistics.
-@dataclasses.dataclass
-class SupervisedTuningDataStats:
-  """Represents detailed tuning dataset stats."""
-
-  tuning_dataset_example_count: int = 0
-  total_tuning_character_count: int = 0
-  total_billable_token_count: int = 0
-  tuning_step_count: int = 0
-  # Represents a histogram and some summary statistics of the number of input
-  # tokens across examples.
-  user_input_token_distribution: SupervisedTuningDatasetDistribution | None = (
-      None
-  )
-  # Represents a histogram and some summary statistics for the number of output
-  # tokens across examples.
-  user_output_token_distribution: SupervisedTuningDatasetDistribution | None = (
-      None
-  )
-  # Represents the number of "messages" (a single-turn conversation will have a
-  # single message) across examples.
-  user_message_per_example_distribution: (
-      SupervisedTuningDatasetDistribution | None
-  ) = None
-  user_dataset_examples: list[str] = dataclasses.field(default_factory=list)
-
-
-def get_dataset_stats(
-    *,
-    raw: Any,
-    templated: Any,
-    template: str,
-    tokenizer: transformers.PreTrainedTokenizer,
-    column: str,
-    effective_batch_size: int,
-) -> Mapping[str, Any]:
-  """Calculates dataset statistics for managed fine-tuning, e.g., total number of tokens."""
-  tokenized_dataset = templated.map(lambda x: tokenizer(x[column]))
-  inputs = tokenized_dataset["input_ids"]
-  tuning_dataset_example_count = int(len(inputs))
-  total_billable_token_count = int(np.sum([len(ex) for ex in inputs]))
-  total_tuning_character_count = int(
-      np.sum([len(ex[column]) for ex in templated])
-  )
-  tuning_step_count = (
-      tuning_dataset_example_count + effective_batch_size - 1
-  ) // effective_batch_size
-
-  # Assume that data is represented as ChatCompletions or Vertex Text-Bison
-  # formats to extract per-example input/output tokens.
-  user_inputs = []
-  user_outputs = []
-  user_input_messages_counts = []
-
-  for ex in raw:
-    if "messages" in ex:
-      messages = ex["messages"]
-      if messages:
-        # For ChatCompletions assume the last turn (i.e. the instruction
-        # response) is the expected output.
-        user_inputs.append({**ex, "messages": messages[:-1]})
-        user_outputs.append({**ex, "messages": messages[-1:]})
-        # Exclude everything but the last message for the number of input
-        # messages.
-        user_input_messages_counts.append(len(messages[:-1]))
-    elif "input_text" in ex:
-      # For Vertex Text-Bison, the `output_text` field is the expected output.
-      user_inputs.append({**ex, "output_text": ""})
-      user_outputs.append(
-          {**ex, "input_text": ex["output_text"], "output_text": ""}
-      )
-      # Vertex Text-Bison goes from input -> output; i.e. there is only a single
-      # input "message".
-      user_input_messages_counts.append(1)
-
-  def calc_histogram(
-      counts: Sequence[int],
-  ) -> SupervisedTuningDatasetDistribution:
-    mean = np.mean(counts)
-    median = np.median(counts).item()
-    max_count = np.max(counts).item()
-    min_count = np.min(counts).item()
-    count_sum = np.sum(counts).item()
-    p5 = np.percentile(counts, 0.05).item()
-    p95 = np.percentile(counts, 0.95).item()
-    hist, bin_edges = np.histogram(counts, bins=10)
-
-    return SupervisedTuningDatasetDistribution(
-        sum=count_sum,
-        billable_sum=count_sum,
-        min=min_count,
-        max=max_count,
-        mean=mean,
-        median=median,
-        p5=p5,
-        p95=p95,
-        buckets=[
-            SupervisedTuningDatasetBucket(
-                count=hist[i].item(),
-                left=bin_edges[i].item(),
-                right=bin_edges[i + 1].item(),
-            )
-            for i in range(len(hist))
-        ],
-    )
-
-  # Tokenize input and output messages separately to generate separate summary
-  # statistics about them.
-  user_input_token_distribution = None
-  if user_inputs:
-    user_input_dataset = dataset_validation_util.format_dataset(
-        datasets.Dataset.from_list(user_inputs), column, template, tokenizer
-    )
-    user_input_tokenized_dataset = user_input_dataset.map(
-        lambda x: tokenizer(x[column])
-    )
-    user_input_tokens = user_input_tokenized_dataset["input_ids"]
-    user_input_token_counts = np.array([len(ex) for ex in user_input_tokens])
-    user_input_token_distribution = calc_histogram(user_input_token_counts)
-
-  user_output_token_distribution = None
-  if user_outputs:
-    user_output_dataset = dataset_validation_util.format_dataset(
-        datasets.Dataset.from_list(user_outputs), column, template, tokenizer
-    )
-    user_output_tokenized_dataset = user_output_dataset.map(
-        lambda x: tokenizer(x[column])
-    )
-    user_output_tokens = user_output_tokenized_dataset["input_ids"]
-    user_output_token_counts = np.array([len(ex) for ex in user_output_tokens])
-    user_output_token_distribution = calc_histogram(user_output_token_counts)
-
-  user_messages_per_example_distribution = None
-  if user_input_messages_counts:
-    user_input_messages_counts = np.array(user_input_messages_counts)
-    user_messages_per_example_distribution = calc_histogram(
-        user_input_messages_counts
-    )
-
-  user_dataset_examples = [
-      json.dumps(ex)
-      for ex in raw.shuffle().select(
-          range(min(len(raw), _MAX_NUM_DATASET_SAMPLES))
-      )
-  ]
-
-  dataset_stats = SupervisedTuningDataStats(
-      tuning_dataset_example_count=tuning_dataset_example_count,
-      total_tuning_character_count=total_tuning_character_count,
-      total_billable_token_count=total_billable_token_count,
-      tuning_step_count=tuning_step_count,
-      user_input_token_distribution=user_input_token_distribution,
-      user_output_token_distribution=user_output_token_distribution,
-      user_message_per_example_distribution=user_messages_per_example_distribution,
-      user_dataset_examples=user_dataset_examples,
-  )
-  return dataclasses.asdict(dataset_stats)
@@ -1,140 +0,0 @@
-"""Util functions for reporting device (GPU, CPU) stats."""
-
-import dataclasses
-
-import psutil
-import pynvml
-import torch
-
-
-@dataclasses.dataclass
-class GpuStats:
-  """Holds information about GPU usage stats.
-
-  For memory related, see
-  https://pytorch.org/docs/stable/notes/cuda.html#cuda-memory-management
-  """
-
-  # device id
-  device_id: int
-  # memory reserved.
-  reserved: float
-  # memory occupied.
-  occupied: float
-  # memory reserved, but not used.
-  unused: float
-  # nvidia-smi usually reports more memory usages than pytorch (for driver,
-  # kernel and etc). `smi_diff` tracks this difference.
-  smi_diff: float
-  # Gpu utilization.
-  util: float
-
-  # Allows unpacking operation like
-  # device_id, reserved, occupied, unused, smi_diff, util = GpuStats(...)
-  # See https://stackoverflow.com/a/70753113
-  def __iter__(self):
-    return iter(dataclasses.astuple(self))
-
-
-def gpu_stats() -> GpuStats:
-  """Reports GPU memory usage and utilization."""
-  # See https://pytorch.org/docs/stable/notes/cuda.html#memory-management
-  bytes_per_gb = 1024.0**3
-  device = torch.cuda.current_device()
-  occupied = torch.cuda.memory_allocated(device) / bytes_per_gb
-  reserved = torch.cuda.memory_reserved(device) / bytes_per_gb
-  unused = reserved - occupied
-
-  def smi_mem(device):
-    try:
-      pynvml.nvmlInit()
-      handle = pynvml.nvmlDeviceGetHandleByIndex(device)
-      info = pynvml.nvmlDeviceGetMemoryInfo(handle)
-      return info.used / bytes_per_gb
-    except pynvml.NVMLError:
-      return 0.0
-
-  mem_used_smi = smi_mem(device)
-  smi_diff = mem_used_smi - reserved
-
-  util = torch.cuda.utilization(device)
-  return GpuStats(device, reserved, occupied, unused, smi_diff, util)
-
-
-def gpu_stats_str(stats: GpuStats | None = None) -> str:
-  if stats is None:
-    stats = gpu_stats()
-  device, reserved, occupied, unused, smi_diff, util = stats
-  return (
-      f"GPU ({device=}) memory: {reserved:.2f}({occupied=:.2f}, {unused=:.2f}),"
-      f" {smi_diff=:.2f} GB. Utilization: {util:.2f}%"
-  )
-
-
-@dataclasses.dataclass
-class CpuStats:
-  """Holds information about CPU usage stats."""
-
-  # Total CPU virtual memory i.e. virtual memory allocated + unallocated.
-  total_virtual_mem: float
-  # CPU virtual memory available for use.
-  unallocated_virtual_mem: float
-  # CPU virtual memory already used.
-  allocated_virtual_mem: float
-  # Total CPU swap memory i.e. swap memory allocated + unallocated.
-  total_swap_mem: float
-  # CPU swap memory available for use.
-  unallocated_swap_mem: float
-  # CPU swap memory already used.
-  allocated_swap_mem: float
-  # CPU utilization percentage.
-  utilization: float
-
-
-def cpu_stats() -> CpuStats:
-  """Reports CPU memory usage and utilization."""
-
-  # https://psutil.readthedocs.io/en/latest/#memory
-  gb = 1024.0**3
-  vmem = psutil.virtual_memory()
-  vmem_total = vmem.total / gb
-  vmem_available = vmem.available / gb
-  vmem_used = vmem_total - vmem_available
-  smem = psutil.swap_memory()
-  swap_total = smem.total / gb
-  swap_free = smem.free / gb
-  swap_used = smem.used / gb
-  # https://psutil.readthedocs.io/en/latest/#psutil.cpu_percent
-  cpu_util = psutil.cpu_percent(interval=1e-6)
-  return CpuStats(
-      total_virtual_mem=vmem_total,
-      unallocated_virtual_mem=vmem_available,
-      allocated_virtual_mem=vmem_used,
-      total_swap_mem=swap_total,
-      unallocated_swap_mem=swap_free,
-      allocated_swap_mem=swap_used,
-      utilization=cpu_util,
-  )
-
-
-def cpu_stats_str(stats: CpuStats | None = None) -> str:
-  """Returns a string representation of the CPU stats."""
-
-  if stats is None:
-    stats = cpu_stats()
-  total, occupied, unused = (
-      stats.total_virtual_mem,
-      stats.allocated_virtual_mem,
-      stats.unallocated_virtual_mem,
-  )
-  virtual_mem = (
-      f"CPU virtual memory: {total:.2f}({occupied=:.2f}, {unused=:.2f}) GB"
-  )
-  total, occupied, unused = (
-      stats.total_swap_mem,
-      stats.allocated_swap_mem,
-      stats.unallocated_swap_mem,
-  )
-  swap_mem = f"CPU swap memory: {total:.2f}({occupied=:.2f}, {unused=:.2f}) GB"
-  percent = stats.utilization
-  return f"{virtual_mem} {swap_mem} CPU Utilization: {percent:.2f}%"
@@ -11,7 +11,7 @@ from transformers.trainer_callback import TrainerCallback
 from transformers.trainer_callback import TrainerControl
 from transformers.trainer_callback import TrainerState

-from util import device_stats
+from vertex_vision_model_garden_peft.train.vmg import utils


 class TrainerStatsCallback(TrainerCallback):
@@ -75,15 +75,13 @@ class TrainerStatsCallback(TrainerCallback):
            state.global_step - 1
        )

-      gpu_stats = device_stats.gpu_stats()
-      self._peak_mem = max(
-          gpu_stats.reserved + gpu_stats.smi_diff, self._peak_mem
-      )
+      gpu_stats = utils.gpu_stats()
+      self._peak_mem = max(gpu_stats.total_mem, self._peak_mem)
      logging.info(
          'on_step_end: Throughput: %.2f token/s. %s, %s',
          throughput,
-          device_stats.gpu_stats_str(gpu_stats),
-          device_stats.cpu_stats_str(),
+          utils.gpu_stats_str(gpu_stats),
+          utils.cpu_stats_str(),
      )

  def on_train_begin(
@@ -97,8 +95,8 @@ class TrainerStatsCallback(TrainerCallback):
      self._start_time = time.time()
      logging.info(
          'on_train_begin: %s, %s',
-          device_stats.gpu_stats_str(),
-          device_stats.cpu_stats_str(),
+          utils.gpu_stats_str(),
+          utils.cpu_stats_str(),
      )

  def on_train_end(
@@ -1,28 +0,0 @@
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: FSDP
-downcast_bf16: 'no'
-enable_cpu_affinity: false
-fsdp_config:
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  fsdp_backward_prefetch: NO_PREFETCH
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_forward_prefetch: false
-  fsdp_offload_params: true
-  fsdp_sharding_strategy: FULL_SHARD
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sync_module_states: true
-  fsdp_use_orig_params: false
-  fsdp_activation_checkpointing: false
-main_training_function: main
-mixed_precision: bf16
-machine_rank: 0
-num_machines: 1
-num_processes: 8
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false
@@ -16,7 +16,6 @@ diffusers==0.25.1
 evaluate==0.4.3
 fsspec==2024.3.1
 gcsfs==2024.3.1
-immutabledict==4.2.1
 ninja==1.11.1 # Needed to avoid `ninja 1.11.1.1 is not supported on this platform` error
 nltk==3.9.1
 optimum==1.17.1
@@ -68,12 +68,10 @@ RUN mkdir -p ./vertex_vision_model_garden_peft/
 COPY model_oss/peft/train/vmg/configs/* ./vertex_vision_model_garden_peft/
 COPY model_oss/peft/train/vmg/*.py ./vertex_vision_model_garden_peft/train/vmg/
 COPY model_oss/peft/train/vmg/templates /diffusers/examples/util/templates
-COPY model_oss/peft/train/util/*.py /diffusers/examples/util/
-COPY model_oss/util/* /diffusers/examples/util/
+COPY model_oss/util /diffusers/examples/util
 COPY model_oss/notebook_util/dataset_validation_util.py /diffusers/examples/util
 COPY model_oss/peft/train/vmg/tests/*.py ./vertex_vision_model_garden_peft/tests/
 COPY model_oss/peft/train/test_utils/test_util.py ./vertex_vision_model_garden_peft/tests/
-COPY model_oss/peft/train/test_utils/command_builder.py ./vertex_vision_model_garden_peft/tests/

 RUN chmod a+rwX -R /diffusers/examples/
 ENV PYTHONPATH /diffusers/examples/
@@ -37,6 +37,7 @@ class EvalConfig:
    steps: The number of steps to run evaluation.
    tasks: The list of tasks to run evaluation on.
    per_device_batch_size: The per device batch size for evaluation.
+    num_fewshot: The number of few-shot examples to use for evaluation.
    limit: The maximum number of examples to evaluate.
    metric_name: The name of the metric to compute.
    tokenize_dataset: Whether to tokenize the dataset.
@@ -49,6 +50,7 @@ class EvalConfig:

  steps: int
  per_device_batch_size: int
+  num_fewshot: int | None
  limit: float | None
  metric_name: Sequence[str]
  tokenize_dataset: bool
@@ -97,7 +99,7 @@ def create_trainer(
  kwargs["tokenizer"] = tokenizer

  try:
-    _, eval_dataset = dataset_validation_util.load_dataset_with_template(
+    eval_dataset = dataset_validation_util.load_dataset_with_template(
        dataset_name=eval_config.dataset_path,
        split=eval_config.split,
        input_column=eval_config.column,
@@ -1,107 +1,17 @@
 """Sync local directory to GCS directory using rsync."""

+from collections.abc import Sequence
 import multiprocessing
 import os
 import subprocess
 import time
-from typing import Optional, Sequence, Tuple

 from absl import logging

-from util import constants
-from util import fileutils
-
 _GCS_COMMAND_RETRIES = 3
 _RSYNC_RETRY_INTERVAL_SECS = 30


-def is_gcs_or_gcsfuse_path(path: str) -> bool:
-  """Returns if the path is a GCS or gcsfuse path.
-
-  Args:
-    path: The path to check.
-
-  Returns:
-    True if the path is a GCS or gcsfuse path.
-  """
-  return path.startswith(
-      (constants.GCS_URI_PREFIX, constants.GCSFUSE_URI_PREFIX)
-  )
-
-
-def manage_sync_path(
-    path: str, node_rank: Optional[int] = None
-) -> Tuple[str, str]:
-  """Returns local dir and GCS location for the given path if the given path is a GCS or gcsfuse path.
-
-  It will also create a local directory if it does not exist. Otherwise, it
-  returns the same path.
-
-  Args:
-    path: The local or GCS path to manage.
-    node_rank: The node rank to be appended to the GCS path.
-
-  Returns:
-    The local and GCS paths.
-  """
-  local_dir = path
-  gcs_dir = path
-  if is_gcs_or_gcsfuse_path(path):
-    local_dir = os.path.join(
-        constants.LOCAL_OUTPUT_DIR,
-        fileutils.force_gcs_fuse_path(path)[1:],
-    )
-    gcs_dir = fileutils.force_gcs_path(path)
-  if not os.path.exists(local_dir):
-    os.makedirs(local_dir, exist_ok=True)
-
-  if node_rank is None:
-    return local_dir, gcs_dir
-  return local_dir, os.path.join(gcs_dir, f"node-{node_rank}")
-
-
-def setup_gcs_rsync(
-    dirs_to_sync: Sequence[Tuple[str, str]],
-    mp_queue: multiprocessing.Queue,
-    gcs_rsync_interval_secs: int,
-) -> multiprocessing.Process:
-  """Sets up the GCS rsync process.
-
-  Args:
-    dirs_to_sync: The absolute directory paths which will be synced to GCS.
-    mp_queue: The multiprocessing queue to check if the training is finished.
-    gcs_rsync_interval_secs: Integer, interval in seconds to run gcs rsync.
-
-  Returns:
-    The GCS rsync process.
-  """
-  rsync_process = multiprocessing.Process(
-      target=start_gcs_rsync,
-      args=(dirs_to_sync, mp_queue, gcs_rsync_interval_secs),
-  )
-  rsync_process.start()
-  return rsync_process
-
-
-def cleanup_gcs_rsync(
-    rsync_process: multiprocessing.Process, mp_queue: multiprocessing.Queue
-) -> None:
-  """Cleans up the GCS rsync process.
-
-  Args:
-    rsync_process: The GCS rsync process.
-    mp_queue: The multiprocessing queue.
-  """
-  mp_queue.put("finish rsync process")
-  rsync_process.join()
-  if rsync_process.exitcode == 0:
-    logging.info("Artifacts have been uploaded to GCS.")
-  else:
-    logging.error(
-        "GCS rsync process failed with exit code %d.", rsync_process.exitcode
-    )
-
-
 def _rsync_local_to_gcs(local_dir: str, gcs_dir: str) -> None:
  """Syncs the local directory to GCS.

@@ -147,7 +57,7 @@ def _rsync_local_to_gcs(local_dir: str, gcs_dir: str) -> None:


 def start_gcs_rsync(
-    dirs_to_sync: Sequence[Tuple[str, str]],
+    dirs_to_sync: Sequence[tuple[str, str]],
    mp_queue: multiprocessing.Queue,
    gcs_rsync_interval_secs: int,
 ) -> None:
@@ -1,6 +1,7 @@
 """Instruct/Chat with LoRA models."""

 from collections.abc import Callable, Mapping, Sequence
+import dataclasses
 import datetime
 import json
 import os
@@ -22,8 +23,6 @@ import trl
 import wandb

 from util import dataset_validation_util
-from util import dataset_stats
-from util import device_stats
 from vertex_vision_model_garden_peft.train.vmg import callbacks
 from vertex_vision_model_garden_peft.train.vmg import eval_lib
 from vertex_vision_model_garden_peft.train.vmg import utils
@@ -232,6 +231,11 @@ _PER_DEVICE_EVAL_BATCH_SIZE = flags.DEFINE_integer(
    'The per device batch size for model evaluation.',
 )

+_EVAL_NUM_FEWSHOT = flags.DEFINE_integer(
+    'eval_num_fewshot',
+    None,
+    'Run N-shot language model evaluation. Not implemented in `builtin_eval`.',
+)

 _EVAL_LIMIT = flags.DEFINE_float(
    'eval_limit',
@@ -251,7 +255,8 @@ _EVAL_METRIC_NAME = flags.DEFINE_list(
 _EVAL_DATASET = flags.DEFINE_string(
    'eval_dataset',
    None,
-    'The Hugging Face dataset name or path to use for evaluation.',
+    'Overrides the default evaluation dataset path. In `builtin_eval` mode,'
+    ' this can be any Hugging Face dataset name or path.',
 )

 # We set the default eval split as `test`, based on observation from
@@ -259,13 +264,13 @@ _EVAL_DATASET = flags.DEFINE_string(
 _EVAL_SPLIT = flags.DEFINE_string(
    'eval_split',
    'test',
-    'Eval split name in the eval dataset.',
+    'Eval split name in the eval dataset for `builtin_eval`.',
 )

 _EVAL_TEMPLATE = flags.DEFINE_string(
    'eval_template',
    None,
-    'Template for formatting language model evaluation data.'
+    'Template for formatting language model evaluation data for `builtin_eval`.'
    ' Must be a filename under `templates` folder, without `.json` extension,'
    ' e.g. `alpaca`, or a Cloud Storage URI to a JSON file.',
 )
@@ -273,7 +278,7 @@ _EVAL_TEMPLATE = flags.DEFINE_string(
 _EVAL_COLUMN = flags.DEFINE_string(
    'eval_column',
    None,
-    'Eval column name in the eval dataset.',
+    'Eval column name in the eval dataset for `builtin_eval`.',
 )

 _METRIC_FOR_BEST_MODEL = flags.DEFINE_string(
@@ -571,8 +576,8 @@ def finetune_instruct(
  """Finetunes instruct."""
  logging.info(
      'on entering instruct_lora, %s,\n%s',
-      device_stats.gpu_stats_str(),
-      device_stats.cpu_stats_str(),
+      utils.gpu_stats_str(),
+      utils.cpu_stats_str(),
  )
  gradient_checkpointing_kwargs = {}
  # DDP provides limited support with the reentrant variant of gradient
@@ -589,7 +594,7 @@ def finetune_instruct(
      access_token=access_token,
  )

-  train_dataset, train_dataset_with_template = (
+  train_dataset_with_template = (
      dataset_validation_util.load_dataset_with_template(
          train_dataset,
          split=train_split,
@@ -616,20 +621,18 @@ def finetune_instruct(
          'getting tuning data stats with effective batch size %s',
          effective_batch_size,
      )
-      train_dataset_stats = dataset_stats.get_dataset_stats(
-          raw=train_dataset,
-          templated=train_dataset_with_template,
-          template=train_template,
-          tokenizer=tokenizer,
-          column=train_column,
-          effective_batch_size=effective_batch_size,
+      train_dataset_stats = utils.get_dataset_stats(
+          train_dataset_with_template,
+          tokenizer,
+          train_column,
+          effective_batch_size,
      )
      logging.info('stats: %s', train_dataset_stats)
      tuning_data_stats_file = dataset_validation_util.force_gcs_fuse_path(
          tuning_data_stats_file
      )
      with open(tuning_data_stats_file, 'w') as out_f:
-        json.dump(train_dataset_stats, out_f)
+        json.dump(dataclasses.asdict(train_dataset_stats), out_f)

  model = utils.load_model(
      pretrained_model_name_or_path=pretrained_model_name_or_path,
@@ -660,9 +663,7 @@ def finetune_instruct(
    # `get_peft_model`, which may revert other changes we did before. That's why
    # we are calling `get_peft_model` explicitly here.
    model = get_peft_model(model, peft_config)
-    adapter_for_eval_dir = os.path.join(output_dir, 'adapter_for_eval')
-    logging.info('saving adapter for evaluation to %s...', adapter_for_eval_dir)
-    peft_config.save_pretrained(adapter_for_eval_dir)
+
    # This is to work-around mix-precision training. This issue is not fixed as
    # of transformers==4.41.2.
    # See b/332760883#comment30 for more details.
@@ -839,6 +840,7 @@ def main(unused_argv: Sequence[str]) -> None:
  if _EVAL_DATASET.value:
    eval_config = eval_lib.EvalConfig(
        per_device_batch_size=_PER_DEVICE_EVAL_BATCH_SIZE.value,
+        num_fewshot=_EVAL_NUM_FEWSHOT.value,
        limit=_EVAL_LIMIT.value,
        metric_name=_EVAL_METRIC_NAME.value,
        steps=_EVAL_STEPS.value,
@@ -31,7 +31,7 @@ _MERGE_BASE_AND_LORA_OUTPUT_DIR = flags.DEFINE_string(

 _MERGE_MODEL_PRECISION_MODE = flags.DEFINE_enum(
    'merge_model_precision_mode',
-    constants.PRECISION_MODE_16B,
+    constants.PRECISION_MODE_16,
    [
        constants.PRECISION_MODE_4,
        constants.PRECISION_MODE_8,
@@ -86,19 +86,10 @@ def main(unused_argv: Sequence[str]) -> None:
        )
    )

-  finetuned_lora_model_dir = fileutils.force_gcs_path(
-      _FINETUNED_LORA_MODEL_DIR.value
-  )
-  if dataset_validation_util.is_gcs_path(finetuned_lora_model_dir):
-    finetuned_lora_model_dir = (
-        dataset_validation_util.download_gcs_uri_to_local(
-            finetuned_lora_model_dir
-        )
-    )
  utils.merge_causal_language_model_with_lora(
      pretrained_model_name_or_path=pretrained_model_name_or_path,
      precision_mode=_MERGE_MODEL_PRECISION_MODE.value,
-      finetuned_lora_model_dir=finetuned_lora_model_dir,
+      finetuned_lora_model_dir=_FINETUNED_LORA_MODEL_DIR.value,
      merged_model_output_dir=_MERGE_BASE_AND_LORA_OUTPUT_DIR.value,
      access_token=_HUGGINGFACE_ACCESS_TOKEN.value,
  )
@@ -0,0 +1,232 @@
+"""Sequence classification with LoRA models."""
+
+from typing import Sequence
+
+from absl import app
+from absl import flags
+from datasets import load_dataset
+import evaluate
+from peft import get_peft_model
+from peft import LoraConfig
+import torch
+from torch.optim import AdamW
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+from transformers import get_linear_schedule_with_warmup
+
+from util import dataset_validation_util
+
+
+_PRETRAINED_MODEL_NAME_OR_PATH = flags.DEFINE_string(
+    "pretrained_model_name_or_path",
+    None,
+    "The pretrained model name or path. Supported models can be causal language"
+    " modeling models from https://github.com/huggingface/peft/tree/main. Note,"
+    " there might be different paddings for different models. This tool assumes"
+    " the pretrained_model_name_or_path contains model name, and then choose"
+    " proper padding methods. e.g. it must contain `llama` for `Llama2"
+    " models`.",
+)
+
+_OUTPUT_DIR = flags.DEFINE_string(
+    "output_dir",
+    None,
+    "The output directory.",
+)
+
+_DATASET_NAME = flags.DEFINE_string(
+    "dataset_name",
+    None,
+    "The dataset name in huggingface.",
+)
+
+_LORA_RANK = flags.DEFINE_integer(
+    "lora_rank",
+    16,
+    "The rank of the update matrices, expressed in int. Lower rank results in"
+    " smaller update matrices with fewer trainable parameters, referring to"
+    " https://huggingface.co/docs/peft/conceptual_guides/lora.",
+)
+
+_LORA_ALPHA = flags.DEFINE_integer(
+    "lora_alpha",
+    32,
+    "LoRA scaling factor, referring to"
+    " https://huggingface.co/docs/peft/conceptual_guides/lora.",
+)
+
+_LORA_DROPOUT = flags.DEFINE_float(
+    "lora_dropout",
+    0.05,
+    "dropout probability of the LoRA layers, referring to"
+    " https://huggingface.co/docs/peft/task_guides/token-classification-lora.",
+)
+
+_NUM_TRAIN_EPOCHS = flags.DEFINE_integer(
+    "num_train_epochs",
+    None,
+    "The number of training epochs.",
+)
+
+_BATCH_SIZE = flags.DEFINE_integer(
+    "batch_size",
+    32,
+    "The batch size.",
+)
+
+_LEARNING_RATE = flags.DEFINE_float(
+    "learning_rate",
+    2e-4,
+    "The learning rate after the potential warmup period.",
+)
+
+
+def finetune_sequence_classification(
+    pretrained_model_name_or_path: str,
+    dataset_name: str,
+    output_dir: str,
+    lora_rank: int = 8,
+    lora_alpha: int = 16,
+    lora_dropout: float = 0.1,
+    num_train_epochs: int = 20,
+    batch_size: int = 32,
+    learning_rate: float = 3e-4,
+) -> None:
+  """Finetunes sequence classification."""
+  task = "mrpc"
+  device = "cuda"
+
+  peft_config = LoraConfig(
+      task_type="SEQ_CLS",
+      inference_mode=False,
+      r=lora_rank,
+      lora_alpha=lora_alpha,
+      lora_dropout=lora_dropout,
+  )
+  if any(k in pretrained_model_name_or_path for k in ("gpt", "opt", "bloom")):
+    padding_side = "left"
+  else:
+    padding_side = "right"
+
+  tokenizer = AutoTokenizer.from_pretrained(
+      pretrained_model_name_or_path, padding_side=padding_side
+  )
+  if getattr(tokenizer, "pad_token_id") is None:
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+
+  datasets = load_dataset(dataset_name, task)
+  metric = evaluate.load(dataset_name, task)
+
+  def tokenize_function(examples):
+    # max_length=None => use the model max length (it's actually the default)
+    outputs = tokenizer(
+        examples["sentence1"],
+        examples["sentence2"],
+        truncation=True,
+        max_length=None,
+    )
+    return outputs
+
+  tokenized_datasets = datasets.map(
+      tokenize_function,
+      batched=True,
+      remove_columns=["idx", "sentence1", "sentence2"],
+  )
+
+  # We also rename the 'label' column to 'labels' which is the expected name for
+  # labels by the models of the transformers library.
+  tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
+
+  def collate_fn(examples):
+    return tokenizer.pad(examples, padding="longest", return_tensors="pt")
+
+  # Instantiate dataloaders.
+  train_dataloader = DataLoader(
+      tokenized_datasets["train"],
+      shuffle=True,
+      collate_fn=collate_fn,
+      batch_size=batch_size,
+  )
+  eval_dataloader = DataLoader(
+      tokenized_datasets["validation"],
+      shuffle=False,
+      collate_fn=collate_fn,
+      batch_size=batch_size,
+  )
+
+  model = AutoModelForSequenceClassification.from_pretrained(
+      pretrained_model_name_or_path, return_dict=True
+  )
+  model = get_peft_model(model, peft_config)
+  model.print_trainable_parameters()
+
+  optimizer = AdamW(params=model.parameters(), lr=learning_rate)
+
+  # Instantiate scheduler
+  lr_scheduler = get_linear_schedule_with_warmup(
+      optimizer=optimizer,
+      num_warmup_steps=0.06 * (len(train_dataloader) * num_train_epochs),
+      num_training_steps=(len(train_dataloader) * num_train_epochs),
+  )
+
+  model.to(device)
+  for epoch in range(num_train_epochs):
+    model.train()
+    for _, batch in enumerate(tqdm(train_dataloader)):
+      batch.to(device)
+      outputs = model(**batch)
+      loss = outputs.loss
+      loss.backward()
+      optimizer.step()
+      lr_scheduler.step()
+      optimizer.zero_grad()
+
+    model.eval()
+    for _, batch in enumerate(tqdm(eval_dataloader)):
+      batch.to(device)
+      with torch.no_grad():
+        outputs = model(**batch)
+      predictions = outputs.logits.argmax(dim=-1)
+      references = batch["labels"]
+      metric.add_batch(
+          predictions=predictions,
+          references=references,
+      )
+
+    eval_metric = metric.compute()
+    print(f"epoch {epoch}:", eval_metric)
+
+  model.save_pretrained(output_dir)
+
+
+def main(unused_argv: Sequence[str]) -> None:
+  if dataset_validation_util.is_gcs_path(_PRETRAINED_MODEL_NAME_OR_PATH.value):
+    pretrained_model_name_or_path = (
+        dataset_validation_util.download_gcs_uri_to_local(
+            _PRETRAINED_MODEL_NAME_OR_PATH.value
+        )
+    )
+  else:
+    pretrained_model_name_or_path = _PRETRAINED_MODEL_NAME_OR_PATH.value
+  pretrained_model_path = dataset_validation_util.force_gcs_fuse_path(
+      pretrained_model_name_or_path
+  )
+  output_dir = dataset_validation_util.force_gcs_fuse_path(_OUTPUT_DIR.value)
+
+  finetune_sequence_classification(
+      pretrained_model_name_or_path=pretrained_model_path,
+      dataset_name=_DATASET_NAME.value,
+      output_dir=output_dir,
+      lora_rank=_LORA_RANK.value,
+      lora_alpha=_LORA_ALPHA.value,
+      lora_dropout=_LORA_DROPOUT.value,
+      num_train_epochs=int(_NUM_TRAIN_EPOCHS.value),
+      batch_size=_BATCH_SIZE.value,
+      learning_rate=_LEARNING_RATE.value,
+  )
+
+
+if __name__ == "__main__":
+  app.run(main)
@@ -1,7 +0,0 @@
-{
-  "description": "Chat template used by Qwen 2.5.",
-  "source": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/tokenizer_config.json#L198",
-  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
-  "instruction_separator": "<|im_start|>user\n",
-  "response_separator": "<|im_start|>assistant\n"
-}
@@ -32,7 +32,6 @@ class DockerCommandBuilder(CommandBuilder):
    super().__init__()
    self._docker_uri = [docker_uri]
    self.privilege_mode = []
-    self.entrypoint = []

    self._defaults = [
        'docker',
@@ -63,9 +62,6 @@ class DockerCommandBuilder(CommandBuilder):
  def add_privilege_mode(self):
    self.privilege_mode = ['--privileged']

-  def add_entrypoint(self, entrypoint: list[str]):
-    self.entrypoint = entrypoint
-
  def build_cmd(self) -> str:
    return (
        self._defaults
@@ -73,7 +69,6 @@ class DockerCommandBuilder(CommandBuilder):
        + self._mount_maps
        + self.privilege_mode
        + self._docker_uri
-        + self.entrypoint
    )


@@ -90,6 +85,3 @@ class PythonCommandBuilder(CommandBuilder):
  def build_cmd(self) -> str:
    os.environ.update(self._env_vars)
    return self._defaults
-
-  def add_entrypoint(self, entrypoint: list[str]):
-    self._defaults = entrypoint
@@ -64,7 +64,6 @@ class TrainerThroughputTest(test_util.TestBase):
          'Mistral-7B-v0.1',
          'Mixtral-8x7B-v0.1',
          'gemma-2-9b-it',
-          'Qwen2.5-32B-Instruct',
      ],
      precision=['4bit', '8bit', 'bfloat16'],
      max_seq_length=list(range(4 * 1024, 24 * 1024 + 1, 4 * 1024)),
@@ -90,7 +89,6 @@ class TrainerThroughputTest(test_util.TestBase):
          'Mistral-7B-v0.1',
          'Mixtral-8x7B-v0.1',
          'gemma-2-9b-it',
-          'Qwen2.5-32B-Instruct',
      ],
      precision=['4bit', '8bit', 'bfloat16'],
      max_seq_length=list(range(4 * 1024, 24 * 1024 + 1, 4 * 1024)),
@@ -121,11 +119,7 @@ class TrainerThroughputTest(test_util.TestBase):
    self.assertEqual(self.run_cmd_and_handle_failure(), 0)

  @parameterized.product(
-      model_name=[
-          'llama3.1-8b-hf',
-          'llama3.1-70b-hf',
-          'Qwen2.5-32B-Instruct',
-      ],
+      model_name=['llama3.1-8b-hf', 'llama3.1-70b-hf'],
      precision=['4bit', '8bit', 'bfloat16'],
      max_seq_length=list(range(4 * 1024, 24 * 1024 + 1, 4 * 1024)),
      num_gpus=[8],
@@ -142,16 +136,9 @@ class TrainerThroughputTest(test_util.TestBase):
        self.test_suite_output_dir,
        f'bm_fsdp_{num_gpus}gpu_{model_name}_{precision}.txt',
    )
-    if 'llama' in model_name.lower():
-      self.task_cmd_builder.config_file = (
-          'vertex_vision_model_garden_peft/llama2_fsdp_8gpu.yaml'
-      )
-    elif 'qwen' in model_name.lower():
-      self.task_cmd_builder.config_file = (
-          'vertex_vision_model_garden_peft/qwen2_fsdp_8gpu.yaml'
-      )
-    else:
-      self.fail(f'Unsupported model: {model_name}')
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/llama_fsdp_8gpu.yaml'
+    )

    self.command_builder.add_env_var(
        'CUDA_VISIBLE_DEVICES', ','.join([str(x) for x in range(0, num_gpus)])
@@ -140,70 +140,6 @@ class TrainedModelQualityTest(test_util.TestBase):

    self.assertEqual(self.run_cmd(), 0)

-  @parameterized.named_parameters(
-      ('Qwen2.5-32B-Instruct', 'Qwen2.5-32B-Instruct'),
-  )
-  def test_qwen_model_deepspeed(self, model_name):
-    self.setup_output_dir(f'test_deepspeed_{model_name}')
-    self.task_cmd_builder.pretrained_model_name_or_path = model_name
-    self.task_cmd_builder.config_file = (
-        'vertex_vision_model_garden_peft/deepspeed_zero2_8gpu.yaml'
-    )
-    self.task_cmd_builder.train_dataset = test_util.get_test_data_path(
-        'llama-tuning-test/opposite-examples-train.jsonl'
-    )
-    self.task_cmd_builder.train_split = 'train'
-    self.task_cmd_builder.train_column = 'messages'
-    self.task_cmd_builder.train_template = 'qwen2_5'
-    self.task_cmd_builder.eval_dataset = test_util.get_test_data_path(
-        'llama-tuning-test/opposite-examples-eval.jsonl'
-    )
-    self.task_cmd_builder.eval_split = 'train'
-    self.task_cmd_builder.eval_column = self.task_cmd_builder.train_column
-    self.task_cmd_builder.eval_template = self.task_cmd_builder.train_template
-
-    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0,1,2,3,4,5,6,7')
-    # Note(lavrai): The following parameters are needed for the opposite-word
-    # dataset to converge properly.
-    self.task_cmd_builder.gradient_accumulation_steps = 1
-    self.task_cmd_builder.num_train_epochs = 10.0
-    self.task_cmd_builder.logging_steps = 1
-
-    self.assertEqual(self.run_cmd(), 0)
-
-  @parameterized.named_parameters(
-      ('Qwen2.5-32B-Instruct', 'Qwen2.5-32B-Instruct'),
-  )
-  def test_qwen_model_fsdp(self, model_name):
-    self.setup_output_dir(f'test_fsdp_{model_name}')
-    self.task_cmd_builder.pretrained_model_name_or_path = (
-        test_util.get_pretrained_model_name_or_path(model_name)
-    )
-    self.task_cmd_builder.config_file = (
-        'vertex_vision_model_garden_peft/qwen2_fsdp_8gpu.yaml'
-    )
-    self.task_cmd_builder.train_dataset = test_util.get_test_data_path(
-        'llama-tuning-test/opposite-examples-train.jsonl'
-    )
-    self.task_cmd_builder.train_split = 'train'
-    self.task_cmd_builder.train_column = 'messages'
-    self.task_cmd_builder.train_template = 'qwen2_5'
-    self.task_cmd_builder.eval_dataset = test_util.get_test_data_path(
-        'llama-tuning-test/opposite-examples-eval.jsonl'
-    )
-    self.task_cmd_builder.eval_split = 'train'
-    self.task_cmd_builder.eval_column = self.task_cmd_builder.train_column
-    self.task_cmd_builder.eval_template = self.task_cmd_builder.train_template
-
-    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0,1,2,3,4,5,6,7')
-    # Note(lavrai): The following parameters are needed for the opposite-word
-    # dataset to converge properly.
-    self.task_cmd_builder.gradient_accumulation_steps = 1
-    self.task_cmd_builder.num_train_epochs = 10.0
-    self.task_cmd_builder.logging_steps = 1
-
-    self.assertEqual(self.run_cmd(), 0)
-

 if __name__ == '__main__':
  absltest.main()
@@ -9,17 +9,19 @@ environment. Otherwise, `python3` is used.

 import argparse
 from collections.abc import MutableSequence, Sequence
+import json
 import multiprocessing
+import os
 import subprocess
 import sys
 from absl import app
 from absl import flags
 from absl import logging
 from util import dataset_validation_util
-from util import cluster_spec
+from vertex_vision_model_garden_peft.train.vmg import gcs_syncer
 from vertex_vision_model_garden_peft.train.vmg import utils
 from util import constants
-from util import gcs_syncer
+from util import fileutils
 from util import hypertune_utils


@@ -39,12 +41,9 @@ _TASK_TO_SCRIPT = {
    constants.INSTRUCT_LORA: (
        'vertex_vision_model_garden_peft/train/vmg/instruct_lora.py'
    ),
-    constants.MERGE_CAUSAL_LANGUAGE_MODEL_LORA: (
-        'vertex_vision_model_garden_peft/train/vmg/merge_causal_language_model_lora.py'
-    ),
-    constants.VALIDATE_DATASET_WITH_TEMPLATE: (
-        'vertex_vision_model_garden_peft/train/vmg/validate_dataset_with_template.py'
-    ),
+    constants.MERGE_CAUSAL_LANGUAGE_MODEL_LORA: 'vertex_vision_model_garden_peft/train/vmg/merge_causal_language_model_lora.py',
+    constants.SEQUENCE_CLASSIFICATION_LORA: 'vertex_vision_model_garden_peft/train/vmg/sequence_classification_lora.py',
+    constants.VALIDATE_DATASET_WITH_TEMPLATE: 'vertex_vision_model_garden_peft/train/vmg/validate_dataset_with_template.py',
    constants.RUN_TESTS: 'vertex_vision_model_garden_peft/tests/run_tests.py',
 }

@@ -72,17 +71,53 @@ def launch_script_cmd(

 def _get_accelerate_args() -> argparse.Namespace:
  """Returns the accelerate args."""
-  primary_node_addr, primary_node_port, node_rank, num_nodes = (
-      cluster_spec.get_cluster_spec()
-  )
+  # For the format of the cluster spec, see
+  # https://cloud.google.com/vertex-ai/docs/training/distributed-training#cluster-spec-format # pylint: disable=line-too-long
+  cluster_spec = os.getenv('CLUSTER_SPEC', default=None)
+  if not cluster_spec:
+    return argparse.Namespace()
+  logging.info('CLUSTER_SPEC: %s', cluster_spec)
+
+  cluster_data = json.loads(cluster_spec)
+  if (
+      'workerpool1' not in cluster_data['cluster']
+      or not cluster_data['cluster']['workerpool1']
+  ):
+    return argparse.Namespace()
+
+  # 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_worker_nodes = len(cluster_data['cluster']['workerpool1'])
+  num_nodes = num_worker_nodes + 1  # Add 1 for the primary node
+  logging.info('num nodes: %s', num_nodes)
+
  accelerate_args = argparse.Namespace()
-  if num_nodes > 1:
-    accelerate_args.machine_rank = node_rank
-    accelerate_args.num_machines = num_nodes
-    accelerate_args.main_process_ip = primary_node_addr
-    accelerate_args.main_process_port = primary_node_port
-    accelerate_args.max_restarts = 0
-    accelerate_args.monitor_interval = 120
+  accelerate_args.machine_rank = node_rank
+  accelerate_args.num_machines = num_nodes
+  accelerate_args.main_process_ip = primary_node_addr
+  accelerate_args.main_process_port = primary_node_port
+  accelerate_args.max_restarts = 0
+  accelerate_args.monitor_interval = 120

  return accelerate_args

@@ -96,6 +131,45 @@ def _append_args_to_command_in_place(
      command.append(f'--{key}={value}')


+def _is_gcs_or_gcsfuse_path(path: str) -> bool:
+  """Returns if the path is a GCS or gcsfuse path.
+
+  Args:
+    path: The path to check.
+
+  Returns:
+    True if the path is a GCS or gcsfuse path.
+  """
+  return path.startswith(
+      (constants.GCS_URI_PREFIX, constants.GCSFUSE_URI_PREFIX)
+  )
+
+
+def _manage_training_path(path: str, node_rank: int) -> tuple[str, str]:
+  """Returns local dir and GCS location for the given path if the given path is a GCS or gcsfuse path.
+
+  It will also create a local directory if it does not exist. Othereise, it
+  returns the same path.
+
+  Args:
+    path: The local or GCS path to manage.
+    node_rank: The node rank to be appended to the GCS path.
+
+  Returns:
+    The local and GCS paths.
+  """
+  local_dir = path
+  gcs_dir = path
+  if _is_gcs_or_gcsfuse_path(path):
+    local_dir = os.path.join(
+        constants.LOCAL_OUTPUT_DIR,
+        dataset_validation_util.force_gcs_fuse_path(path)[1:],
+    )
+    gcs_dir = fileutils.force_gcs_path(path)
+    os.makedirs(local_dir, exist_ok=True)
+  return local_dir, os.path.join(gcs_dir, f'node-{node_rank}')
+
+
 def _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
    task_type: str, config_file: str, unknown: Sequence[str]
 ) -> Sequence[Sequence[str]]:
@@ -129,11 +203,11 @@ def _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
      dataset_validation_util.force_gcs_fuse_path(training_args.output_dir)
  )

-  local_output_dir, gcs_output_dir = gcs_syncer.manage_sync_path(
+  local_output_dir, gcs_output_dir = _manage_training_path(
      training_args.output_dir, node_rank
  )
  training_args.output_dir = local_output_dir
-  if gcs_syncer.is_gcs_or_gcsfuse_path(gcs_output_dir):
+  if _is_gcs_or_gcsfuse_path(gcs_output_dir):
    dirs_to_sync.append((local_output_dir, gcs_output_dir))

  # Merge only flags.
@@ -143,11 +217,11 @@ def _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
  merge_args, unknown = merge_parser.parse_known_args(unknown)

  if merge_args.merge_base_and_lora_output_dir:
-    merge_local_dir, merge_gcs_dir = gcs_syncer.manage_sync_path(
-        merge_args.merge_base_and_lora_output_dir, None
+    merge_local_dir, merge_gcs_dir = _manage_training_path(
+        merge_args.merge_base_and_lora_output_dir, node_rank
    )
    merge_args.merge_base_and_lora_output_dir = merge_local_dir
-    if gcs_syncer.is_gcs_or_gcsfuse_path(merge_gcs_dir):
+    if _is_gcs_or_gcsfuse_path(merge_gcs_dir):
      dirs_to_sync.append((merge_local_dir, merge_gcs_dir))

  # Common flags shared by merging and training.
@@ -165,10 +239,8 @@ def _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
  # Only the main node runs merging.
  if merge_args.merge_base_and_lora_output_dir and node_rank == 0:
    lora_dir = utils.get_final_checkpoint_path(training_args.output_dir)
-    lora_local_dir, lora_gcs_dir = gcs_syncer.manage_sync_path(
-        lora_dir, node_rank
-    )
-    if gcs_syncer.is_gcs_or_gcsfuse_path(lora_gcs_dir):
+    lora_local_dir, lora_gcs_dir = _manage_training_path(lora_dir, node_rank)
+    if _is_gcs_or_gcsfuse_path(lora_gcs_dir):
      dirs_to_sync.append((lora_local_dir, lora_gcs_dir))

    merge_cmd = [
@@ -191,37 +263,46 @@ def _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
  return commands, dirs_to_sync


-def _get_merge_cmd_and_dirs_to_sync(
-    task_type: str, config_file: str, unknown: Sequence[str]
-) -> Sequence[Sequence[str]]:
-  """Returns the merge command and dirs to sync.
+def _setup_gcs_rsync(
+    dirs_to_sync: Sequence[tuple[str, str]],
+    mp_queue: multiprocessing.Queue,
+    gcs_rsync_interval_secs: int,
+) -> multiprocessing.Process:
+  """Sets up the GCS rsync process.

  Args:
-    task_type: The task type.
-    config_file: The accelerate config file path.
-    unknown: The unknown args which are not recognised by the parser.
+    dirs_to_sync: The absolute directory paths which will be synced to GCS.
+    mp_queue: The multiprocessing queue to check if the training is finished.
+    gcs_rsync_interval_secs: Integer, interval in seconds to run gcs rsync.

  Returns:
-    The bash commands to execute and the directories to sync.
+    The GCS rsync process.
  """
-  # Merge only flags.
-  merge_parser = argparse.ArgumentParser()
-  merge_parser.add_argument('--merge_base_and_lora_output_dir')
-  merge_args, unknown = merge_parser.parse_known_args(unknown)
+  rsync_process = multiprocessing.Process(
+      target=gcs_syncer.start_gcs_rsync,
+      args=(dirs_to_sync, mp_queue, gcs_rsync_interval_secs),
+  )
+  rsync_process.start()
+  return rsync_process

-  dirs_to_sync = []
-  if merge_args.merge_base_and_lora_output_dir:
-    merge_local_dir, merge_gcs_dir = gcs_syncer.manage_sync_path(
-        merge_args.merge_base_and_lora_output_dir, None
+
+def _cleanup_gcs_rsync(
+    rsync_process: multiprocessing.Process, mp_queue: multiprocessing.Queue
+) -> None:
+  """Cleans up the GCS rsync process.
+
+  Args:
+    rsync_process: The GCS rsync process.
+    mp_queue: The multiprocessing queue.
+  """
+  mp_queue.put('training finished')
+  rsync_process.join()
+  if rsync_process.exitcode == 0:
+    logging.info('Artifacts have been uploaded to GCS.')
+  else:
+    logging.error(
+        'GCS rsync process failed with exit code %d.', rsync_process.exitcode
    )
-    merge_args.merge_base_and_lora_output_dir = merge_local_dir
-    if gcs_syncer.is_gcs_or_gcsfuse_path(merge_gcs_dir):
-      dirs_to_sync.append((merge_local_dir, merge_gcs_dir))
-
-  cmd = launch_script_cmd(_TASK_TO_SCRIPT[task_type], config_file)
-  _append_args_to_command_in_place(merge_args, cmd)
-  cmd.extend(unknown)
-  return [cmd], dirs_to_sync


 def main(unused_argv: Sequence[str]) -> None:
@@ -254,10 +335,6 @@ def main(unused_argv: Sequence[str]) -> None:
    commands, dirs_to_sync = _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
        task_type=task, config_file=args.config_file, unknown=unknown
    )
-  elif task in [constants.MERGE_CAUSAL_LANGUAGE_MODEL_LORA]:
-    commands, dirs_to_sync = _get_merge_cmd_and_dirs_to_sync(
-        task_type=task, config_file=args.config_file, unknown=unknown
-    )
  else:
    assert task in _TASK_TO_SCRIPT
    cmd = launch_script_cmd(_TASK_TO_SCRIPT[task], args.config_file)
@@ -267,7 +344,7 @@ def main(unused_argv: Sequence[str]) -> None:
  rsync_process = None
  mp_queue = multiprocessing.Queue(maxsize=1)
  if dirs_to_sync:
-    rsync_process = gcs_syncer.setup_gcs_rsync(
+    rsync_process = _setup_gcs_rsync(
        dirs_to_sync, mp_queue, args.gcs_rsync_interval_secs
    )

@@ -284,7 +361,7 @@ def main(unused_argv: Sequence[str]) -> None:
        rsync_process.terminate()
      raise e
  if rsync_process is not None:
-    gcs_syncer.cleanup_gcs_rsync(rsync_process, mp_queue)
+    _cleanup_gcs_rsync(rsync_process, mp_queue)


 if __name__ == '__main__':
@@ -1,6 +1,7 @@
 """Common libraries for PEFT."""

 from collections.abc import Mapping, Sequence
+import dataclasses
 import datetime
 import gc
 import os
@@ -10,9 +11,12 @@ from absl import logging
 import accelerate
 from accelerate import DistributedType
 from accelerate import PartialState
+import numpy as np
 import peft
 from peft import PeftModel
 from peft import prepare_model_for_kbit_training
+import psutil
+import pynvml
 import torch
 import transformers
 from transformers import AutoModelForCausalLM
@@ -24,6 +28,7 @@ import trl
 from util import dataset_validation_util
 from util import constants

+
 _LLAMA_3_1_405B_MODEL_ID = "Meta-Llama-3.1-405B"
 _LOCAL_MERGED_MODEL_DIR = "/tmp/merged_model"
 _GEMMA2_MODEL = "gemma-2"
@@ -121,7 +126,7 @@ def load_model(
      "device_map": device_map,
      "torch_dtype": torch_dtype,
      "quantization_config": quantization_config,
-      "trust_remote_code": False,
+      "trust_remote_code": True,
      "token": access_token,
      "attn_implementation": attn_implementation,
  }
@@ -305,12 +310,171 @@ def convert_model_to_fp8(
  PartialState().wait_for_everyone()


+@dataclasses.dataclass
+class TuningDataStats:
+  tuning_dataset_example_count: int
+  total_billable_token_count: int
+  tuning_step_count: int
+
+
+def get_dataset_stats(
+    dataset: Any,
+    tokenizer: transformers.PreTrainedTokenizer,
+    column: str,
+    effective_batch_size: int,
+) -> TuningDataStats:
+  """Calculates dataset statistics, e.g., total number of tokens."""
+  tokenized_dataset = dataset.map(lambda x: tokenizer(x[column]))
+  inputs = tokenized_dataset["input_ids"]
+  tuning_dataset_example_count = int(len(inputs))
+  total_billable_token_count = int(np.sum([len(ex) for ex in inputs]))
+  tuning_step_count = (
+      tuning_dataset_example_count + effective_batch_size - 1
+  ) // effective_batch_size
+  return TuningDataStats(
+      tuning_dataset_example_count,
+      total_billable_token_count,
+      tuning_step_count,
+  )
+
+
 def force_gc():
  """Collects garbage immediately to release unused CPU/GPU resources."""
  gc.collect()
  torch.cuda.empty_cache()


+@dataclasses.dataclass
+class GpuStats:
+  """Holds information about GPU usage stats.
+
+  For memory related, see
+  https://pytorch.org/docs/stable/notes/cuda.html#cuda-memory-management
+  """
+
+  # total memory
+  total_mem: float
+  # memory occupied.
+  occupied: float
+  # memory reserved, but not used.
+  unused: float
+  # nvidia-smi usually reports more memory usages than pytorch (for driver,
+  # kernel and etc). `smi_diff` tracks this difference.
+  smi_diff: float
+  # Gpu utilization.
+  util: float
+
+  # Allows unpacking operation like
+  # total_mem, occupied, unused, smi_diff, util = GpuStats(...)
+  # See https://stackoverflow.com/a/70753113
+  def __iter__(self):
+    return iter(dataclasses.astuple(self))
+
+
+def gpu_stats() -> GpuStats:
+  """Reports GPU memory usage and utilization."""
+  # See https://pytorch.org/docs/stable/notes/cuda.html#memory-management
+  bytes_per_gb = 1024.0**3
+  device = torch.cuda.current_device()
+  occupied = torch.cuda.memory_allocated(device) / bytes_per_gb
+  reserved = torch.cuda.memory_reserved(device) / bytes_per_gb
+  unused = reserved - occupied
+
+  def smi_mem(device):
+    try:
+      pynvml.nvmlInit()
+      handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+      info = pynvml.nvmlDeviceGetMemoryInfo(handle)
+      return info.used / bytes_per_gb
+    except pynvml.NVMLError:
+      return 0.0
+
+  mem_used_smi = smi_mem(device)
+  smi_diff = mem_used_smi - reserved
+
+  util = torch.cuda.utilization(device)
+  return GpuStats(mem_used_smi, occupied, unused, smi_diff, util)
+
+
+def gpu_stats_str(stats: GpuStats | None = None) -> str:
+  if stats is None:
+    stats = gpu_stats()
+  total, occupied, unused, smi_diff, util = stats
+  return (
+      f"GPU memory: {total:.2f}({occupied=:.2f}, {unused=:.2f},"
+      f" {smi_diff=:.2f}) GB. Utilization: {util:.2f}%"
+  )
+
+
+@dataclasses.dataclass
+class CpuStats:
+  """Holds information about CPU usage stats."""
+
+  # Total CPU virtual memory i.e. virtual memory allocated + unallocated.
+  total_virtual_mem: float
+  # CPU virtual memory available for use.
+  unallocated_virtual_mem: float
+  # CPU virtual memory already used.
+  allocated_virtual_mem: float
+  # Total CPU swap memory i.e. swap memory allocated + unallocated.
+  total_swap_mem: float
+  # CPU swap memory available for use.
+  unallocated_swap_mem: float
+  # CPU swap memory already used.
+  allocated_swap_mem: float
+  # CPU utilization percentage.
+  utilization: float
+
+
+def cpu_stats() -> CpuStats:
+  """Reports CPU memory usage and utilization."""
+
+  # https://psutil.readthedocs.io/en/latest/#memory
+  gb = 1024.0**3
+  vmem = psutil.virtual_memory()
+  vmem_total = vmem.total / gb
+  vmem_available = vmem.available / gb
+  vmem_used = vmem_total - vmem_available
+  smem = psutil.swap_memory()
+  swap_total = smem.total / gb
+  swap_free = smem.free / gb
+  swap_used = smem.used / gb
+  # https://psutil.readthedocs.io/en/latest/#psutil.cpu_percent
+  cpu_util = psutil.cpu_percent(interval=1e-6)
+  return CpuStats(
+      total_virtual_mem=vmem_total,
+      unallocated_virtual_mem=vmem_available,
+      allocated_virtual_mem=vmem_used,
+      total_swap_mem=swap_total,
+      unallocated_swap_mem=swap_free,
+      allocated_swap_mem=swap_used,
+      utilization=cpu_util,
+  )
+
+
+def cpu_stats_str(stats: CpuStats | None = None) -> str:
+  """Returns a string representation of the CPU stats."""
+
+  if stats is None:
+    stats = cpu_stats()
+  total, occupied, unused = (
+      stats.total_virtual_mem,
+      stats.allocated_virtual_mem,
+      stats.unallocated_virtual_mem,
+  )
+  virtual_mem = (
+      f"CPU virtual memory: {total:.2f}({occupied=:.2f}, {unused=:.2f}) GB"
+  )
+  total, occupied, unused = (
+      stats.total_swap_mem,
+      stats.allocated_swap_mem,
+      stats.unallocated_swap_mem,
+  )
+  swap_mem = f"CPU swap memory: {total:.2f}({occupied=:.2f}, {unused=:.2f}) GB"
+  percent = stats.utilization
+  return f"{virtual_mem} {swap_mem} CPU Utilization: {percent:.2f}%"
+
+
 def init_partial_state(
    timeout: datetime.timedelta = datetime.timedelta(seconds=600),
 ) -> None:
@@ -1,12 +1,9 @@
 """Fileutil lib to copy files between gcs and local."""

-import filecmp
 import fnmatch
 import os
 import pathlib
 import shutil
-import subprocess
-import time
 from typing import List, Optional, Tuple
 import uuid

@@ -60,96 +57,6 @@ def force_gcs_path(uri: str) -> str:
    return uri


-def is_file_available(
-    file_path: str, retry_interval_secs: int = 60, timeout_secs: int = 3600
-) -> bool:
-  """Checks and waits for a file to be available in GCS.
-
-  Args:
-    file_path: The file path to check.
-    retry_interval_secs: The interval in seconds to check the file.
-    timeout_secs: The timeout in seconds to wait for the file.
-
-  Returns:
-    True if the file is available, False otherwise.
-  """
-  start_time = time.time()
-  while True:
-    try:
-      file_check_cmd = ['gcloud', 'storage', 'ls', file_path]
-      result = subprocess.run(
-          file_check_cmd, capture_output=True, text=True, check=True
-      )
-      if file_path in result.stdout:
-        logging.info('File %s exists.', file_path)
-        return True
-    except subprocess.CalledProcessError as e:
-      elapsed_time = time.time() - start_time
-      if elapsed_time > timeout_secs:
-        logging.info(
-            "Timeout: File '%s' not found after %d seconds. Error: %s",
-            file_path,
-            elapsed_time,
-            e,
-        )
-        return False
-
-      logging.info(
-          "File '%s' not found yet. Checking again in %d seconds. Error: %s",
-          file_path,
-          retry_interval_secs,
-          e,
-      )
-      time.sleep(retry_interval_secs)
-
-
-def compare_dirs(
-    local_dir: str,
-    gcsfuse_dir: str,
-    retry_interval_secs: int = 30,
-    timeout_secs: int = 3600,
-) -> bool:
-  """Compares two directories and returns True if they are the same.
-
-  Args:
-    local_dir: The local directory.
-    gcsfuse_dir: The gcsfuse directory.
-    retry_interval_secs: The interval in seconds to check the directories.
-    timeout_secs: The timeout in seconds to wait for the directories.
-
-  Returns:
-    True if the directories are the same, False otherwise.
-  """
-  start_time = time.time()
-  while True:
-    if os.path.exists(local_dir) and os.path.exists(gcsfuse_dir):
-      comparison = filecmp.dircmp(local_dir, gcsfuse_dir)
-      if (
-          not comparison.left_only
-          and not comparison.right_only
-          and not comparison.diff_files
-      ):
-        return True
-    elapsed_time = time.time() - start_time
-    if elapsed_time > timeout_secs:
-      logging.info(
-          "Timeout: Directories '%s' and '%s' do not match after %d seconds.",
-          local_dir,
-          gcsfuse_dir,
-          elapsed_time,
-      )
-      return False
-
-    logging.info(
-        "Directories '%s' and '%s' do not match yet. Checking again in %d"
-        ' seconds.',
-        local_dir,
-        gcsfuse_dir,
-        retry_interval_secs,
-    )
-    time.sleep(retry_interval_secs)
-
-
 def download_gcs_file_to_memory(gcs_uri: str) -> bytes:
  """Downloads a gcs file to in memory.

@@ -445,15 +352,3 @@ def get_output_video_file(video_output_file_path: str) -> str:
      file_extension, '_overlay' + file_extension
  )
  return out_local_video_file_name
-
-
-def delete_local_file(local_file_path: str) -> None:
-  """Deletes a local file."""
-  if os.path.exists(local_file_path):
-    os.remove(local_file_path)
-
-
-def delete_local_dir(local_dir: str) -> None:
-  """Deletes a local directory recursively."""
-  if os.path.exists(local_dir):
-    shutil.rmtree(local_dir)
@@ -1,119 +0,0 @@
-#!/bin/bash
-#
-# This launcher downloads model files from GCS to local model directory before
-# launching the actual command.
-#
-# If GCS URI is passed as an environment variable, set GCS_URI_ENV_KEY to the
-# environment variable name.
-# If GCS URI is passed as an argument, set GCS_URI_ARG_KEY to the argument name.
-# The argument must be in the format of '--$GCS_URI_ARG_KEY=gs://*'. Do not
-# separate argument name and value with spaces.
-# This script will also try reading from AIP_STORAGE_URI or AIP_STORAGE_DIR.
-# Note that AIP_STORAGE_DIR is expected to be a local path, so it bypasses the
-# download process.
-#
-# Input priority: AIP_STORAGE_DIR > AIP_STORAGE_URI > GCS_URI_ENV_KEY > GCS_URI_ARG_KEY.
-# Will output the local model directory to GCS_URI_ENV_KEY and GCS_URI_ARG_KEY
-# if they are set. Both will be updated if both set.
-#
-# Requires google-cloud-sdk as a dependency (for gcloud storage CLI).
-
-set -e
-
-readonly LOCAL_MODEL_DIR=${LOCAL_MODEL_DIR:-"/tmp/model_dir"}
-readonly LOCAL_ARGS_FILE=${LOCAL_ARGS_FILE:-"/tmp/args.txt"}
-
-update_model_id() {
-  if [[ ! -z "$GCS_URI_ENV_KEY" ]]; then
-    echo "Updating env var $GCS_URI_ENV_KEY to $AIP_STORAGE_DIR."
-    export "$GCS_URI_ENV_KEY"="$AIP_STORAGE_DIR"
-  fi
-
-  if [[ ! -z "$GCS_URI_ARG_KEY" ]]; then
-    echo "Updating args $GCS_URI_ARG_KEY to $AIP_STORAGE_DIR."
-    updated=0
-    for (( i=1; i <= $#; i++)); do
-      arg="${!i}"
-      if [[ "$arg" == "--$GCS_URI_ARG_KEY="* ]]; then
-        echo "Found $arg, updating to $AIP_STORAGE_DIR."
-        set -- "${@:1:(($i-1))}" "--$GCS_URI_ARG_KEY=$AIP_STORAGE_DIR" "${@:$(($i+1))}";
-        updated=1
-        break
-      fi
-    done
-    if [[ $updated -eq 0 ]]; then
-      echo "Appending args $GCS_URI_ARG_KEY to $AIP_STORAGE_DIR."
-      set -- "$@" "--$GCS_URI_ARG_KEY=$AIP_STORAGE_DIR";
-    fi
-  fi
-  echo "$*" > "$LOCAL_ARGS_FILE"
-}
-
-maybe_download_model() {
-  if [[ -z "$GCS_URI_ENV_KEY" ]] && [[ -z "$GCS_URI_ARG_KEY" ]]; then
-    echo "Internal error: Required GCS_URI_ENV_KEY or GCS_URI_ARG_KEY."
-    exit 1
-  fi
-
-  echo "$*" > "$LOCAL_ARGS_FILE"
-  gcs_uri=""
-  if [[ ! -z "$AIP_STORAGE_DIR" ]]; then
-    # AIP_STORAGE_DIR is expected to be a local path.
-    echo "AIP_STORAGE_DIR set, proceeding to run the launcher."
-    update_model_id "$@"
-    return
-  elif [[ $AIP_STORAGE_URI == gs://* ]]; then
-    # Check AIP_STORAGE_URI environment variable.
-    echo "AIP_STORAGE_URI set and starts with 'gs://', proceeding to download from GCS."
-    gcs_uri="$AIP_STORAGE_URI"
-  elif [[ ! -z "$GCS_URI_ENV_KEY" ]] && [[ ${!GCS_URI_ENV_KEY} == gs://* ]]; then
-    # Check custom environment variable.
-    echo "Custom environment variable ${GCS_URI_ENV_KEY} set and starts with 'gs://', proceeding to download from GCS."
-    gcs_uri="${!GCS_URI_ENV_KEY}"
-  elif [[ ! -z "$GCS_URI_ARG_KEY" ]]; then
-    # Check custom args.
-    for arg in "$@"; do
-      if [[ "$arg" == "--$GCS_URI_ARG_KEY=gs://"* ]]; then
-        gcs_uri="${arg#*=}"
-        echo "Custom args ${GCS_URI_ARG_KEY} set and starts with 'gs://', proceeding to download from GCS."
-        break
-      elif [[ "$arg" == "--$GCS_URI_ARG_KEY" ]]; then
-        echo "Found $GCS_URI_ARG_KEY, but it's not in the format of '--$GCS_URI_ARG_KEY=gs://*'."
-        echo "Ensure the value of $GCS_URI_ARG_KEY is within the same arg, separated by '='."
-        exit 1
-      fi
-    done
-  fi
-
-  if [[ -z "$gcs_uri" ]]; then
-    echo "No GCS URI found, proceeding to run the launcher."
-    return
-  fi
-
-  # Remove trailing '/' if any.
-  gcs_uri="${gcs_uri%%/}"
-  export AIP_STORAGE_DIR="$LOCAL_MODEL_DIR/${gcs_uri##gs://}"
-
-  # Create the target directory.
-  mkdir -p "$AIP_STORAGE_DIR"
-  echo "Downloading model from ${gcs_uri} to ${AIP_STORAGE_DIR}."
-
-  # Use gcloud storage CLI to copy the content from GCS to the target directory.
-  if gcloud storage cp -r "$gcs_uri/*" "$AIP_STORAGE_DIR"; then
-    echo "Model downloaded successfully to ${AIP_STORAGE_DIR}."
-    update_model_id "$@"
-  else
-    echo "Failed to download model from GCS."
-    exit 1
-  fi
-}
-
-run_local_command() {
-  command=$(cat "$LOCAL_ARGS_FILE")
-  rm -f "$LOCAL_ARGS_FILE"
-  echo "Launch command: $command"
-  eval "$command"
-}
-
-maybe_download_model "$@"
-run_local_command
@@ -1,35 +0,0 @@
-#!/bin/bash
-
-# !/bin/bash
-# The Startup prober built to check whether models listed in local disk are
-# loaded in memory and are ready to serve traffic. The script returns 0 if
-# succeed. Any other returned value are consider as an error. More detail could be
-# found from [shell script Exit codes](http://shellscript.sh/exitcodes.html).
-#
-# TorchServe: The Management API listens on port 8081 and is only accessible
-# from localhost by default.
-
-if [[ -z "${MNG_PORT}" ]]; then
-  MNG_PORT=7081  # We default the management_port to 7081.
-else
-  MNG_PORT="${MNG_PORT}"
-fi
-
-check_model_availability(){
-  local MODEL_NAME=$1
-  # Returns whether "READY" is found in the model status.
-  # Reference: https://pytorch.org/serve/management_api.html#describe-model.
-  curl -s "http://localhost:${MNG_PORT}/models/${MODEL_NAME}" | grep "READY" -q
-}
-
-main(){
-  check_model_availability "$MODEL"  # Assume Dockerfile sets MODEL environment parameter.
-  local available=$?
-  if [[ $available -gt 0 ]]
-  then
-    echo "Warning: Model(${MODEL}) is not yet available."
-    return 1
-  fi
-  return 0
-}
-main
@@ -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",
--- a/Show More
+++ b/Show More