Davidnet

#!/bin/bash
uv run --env-file .env main.py download-files

Davidnet

set -euxf -o pipefail

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
    
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker

Davidnet

# Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

# Add the repository to Apt sources:
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \

Davidnet

# Download Python x source
RUN cd /tmp && \
    wget https://www.python.org/ftp/python/$PYTHON_VERSION/Python-$PYTHON_VERSION.tar.xz && \
    tar -xf Python-$PYTHON_VERSION.tar.xz

# Compile Python 
RUN cd /tmp/Python-$PYTHON_VERSION && \
    ./configure --enable-optimizations --enable-shared && \
    make -j 8 && \

Davidnet

distribution=$(. /etc/os-release;echo "$ID""$VERSION_ID") \
      && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
      && curl -s -L https://nvidia.github.io/libnvidia-container/"$distribution"/libnvidia-container.list | \
            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker

Davidnet

cmake_minimum_required(VERSION 3.17)

project(tritonclient LANGUAGES C CXX)

# Use C++17 standard as Triton's minimum required.
set(TRITON_MIN_CXX_STANDARD 17 CACHE STRING "The minimum C++ standard which features are requested to build this target.")

#
# Options
#

Davidnet

[tool.poetry]
name = "aiplatform-issue"
version = "0.1.0"
description = ""
authors = ["Your Name <[email protected]>"]
readme = "README.md"

[tool.poetry.dependencies]
python = ">=3.11,<3.12.0"
kfp = "2.4.0"

Davidnet

Hello everyone I am trying to run the following pipeline with TPUs,

In GCP I have quotas, and credits, yet in vertex it fails with the same message: "Internal Error" please contact Vertex AI, but to contact them, yet does anyone see anything wrong with the following definition?


 
 from kfp import dsl
 import google_cloud_pipeline_components.v1.custom_job as gcp_components_custom_job
 
 

Davidnet

https://www.youtube.com/watch?v=CUQT-YccpR8

Davidnet

https://www.youtube.com/watch?v=MdwANJnUFng