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NVIDIA NIM Now Available on Hugging Face with Inference-as-a-Service
Hugging Face has announced the launch of an inference-as-a-service capability powered by NVIDIA NIM. This new service will provide developers easy access to NVIDIA-accelerated inference for popular AI models.
The new service allows developers to rapidly deploy leading large language models such as the Llama 3 family and Mistral AI models with optimization from NVIDIA NIM microservices running on NVIDIA DGX Cloud. This will help developers quickly prototype with open-source AI models hosted on the Hugging Face Hub and deploy them in production.
The Hugging Face inference-as-a-service on NVIDIA DGX Cloud powered by NIM microservices offers easy access to compute resources that are optimized for AI deployment. The NVIDIA DGX Cloud platform is purpose-built for generative AI and provides scalable GPU resources that support every step of AI development, from prototype to production.
To use the service, users must have access to an Enterprise Hub organization and a fine-grained token for authentication. The NVIDIA NIM Endpoints for supported Generative AI models can be found on the model page of the Hugging Face Hub.https://www.infoq.com/news/2024/08/nvidia-nim-huggingface/
Hugging Face has announced the launch of an inference-as-a-service capability powered by NVIDIA NIM. This new service will provide developers easy access to NVIDIA-accelerated inference for popular AI models.
The new service allows developers to rapidly deploy leading large language models such as the Llama 3 family and Mistral AI models with optimization from NVIDIA NIM microservices running on NVIDIA DGX Cloud. This will help developers quickly prototype with open-source AI models hosted on the Hugging Face Hub and deploy them in production.
The Hugging Face inference-as-a-service on NVIDIA DGX Cloud powered by NIM microservices offers easy access to compute resources that are optimized for AI deployment. The NVIDIA DGX Cloud platform is purpose-built for generative AI and provides scalable GPU resources that support every step of AI development, from prototype to production.
To use the service, users must have access to an Enterprise Hub organization and a fine-grained token for authentication. The NVIDIA NIM Endpoints for supported Generative AI models can be found on the model page of the Hugging Face Hub.https://www.infoq.com/news/2024/08/nvidia-nim-huggingface/
InfoQ
NVIDIA NIM Now Available on Hugging Face with Inference-as-a-Service
Hugging Face has announced the launch of an inference-as-a-service capability powered by NVIDIA NIM. This new service will provide developers easy access to NVIDIA-accelerated inference for popular AI models.
Serverless Inference API has shorter context length than the model?
I tried Llama 3.1 70B with Huggingface Serverless Inference API but got an error with 20k tokens even if the model has 128k context length. Does Huggingface limit the context length on top of the model and is there any workaround for this?
I tried Llama 3.1 70B with Huggingface Serverless Inference API but got an error with 20k tokens even if the model has 128k context length. Does Huggingface limit the context length on top of the model and is there any workaround for this?
Need Help Integrating black-forest-labs/FLUX.1-dev Text-to-Image Model in Next.js App
I'm trying to build a Next.js app using the black-forest-labs/FLUX.1-dev text-to-image model, but I've been struggling to get it working for the past few days. I've tried using the Next.js AI SDK and the HfInference library, but I'm not sure how to properly integrate them. Has anyone had experience with this or could offer some guidance? Any help would be greatly appreciated!
I'm trying to build a Next.js app using the black-forest-labs/FLUX.1-dev text-to-image model, but I've been struggling to get it working for the past few days. I've tried using the Next.js AI SDK and the HfInference library, but I'm not sure how to properly integrate them. Has anyone had experience with this or could offer some guidance? Any help would be greatly appreciated!
Difficulties to deal with HuggingFace transformers
Hi,
I am currently working with HuggingFace's transformers library. It is somewhat convenient to load models. I am not a troll. But the deeper I go, the more difficulties arise and I got the impression that the api is not well designed.
It allows for setting the same option at various places, and it is not documented how they interplay. For instance, it seems there is no uniform way to handle special tokens such as EOS. One can set these tokens 1. in the model, 2. in the tokenizer, and 3. in the pipeline. It is unclear to me how exactly these options interplay, and also the documentation does not say anything about it. Sometimes parameters are just ignored, and the library does not warn you about it. For instance, the parameter "add_eos_token" of the tokenizer seems to have no effect in some cases, and I am not the only one with this issue (https://github.com/huggingface/transformers/issues/30947).
It seems that it strongly depends on the model where and how you actually configure options, what effects they will have, or which settings work at all. This somehow contrasts the purpose of the api. It wants to make it easy to switch from one model to another, giving the impression that everything is controlled by just the model id. But when you go deeper it turns out that many small things have to be tailored to the model (even if restricted to a certain class such as generative text LLM). A look into the sourcecode of the transformers library confirms that it makes distinctions depending on the model id. That is, internally the library seems to exploit knowledge about the different models. That's not what one expects from a platform that pretends to work with arbitrary models.
Anyone having thoughts like this?
Hi,
I am currently working with HuggingFace's transformers library. It is somewhat convenient to load models. I am not a troll. But the deeper I go, the more difficulties arise and I got the impression that the api is not well designed.
It allows for setting the same option at various places, and it is not documented how they interplay. For instance, it seems there is no uniform way to handle special tokens such as EOS. One can set these tokens 1. in the model, 2. in the tokenizer, and 3. in the pipeline. It is unclear to me how exactly these options interplay, and also the documentation does not say anything about it. Sometimes parameters are just ignored, and the library does not warn you about it. For instance, the parameter "add_eos_token" of the tokenizer seems to have no effect in some cases, and I am not the only one with this issue (https://github.com/huggingface/transformers/issues/30947).
It seems that it strongly depends on the model where and how you actually configure options, what effects they will have, or which settings work at all. This somehow contrasts the purpose of the api. It wants to make it easy to switch from one model to another, giving the impression that everything is controlled by just the model id. But when you go deeper it turns out that many small things have to be tailored to the model (even if restricted to a certain class such as generative text LLM). A look into the sourcecode of the transformers library confirms that it makes distinctions depending on the model id. That is, internally the library seems to exploit knowledge about the different models. That's not what one expects from a platform that pretends to work with arbitrary models.
Anyone having thoughts like this?
LLM Political Compass, Grok is the most liberal and libertarian
I just want to grill
From this research Paper https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0306621
I just want to grill
From this research Paper https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0306621
Flux Schnell NF4 V2 Released
Resource - Update
Today I made an extension for ComfyUI which allows you to load Flux NF4 UNet separately from CLIP and VAE (post with details) and tried to figure out differences between NF4 Dev v2 and v1
So, I did it and applied this to schnell model
Here's the repo on HuggingFace with all 4 UNets (dev, dev-v2, schnell, schnell-v2) for you to use!
A comparison between NF4 Schnell and NF4 Schnell v2:
Full res comparison
I didn't tried it on diverse prompts and not able to compare with fp8 or fp16 at this moment, so I'll be happy to see your comparisons in the comments
https://huggingface.co/duuuuuuuden/flux1-nf4-unet
Resource - Update
Today I made an extension for ComfyUI which allows you to load Flux NF4 UNet separately from CLIP and VAE (post with details) and tried to figure out differences between NF4 Dev v2 and v1
So, I did it and applied this to schnell model
Here's the repo on HuggingFace with all 4 UNets (dev, dev-v2, schnell, schnell-v2) for you to use!
A comparison between NF4 Schnell and NF4 Schnell v2:
Full res comparison
I didn't tried it on diverse prompts and not able to compare with fp8 or fp16 at this moment, so I'll be happy to see your comparisons in the comments
https://huggingface.co/duuuuuuuden/flux1-nf4-unet
https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit
The NVIDIA Jetson Nano Developer Kit B01 is a powerful and compact development platform designed for AI and robotics applications. It is an upgraded version of the original Jetson Nano Developer Kit, offering enhanced performance and additional features. Here are the key details and specifications of the Jetson Nano Developer Kit B01:
Key Specifications:
GPU: 128-core NVIDIA Maxwell architecture
CPU: Quad-core ARM Cortex-A57 MPCore processor
Memory: 4 GB 64-bit LPDDR4
Storage: microSD card slot (for operating system and data storage)
Connectivity:
Gigabit Ethernet
Wi-Fi (optional module)
Bluetooth (optional module)
4x USB 3.0 ports
HDMI 2.0 or DisplayPort 1.2
MIPI CSI-2 camera connector
GPIO pins
Power: 5V DC, 4A (20W)
The NVIDIA Jetson Nano Developer Kit B01 is a powerful and compact development platform designed for AI and robotics applications. It is an upgraded version of the original Jetson Nano Developer Kit, offering enhanced performance and additional features. Here are the key details and specifications of the Jetson Nano Developer Kit B01:
Key Specifications:
GPU: 128-core NVIDIA Maxwell architecture
CPU: Quad-core ARM Cortex-A57 MPCore processor
Memory: 4 GB 64-bit LPDDR4
Storage: microSD card slot (for operating system and data storage)
Connectivity:
Gigabit Ethernet
Wi-Fi (optional module)
Bluetooth (optional module)
4x USB 3.0 ports
HDMI 2.0 or DisplayPort 1.2
MIPI CSI-2 camera connector
GPIO pins
Power: 5V DC, 4A (20W)
NVIDIA Developer
Get Started With Jetson Nano Developer Kit
A small AI computer for makers, learners, and developers to build practical AI applications, cool AI robots, and more.
Jetson Nano B01 is a single-board computer developed by NVIDIA, designed for AI and robotics applications. It is an upgrade from the original Jetson Nano, featuring improved performance and additional features. Here are some key specifications and features of the Jetson Nano B01:
Key Specifications:
GPU: 128-core NVIDIA Maxwell architecture
CPU: Quad-core ARM Cortex-A57 MPCore processor
Memory: 4 GB 64-bit LPDDR4https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/product-development/
Storage: microSD card slot (for operating system and data storage)
Connectivity:
Gigabit Ethernet
Wi-Fi (optional module)
Bluetooth (optional module)
4x USB 3.0 ports
HDMI 2.0 or DisplayPort 1.2
MIPI CSI-2 camera connector
GPIO pins
Power: 5V DC, 4A (20W)
Features:
AI and Machine Learning: Supports NVIDIA JetPack SDK, which includes CUDA, cuDNN, and TensorRT for accelerating AI workloads.
Robotics and IoT: Suitable for developing robotics projects and IoT devices with AI capabilities.
Development Environment: Compatible with various development environments, including JetPack SDK, Ubuntu, and NVIDIA SDK Manager.
Expansion Capabilities: Supports various expansion modules and carrier boards through its GPIO pins and connectors.
Use Cases:
AI and Machine Learning Projects: Training and deploying deep learning models for image recognition, object detection, and more.
Robotics: Building and programming robots with advanced AI capabilities.
Smart Cameras: Developing smart camera systems with real-time video analytics.
IoT Devices: Creating IoT devices with embedded AI processing.
Getting Started:
Setup: Install the JetPack SDK and required libraries using the NVIDIA SDK Manager.
Development: Use Python, C++, or other supported languages to develop applications.
Deployment: Deploy your applications on the Jetson Nano B01 and connect peripherals as needed.
The Jetson Nano B01 is a powerful and versatile platform for developers and hobbyists looking to explore and implement AI and robotics projects. Its compact size, robust performance, and extensive support for AI libraries make it an excellent choice for a wide range of applications.
Key Specifications:
GPU: 128-core NVIDIA Maxwell architecture
CPU: Quad-core ARM Cortex-A57 MPCore processor
Memory: 4 GB 64-bit LPDDR4https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/product-development/
Storage: microSD card slot (for operating system and data storage)
Connectivity:
Gigabit Ethernet
Wi-Fi (optional module)
Bluetooth (optional module)
4x USB 3.0 ports
HDMI 2.0 or DisplayPort 1.2
MIPI CSI-2 camera connector
GPIO pins
Power: 5V DC, 4A (20W)
Features:
AI and Machine Learning: Supports NVIDIA JetPack SDK, which includes CUDA, cuDNN, and TensorRT for accelerating AI workloads.
Robotics and IoT: Suitable for developing robotics projects and IoT devices with AI capabilities.
Development Environment: Compatible with various development environments, including JetPack SDK, Ubuntu, and NVIDIA SDK Manager.
Expansion Capabilities: Supports various expansion modules and carrier boards through its GPIO pins and connectors.
Use Cases:
AI and Machine Learning Projects: Training and deploying deep learning models for image recognition, object detection, and more.
Robotics: Building and programming robots with advanced AI capabilities.
Smart Cameras: Developing smart camera systems with real-time video analytics.
IoT Devices: Creating IoT devices with embedded AI processing.
Getting Started:
Setup: Install the JetPack SDK and required libraries using the NVIDIA SDK Manager.
Development: Use Python, C++, or other supported languages to develop applications.
Deployment: Deploy your applications on the Jetson Nano B01 and connect peripherals as needed.
The Jetson Nano B01 is a powerful and versatile platform for developers and hobbyists looking to explore and implement AI and robotics projects. Its compact size, robust performance, and extensive support for AI libraries make it an excellent choice for a wide range of applications.
This text mainly introduces the detailed process of setting up and managing Coolify on a server, including server configuration, installing Coolify, setting up user account security, deploying projects (such as static websites and Next.js applications), configuring domain names and redirects, selecting proxy servers, and handling related security and optimization settings.
Highlights
Server Configuration: Details the selection and configuration requirements of the server, such as CPU, memory, and storage, and also introduces how to set up SSH keys, firewalls, and cloud configuration.
Coolify Installation: Emphasizes the steps to install Coolify, including obtaining the installation script, running as the root user, and basic settings after installation.
User Account Security: Covers security measures such as setting user passwords and enabling two-factor authentication.
Project Deployment: Introduces the deployment process of static websites and Next.js applications, including resource selection, environment settings, and build package selection.
Domain Name and Redirection Configuration: Explains how to set up DNS records, specify domain names in Coolify, and configure proxy servers for https and redirection.
Proxy Server Selection: Compares the characteristics and configuration methods of two proxy servers, Caddy and Traffic.
Complex application deployment: Using a Next.js application as an example, this article illustrates the advantages and customizability of one-click deployment using Nyx packs.https://www.youtube.com/watch?v=taJlPG82Ucw
Highlights
Server Configuration: Details the selection and configuration requirements of the server, such as CPU, memory, and storage, and also introduces how to set up SSH keys, firewalls, and cloud configuration.
Coolify Installation: Emphasizes the steps to install Coolify, including obtaining the installation script, running as the root user, and basic settings after installation.
User Account Security: Covers security measures such as setting user passwords and enabling two-factor authentication.
Project Deployment: Introduces the deployment process of static websites and Next.js applications, including resource selection, environment settings, and build package selection.
Domain Name and Redirection Configuration: Explains how to set up DNS records, specify domain names in Coolify, and configure proxy servers for https and redirection.
Proxy Server Selection: Compares the characteristics and configuration methods of two proxy servers, Caddy and Traffic.
Complex application deployment: Using a Next.js application as an example, this article illustrates the advantages and customizability of one-click deployment using Nyx packs.https://www.youtube.com/watch?v=taJlPG82Ucw
What are Elasticsearch Plugins?
Elasticsearch is an open source, scalable search engine. Although Elasticsearch supports a large number of features out-of-the-box, it can also be extended with a variety of plugins to provide advanced analytics and process different data types.
This guide will show to how install the following Elasticsearch plugins and interact with them using the Elasticsearch API:
ingest-attachment: allows Elasticsearch to index and search base64-encoded documents in formats such as RTF, PDF, and PPT.
analysis-phonetic: identifies search results that sound similar to the search term.
ingest-geoip: adds location information to indexed documents based on any IP addresses within the document.
ingest-user-agent: parses the User-Agent header of HTTP requests to provide identifying information about the client that sent each request.https://www.linode.com/docs/guides/a-guide-to-elasticsearch-plugins/
Elasticsearch is an open source, scalable search engine. Although Elasticsearch supports a large number of features out-of-the-box, it can also be extended with a variety of plugins to provide advanced analytics and process different data types.
This guide will show to how install the following Elasticsearch plugins and interact with them using the Elasticsearch API:
ingest-attachment: allows Elasticsearch to index and search base64-encoded documents in formats such as RTF, PDF, and PPT.
analysis-phonetic: identifies search results that sound similar to the search term.
ingest-geoip: adds location information to indexed documents based on any IP addresses within the document.
ingest-user-agent: parses the User-Agent header of HTTP requests to provide identifying information about the client that sent each request.https://www.linode.com/docs/guides/a-guide-to-elasticsearch-plugins/
How to install elastic plugin?
User Agent Processor Plugin
Install the plugin: sudo /usr/share/elasticsearch/bin/elasticsearch-plugin install ingest-user-agent.
Restart Elasticsearch: sudo systemctl restart elasticsearch.
Confirm the plugin is installed: GET /_cat/plugins.
User Agent Processor Plugin
Install the plugin: sudo /usr/share/elasticsearch/bin/elasticsearch-plugin install ingest-user-agent.
Restart Elasticsearch: sudo systemctl restart elasticsearch.
Confirm the plugin is installed: GET /_cat/plugins.
How to install extensions in Ubuntu terminal?
Step 1: Install the Browser Add-on. Install the official browser extension first. ...
Step 2: Install 'Chrome GNOME Shell' package. Step two is to install the native connector package that lets the browser extension communicate with the GNOME Shell desktop. ...
Step 3: Install Extensions.
Nov 14, 2023
Step 1: Install the Browser Add-on. Install the official browser extension first. ...
Step 2: Install 'Chrome GNOME Shell' package. Step two is to install the native connector package that lets the browser extension communicate with the GNOME Shell desktop. ...
Step 3: Install Extensions.
Nov 14, 2023
To manually add a plugin to your WordPress website:
Download the desired plugin as a . ...
From your WordPress dashboard, choose Plugins > Add New.
Click Upload Plugin at the top of the page.
Click Choose File, locate the plugin . ...
After the installation is complete, click Activate Plugin.
Jan 24, 2022
Download the desired plugin as a . ...
From your WordPress dashboard, choose Plugins > Add New.
Click Upload Plugin at the top of the page.
Click Choose File, locate the plugin . ...
After the installation is complete, click Activate Plugin.
Jan 24, 2022
How to install plugins in terminal?
To install the plug-ins:
Click Start, and enter cmd in the Search box. Command Prompt appears.
Click Run as administrator.
Navigate to the Enterprise Client installation path. For example, C:\Program Files (x86)\Automation Anywhere\Enterprise\Client.
To install the plug-ins:
Click Start, and enter cmd in the Search box. Command Prompt appears.
Click Run as administrator.
Navigate to the Enterprise Client installation path. For example, C:\Program Files (x86)\Automation Anywhere\Enterprise\Client.
How to install elastic on Ubuntu server?
Elasticsearch Tutorial: How to Install Elasticsearch on Ubuntu
1.Step 1: Update Your Ubuntu. ...
2.Step 2: Install Java. ...
3.Step 3: Download Elasticsearch. ...
4.Step 4: Install Elasticsearch Ubuntu and Configure. ...
5.Step 5: Start Elasticsearch and Test It. ...
6.Step 6: Secure Elasticsearch on Ubuntu.
Elasticsearch Tutorial: How to Install Elasticsearch on Ubuntu
1.Step 1: Update Your Ubuntu. ...
2.Step 2: Install Java. ...
3.Step 3: Download Elasticsearch. ...
4.Step 4: Install Elasticsearch Ubuntu and Configure. ...
5.Step 5: Start Elasticsearch and Test It. ...
6.Step 6: Secure Elasticsearch on Ubuntu.
How to install plugin in Ubuntu?
1.How do I add plugins in Chrome in Ubuntu?
2.Method 1: Download or Install Google Chrome on Ubuntu.
3.Method 2: Access the Google Chrome Web Store.
4.Method 3: Chrome Search and Select the Plugin option.
5.Method 4: Download or Install the Plugin extension.
6.Method 5: Manage all the internal Plugins.
7.Conclusion.
1.How do I add plugins in Chrome in Ubuntu?
2.Method 1: Download or Install Google Chrome on Ubuntu.
3.Method 2: Access the Google Chrome Web Store.
4.Method 3: Chrome Search and Select the Plugin option.
5.Method 4: Download or Install the Plugin extension.
6.Method 5: Manage all the internal Plugins.
7.Conclusion.
How to install stm32 IDE in Ubuntu?
Running on Ubuntu, this comprehensive integrated development environment (IDE) offers everything you need for software development.
Open the Terminal. ...
Update and Upgrade. ...
Download STM32Cube. ...
Install Java Runtime Environment. ...
Extract and Install STM32Cube. ...
Launch STM32Cube.
Running on Ubuntu, this comprehensive integrated development environment (IDE) offers everything you need for software development.
Open the Terminal. ...
Update and Upgrade. ...
Download STM32Cube. ...
Install Java Runtime Environment. ...
Extract and Install STM32Cube. ...
Launch STM32Cube.
How to install zsh plugins on Ubuntu?
Step 2: Install Zsh. To install Zsh, enter the following command: sudo apt install zsh.
Step 3: Set Zsh as Your Default Shell. Once installed, you can set Zsh as your default shell with this command: chsh -s $(which zsh) ...
Step 4: Install Oh My Zsh (Optional, but Recommended) ...
Step 5: Customize Zsh with Themes and Plugins.
Step 2: Install Zsh. To install Zsh, enter the following command: sudo apt install zsh.
Step 3: Set Zsh as Your Default Shell. Once installed, you can set Zsh as your default shell with this command: chsh -s $(which zsh) ...
Step 4: Install Oh My Zsh (Optional, but Recommended) ...
Step 5: Customize Zsh with Themes and Plugins.
how to install coolify on etc ubuntu？
What is Coolify?
Before we get our hands dirty, let's understand what Coolify is. Coolify is an open-source, self-hostable platform that allows you to deploy your web apps, static sites, and databases directly to your servers. It's like having your own Heroku but with the freedom to control every aspect of the infrastructure.
Why Ubuntu?
Ubuntu is known for its stability and widespread support, making it a favorite among developers for hosting applications. It's also well-documented and easy to use, providing a solid foundation for our Coolify installation.
Prerequisites
Before we begin, ensure you have the following:
- An Ubuntu server (20.04 LTS recommended)
- SSH access to your server
- Basic knowledge of the Linux command line
Step 1: Update Your Server
First things first, let’s make sure your Ubuntu server is up-to-date. Connect to your server via SSH and run the following commands:
sudo apt update
sudo apt upgrade -y
This will fetch the latest versions of the packages and upgrade them.
Step 2: Install Docker
Coolify runs on Docker, so our next step is to install Docker on your Ubuntu server. Execute the following commands:
sudo apt install apt-transport-https ca-certificates curl software-properties-common -y
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
sudo apt update
sudo apt install docker-ce -y
To ensure Docker is installed correctly, run:
sudo systemctl status docker
You should see Docker running as a service.
Step 3: Install Docker Compose
Although Coolify uses its own version of Docker Compose, it’s good practice to have the official version installed:
sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
Verify the installation with:
docker-compose --version
Step 4: Install Coolify
Now, we’re ready to install Coolify. Clone the Coolify repository and run the installation script:
git clone https://github.com/coollabsio/coolify.git
cd coolify/scripts
./install.sh
Follow the on-screen instructions to complete the setup.
Congratulations! You have successfully set up the environment necessary for running Coolify on your Ubuntu server. In the next part, i will cover how to configure Coolify, secure your setup, and deploy your first application.
Stay tuned, and happy deploying!
What is Coolify?
Before we get our hands dirty, let's understand what Coolify is. Coolify is an open-source, self-hostable platform that allows you to deploy your web apps, static sites, and databases directly to your servers. It's like having your own Heroku but with the freedom to control every aspect of the infrastructure.
Why Ubuntu?
Ubuntu is known for its stability and widespread support, making it a favorite among developers for hosting applications. It's also well-documented and easy to use, providing a solid foundation for our Coolify installation.
Prerequisites
Before we begin, ensure you have the following:
- An Ubuntu server (20.04 LTS recommended)
- SSH access to your server
- Basic knowledge of the Linux command line
Step 1: Update Your Server
First things first, let’s make sure your Ubuntu server is up-to-date. Connect to your server via SSH and run the following commands:
sudo apt update
sudo apt upgrade -y
This will fetch the latest versions of the packages and upgrade them.
Step 2: Install Docker
Coolify runs on Docker, so our next step is to install Docker on your Ubuntu server. Execute the following commands:
sudo apt install apt-transport-https ca-certificates curl software-properties-common -y
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
sudo apt update
sudo apt install docker-ce -y
To ensure Docker is installed correctly, run:
sudo systemctl status docker
You should see Docker running as a service.
Step 3: Install Docker Compose
Although Coolify uses its own version of Docker Compose, it’s good practice to have the official version installed:
sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
Verify the installation with:
docker-compose --version
Step 4: Install Coolify
Now, we’re ready to install Coolify. Clone the Coolify repository and run the installation script:
git clone https://github.com/coollabsio/coolify.git
cd coolify/scripts
./install.sh
Follow the on-screen instructions to complete the setup.
Congratulations! You have successfully set up the environment necessary for running Coolify on your Ubuntu server. In the next part, i will cover how to configure Coolify, secure your setup, and deploy your first application.
Stay tuned, and happy deploying!
GitHub
GitHub - coollabsio/coolify: An open-source & self-hostable Heroku / Netlify / Vercel alternative.
An open-source & self-hostable Heroku / Netlify / Vercel alternative. - coollabsio/coolify
Введение в библиотеку Transformers и платформу Hugging Face
Исходники: https://github.com/huggingface/transformers
Документация: https://huggingface.co/docs/transformers/main/en/index
Платформа Hugging Face это коллекция готовых современных предварительно обученных Deep Learning моделей. А библиотека Transformers предоставляет инструменты и интерфейсы для их простой загрузки и использования. Это позволяет вам экономить время и ресурсы, необходимые для обучения моделей с нуля.
Модели решают весьма разнообразный спектр задач:
NLP: classification, NER, question answering, language modeling, summarization, translation, multiple choice, text generation.
CV: classification, object detection,segmentation.
Audio: classification, automatic speech recognition.
Multimodal: table question answering, optical character recognition, information extraction from scanned documents, video classification, visual question answering.
Reinforcement Learning
Time Series
Одна и та же задача может решаться различными архитектурами и их список впечатляет - более 150 на текущий момент. Из наиболее известных: Vision Transformer (ViT), T5, ResNet, BERT, GPT2. На этих архитектурах обучены более 60 000 моделей.
Исходники: https://github.com/huggingface/transformers
Документация: https://huggingface.co/docs/transformers/main/en/index
Платформа Hugging Face это коллекция готовых современных предварительно обученных Deep Learning моделей. А библиотека Transformers предоставляет инструменты и интерфейсы для их простой загрузки и использования. Это позволяет вам экономить время и ресурсы, необходимые для обучения моделей с нуля.
Модели решают весьма разнообразный спектр задач:
NLP: classification, NER, question answering, language modeling, summarization, translation, multiple choice, text generation.
CV: classification, object detection,segmentation.
Audio: classification, automatic speech recognition.
Multimodal: table question answering, optical character recognition, information extraction from scanned documents, video classification, visual question answering.
Reinforcement Learning
Time Series
Одна и та же задача может решаться различными архитектурами и их список впечатляет - более 150 на текущий момент. Из наиболее известных: Vision Transformer (ViT), T5, ResNet, BERT, GPT2. На этих архитектурах обучены более 60 000 моделей.
GitHub
GitHub - huggingface/transformers: 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. - huggingface/transformers