AI Archives | Clever Cloud From Code to Product Fri, 25 Oct 2024 07:42:52 +0000 en-GB hourly 1 https://staging-cc-assetsv6.cellar-c2.services.clever-cloud.com/uploads/2023/03/cropped-cropped-favicon-32x32.png AI Archives | Clever Cloud 32 32 Spécial Devoxx Maroc 2024 – 1/2 https://stagingv6.cleverapps.io/podcast/special-devoxx-maroc-2024-1-2/ https://stagingv6.cleverapps.io/podcast/special-devoxx-maroc-2024-1-2/#respond Fri, 25 Oct 2024 07:42:50 +0000 https://stagingv6.cleverapps.io/?post_type=podcast&p=14512

Animé par Horacio Gonzalez - @LostInBrittany
Épisode enregistré le 2 octobre 2024 à Marrakech

Voir sur Youtube

Les invités

00:00:16 : Sébastien BLANC (@sebi2706) Staff Developer Advocate @ Aiven

00:11:19 : Badr El HAOURI (@badrelhouari) Chairman @ Devoxx Morocco

00:22:06 : Eléa PETTON (@EleaPetton) Machine Learning Engineer @ OVH Cloud

00:29:43 : Raphaël SEMETEYS (@RaphaelSemeteys) Architecte, DevRel, expert open source @ Wordline

00:38:40 : Yassine BENABBAS (@yostane) Teacher, and Software Development Community Advocate at Worldline @ Wordline 

00:45:00 : Natalie GODEC (@ouvessvit) Senior Cloud Architect @ Zencore

Liens vers les talks

Coming soon...

]]>

Animé par Horacio Gonzalez - @LostInBrittany
Épisode enregistré le 2 octobre 2024 à Marrakech

Voir sur Youtube

Les invités

00:00:16 : Sébastien BLANC (@sebi2706) Staff Developer Advocate @ Aiven

00:11:19 : Badr El HAOURI (@badrelhouari) Chairman @ Devoxx Morocco

00:22:06 : Eléa PETTON (@EleaPetton) Machine Learning Engineer @ OVH Cloud

00:29:43 : Raphaël SEMETEYS (@RaphaelSemeteys) Architecte, DevRel, expert open source @ Wordline

00:38:40 : Yassine BENABBAS (@yostane) Teacher, and Software Development Community Advocate at Worldline @ Wordline 

00:45:00 : Natalie GODEC (@ouvessvit) Senior Cloud Architect @ Zencore

Liens vers les talks

Coming soon...

]]> https://stagingv6.cleverapps.io/podcast/special-devoxx-maroc-2024-1-2/feed/ 0 Deploy llama, mistral, openchat or your own model on Clever Cloud https://stagingv6.cleverapps.io/blog/engineering/2023/11/27/deploy-llama-mistral-openchat-or-your-own-model-on-clever-cloud/ Mon, 27 Nov 2023 09:16:24 +0000 https://stagingv6.cleverapps.io/?p=11185 bannier which illustrate how to deploy AI on Clever Cloud

If AI has been in the news recently, it's mainly through large language models (LLMs), which require substantial computing power for training or inference. Thus, it's common to see developers using them via turnkey APIs, as we did in a previous article. But this is changing thanks to more open and efficient models and tools, which you can easily deploy on Clever Cloud.

Indeed, while our platform was designed for a variety of uses, we always paid particular attention to its flexibility and the freedom it gave our customers. This means they can easily exploit it to address new markets, with minimum effort spent on adaptation.

To confirm this, we recently tried to deploy open source AI models such as those derived from LLaMA and Mistral. Their particularity is that they exist in lightweight versions, which don't necessarily require a GPU to run. As a result, they can be hosted as simple Clever Cloud applications, as long as they have been allocated enough memory.

Let's ollama (with a web UI) !

To do it, we've chosen to use ollama, an MIT-licensed project that lets you download models from a registry, or even your own. It works as a server that communicates via an API, and is very easy to host on our infrastructures.

To get started, you’ll need a Clever Cloud account. You can create the application and follow the next steps using our web interface, the Console, but also through our open source CLI: Clever Tools. That’s what we’ll describe below.

We’ll assume that your machine is running git and a recent version of Node.js. If you haven’t yet installed Clever Tools on your system, type (with system administrator rights, or sudo if necessary):

npm i -g clever-tools
clever login

Once logged in, you can check that everything has gone smoothly with the following command:

clever profile

Next, we create a Clever Cloud application using Node.js. It will install ollama to download and run our models, adding a web interface (chatbot-ollama, also under MIT license) that will allow us to query the server from any browser, run from a computer or a simple smartphone.

Chatbot Ollama

This solution also offers local storage of chats in the browser. It therefore requires no database, but offers an import/export function. You can also choose between different models, set some of their parameters, configure system instructions, various prompts, and so on.

mkdir myollama && cd myollama
git init
curl -L https://github.com/ivanfioravanti/chatbot-ollama/archive/refs/heads/main.tar.gz | tar -xz --strip-components=1

clever create -t node myollama && clever scale --flavor L
clever env set CC_POST_BUILD_HOOK "npx next telemetry disable && npm run build"
clever env set CC_PRE_BUILD_HOOK "./ollama_setup.sh"
clever env set CC_PRE_RUN_HOOK "./ollama_start.sh"

echo "orca-mini" > models.list
echo "mistral" >> models.list
echo "codellama" >> models.list

The code above creates the application. We add a file containing the list of models we wish to fetch during startup. We've chosen an L instance, the minimum size for loading such models into memory. We also declare several environment variables for running scripts to install ollama, start the server and disable telemetry for the Next.js framework used to build the web interface after the dependencies have been installed.

Now add some scripts

Our application is almost ready. We still have to create two executable files:

touch ollama_setup.sh && chmod +x ollama_setup.sh
touch ollama_start.sh && chmod +x ollama_start.sh

ollama_setup.sh download the ollama binary and the models from our list. Open it with your favorite editor and insert the following content:

#!/bin/bash

# We define the folder (in $PATH), where the ollama binary will be placed
BIN_DIR=${HOME}/.local/bin
mkdir -p ${BIN_DIR} 

ollama_start() {
    echo "Downloading and launching ollama..."
    curl -Ls https://ollama.ai/download/ollama-linux-amd64 -o ${BIN_DIR}/ollama
    chmod +x ${BIN_DIR}/ollama
    ollama serve &> /dev/null &
    echo -e "Finished: \033[32m✔\033[0m\n"
}

get_models() {
    MODELS_FILE="models.list"
    MODELS_DEFAULT="orca-mini"

    # We check if the models file exists
    if [ ! -f ${MODELS_FILE} ]; then
        echo "File ${MODELS_FILE} not found, ${MODELS_DEFAULT} used by default."
        echo ${MODELS_DEFAULT} > ${MODELS_FILE}
    fi

    # Loop through each line and pull model
    while IFS= read -r line; do
        echo "Pulling ${line} model..."
        ollama pull "${line}" > /dev/null 2>&1
        echo -e "Finished: \033[32m✔\033[0m\n"

    done < ${MODELS_FILE}
}

# We start ollama, and wait for it to respond
ollama_start
if [ $? -ne 0 ]; then
    echo -e "\nError during ollama setup."
    exit 1
fi

count=0
MAX_TIME=10

while ! pgrep -x "ollama" > /dev/null; do
    if [ ${count} -lt ${MAX_TIME} ]; then
        sleep 1
        count=$((count+1))
    else
        echo "Application 'ollama' did not launch within 10 seconds."
        exit 1
    fi
done

get_models
if [ $? -ne 0 ]; then
    echo -e "\nError during models downloading."
    exit 1
fi

This code may seem a little long, but that's mainly because we've made it modular, logging only successful steps and any errors.

ollama_start.sh detects whether the ollama server is already present before starting the web interface. If it isn't, we launch it in the background (we'll see why below).

Open it with your favorite editor and insert the following content:

if ! pgrep -f "ollama" > /dev/null; then
    # If 'ollama' is not running, start it in the background
    ollama serve &
    echo "Application 'ollama' started."
else
    # If 'ollama' is already running
    echo "'ollama' application is already running."
fi

Once these two files have been created, we can deploy the application:

git add . && git commit -m "Init application"
clever deploy
clever domain

You'll then see the domain to access it (which you can customize).

Cache and quick (re)start

Using Clever Cloud not only means deploying such an application easily, with logs and monitoring, and being able to instantly change its horizontal and/or vertical scaling. It also means being able to turn it into an image ready for a fast redeployment.

In fact, every application we host is stored as an archive just before dependencies are installed. This means it can be stopped and restarted in this form, so we don't have to re-download the models every time. To do this, simply specify the folders to be added to the archive:

clever env set CC_OVERRIDE_BUILDCACHE "/:../.ollama/:../.local/bin/"
clever restart --without-cache --follow

The application will restart, but this time it will take the built web interface, ollama and models from its cache, as the CC_PRE_BUILD_HOOK and CC_PRE_BUILD_HOOK steps are not executed. This is one of the reasons why we need to make sure that we start the ollama server with CC_PRE_RUN_HOOK, but check that this hasn't already been done in a deployment where it is used to download models before running them.

Once this step is complete, you can stop the application and restart it whenever you like - it'll be much faster from now on. And you'll only be charged for the time you use it, by the second. If you wish to modify the list of models to use, do so in the dedicated file. A git push will be enough to rebuild the new cache.

# Stop/Restart the application
clever stop
clever restart

# After a modification in models.list, git push to recreate the cache
git add models.list && git commit -m "New models" 
clever deploy

You'll find the files you need for this project in this GitHub repository, with the environment variables ready to import (clever env import < .env).

And if you want to push things further, deploy other models and tools for your applications that require more power and/or GPUs, let us know!

]]> bannier which illustrate how to deploy AI on Clever Cloud

If AI has been in the news recently, it's mainly through large language models (LLMs), which require substantial computing power for training or inference. Thus, it's common to see developers using them via turnkey APIs, as we did in a previous article. But this is changing thanks to more open and efficient models and tools, which you can easily deploy on Clever Cloud.

Indeed, while our platform was designed for a variety of uses, we always paid particular attention to its flexibility and the freedom it gave our customers. This means they can easily exploit it to address new markets, with minimum effort spent on adaptation.

To confirm this, we recently tried to deploy open source AI models such as those derived from LLaMA and Mistral. Their particularity is that they exist in lightweight versions, which don't necessarily require a GPU to run. As a result, they can be hosted as simple Clever Cloud applications, as long as they have been allocated enough memory.

Let's ollama (with a web UI) !

To do it, we've chosen to use ollama, an MIT-licensed project that lets you download models from a registry, or even your own. It works as a server that communicates via an API, and is very easy to host on our infrastructures.

To get started, you’ll need a Clever Cloud account. You can create the application and follow the next steps using our web interface, the Console, but also through our open source CLI: Clever Tools. That’s what we’ll describe below.

We’ll assume that your machine is running git and a recent version of Node.js. If you haven’t yet installed Clever Tools on your system, type (with system administrator rights, or sudo if necessary):

npm i -g clever-tools
clever login

Once logged in, you can check that everything has gone smoothly with the following command:

clever profile

Next, we create a Clever Cloud application using Node.js. It will install ollama to download and run our models, adding a web interface (chatbot-ollama, also under MIT license) that will allow us to query the server from any browser, run from a computer or a simple smartphone.

Chatbot Ollama

This solution also offers local storage of chats in the browser. It therefore requires no database, but offers an import/export function. You can also choose between different models, set some of their parameters, configure system instructions, various prompts, and so on.

mkdir myollama && cd myollama
git init
curl -L https://github.com/ivanfioravanti/chatbot-ollama/archive/refs/heads/main.tar.gz | tar -xz --strip-components=1

clever create -t node myollama && clever scale --flavor L
clever env set CC_POST_BUILD_HOOK "npx next telemetry disable && npm run build"
clever env set CC_PRE_BUILD_HOOK "./ollama_setup.sh"
clever env set CC_PRE_RUN_HOOK "./ollama_start.sh"

echo "orca-mini" > models.list
echo "mistral" >> models.list
echo "codellama" >> models.list

The code above creates the application. We add a file containing the list of models we wish to fetch during startup. We've chosen an L instance, the minimum size for loading such models into memory. We also declare several environment variables for running scripts to install ollama, start the server and disable telemetry for the Next.js framework used to build the web interface after the dependencies have been installed.

Now add some scripts

Our application is almost ready. We still have to create two executable files:

touch ollama_setup.sh && chmod +x ollama_setup.sh
touch ollama_start.sh && chmod +x ollama_start.sh

ollama_setup.sh download the ollama binary and the models from our list. Open it with your favorite editor and insert the following content:

#!/bin/bash

# We define the folder (in $PATH), where the ollama binary will be placed
BIN_DIR=${HOME}/.local/bin
mkdir -p ${BIN_DIR} 

ollama_start() {
    echo "Downloading and launching ollama..."
    curl -Ls https://ollama.ai/download/ollama-linux-amd64 -o ${BIN_DIR}/ollama
    chmod +x ${BIN_DIR}/ollama
    ollama serve &> /dev/null &
    echo -e "Finished: \033[32m✔\033[0m\n"
}

get_models() {
    MODELS_FILE="models.list"
    MODELS_DEFAULT="orca-mini"

    # We check if the models file exists
    if [ ! -f ${MODELS_FILE} ]; then
        echo "File ${MODELS_FILE} not found, ${MODELS_DEFAULT} used by default."
        echo ${MODELS_DEFAULT} > ${MODELS_FILE}
    fi

    # Loop through each line and pull model
    while IFS= read -r line; do
        echo "Pulling ${line} model..."
        ollama pull "${line}" > /dev/null 2>&1
        echo -e "Finished: \033[32m✔\033[0m\n"

    done < ${MODELS_FILE}
}

# We start ollama, and wait for it to respond
ollama_start
if [ $? -ne 0 ]; then
    echo -e "\nError during ollama setup."
    exit 1
fi

count=0
MAX_TIME=10

while ! pgrep -x "ollama" > /dev/null; do
    if [ ${count} -lt ${MAX_TIME} ]; then
        sleep 1
        count=$((count+1))
    else
        echo "Application 'ollama' did not launch within 10 seconds."
        exit 1
    fi
done

get_models
if [ $? -ne 0 ]; then
    echo -e "\nError during models downloading."
    exit 1
fi

This code may seem a little long, but that's mainly because we've made it modular, logging only successful steps and any errors.

ollama_start.sh detects whether the ollama server is already present before starting the web interface. If it isn't, we launch it in the background (we'll see why below).

Open it with your favorite editor and insert the following content:

if ! pgrep -f "ollama" > /dev/null; then
    # If 'ollama' is not running, start it in the background
    ollama serve &
    echo "Application 'ollama' started."
else
    # If 'ollama' is already running
    echo "'ollama' application is already running."
fi

Once these two files have been created, we can deploy the application:

git add . && git commit -m "Init application"
clever deploy
clever domain

You'll then see the domain to access it (which you can customize).

Cache and quick (re)start

Using Clever Cloud not only means deploying such an application easily, with logs and monitoring, and being able to instantly change its horizontal and/or vertical scaling. It also means being able to turn it into an image ready for a fast redeployment.

In fact, every application we host is stored as an archive just before dependencies are installed. This means it can be stopped and restarted in this form, so we don't have to re-download the models every time. To do this, simply specify the folders to be added to the archive:

clever env set CC_OVERRIDE_BUILDCACHE "/:../.ollama/:../.local/bin/"
clever restart --without-cache --follow

The application will restart, but this time it will take the built web interface, ollama and models from its cache, as the CC_PRE_BUILD_HOOK and CC_PRE_BUILD_HOOK steps are not executed. This is one of the reasons why we need to make sure that we start the ollama server with CC_PRE_RUN_HOOK, but check that this hasn't already been done in a deployment where it is used to download models before running them.

Once this step is complete, you can stop the application and restart it whenever you like - it'll be much faster from now on. And you'll only be charged for the time you use it, by the second. If you wish to modify the list of models to use, do so in the dedicated file. A git push will be enough to rebuild the new cache.

# Stop/Restart the application
clever stop
clever restart

# After a modification in models.list, git push to recreate the cache
git add models.list && git commit -m "New models" 
clever deploy

You'll find the files you need for this project in this GitHub repository, with the environment variables ready to import (clever env import < .env).

And if you want to push things further, deploy other models and tools for your applications that require more power and/or GPUs, let us know!

]]> Les IA s’entraînent offshore pour générer des malwares à destination des sangliers https://stagingv6.cleverapps.io/podcast/les-ia-sentrainent-offshore-pour-generer-des-malwares-a-destination-des-sangliers/ https://stagingv6.cleverapps.io/podcast/les-ia-sentrainent-offshore-pour-generer-des-malwares-a-destination-des-sangliers/#respond Fri, 10 Nov 2023 15:01:50 +0000 https://stagingv6.cleverapps.io/?post_type=podcast&p=11126 Bannière MACI 95

Voir sur Youtube

Animé par Jean-Baptiste KAISER - @ArendSyl
avec la participation de :
- Guillaume ASSIER : @GuillaumeAssier
- Eloi DEMOLIS : @Wonshtrum
- Sylvain WALLEZ : @bluxte

Épisode enregistré le 7 novembre 2023

👋 Venez discuter avec nous sur @clever_cloudFR pour nous dire ce que vous avez pensé de ce nouvel épisode.

➡️ Pour découvrir ou réécouter d’anciens épisodes c’est par ici !

Chapitrage et liens

00:01:24 Executive Order sur l’IA https://bsky.app/profile/dashishka.bsky.social/post/3kd25ppdlek2l
https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/

00:07:03 Platforme d’entrainement d’IA en pleine mer https://www.numerama.com/tech/1547042-pour-echapper-aux-regulations-sur-lia-une-entreprise-veut-creer-un-etat-en-pleine-mer.html

00:12:01 Adoption de WebGPU (and “Rust is the defacto standard for graphics in Rust”) https://www.youtube.com/watch?v=CklvTsKogTg

00:22:15 Alternant chez Orange le jour, dev de malwares la nuit
https://www.zdnet.fr/actualites/alternant-chez-orange-cyberdefense-le-jour-developpeur-de-programmes-malveillants-la-nuit-39962084.htm

00:28:43 React serveur
https://twitter.com/manekinekko/status/1717957681359495449

00:36:27 ChatGPT Builder
https://openai.com/blog/introducing-gpts
https://twitter.com/heyBarsee/status/1721792544814588141

00:46:50 Text to 3D generative AI
https://research.nvidia.com/labs/dir/magic3d/

00:51:28 #sanglicam
https://twitter.com/hashtag/sanglicam?f=live

00:56:42 Musique de fin
Eloi (6-10) : OOGIE BOOGIE'S SONG | The Nightmare Before Christmas | VoicePlay A Cappella Cover : https://www.youtube.com/watch?v=EbxkOntz2SA

]]> Bannière MACI 95

Voir sur Youtube

Animé par Jean-Baptiste KAISER - @ArendSyl
avec la participation de :
- Guillaume ASSIER : @GuillaumeAssier
- Eloi DEMOLIS : @Wonshtrum
- Sylvain WALLEZ : @bluxte

Épisode enregistré le 7 novembre 2023

👋 Venez discuter avec nous sur @clever_cloudFR pour nous dire ce que vous avez pensé de ce nouvel épisode.

➡️ Pour découvrir ou réécouter d’anciens épisodes c’est par ici !

Chapitrage et liens

00:01:24 Executive Order sur l’IA https://bsky.app/profile/dashishka.bsky.social/post/3kd25ppdlek2l
https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/

00:07:03 Platforme d’entrainement d’IA en pleine mer https://www.numerama.com/tech/1547042-pour-echapper-aux-regulations-sur-lia-une-entreprise-veut-creer-un-etat-en-pleine-mer.html

00:12:01 Adoption de WebGPU (and “Rust is the defacto standard for graphics in Rust”) https://www.youtube.com/watch?v=CklvTsKogTg

00:22:15 Alternant chez Orange le jour, dev de malwares la nuit
https://www.zdnet.fr/actualites/alternant-chez-orange-cyberdefense-le-jour-developpeur-de-programmes-malveillants-la-nuit-39962084.htm

00:28:43 React serveur
https://twitter.com/manekinekko/status/1717957681359495449

00:36:27 ChatGPT Builder
https://openai.com/blog/introducing-gpts
https://twitter.com/heyBarsee/status/1721792544814588141

00:46:50 Text to 3D generative AI
https://research.nvidia.com/labs/dir/magic3d/

00:51:28 #sanglicam
https://twitter.com/hashtag/sanglicam?f=live

00:56:42 Musique de fin
Eloi (6-10) : OOGIE BOOGIE'S SONG | The Nightmare Before Christmas | VoicePlay A Cappella Cover : https://www.youtube.com/watch?v=EbxkOntz2SA

]]> https://stagingv6.cleverapps.io/podcast/les-ia-sentrainent-offshore-pour-generer-des-malwares-a-destination-des-sangliers/feed/ 0 Depuis Anvers, le mistral emporte l’IA dans une java binaire https://stagingv6.cleverapps.io/podcast/depuis-anvers-le-mistral-emporte-lia-dans-une-java-binaire/ https://stagingv6.cleverapps.io/podcast/depuis-anvers-le-mistral-emporte-lia-dans-une-java-binaire/#respond Fri, 06 Oct 2023 13:38:36 +0000 https://stagingv6.cleverapps.io/?post_type=podcast&p=10505 bannière de l'épisode 91

Voir sur Youtube


Animé par Quentin ADAM

avec la participation de :

  • Sun Tan - @_sunix
  • Horacio Gonzalez - @lostinbritany
  • Sebastien Blanc - @sebi2706

👋 Venez discuter avec nous sur @clever_cloudFR pour nous dire ce que vous avez pensé de ce nouvel épisode.

➡️ Pour découvrir ou réécouter d’anciens épisodes c’est par ici !

Timecode et liens

00:00:16 - Présentation des participants
00:03:38 - Devoxx Belgique
00:13:01 - IA everywhere, Java et le binaire
00:39:12 - Broadcom et vmware
00:43:45 - MISTRAL 7B premier modèle ouvert d'AI
https://www.usine-digitale.fr/article/la-start-up-francaise-mistral-ai-devoile-mistral-7b-son-grand-modele-de-langage-open-source.N2176022

OVHcloud s’équipe en GPU nouvelle génération : https://www.lemagit.fr/actualites/366553735/VDI-rasterisation-IA-OVHcloud-sequipe-en-GPU-nouvelle-generation

00:51:15 - L’iA et le numérique comme tremplin politique :
https://www.wired.com/story/what-openai-really-wants/

01:10:59 - Musique de fin : Music Thylacine - Vivaldi (and 74 musicians) (Official Video) https://www.youtube.com/watch?v=3zyVu8xn60I

]]> bannière de l'épisode 91

Voir sur Youtube


Animé par Quentin ADAM

avec la participation de :

  • Sun Tan - @_sunix
  • Horacio Gonzalez - @lostinbritany
  • Sebastien Blanc - @sebi2706

👋 Venez discuter avec nous sur @clever_cloudFR pour nous dire ce que vous avez pensé de ce nouvel épisode.

➡️ Pour découvrir ou réécouter d’anciens épisodes c’est par ici !

Timecode et liens

00:00:16 - Présentation des participants
00:03:38 - Devoxx Belgique
00:13:01 - IA everywhere, Java et le binaire
00:39:12 - Broadcom et vmware
00:43:45 - MISTRAL 7B premier modèle ouvert d'AI
https://www.usine-digitale.fr/article/la-start-up-francaise-mistral-ai-devoile-mistral-7b-son-grand-modele-de-langage-open-source.N2176022

OVHcloud s’équipe en GPU nouvelle génération : https://www.lemagit.fr/actualites/366553735/VDI-rasterisation-IA-OVHcloud-sequipe-en-GPU-nouvelle-generation

00:51:15 - L’iA et le numérique comme tremplin politique :
https://www.wired.com/story/what-openai-really-wants/

01:10:59 - Musique de fin : Music Thylacine - Vivaldi (and 74 musicians) (Official Video) https://www.youtube.com/watch?v=3zyVu8xn60I

]]> https://stagingv6.cleverapps.io/podcast/depuis-anvers-le-mistral-emporte-lia-dans-une-java-binaire/feed/ 0 Clever Grid: Using GPUs has never been so simple https://stagingv6.cleverapps.io/blog/features/2019/07/08/gpu-machine-learning-with-clevergrid/ Mon, 08 Jul 2019 15:35:00 +0000 https://www2.cleverapps.io/wp/blog/technology/2019/07/08/gpu-machine-learning-with-clevergrid/

Today the Clever Cloud team is happy to release Clever Grid, our new GPUs as a Service offering. Because we think life should be as easy for data scientists as it is for developers when it comes to running code.

TL;DR

  • No hidden cost starting from 0.42 euros an hour
  • Billed by the minute
  • Datacenter located in Europe to comply with GDPR
  • up to 4 GeForce GTX1070 on the same machine
  • Fully automated setup with CUDA, Tensorflow, Keras and more

That's right, the level of automation given to developers using Clever Cloud is now available for Data Scientist too! No more tedious CUDA driver setup, no more dependency hell, you can focus on what matters: your code. And it's just a git push away. Try it now on dashboard.clevergrid.io! Note that Clever Grid login is compatible with your Clever Cloud account, but we'll detail this below.

0.42 euros an hour […] with 8 CPU, 1 GPU (Nvidia GeForce GTX1070), 6GB of Ram and 250GB of storage space

But by now you should be wondering, how much is it going to cost me? It starts at 10 euros a day, billed by the minute. That's 0.42 euros per hour. And what do you get for this low starting price? You get a VM with 8 CPU, 1 GPU (GeForce GTX1070), 6GB of Ram and 250GB of storage space. And our Datacenters are located in Europe so you can comply with GDPR!

Here's our complete pricing list, please keep in mind that this is the only thing you will pay. There are no hidden costs, no additional bandwith fees and the like.

Clever Grid price list

You will be able to choose the size of the VM you want when creating the application. But first you need to give it a name, a description and choose its type. We have six different ways of running your code. You can use Python, Java or Docker as runner or webapp. Basically a runner starts, executes your scripts and stops. It's great to train your models. A webapp is the more traditional way of deploying applications on Clever Cloud. It's as its name indicates a web application.

As usual with Clever Cloud you can start your application by pushing your code to a git remote branch we provide upon application creation. Or you can start from one of our template and start editing the code from the dashboard. We currently provide a template to automatically run a Jupyter notebook or to train a style transfer model.

And if you are already a Clever Cloud user, know that everything is available from the Clever Cloud console as well. Clever Grid is basically a new Web Console written from scratch using Vue.js and our public API, allowing you to deploy only in Zones where GPUs are available. Keep in mind that VM disks are stateless, if your need to keep your data, use persistent storage like Clever Cloud Cellar, a S3-like storage service (free up to 100MB).

We hope you will enjoy using Clever Grid to innovate faster with Machine Learning and Artificial Intelligence. And always remember that ethics is super important in science!

PS: You can find our ready-to-go demos here on Github. Check the documentation of Clever Grid for installation instructions.

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Today the Clever Cloud team is happy to release Clever Grid, our new GPUs as a Service offering. Because we think life should be as easy for data scientists as it is for developers when it comes to running code.

TL;DR

  • No hidden cost starting from 0.42 euros an hour
  • Billed by the minute
  • Datacenter located in Europe to comply with GDPR
  • up to 4 GeForce GTX1070 on the same machine
  • Fully automated setup with CUDA, Tensorflow, Keras and more

That's right, the level of automation given to developers using Clever Cloud is now available for Data Scientist too! No more tedious CUDA driver setup, no more dependency hell, you can focus on what matters: your code. And it's just a git push away. Try it now on dashboard.clevergrid.io! Note that Clever Grid login is compatible with your Clever Cloud account, but we'll detail this below.

0.42 euros an hour […] with 8 CPU, 1 GPU (Nvidia GeForce GTX1070), 6GB of Ram and 250GB of storage space

But by now you should be wondering, how much is it going to cost me? It starts at 10 euros a day, billed by the minute. That's 0.42 euros per hour. And what do you get for this low starting price? You get a VM with 8 CPU, 1 GPU (GeForce GTX1070), 6GB of Ram and 250GB of storage space. And our Datacenters are located in Europe so you can comply with GDPR!

Here's our complete pricing list, please keep in mind that this is the only thing you will pay. There are no hidden costs, no additional bandwith fees and the like.

Clever Grid price list

You will be able to choose the size of the VM you want when creating the application. But first you need to give it a name, a description and choose its type. We have six different ways of running your code. You can use Python, Java or Docker as runner or webapp. Basically a runner starts, executes your scripts and stops. It's great to train your models. A webapp is the more traditional way of deploying applications on Clever Cloud. It's as its name indicates a web application.

As usual with Clever Cloud you can start your application by pushing your code to a git remote branch we provide upon application creation. Or you can start from one of our template and start editing the code from the dashboard. We currently provide a template to automatically run a Jupyter notebook or to train a style transfer model.

And if you are already a Clever Cloud user, know that everything is available from the Clever Cloud console as well. Clever Grid is basically a new Web Console written from scratch using Vue.js and our public API, allowing you to deploy only in Zones where GPUs are available. Keep in mind that VM disks are stateless, if your need to keep your data, use persistent storage like Clever Cloud Cellar, a S3-like storage service (free up to 100MB).

We hope you will enjoy using Clever Grid to innovate faster with Machine Learning and Artificial Intelligence. And always remember that ethics is super important in science!

PS: You can find our ready-to-go demos here on Github. Check the documentation of Clever Grid for installation instructions.

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