Welcome to DS2.ai!
Hello ๐Ÿ‘‹ Introduce the entire process of customized AI development, the DS2.ai solution.
DS2.ai's User Guide, a platform that automates everything from data preparation to labeling, artificial intelligence development, deployment, maintenance, and maintenance. For customers who are not familiar with artificial intelligence, it provides an environment where they can develop artificial intelligence with a simple click from AI introduction to deployment, and for developers and data scientists, they can focus on development and AI building through stable server management.

Step 1 : Run the installation script

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Proceed with the installation as follows:
1. In order to install DS2.ai, nvidia-docker2 & docker-compose installation is essential. Please click the link to complete the installation first.
2. After nvidia-docker2 & docker-compose installation is complete, open a terminal and run the following command.
GPU version (nvidia-docker2 need)
CPU version
mkdir ds2ai
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cd ds2ai
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wget https://assetdslab.s3.ap-southeast-1.amazonaws.com/gpu/docker-compose.yml
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sudo docker-compose up -d
mkdir ds2ai
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cd ds2ai
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wget https://assetdslab.s3.ap-southeast-1.amazonaws.com/cpu/docker-compose.yml
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sudo docker-compose up -d
3. After successful installation, you can connect to http://localhost:13000 and run ds2.ai.
After the initial installation is completed, you can connect after the update takes about 3 to 5 minutes.
* To uninstall, run the following command.
sudo docker stop ds2ai-container
sudo docker rm ds2ai-container
sudo docker rmi dslabglobal/ds2ai:stable

Step 2: Register the first administrator user

After installation is complete, if you connect to http://localhost:13000, a page for registering an administrator will appear. After signing up, an initial administrator account for ds2.ai is set up.

Step 3: Start labeling data

Supports training data labeling tools needed to create artificial intelligence models.
After clicking Labeling on the top menu, upload the dataset, and you can start labeling by selecting the desired function between manual labeling and auto-labeling tool.

Step 4: Training AI models

After labeling is complete, artificial intelligence can be developed using the learning data. Click the โ€œStart AI Developmentโ€ button on the dataset or labeling project screen to enter the setting screen for AI development.
On the setting screen, three types of development environments are supported.
  • Manual setting: Select the desired deep learning & machine learning library (Pytorch, Tensorflow, XGboost, etc.)
  • Fast learning speed (AutoML): A function that creates a model by speeding up the learning rate among AutoML learning techniques
  • High Accuracy (AutoML): A function that creates a model with high accuracy among AutoML learning techniques
After selecting the desired learning method, click the Start button on the right to start learning. If you click the "Model" tab at the bottom after starting, you can check the progress of the model being developed. It provides the function of "distribute" and the function of "analyzing" through the data set of the prediction result created by the model.

Step 5 : Deploy AI models

You can use the Deploy Model function by completing training through DS2.ai or uploading a model you have already created to DS2.ai. (The ability to upload models directly supports Pytorch and Tensorflow2 models.)
You can upload by clicking the "Deploy" menu button at the top or distribute the developed model through the "Deploy" function in the "Learning" menu.
The deployed model can be managed through a separate endpoint, and the number of API calls can be monitored.

Step 6 : Use Python SDK

One of the powerful features of DS2.ai is the manual setting function that can easily set up learning under various conditions to derive an optimal artificial intelligence model.
pip install ds2ai
After completing the installation of the ds2ai Python library, you can start learning using the example below.
import ds2ai
ds2 = ds2ai.DS2("your-app-code")
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project = ds2.train(
"BankMarketing.csv",
option="custom",
training_method="normal",
value_for_predict="is_charge",
algorithm="keras_ann",
hyper_params={
"layer_width": [20,3,5],
"layer_deep": [3],
"epochs": [10],
"loss_function": ["mean_squared_error"],
"optimizer": [
{
"clipvalue": 0.5,
"learning_rate": 0.001,
"beta_1": 0.9,
"beta_2": 0.9999,
"epsilon": None,
"decay": 0,
"amsgrad": False,
"function_name": "Adam"
}
],
"activation": ["relu"],
"batch_size": [32],
"output_activation": ["relu"]
}
)
You can check the app code by clicking the user name in the upper right corner. You can start learning with the code above after putting this app code as shown below.
ds2 = ds2ai.DS2("Your App code")
After the code is executed, the ability to predict or deploy the job situation and the trained model is available in ds2.ai as-is. For more information on how to use, refer to "SDK | PYTHON" in the left menu.
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Next step

The advanced functions can work synergistically not only in the UI, but also by linking with the code you were working on through the API or Python SDK. From the menu on the left of the guide, you can practice various examples through Recipe, and you can use the โ€œVerifyโ€ menu to verify AI models or the โ€œJupyterโ€ menu to manage Jupyter by separating it into multiple ports. It is equipped with functions.
For more detailed guides, you can proceed sequentially through the buttons below.