General information about the Data Science Virtual Machine product
Data Science Virtual Machine (DSVM) is a cloud server with an out-of-the-box operating system image and a pre-installed tools For machine learning developers and data analysts.
With DSVM you can:
- develop applications for chatbots, recommendation services, recognizing objects in photos and videos, speech synthesis and recognition, and prediction services;
- to train the model;
- perform experiments with data.
Tools
Tools are pre-installed in the image for DSVM (more details in their official documentation):
- Python 3.10;
- pip;
- PyTorch;
- TensorFlow;
- JupyterLab;
- Jupyter Notebook;
- Keras;
- scikit-learn;
- NumPy;
- SciPy;
- pandas;
- NLTK;
- OpenCV;
- CatBoost;
- XGBoost;
- LightGBM.
Cost
When using DSVM, only cloud platform resources by cloud platform payment models.
Before using DSVM top up.
Resource prices can be viewed at selectel.ru.
Create DSVM
Use the instructions Create a cloud server.
Select:
- source is a ready-made Data Science VM image (Ubuntu 22.04 LTS 64-bit);
- configuration — with RAM from 2 GB and boot disk capacity from 40 GB. For fast model training we recommend using fixed line configurations GPU Line or custom configurations with GPU — they allow up to eight GPUs to be used.
Start JupyterLab
-
Open a page in your browser
http://<ip_address>Specify
<ip_address>— The public IP address of the cloud server can be viewed in control panel under Cloud platform → Servers → server page → tab Ports → column Public IP. -
Enter the password. By default, the UUID of the cloud server is used as the password. You can view it in control panels under Cloud platform → Servers → server page → alphanumeric value under the server name.