Data Analytics Virtual Machine

Last update: June 04 2025

Data Analytics Virtual Machine (DAVM) is a cloud server with an out-of-the-box operating system image and pre-installed tools for data analytics and machine learning.

With DAVM you can:

• Develop and train machine learning models;
• build data processing processes (ETL/ELT);
• visualize data (BI).

Once the DAVM is created, Jupyter Notebooks will have detailed instructions on how to use JupyterLab, Prefect, Apache Superset, Keycloak, PyTorch, TensorFlow, and code samples.

Tools⁠

The image for DAVM Data Analytics VM (Ubuntu 22.04 LTS 64-bit) has the tools pre-installed (more details in their official documentation):

• JupyterLab (version 3.6.3) — a unified development environment for working with Jupyter Notebooks, program code and data;
• Prefect (version 2.10.16) — software for managing data collection, monitoring and batch processing tasks;
• Apache Superset (version 3.0.0) is a web application for visualization, reporting and dashboards;
• PostgreSQL — relational DBMS for data storage;
• set of libraries for machine learning: TensorFlow, PyTorch and others. See the list in the Library Versions table;
• GPU drivers version 550;
• CUDA drivers version 11.8.

Create DAVM⁠

Use the instructions to Create a cloud server.

Select:

• pool segment — ru-7 or ru-9;
• source — ready-made Data Analytics VM image (Ubuntu 22.04 LTS 64-bit). The image contains drivers required to work with graphics processors;
• configuration — with RAM of 8 GB or more and boot disk capacity of 100 GB or more.

Start DAVM⁠

1. Make sure that more than seven minutes have passed since the server was created — this is the time required to deploy additional services and create DNS records.

2. Open the DAVM page in your browser. Use the link:

   • home-<ip_address>.pl.davm.selcloud.ru

     Specify <ip_address> — public IP address of the cloud server, can be viewed in the control panel: in the top menu, click Products → Cloud Servers → Server page → Ports tab → in the port card, click next to the public IP address. In the IP address, replace the characters . with -, for example 203-0-113-10;

   • or a link from the operating system message that appears when you connect to the cloud server through the console in the control panel.

3. Authorize Keycloak. Use:

   • admin;
   • password — cloud server ID, can be viewed in the control panel: in the top menu click Products → Cloud Servers → Server page.

4. Enter a new password. Save it — if you lose your password, we will not be able to reset it.

Library versions⁠

absl-py	2.0.0	Jinja2	3.1.2	PyJWT	2.8.0
accelerate	0.23.0	jmespath	1.0.1	pyOpenSSL	23.3.0
aiobotocore	2.5.4	joblib	1.3.2	pyparsing	3.1.1
aiofiles	22.1.0	json5	0.9.14	pyproject_hooks	1.0.0
aiohttp	3.8.6	jsonpatch	1.33	PySocks	1.7.1
aioitertools	0.11.0	jsonpointer	2.4	python-dateutil	2.8.2
aiosignal	1.3.1	jsonschema	4.19.2	python-json-logger	2.0.7
aiosqlite	0.19.0	jsonschema-specifications	2023.7.1	python-slugify	8.0.1
alembic	1.12.1	jupyter	1.0.0	pytz	2023.3.post1.
altair	5.1.2	jupyter_client	8.6.0	pytzdata	2020.1
ansiwrap	0.8.4	jupyter-console	6.6.3	PyWavelets	1.4.1
anyio	3.7.1	jupyter_core	5.5.0	PyYAML	6.0.1
apprise	1.6.0	jupyter-events	0.9.0	pyzmq	25.1.1
archspec	0.2.2	jupyter_scheduler	1.3.1	qtconsole	5.5.0
argon2-cffi	23.1.0	jupyter_server	2.10.0	QtPy	2.4.1
argon2-cffi-bindings	21.2.0	jupyter_server_fileid	0.9.0	readchar	4.0.5
arrow	1.3.0	jupyter-server-mathjax	0.2.6	referencing	0.30.2
asgi-lifespan	2.1.0	jupyter_server_terminals	0.4.4	regex	2023.10.3
asttokens	2.4.1	jupyter_server_ydoc	0.8.0	requests	2.31.0
astunparse	1.6.3	jupyter-telemetry	0.1.0	requests-oauthlib	1.3.1
async-generator	1.1	jupyter-ydoc	0.2.4	rfc3339-validator	0.1.4
async-timeout	4.0.3	jupyterhub	4.0.0	rfc3986-validator	0.1.1
asyncpg	0.29.0	jupyterlab	3.6.3	rich	13.6.0
attrs	23.1.0	jupyterlab-git	0.41.0	rpds-py	0.12.0
Babel	2.13.1	jupyterlab-pygments	0.2.2	rpy2	3.5.11
backports.functools-lru-cache	1.6.5	jupyterlab-s3-browser	0.12.0	rsa	4.9
beautifulsoup4	4.12.2	jupyterlab_server	2.25.1	ruamel.yaml	0.18.5
bleach	6.1.0	jupyterlab-widgets	3.0.9	ruamel.yaml.clib	0.2.7
blinker	1.7.0	keras	2.14.0	s3fs	2023.6.0
bokeh	3.3.0	kiwisolver	1.4.5	s3transfer	0.6.2
boltons	23.0.0	kubernetes	28.1.0	safetensors	0.4.0
boto3	1.28.17	lab	8	scikit-image	0.22.0
botocore	1.31.17	lazy_loader	0.3	scikit-learn	1.3.2
Bottleneck	1.3.7	libclang	16.0.6	scipy	1.11.3
Brotli	1.1.0	libmambapy	1.5.3	seaborn	0.13.0
build	1.0.3	lit	15.0.7	Send2Trash	1.8.2
cached-property	1.5.2	llvmlite	0.41.1	setuptools	68.2.2
cachetools	5.3.2	locket	1.0.0	simplegeneric	0.8.1
certifi	2023.7.22	lz4	4.3.2	simplejson	3.19.2
certipy	0.1.3	Mako	1.3.0	singleton-decorator	1.0.0
cffi	1.16.0	mamba	1.5.3	six	1.16.0
charset-normalizer	3.3.2	Markdown	3.5.1	smmap	5.0.1
click	8.1.7	markdown-it-py	3.0.0	sniffio	1.3.0
cloudpickle	3.0.0	MarkupSafe	2.1.3	sortedcontainers	2.4.0
cmake	3.25.0	matplotlib	3.8.1	soupsieve	2.5
colorama	0.4.6	matplotlib-inline	0.1.6	SQLAlchemy	1.4.50
comm	0.1.4	mdurl	0.1.2	stack-data	0.6.2
conda	23.10.0	mistune	3.0.2	starlette	0.27.0
conda-libmamba-solver	23.11.0	ml-dtypes	0.2.0	statsmodels	0.14.0
conda-package-handling	2.2.0	mpmath	1.3.0	sympy	1.12
conda_package_streaming	0.9.0	msgpack	1.0.6	tables	3.9.1
contourpy	1.2.0	multidict	6.0.4	tblib	2.0.0
coolname	2.2.0	multiprocess	0.70.15	tenacity	8.2.3
croniter	2.0.1	munkres	1.1.4	tensorboard	2.14.1
cryptography	41.0.5	nbclassic	1.0.0	tensorboard-data-server	0.7.2
cycler	0.12.1	nbclient	0.8.0	tensorflow	2.14.0
Cython	3.0.5	nbconvert	7.11.0	tensorflow-estimator	2.14.0
cytoolz	0.12.2	nbdime	3.2.1	tensorflow-io-gcs-filesystem	0.34.0
dask	2023.10.1	nbformat	5.9.2	termcolor	2.3.0
datasets	2.14.5	nest-asyncio	1.5.8	terminado	0.17.1
dateparser	1.1.8	networkx	3.2.1	text-unidecode	1.3
debugpy	1.8.0	notebook	6.5.4	textwrap3	0.9.2
decorator	5.1.1	notebook_shim	0.2.3	threadpoolctl	3.2.0
defusedxml	0.7.1	numba	0.58.1	tifffile	2023.9.26
dill	0.3.7	numexpr	2.8.7	tinycss2	1.2.1
distributed	2023.10.1	numpy	1.26.0	tokenizers	0.14.1
docker	6.1.3	nvidia-ml-py	12.535.133	toml	0.10.2
entrypoints	0.4	nvitop	1.3.1	tomli	2.0.1
et-xmlfile	1.1.0	oauthlib	3.2.2	toolz	0.12.0
exceptiongroup	1.1.3	openpyxl	3.1.2	torch	2.0.1+cu118.
executing	2.0.1	opt-einsum	3.3.0	torchaudio	2.0.2+cu118.
fastapi	0.104.1	orjson	3.9.10	torchvision	0.15.2+cu118
fastjsonschema	2.18.1	overrides	7.4.0	tornado	6.3.3
filelock	3.9.0	packaging	23.2	tqdm	4.66.1
flatbuffers	23.5.26	pamela	1.1.0	traitlets	5.9.0
fonttools	4.44.0	pandas	2.1.2	transformers	4.34.0
fqdn	1.5.1	pandocfilters	1.5.0	triton	2.0.0
frozenlist	1.4.0	papermill	2.4.0	truststore	0.8.0
fsspec	2023.6.0	parso	0.8.3	txt2tags	3.9
gast	0.5.4	partd	1.4.1	typer	0.9.0
gitdb	4.0.11	pathspec	0.11.2	types-python-dateutil	2.8.19.14
GitPython	3.1.40	patsy	0.5.3	typing_extensions	4.8.0
gmpy2	2.1.2	pendulum	2.1.2	typing-utils	0.1.0
google-auth	2.23.4	pexpect	4.8.0	tzdata	2023.3
google-auth-oauthlib	1.0.0	pickleshare	0.7.5	tzlocal	5.2
google-pasta	0.2.0	Pillow	10.1.0	unicodedata2	15.1.0
greenlet	3.0.1	pip	23.3.1	uri-template	1.3.0
griffe	0.36.9	pkgutil_resolve_name	1.3.10	urllib3	1.26.18
grpcio	1.59.2	platformdirs	3.11.0	uvicorn	0.24.0.post1
h11	0.14.0	plotly	5.14.1	wcwidth	0.2.9
h2	4.1.0	pluggy	1.3.0	webcolors	1.13
h5py	3.10.0	prefect	2.10.16	webencodings	0.5.1
hpack	4.0.0	prometheus-client	0.18.0	websocket-client	1.6.4
httpcore	1.0.1	prompt-toolkit	3.0.39	websockets	12
httpx	0.25.1	protobuf	4.24.4	Werkzeug	3.0.1
huggingface-hub	0.17.3	psutil	5.9.5	wheel	0.41.3
hyperframe	6.0.1	psycopg2-binary	2.9.6	widgetsnbextension	4.0.9
idna	3.4	ptyprocess	0.7.0	wrapt	1.14.1
imagecodecs	2023.9.18	pure-eval	0.2.2	xformers	0.0.22
imageio	2.31.5	py-cpuinfo	9.0.0	xlrd	2.0.1
importlib-metadata	6.8.0	pyarrow	14.0.0	xxhash	3.4.1
importlib-resources	6.1.1	pyasn1	0.5.0	xyzservices	2023.10.1
ipykernel	6.26.0	pyasn1-modules	0.3.0	y-py	0.5.9
ipympl	0.9.3	pycosat	0.6.6	yarl	1.9.2
ipython	8.17.2	pycparser	2.21	ypy-websocket	0.8.2
ipython-genutils	0.2.0	pycurl	7.45.1	zict	3.0.0
ipywidgets	8.1.1	pydantic	1.10.12	zipp	3.17.0
isoduration	20.11.0	Pygments	2.16.1	zstandard	0.22.0
jedi	0.19.1