diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..8e18737933d6d52417593b7fc8db2b9ce29498c6
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,168 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+.secrets
+.tmp
+.testdata
+*.out
+*.err
+*lock
+out
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000000000000000000000000000000000000..3b664107303df336bab8010caad42ddaed24550e
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,3 @@
+{
+    "git.ignoreLimitWarning": true
+}
\ No newline at end of file
diff --git a/1_batch_job/main.py b/1_batch_job/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..85d4a4b3adac3733fec579929bcc82de8d2a3a16
--- /dev/null
+++ b/1_batch_job/main.py
@@ -0,0 +1,35 @@
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+
+# Env config
+print(f"The total number of CPU cores on this node is: {os.cpu_count()}")
+
+# Generate some synthetic data
+np.random.seed(42)
+X = 2 * np.random.rand(100, 1)
+y = 4 + 3 * X + np.random.randn(100, 1)  # y = 4 + 3X + noise
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Create and train the model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Make predictions
+y_pred = model.predict(X_test)
+
+# Print model parameters
+print(f"Intercept: {model.intercept_[0]:.2f}")
+print(f"Coefficient: {model.coef_[0][0]:.2f}")
+
+# Evaluate the model
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+print(f"Mean Squared Error: {mse:.2f}")
+print(f"R-squared: {r2:.2f}")
\ No newline at end of file
diff --git a/1_batch_job/start_batch.job b/1_batch_job/start_batch.job
new file mode 100644
index 0000000000000000000000000000000000000000..33cbcbf87c69a8e8ce6147f5fbe41273d1a98e4e
--- /dev/null
+++ b/1_batch_job/start_batch.job
@@ -0,0 +1,24 @@
+#!/bin/bash
+        
+#SBATCH --job-name=jupyter_environ
+#SBATCH --time=00:30:00
+#SBATCH --account=cin_staff # Change with your account
+#SBATCH --partition=dcgp_usr_prod # Change with the partition you want to use
+#SBATCH --qos=dcgp_qos_dbg # Replace with normal or the queue of your choice
+#SBATCH --nodes=1
+#SBATCH --tasks-per-node=1
+#SBATCH --cpus-per-task=112 # Use a number of cpus appropriate to the partition you are in
+#SBATCH --gres=gpu:0 # Use a number of gpus appropriate to the partition you are in
+#SBATCH --exclusive # Here we ask for an entire node
+#SBATCH --error jupyter-%j.err
+#SBATCH --output jupyter-%j.out
+
+# Load the python module and enable the venv
+module load python/3.11.6--gcc--8.5.0
+source venv/bin/activate
+
+echo [INFO]: Starting your script 
+
+python3 1_batch_job/main.py
+
+echo [INFO]: Execution ended
\ No newline at end of file
diff --git a/2_interactive_job/main.ipynb b/2_interactive_job/main.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..03bc5a04bb10823d512af271048bb20ee662ccdb
--- /dev/null
+++ b/2_interactive_job/main.ipynb
@@ -0,0 +1,153 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import mean_squared_error, r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of CPU cores on this node is: 112\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"The total number of CPU cores on this node is: {os.cpu_count()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate some synthetic data\n",
+    "np.random.seed(42)\n",
+    "X = 2 * np.random.rand(100, 1)\n",
+    "y = 4 + 3 * X + np.random.randn(100, 1)  # y = 4 + 3X + noise\n",
+    "\n",
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Create and train the model\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Make predictions\n",
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model inspection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intercept: 4.14\n",
+      "Coefficient: 2.80\n",
+      "Mean Squared Error: 0.65\n",
+      "R-squared: 0.81\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Print model parameters\n",
+    "print(f\"Intercept: {model.intercept_[0]:.2f}\")\n",
+    "print(f\"Coefficient: {model.coef_[0][0]:.2f}\")\n",
+    "\n",
+    "# Evaluate the model\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "print(f\"Mean Squared Error: {mse:.2f}\")\n",
+    "print(f\"R-squared: {r2:.2f}\")\n",
+    "\n",
+    "# Plot the results\n",
+    "plt.scatter(X_test, y_test, label=\"Actual data\")\n",
+    "plt.plot(X_test, y_pred, color=\"red\", label=\"Regression line\")\n",
+    "plt.xlabel(\"X\")\n",
+    "plt.ylabel(\"y\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/2_interactive_job/start_interactive.job b/2_interactive_job/start_interactive.job
new file mode 100644
index 0000000000000000000000000000000000000000..3a0bf6bb15ebeb13e2b8248c39fb57dbcb5370d5
--- /dev/null
+++ b/2_interactive_job/start_interactive.job
@@ -0,0 +1,46 @@
+#!/bin/bash
+        
+#SBATCH --job-name=jupyter_environ
+#SBATCH --time=00:30:00
+#SBATCH --account=cin_staff # Change with your account
+#SBATCH --partition=dcgp_usr_prod # Change with the partition you want to use
+#SBATCH --qos=dcgp_qos_dbg # Replace with normal or the queue of your choice
+#SBATCH --nodes=1
+#SBATCH --tasks-per-node=1
+#SBATCH --cpus-per-task=112 # Use a number of cpus appropriate to the partition you are in
+#SBATCH --gres=gpu:0 # Use a number of gpus appropriate to the partition you are in
+#SBATCH --exclusive # Here we ask for an entire node
+#SBATCH --error jupyter-%j.err
+#SBATCH --output jupyter-%j.out
+
+# Load the python module and enable the venv
+module load python/3.11.6--gcc--8.5.0
+source venv/bin/activate
+
+# Get the worker list associated to this slurm job
+worker_list=($(scontrol show hostnames "$SLURM_JOB_NODELIST"))
+
+# Set the first worker as the head node and get his ip
+worker_node=${worker_list[0]}
+worker_node_ip=$(srun --nodes=1 --ntasks=1 -w "$worker_node" hostname --ip-address)
+
+# Print ssh tunnel instruction
+jupyter_port=$((1024 + $RANDOM % 64511))
+jupyter_token=${USER}_${jupyter_port}
+echo ===================================================
+echo [INFO]: To access the Jupyter server, remember to open a ssh tunnel with: 
+echo ssh -L $jupyter_port:$worker_node_ip:$jupyter_port ${USER}@login02-ext.leonardo.cineca.it -N
+echo then you can connect to the jupyter server at http://127.0.0.1:$jupyter_port/lab?token=$jupyter_token
+echo ===================================================
+
+# Start the head node
+echo [INFO]: Starting jupyter notebook server on $worker_node 
+
+# Note that the jupyter notebook command is available only because we have enabled the venv
+command="jupyter lab --ip=0.0.0.0 --port=${jupyter_port} --NotebookApp.token=${jupyter_token}"
+echo [INFO]: $command
+$command &
+
+echo [INFO]: Your env is up and running.
+
+sleep infinity
diff --git a/README.md b/README.md
index 05e6ae20eed0f0f47e6017ba15643705d494bc21..a083d00bbd8a4ac6d075fd915f0a1f508ad5399c 100644
--- a/README.md
+++ b/README.md
@@ -1,93 +1,21 @@
-# leonardo_minimal_examples
+# leonardo_minimal_examples  
 
+In this repo you will find some minimal examples of how to use Leonardo. To run the examples you need to have some python dependencies installed.  
+In order to configure the environment, you must run the `configure.sh` script by running `bash configure.sh` in the root folder of this project.    
 
+## Running a batch job  
 
-## Getting started
+Running a batch job is as simple as running the sbatch associated to the job. In the folder `1_batch_job` you will find a script (the file `main.py`) that must be run in sbatch mode and its associated sbatch file.  
+To start the job you simply need to run this command: `sbatch 1_batch_job/start_batch.job`.  
 
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
+## Running an interactive job  
 
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
+To run an interactive job, you must provision an interactive environment yourself. A working interactive environment can be built from scratch creating a virtual environment with the following packages:  
+1. `jupyterlab`;
+2. (Optional) you can install `jupyterlab_nvdashboard` to monitor the gpu usage;  
 
-## Add your files
+If you executed the `configure.sh` script, your environment is already equipped with all the dependencies to instantiate an interactive environment.  
 
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
+At this point you need to start the environment by executing its associated job (check the `start_interactive.job` file). To start the environment use the following command: `sbatch 2_interactive_job/start_interactive.job`. 
+After starting the job, you can connect to the interactive environment throught an ssh tunnel. The instructions to instantiate an ssh tunnel are printed in the job output file.  
 
-```
-cd existing_repo
-git remote add origin https://gitlab.hpc.cineca.it/rmioli00/leonardo_minimal_examples.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.hpc.cineca.it/rmioli00/leonardo_minimal_examples/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
diff --git a/configure.sh b/configure.sh
new file mode 100644
index 0000000000000000000000000000000000000000..fe6b7892f5dea664566f25742a343c7962129251
--- /dev/null
+++ b/configure.sh
@@ -0,0 +1,17 @@
+# Prune the environment in case it is already present
+rm -rf venv
+
+# Load the python module. We don't need to activate any profile because the python module is included in the profile/base profile
+module load python/3.11.6--gcc--8.5.0
+
+# Check what python binary we are using
+which python3
+
+# Create the venv
+python3 -m venv venv
+
+# Enable the venv
+source venv/bin/activate
+
+# Install all the requirements
+pip3 install -r requirements.txt
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..09ec0550982a7acc205ebd2a659079039679117b
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,144 @@
+aiohappyeyeballs==2.4.6
+aiohttp==3.11.12
+aiohttp-cors==0.7.0
+aiosignal==1.3.2
+annotated-types==0.7.0
+anyio==4.8.0
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asttokens==3.0.0
+async-lru==2.0.4
+attrs==25.1.0
+babel==2.17.0
+beautifulsoup4==4.13.3
+bleach==6.2.0
+cachetools==5.5.2
+certifi==2025.1.31
+cffi==1.17.1
+charset-normalizer==3.4.1
+click==8.1.8
+colorful==0.5.6
+comm==0.2.2
+contourpy==1.3.1
+cycler==0.12.1
+debugpy==1.8.12
+decorator==5.2.1
+defusedxml==0.7.1
+distlib==0.3.9
+executing==2.2.0
+fastjsonschema==2.21.1
+filelock==3.17.0
+fonttools==4.56.0
+fqdn==1.5.1
+frozenlist==1.5.0
+google-api-core==2.24.1
+google-auth==2.38.0
+googleapis-common-protos==1.68.0
+grpcio==1.70.0
+h11==0.14.0
+httpcore==1.0.7
+httpx==0.28.1
+idna==3.10
+ipykernel==6.29.5
+ipython==8.32.0
+isoduration==20.11.0
+jedi==0.19.2
+Jinja2==3.1.5
+joblib==1.4.2
+json5==0.10.0
+jsonpointer==3.0.0
+jsonschema==4.23.0
+jsonschema-specifications==2024.10.1
+jupyter-events==0.12.0
+jupyter-lsp==2.2.5
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+jupyter_server==2.15.0
+jupyter_server_terminals==0.5.3
+jupyterlab==4.3.5
+jupyterlab_nvdashboard==0.11.0
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.27.3
+kiwisolver==1.4.8
+MarkupSafe==3.0.2
+matplotlib==3.10.0
+matplotlib-inline==0.1.7
+mistune==3.1.2
+msgpack==1.1.0
+multidict==6.1.0
+nbclient==0.10.2
+nbconvert==7.16.6
+nbformat==5.10.4
+nest-asyncio==1.6.0
+notebook==7.3.2
+notebook_shim==0.2.4
+numpy==2.2.3
+nvidia-ml-py==12.570.86
+opencensus==0.11.4
+opencensus-context==0.1.3
+overrides==7.7.0
+packaging==24.2
+pandas==2.2.3
+pandocfilters==1.5.1
+parso==0.8.4
+patsy==1.0.1
+pexpect==4.9.0
+pillow==11.1.0
+platformdirs==4.3.6
+prometheus_client==0.21.1
+prompt_toolkit==3.0.50
+propcache==0.3.0
+proto-plus==1.26.0
+protobuf==5.29.3
+psutil==7.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+py-spy==0.4.0
+pyasn1==0.6.1
+pyasn1_modules==0.4.1
+pycparser==2.22
+pydantic==2.10.6
+pydantic_core==2.27.2
+Pygments==2.19.1
+pynvml==12.0.0
+pyparsing==3.2.1
+python-dateutil==2.9.0.post0
+python-json-logger==3.2.1
+pytz==2025.1
+PyYAML==6.0.2
+pyzmq==26.2.1
+ray==2.41.0
+referencing==0.36.2
+requests==2.32.3
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rpds-py==0.23.1
+rsa==4.9
+scikit-learn==1.6.1
+scipy==1.15.2
+seaborn==0.13.2
+Send2Trash==1.8.3
+six==1.17.0
+smart-open==7.1.0
+sniffio==1.3.1
+soupsieve==2.6
+stack-data==0.6.3
+statsmodels==0.14.4
+terminado==0.18.1
+threadpoolctl==3.5.0
+tinycss2==1.4.0
+tornado==6.4.2
+traitlets==5.14.3
+types-python-dateutil==2.9.0.20241206
+typing_extensions==4.12.2
+tzdata==2025.1
+uri-template==1.3.0
+urllib3==2.3.0
+virtualenv==20.29.2
+wcwidth==0.2.13
+webcolors==24.11.1
+webencodings==0.5.1
+websocket-client==1.8.0
+wrapt==1.17.2
+yarl==1.18.3