<jupyter_start><jupyter_text>Getting Started with Amazon SageMaker Studio Lab  Welcome to your SageMaker Studio Lab project Your SageMaker Studio Lab project is a notebook development environment with 25 GB of persistent storage and access to a CPU or GPU runtime. Everything about your project is automatically saved (notebooks, source code files, datasets, Git repos you have cloned, Conda environments, JupyterLab extensions, etc.) so each time you launch your project you can pick up where you left off. SageMaker Studio Lab is based on the open-source JupyterLab, so you can take advantage of open-source Jupyter extensions in your project.  Running Python code This Getting Started document is a [Jupyter notebook](https://jupyter.org/). Notebooks enable you to combine live code, equations, Markdown, images, visualizations, and other content into a single document that you can share with other people. To run the following Python code, select the cell with a click, and then type `Shift-Enter` on your keyboard or click the play button in the notebook toolbar at the top of the document.<jupyter_code>a = 10
b = 20
c = a + b
print(a, b, c)<jupyter_output>10 20 30<jupyter_text>To learn more about Python see [The Python Tutorial](https://docs.python.org/3/tutorial/).  Creating notebooks, source code files and accessing the Terminal SageMaker Studio Lab lets you create notebooks, source code files, and access the built-in Terminal. You can do this by clicking on the "+" button at the top of the file browser in the left panel to open the Launcher:![Launcher Button](images/launcher_button.png) In the Launcher, there are a set of cards that allow you to launch notebooks in different environments, create source code files, or access the Terminal:![Launcher Cards](images/launcher_cards.png) All of the notebooks, files, and datasets that you create are saved in your persistent project directory and are available when you open your project. To get help or access documentation, click on the **Help** menu in the menu bar at the top of the page.  Installing Python packages The simplest way of installing Python packages is to use either of the following magic commands in a code cell of a notebook:`%conda install ``%pip install `These magic commands will always install packages into the environment used by that notebook and any packages you install are saved in your persistent project directory. Note: we don't recommend using `!pip` or `!conda` as those can behave in unexpected ways when you have multiple environments.Here is an example that shows how to install NumPy into the environment used by this notebook:<jupyter_code>%conda install numpy<jupyter_output>Collecting package metadata (current_repodata.json): done
Solving environment: done


==> WARNING: A newer version of conda exists. <==
  current version: 4.10.3
  latest version: 4.11.0

Please update conda by running

    $ conda update -n base conda



# All requested packages already installed.


Note: you may need to restart the kernel to use updated packages.<jupyter_text>Now you can use NumPy:<jupyter_code>import numpy as np
np.random.rand(10)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.052083333333333336, 'fraction_numerical': 0.00726010101010101, 'mean_word_length': 5.0018939393939394, 'pattern_counts': {'":': 0, '<': 12, '<?xml version=': 0, '>': 12, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '43230', 'license': 'Apache-2.0', 'n_tokens_mistral': 837, 'n_tokens_neox': 791, 'n_words': 455, 'path': 'tp2/.ipynb_checkpoints/Instrucciones-checkpoint.ipynb', 'repo_name': 'stoklemariano/digitalhouse-group2-tp2', 'size': '3168'}