Skip to main content

How to install Anaconda or Miniconda in Colab

Colab is the Google's platform for working with Python notebooks and practice Deep Learning using different frameworks. It is a powerful platform, there is availability of GPUs or TPUs, it allows to use your Google Drive space for notebooks and data, has a friendly user interface and lots of useful features, but in order to install/update Python packages, it comes by default only with pip and no availability for conda. If you need to import a Python package which is available in Anaconda, but not in PyPi you need to install Anaconda or Miniconda yourself from a notebook. In this post I am explaining the simple steps to do it.

Anaconda installation
Create your notebook and from a code cell download the Anaconda installer:

!wget -c https://repo.continuum.io/archive/Anaconda3-5.1.0-Linux-x86_64.sh

This is the version that works fine for me. I have tried also with the latest release 2019.10, but the configuration then would have extra complexity.

Now you need to make the downloaded file executable:

!chmod +x Anaconda3-5.1.0-Linux-x86_64.sh

and then you can run it:

!bash ./Anaconda3-5.1.0-Linux-x86_64.sh -b -f -p /usr/local

Before installing packages, a last configuration setting is needed:

import sys
sys.path.append('/usr/local/lib/python3.6/site-packages/')


This way the path to the packages managed through conda has been appended to the PYTHONPATH environment variable.

Miniconda installation
In most cased Miniconda would be enough. This is the way you can install it.
Create your notebook and from a code cell download the installer:

!wget https://repo.continuum.io/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh

Make it executable:

!chmod +x Miniconda3-4.5.4-Linux-x86_64.sh

and finally execute it.

!bash ./Miniconda3-4.5.4-Linux-x86_64.sh -b -f -p /usr/local

And you're ready to go.

Comments

  1. DevOps technology is designed with combining Development, Best Software Training Institute for DevOps
    Online Training, Provides DevOps Online Training Course, Classes by Real-Time Experts- Provides DevOps
    Online Training Course, Classes by Real-Time Experts- Naresh IT-Best online training Institute in Hyderabad.

    devops Online Training

    ReplyDelete

Post a Comment

Popular posts from this blog

jOOQ: code generation in Eclipse

jOOQ allows code generation from a database schema through ANT tasks, Maven and shell command tools. But if you're working with Eclipse it's easier to create a new Run Configuration to perform this operation. First of all you have to write the usual XML configuration file for the code generation starting from the database: <?xml version="1.0" encoding="UTF-8" standalone="yes"?> <configuration xmlns="http://www.jooq.org/xsd/jooq-codegen-2.0.4.xsd">   <jdbc>     <driver>oracle.jdbc.driver.OracleDriver</driver>     <url>jdbc:oracle:thin:@dbhost:1700:DBSID</url>     <user>DB_FTRS</user>     <password>password</password>   </jdbc>   <generator>     <name>org.jooq.util.DefaultGenerator</name>     <database>       <name>org.jooq.util.oracle.OracleDatabase</name>     ...

Exporting InfluxDB data to a CVS file

Sometimes you would need to export a sample of the data from an InfluxDB table to a CSV file (for example to allow a data scientist to do some offline analysis using a tool like Jupyter, Zeppelin or Spark Notebook). It is possible to perform this operation through the influx command line client. This is the general syntax: sudo /usr/bin/influx -database '<database_name>' -host '<hostname>' -username '<username>'  -password '<password>' -execute 'select_statement' -format '<format>' > <file_path>/<file_name>.csv where the format could be csv , json or column . Example: sudo /usr/bin/influx -database 'telegraf' -host 'localhost' -username 'admin'  -password '123456789' -execute 'select * from mem' -format 'csv' > /home/googlielmo/influxdb-export/mem-export.csv

Turning Python Scripts into Working Web Apps Quickly with Streamlit

 I just realized that I am using Streamlit since almost one year now, posted about in Twitter or LinkedIn several times, but never wrote a blog post about it before. Communication in Data Science and Machine Learning is the key. Being able to showcase work in progress and share results with the business makes the difference. Verbal and non-verbal communication skills are important. Having some tool that could support you in this kind of conversation with a mixed audience that couldn't have a technical background or would like to hear in terms of results and business value would be of great help. I found that Streamlit fits well this scenario. Streamlit is an Open Source (Apache License 2.0) Python framework that turns data or ML scripts into shareable web apps in minutes (no kidding). Python only: no front‑end experience required. To start with Streamlit, just install it through pip (it is available in Anaconda too): pip install streamlit and you are ready to execute the working de...