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Environments

Table of Contents​

Overview​

Using Jarvislabs, you can create one of the following instances:

  • PyTorch
  • FastAI
  • Tensorflow

To simplify your workflow, we regularly install and update the latest versions of these software packages. As your projects grow more complex, you may want to create and maintain separate environments. You can create new environments using:

  • Conda
  • Pyenv

Creating and Managing Environments​

Creating and Managing a New Environment Using Conda​

Create a New Environment​

To create a new environment with Python 3.10, use the following command:

conda create --prefix </path/yourEnvName> <python-version>

Example:

conda create --prefix /home/myenv python=3.10 ipykernel -y

The --prefix option allows you to specify the installation path. Installations in the /home directory persist when you pause and resume an instance.

Note: If you do not use the --prefix option, the environment is installed in the /root location, which resets when you pause and resume an instance.

Activate the New Conda Environment​

To install new libraries in the environment, activate it using:

conda activate /home/myenv/

If you encounter errors while activating, try the following commands in a terminal:

conda init bash
source .bashrc

Start a New Kernel from JupyterLab​

To use the new environment in JupyterLab, set up the kernel:

conda activate /home/myenv/
python -m ipykernel install --user --name=myenv

After refreshing JupyterLab, you can create a notebook with the new conda environment.

Jarvislabs.ai Launch

Creating a New Environment Using Venv​

To create a new environment with the existing Python version, use:

python -m venv NAME

Replace NAME with your desired environment name.

Example:

python -m venv myenv

To use a different Python version:

python3.12 -m venv new_venv

Ensure Python 3.12 is installed on your system.

Activate the environment you just created:

source NAME/bin/activate