Conda Commands

Refer to Conda Interpreter Commands for a table of supported conda commands.

Conda Help

To get help for conda commands, run the command name followed by the --help flag.

Note:

The conda command is not run explicitly because the %conda interpreter provides the conda context.

Get help for all conda commands
```
%conda

--help
```
Get help for a specific conda command. Run the following command to get help with the install command:
```
%conda

install --help
```

Conda Info

The info command displays information about the conda installation, including the conda version and available channels.

%conda

info

Conda Search

The search command allows the user to search for packages and display associated information, including the package version and the channel where it resides.

Search for a specific package. Run the following command to search for the package scikit-learn.
```
%conda

search scikit-learn
```
Search for packages containing 'scikit' in the package name.
```
%conda

search '*scikit*'
```

Search for a specific version of a package.

%conda

search 'numpy==1.12'

%conda

search 'numpy>=1.12'

Search for a specific version on a specific channel.
```
%conda

search conda-forge::numpy
```

Enhanced Conda Commands

A set of enhanced conda commands in the conda environment lifecycle management package env-lcm supports the management of environments saved to Object Storage, including uploading, downloading, listing, and deleting available environments.

Help for conda lifecycle environment commands.

%conda

env-lcm --help

Usage: conda-env-lcm [OPTIONS] COMMAND [ARGS]...

  ADB-S Command Line Interface (CLI) to manage persistence of conda
  environments

Options:
  -v, --version  Show the version and exit.
  --help         Show this message and exit.

Commands:
  delete           Delete a saved conda environment
  download         Download a saved conda environment
  import           Create or update a conda environment from saved metadata
  list-local-envs  List locally available environments for use
  list-saved-envs  List saved conda environments
  upload           Save conda environment for later use

Creating Conda Environments

This section demonstrates creating and installing packages to a conda environment, then removing the environment. Here commonly used options available for environment creation and testing are illustrated. The environment exists for the duration of the notebook session and does not persist between sessions unless it is saved to Object Storage. For instructions that include both creating and persisting an environment for OML users, refer to Administrative task to create and save the conda environments. As an ADMIN user:

Use the create command to create an environment myenv and install the Python keras package.
Verify that the new environment is created, and activate the environment.
Install, then uninstall an additional Python package, pytorch, in the environment.
Deactivate and remove the environment.

Note:

The ADMIN user can access the conda environment from Python and R, but does not have the capability to run embedded Python and R execution commands.

For help with the conda create command, enter create --help in a %conda paragraph.

List Environments

Start by listing the environments available by default. Conda contains default environments with some core system libraries and conda dependencies. The active environment is marked with an asterisk (*).

%conda

env list

# conda environments:
#
base                   * /usr
conda-pack-env           /usr/envs/conda-pack-env

Create Conda Environment

Create conda environment called myenv with Python 3.10 for OML4Py compatibility and install the keras package.

%conda 

create -n myenv python=3.10 keras

Verify Environment Creation

Verify the myenv environment is in the list of environments. The asterisk (*) indicates active environments. The new environment is created but not activated.

%conda

env list

# conda environments:
#
myenv                   /u01/.conda/envs/myenv
base                  * /usr
conda-pack-env          /usr/envs/conda-pack-env

Activate the Environment

Activate the myenv environment and list the environments to verify the activation. The asterisk (*) next to the environment name confirms the activation.

%conda

activate myenv

Conda environment 'myenv' activated

List the environments available by default.


%conda

env list

# conda environments:
#
myenv                 * /u01/.conda/envs/myenv
base                    /usr
conda-pack-env          /usr/envs/conda-pack-env

Installing and Uninstalling Libraries

The ADMIN user can install and uninstall libraries into an environment using the install and uninstall commands. For help with the conda install and uninstall commands, type install --help and uninstall --help in a %conda paragraph.

Note:

When conda installs a package into an environment, it also installs any required dependencies. As shown here, it’s possible to install packages to an existing environment. As a best practice, to avoid dependency conflicts, simultaneously install all the packages you need in a specific environment.

Install Additional Packages

Install the pytorch package into the activated myenv environment.

%conda

install pytorch

List Packages in the Current Environment

List the packages installed in the current environment, and confirm that keras and pytorch are installed.

%conda

list

The output is similar to the following:

# packages in environment at /u01/.conda/envs/myenv:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                        main  
_openmp_mutex             5.1                       1_gnu  
blas                      1.0                         mkl  
.
.
.
fftw                      3.3.9                h27cfd23_1  
future                    0.18.2          py310h06a4308_1  
intel-openmp              2021.4.0          h06a4308_3561  
keras                     2.10.0          py310h06a4308_0  
keras-preprocessing       1.1.2              pyhd3eb1b0_0  
ld_impl_linux-64          2.38                 h1181459_1  
libffi                    3.3                  he6710b0_2  
libgcc-ng                 11.2.0               h1234567_1  
.
.
.
numpy-base                1.23.3          py310h8e6c178_1  
openssl                   1.1.1s               h7f8727e_0  
pip                       22.2.2          py310h06a4308_0  
pycparser                 2.21                     pypi_0    pypi
python                    3.10.6               haa1d7c7_1  
pytorch                   1.10.2          cpu_py310h6894f24_0  
readline                  8.2                  h5eee18b_0  
.
.
.
xz                        5.2.6                h5eee18b_0  
zlib                      1.2.13               h5eee18b_0

The output above has been truncated and does not show the complete list of packages.

Uninstall Package

Libraries can be uninstalled from an environment using the uninstall command. Let’s uninstall the pytorch package from the current environment.

%conda

uninstall pytorch

Verify Package was Uninstalled

List packages in current environment and verify that the pytorch package was uninstalled.

%conda

list

The output shown below does not contain the pytorch package.

# packages in environment at /u01/.conda/envs/myenv:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                        main  
_openmp_mutex             5.1                       1_gnu  
blas                      1.0                         mkl  
bzip2                     1.0.8                h7b6447c_0  
ca-certificates           2022.10.11           h06a4308_0  
certifi                   2022.9.24       py310h06a4308_0  
cffi                      1.15.1          py310h74dc2b5_0  
fftw                      3.3.9                h27cfd23_1  
future                    0.18.2          py310h06a4308_1  
intel-openmp              2021.4.0          h06a4308_3561  
keras                     2.10.0          py310h06a4308_0  
keras-preprocessing       1.1.2              pyhd3eb1b0_0  
ld_impl_linux-64          2.38                 h1181459_1  
libffi                    3.3                  he6710b0_2  
libgcc-ng                 11.2.0               h1234567_1  
libgfortran-ng            11.2.0               h00389a5_1  
libgfortran5              11.2.0               h1234567_1  
libgomp                   11.2.0               h1234567_1  
libstdcxx-ng              11.2.0               h1234567_1  
libuuid                   1.0.3                h7f8727e_2  
mkl                       2021.4.0           h06a4308_640  
mkl-service               2.4.0           py310h7f8727e_0  
mkl_fft                   1.3.1           py310hd6ae3a3_0  
mkl_random                1.2.2           py310h00e6091_0  
ncurses                   6.3                  h5eee18b_3  
ninja                     1.10.2               h06a4308_5  
ninja-base                1.10.2               hd09550d_5  
numpy                     1.23.3          py310hd5efca6_1  
numpy-base                1.23.3          py310h8e6c178_1  
openssl                   1.1.1s               h7f8727e_0  
pip                       22.2.2          py310h06a4308_0  
pycparser                 2.21                     pypi_0    pypi
python                    3.10.6               haa1d7c7_1  
readline                  8.2                  h5eee18b_0  
scipy                     1.9.3           py310hd5efca6_0  
setuptools                65.5.0          py310h06a4308_0  
six                       1.16.0             pyhd3eb1b0_1  
sqlite                    3.39.3               h5082296_0  
tk                        8.6.12               h1ccaba5_0  
typing-extensions         4.3.0           py310h06a4308_0  
typing_extensions         4.3.0           py310h06a4308_0  
tzdata                    2022f                h04d1e81_0  
wheel                     0.37.1             pyhd3eb1b0_0  
xz                        5.2.6                h5eee18b_0  
zlib                      1.2.13               h5eee18b_0

Removing Environments

If you don’t intend to upload the environment to Object Storage for the OML users in the database, you can simply exit the notebook session and it will go out of scope. Alternatively, it can be explicitly removed using the env remove command. Remove the myenv environment and verify it was removed. A best practice is to deactivate the environment prior to removal. For help on the env remove command, type env remove --help in the %conda interpreter.

Deactivate the environment.

%conda

deactivate

Conda environment deactivated

Remove the environment.

%conda

env remove -n myenv

List the environment to see if the environment is removed or not.

env list

# conda environments:
#
myrenv                   /u01/.conda/envs/myrenv
base                  *  /usr
conda-pack-env           /usr/envs/conda-pack-env

Remove all packages in environment /u01/.conda/envs/myenv.

Specify Packages for Installation

Install Packages from the conda-forge Channel

Conda channels are the locations where packages are stored. They serve as the base for hosting and managing packages. Conda packages are downloaded from remote channels, which are URLs to directories containing conda packages. The conda command searches a set of channels. By default, packages are automatically downloaded and updated from the default channel. The conda-forge channel is free for all to use. You can modify what remote channels are automatically searched. You might want to do this to maintain a private or internal channel. We use the conda-forge channel, a community channel made up of thousands of contributors, in the following examples.

Install a specific version of a Package.

To install a specific version of a package, use <package_name>=<version>.

Create an environment using conda-forge.


%conda

create -n mychannelenv -c conda-forge python=3.10

activate mychannelenv

Install a package from conda-forge by specifying the channel.
```
%conda

install scipy --channel conda-forge
```
Install a specific version of a package.
```
%conda

install scipy=0.15.0
```