AIME MLC - Machine Learning Container management system

Easily install, run and manage Docker containers for Pytorch and Tensorflow deep learning frameworks.

Features

Setup and run a specific version of Pytorch or Tensorflow with one simple command
Run different versions of machine learning frameworks and required libraries in parallel
manages required libraries (CUDA, CUDNN, CUBLAS, etc.) in containers, without compromising the host installation
Clear separation of user code and framework installation, test your code with a different framework version in minutes
multi session: open and run many shell session on a single container simultaneously
multi user: separate container space for each user
multi GPU: allocate GPUs per user, container or session
Runs with the same performance as a bare metal installation
Repository of all major deep learning framework versions as containers
(New): create and configure containers in interactive mode
(New): GPU architecture switchable by flag or an environment variable

Installation

AIME machines come pre installed with AIME machine learning container management system for more information see: https://www.aime.info/mlc

Please read on in the AIME MLC Installation Guide how to install AIME MLC on your PC, workstation or server.

Usage

To view detailed information about all available commands, use:

mlc -h

To get help for a specific command (e.g., create), use:

mlc create -h

Create a machine learning container

mlc create container_name framework version [-w workspace_dir] [-d data_dir] [-m models_dir] [-s|--script] [-arch|--architecture gpu_architecture] [-g|--num_gpus all]

Create a new machine learning container

Available frameworks:

Pytorch, Tensorflow

The following architectures are currently available:

CUDA_BLACKWELL: NVIDIA RTX 50x0, RTX PRO 5000/6000 Blackwell

CUDA_ADA: NVIDIA RTX 40x0, RTX 5000/6000 Ada, L40, H100, H200

CUDA_AMPERE: NVIDIA RTX 30x0, RTX A5000/A6000, A100

CUDA: NVIDIA GTX 1080TI, RTX 2080TI, Titan RTX, Quadro 5000/6000, Tesla V100

ROCM6: AMD RX 7900, RX 9070, MI210, MI250X, MI300X, MI325X

ROCM5: AMD Radeon VII, MI100

Frameworks of later version will be mostly supported by the previous GPU generations if the installed driver on the host system doe support the required CUDA/ROCM version. Frameworks compiled for older GPU architectures are not compatible to newer GPU generations.

To print the current available gpu architectures, frameworks and corresponding versions, use:

mlc create --info

For CUDA_ADA architecture it will output:

Available gpu architectures (currently used):
CUDA, *CUDA_ADA*, CUDA_AMPERE, CUDA_BLACKWELL

Available frameworks and versions:

Pytorch:
2.7.0-aime, 2.7.0, 2.6.0, 2.5.1, 2.5.0, 2.4.0, 2.3.1-aime, 2.3.0, 2.2.2, 2.2.0, 2.1.2-aime, 2.1.1-aime, 2.1.0-aime, 2.1.0,
2.0.1-aime, 2.0.1, 2.0.0, 1.14.0a-nvidia, 1.13.1-aime, 1.13.0a-nvidia, 1.12.1-aime

Tensorflow:
2.16.1, 2.15.0, 2.14.0, 2.13.1-aime, 2.13.0, 2.12.0, 2.11.0-nvidia, 2.11.0-aime, 2.10.1-nvidia, 2.10.0-nvidia,
2.9.1-nvidia

Example to create a container in script mode using Pytorch 2.4.0 with the name 'my-container' and with mounted user home directory as workspace, /data and /models as data and models directory, use:

mlc create my-container Pytorch 2.6.0 -w /home/user_name/workspace -d /data -m /models -s -arch CUDA_AMPERE

To provide greater flexibility in selecting a GPU architecture, users can specify the desired architecture for the current container using the -arch cuda_architecture flag (default: host gpu architecture, auto-detected). If a fixed architecture is preferred for an entire session, it can be set by saving the desired GPU architecture in the MLC_ARCH environment variable, for example: export MLC_ARCH=CUDA_AMPERE

Open a machine learning container

mlc open container_name [-s|--script]

To open the created machine learning container "my-container" using the script mode

mlc open my-container -s

Will output:

[my-container] starting container
[my-container] opening shell to container

________                               _______________
___  __/__________________________________  ____/__  /________      __
__  /  _  _ \_  __ \_  ___/  __ \_  ___/_  /_   __  /_  __ \_ | /| / /
_  /   /  __/  / / /(__  )/ /_/ /  /   _  __/   _  / / /_/ /_ |/ |/ /
/_/    \___//_/ /_//____/ \____//_/    /_/      /_/  \____/____/|__/


You are running this container as user with ID 1000 and group 1000,
which should map to the ID and group for your user on the Docker host. Great!

[my-container] admin@aime01:/workspace$

The container is run with the access rights of the user. To use privileged rights like for installing packages with 'apt' within the container use 'sudo'. The default is that no password is needed for sudo, to change this behaviour set a password with 'passwd'.

Multiple instances of a container can be opened with mlc open. Each instance runs in its own process.

To exit an opened shell to the container type 'exit' on the command line. The last exited shell will automatically stop the container.

List available machine learning containers

mlc list will list all available containers for the current user

mlc list

will output for example:

Available ml-containers are:

CONTAINER           FRAMEWORK                  STATUS
[torch-vid2vid]     Pytorch-1.2.0              Up 2 days
[tf1.15.0]          Tensorflow-1.15.0          Up 8 minutes
[tf1-nvidia]        Tensorflow-1.14.0_nvidia   Exited (137) 1 week ago
[tf1.13.2]          Tensorflow-1.13.2          Exited (137) 2 weeks ago
[torch1.3]          Pytorch-1.3.0              Exited (137) 3 weeks ago
[tf2-gpt2]          Tensorflow-2.0.0           Exited (137) 7 hours ago

In case that you wish to see all containers created by all users:

mlc list -au

List the stats of active machine learning containers

mlc stats show all current running ml containers and their CPU and memory usage

mlc stats

Running ml-containers are:

CONTAINER           CPU %               MEM USAGE / LIMIT
[torch-vid2vid]     4.93%               8.516GiB / 63.36GiB
[tf1.15.0]          7.26%               9.242GiB / 63.36GiB

Start machine learning containers

mlc start container_name [-s|--script] to explicitly start a container

'mlc start' is a way to start the container to run installed background processes, like an installed web server, on the container without the need to open an interactive shell to it.

For opening a shell to the container just use 'mlc open', which will automatically start the container if the container is not already running.

Stop machine learning containers

mlc stop container_name [-s|--script] [-f|--force] to explicitly stop a container.

'mlc stop' on a container is comparable to a shutdown of a computer, all activate processes and open shells to the container will be terminated.

To force a stop on a container use:

mlc stop my-container -f

As by the other commands, the script mode is available using the flag -s:

mlc stop my-container -s

Remove/Delete a machine learning container

mlc remove container_name [-s|--script] [-f|--force] to remove the container.

Warning: the container will be unrecoverable deleted only data stored in the /workspace directory will be kept. Only use to clean up containers which are not needed any more.

mlc remove my-container

Update MLC

mlc update-sys to update the container managment system to latest version.

The container system and container repo will be updated to latest version. Run this command to check if new framework versions are available. On most systems privileged access (sudo password) is required to do so.

mlc update-sys

The force option (-f) is available too.

Supported ML containers

Pytorch Containers

Container Name	GPU Arch.	Build	Pytorch	Ubuntu Version	Python Version	Package Manager	CUDA/ROCM Version	CuDNN/MIOpen Version	NVIDIA/ROCM driver version
2.7.1-aime	CUDA_BLACKWELL	AIME	2.7.1	24.04	3.12.3	pip 24.0	12.8.93	9.7.1.26	570.124.06
2.7.1	CUDA_BLACKWELL	Official	2.7.1	22.04	3.11.13	conda 25.5.0	12.8.61	9.7.1.26	570.86.10
2.7.1-aime	CUDA_ADA	AIME	2.7.1	22.04	3.10.12	pip 22.0.2	12.6.20	9.5.1.17	560.28.03
2.7.1	CUDA_ADA	Official	2.7.1	22.04	3.11.13	conda 25.5.0	12.6.85	9.5.1.17	560.35.05
2.7.1-aime	CUDA_AMPERE	AIME	2.7.1	22.04	3.10.12	pip 22.0.2	11.8.89	9.1.0.70	520.61.05
2.7.1	CUDA_AMPERE	Official	2.7.1	22.04	3.11.13	conda 25.5.0	11.8.89	9.1.0.70	520.61.05
2.7.0-aime	CUDA_BLACKWELL	AIME	2.7.0	24.04	3.12.3	pip 24.0	12.8.93	9.7.1.26	570.124.06
2.7.0	CUDA_BLACKWELL	Official	2.7.0	22.04	3.11.12	conda 25.3.1	12.8.61	9.7.1.26	570.86.10
2.7.0-aime	CUDA_ADA	AIME	2.7.0	22.04	3.10.12	pip 22.0.2	12.6.20	9.5.1.17	560.28.03
2.7.0	CUDA_ADA	Official	2.7.0	22.04	3.11.12	conda 25.3.1	12.6.85	9.5.1.17	560.35.05
2.7.0-aime	CUDA_AMPERE	AIME	2.7.0	22.04	3.10.12	pip 22.0.2	11.8.89	9.1.0.70	520.61.05
2.7.0	CUDA_AMPERE	Official	2.7.0	22.04	3.11.12	conda 25.3.1	11.8.89	9.1.0.70	520.61.05
2.6.0	ROCM6	Official	2.6.0	22.04	3.10.12	pip 22.0.2	6.2.2	3.2.0	6.10.5
2.6.0	CUDA_ADA	AIME	2.6.0	22.04	3.10.12	pip 22.0.2	12.4.131	9.1.0.70	550.54.15
2.6.0	CUDA_AMPERE	AIME	2.6.0	22.04	3.10.12	pip 22.0.2	11.8.89	8.9.6.50	520.61.05
2.5.1	CUDA_ADA	AIME	2.5.1	22.04	3.10.12	pip 22.0.2	12.1.105	8.9.0.131	530.30.02
2.5.1	CUDA_AMPERE	AIME	2.5.1	22.04	3.10.12	pip 22.0.2	11.8.89	8.9.6.50	520.61.05
2.5.0	CUDA_ADA	Official	2.5.0	22.04	3.11.10	conda 24.9.2	12.1.105	9.1.0	530.30.02
2.5.0	CUDA_AMPERE	Official	2.5.0	22.04	3.11.10	conda 24.9.2	11.8.89	9.1.0	520.61.05
2.4.0	CUDA_ADA	Official	2.4.0	22.04	3.11.9	conda 24.5	12.1.105	9.1.0	530.30.02
2.4.0	CUDA_AMPERE	Official	2.4.0	22.04	3.11.9	conda 24.5	11.8.89	9.1.0	520.61.05
2.3.1-aime	CUDA_ADA	AIME	2.3.1	22.04	3.10.12	pip 22.0.2	12.1.105	8.9.7.29	530.30.02
2.3.1-aime	CUDA_AMPERE	AIME	2.3.1	22.04	3.10.12	pip 22.0.2	11.8.87	8.9.6.50	520.61.05
2.3.0	CUDA_ADA	Official	2.3.0	22.04	3.10.14	conda 23.5.2	12.1.105	8.9.7.29	530.30.02
2.2.2	CUDA_ADA	AIME	2.2.2	22.04	3.10.12	pip 22.0.2	12.1.105	8.9.7.29	530.30.02
2.2.0	CUDA_ADA	Official	2.2.0	22.04	3.10.13	conda 23.9.0	12.1.105	8.9.0.131	530.30.02
2.1.2-aime	CUDA_ADA	AIME	2.1.2	22.04	3.10.12	pip 22.0.2	12.1.105	8.9.0.131	530.30.02
2.1.1-aime	CUDA_ADA	AIME	2.1.1	22.04	3.10.12	pip 22.0.2	12.1.105	8.9.0.131	530.30.02
2.1.0-aime	CUDA_ADA, CUDA_AMPERE	AIME	2.1.0	22.04	3.10.12	pip 22.0.2	11.8.89	8.9.0.131	520.61.05
2.1.0	CUDA_ADA	Official	2.1.0	22.04	3.10.13	conda 23.9.0	12.1.105	8.9.0.131	530.30.02
2.0.1-aime	CUDA_ADA, CUDA_AMPERE	AIME	2.0.1	22.04	3.10.12	pip 22.0.2	11.8.89	8.9.0.131	520.61.05
2.0.1	CUDA_ADA, CUDA_AMPERE	Official	2.0.1	20.04	3.8.10	pip 20.0.2	11.8.89	8.9.0.131	520.61.05
2.0.0	CUDA_ADA, CUDA_AMPERE	Official	2.0.0	20.04	3.8.10	pip 20.0.2	11.8.89	8.6.0.163	520.61.05
1.14.0a-nvidia	CUDA_ADA	NVIDIA	1.14.0a0	20.04	3.8.10	pip 21.2.4	12.0.146	8.7.0.84	525.85.11
1.13.1-aime	CUDA_ADA	AIME	1.13.1	20.04	3.8.10	pip 20.0.2	11.8.89	8.6.0.163	520.61.05
1.13.0a-nvidia	CUDA_ADA	NVIDIA	1.13.0a0	20.04	3.8.13	pip 21.2.4	11.8.89	8.6.0.163	520.61.03
1.12.1-aime	CUDA_ADA	AIME	1.12.1	20.04	3.8.10	pip 20.0.2	11.8.89	8.6.0.163	520.61.05

Tensorflow Containers

Container Name	GPU Arch.	Build	Tensorflow Version	Ubuntu Version	Python Version	Package Manager	CUDA Version	CuDNN Version	NVIDIA driver version
2.16.1	CUDA_ADA	Official	2.16.1	22.04	3.11.0rc1	pip 24.0	12.3.107	8.9.6.50	545.23.06
2.15.0	CUDA_ADA	Official	2.15.0	22.04	3.11.0rc1	pip 23.3.1	12.3.103	8.9.6.50	545.23.06
2.14.0	CUDA_ADA, CUDA_AMPERE	Official	2.14.0	22.04	3.11.0rc1	pip 23.2.1	11.8.89	8.6.0.163	525.85.12
2.13.1-aime	CUDA_ADA, CUDA_AMPERE	AIME	2.13.1	22.04	3.10.12	pip 22.0.2	11.8.89	8.9.0.131	520.61.05
2.13.0	CUDA_ADA, CUDA_AMPERE	Official	2.13.0	20.04	3.8.10	pip 23.0.1	11.8.89	8.6.0.163	525.85.12
2.12.0	CUDA_ADA, CUDA_AMPERE	Official	2.12.0	20.04	3.8.10	pip 23.0.1	11.8.89	8.6.0.163	525.85.12
2.11.0-nvidia	CUDA_ADA	NVIDIA	2.11.0	20.04	3.8.10	pip 22.3.1	12.0.146	8.7.0.84	525.85.12
2.11.0-aime	CUDA_ADA	AIME	2.11.0	20.04	3.8.10	pip 20.0.2	11.8.89	8.6.0.163	520.61.05
2.10.1-nvidia	CUDA_ADA	NVIDIA	2.10.1	20.04	3.8.10	pip 22.3.1	11.8.89	8.7.0.84	520.61.05
2.10.0-nvidia	CUDA_ADA	NVIDIA	2.10.0	20.04	3.8.10	pip 22.2.2	11.8.89	8.6.0.163	520.61.05
2.9.1-nvidia	CUDA_ADA	NVIDIA	2.9.1	20.04	3.8.10	pip 22.2.2	11.8.89	8.6.0.163	520.61.03

Name		Name	Last commit message	Last commit date
Latest commit History 89 Commits
LICENSE.txt		LICENSE.txt
README.md		README.md
aime-mlc-installation-guide.md		aime-mlc-installation-guide.md
ml_images.repo		ml_images.repo
mlc		mlc
mlc-create		mlc-create
mlc-export		mlc-export
mlc-import		mlc-import
mlc-list		mlc-list
mlc-open		mlc-open
mlc-remove		mlc-remove
mlc-start		mlc-start
mlc-stats		mlc-stats
mlc-stop		mlc-stop
mlc-update-sys		mlc-update-sys
mlc.py		mlc.py

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AIME MLC - Machine Learning Container management system

Features

Installation

Usage

Create a machine learning container

Open a machine learning container

List available machine learning containers

List the stats of active machine learning containers

Start machine learning containers

Stop machine learning containers

Remove/Delete a machine learning container

Update MLC

Supported ML containers

Pytorch Containers

Tensorflow Containers

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AIME MLC - Machine Learning Container management system

Features

Installation

Usage

Create a machine learning container

Open a machine learning container

List available machine learning containers

List the stats of active machine learning containers

Start machine learning containers

Stop machine learning containers

Remove/Delete a machine learning container

Update MLC

Supported ML containers

Pytorch Containers

Tensorflow Containers

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