DashAI 0.9.5

DashAI: a graphical toolbox for training, evaluating and deploying state-of-the-art AI models.

A graphical toolbox for training, evaluating and deploying state-of-the-art AI models

Desktop installers (Windows / macOS / Linux)

The easiest way to get started. Desktop installers, ready to use, are published with every release. They are CPU only and bundle everything you need, so no Python or extra setup is required.

Download the file for your system from the latest release:

• Windows (x64): dashAI-<version>-x64-windows.exe

• macOS (Apple Silicon): dashAI-<version>-arm-osx.dmg

• macOS (Intel): dashAI-<version>-x64-osx.dmg

• Linux (x64): dashAI-<version>-x64-linux.AppImage

On Windows and macOS, run the installer, launch dashAI, and the graphical interface opens automatically.

On Linux, make the AppImage executable and run it:

$ chmod +x dashAI-<version>-x64-linux.AppImage
$ ./dashAI-<version>-x64-linux.AppImage

The AppImage bundles its own Python, so nothing needs to be installed. It requires glibc 2.35 or newer (Ubuntu 22.04+, Debian 12+, Fedora 36+, and most distributions from 2022 on) and FUSE 2 to mount. If FUSE is missing, run it with ./dashAI-<version>-x64-linux.AppImage --appimage-extract-and-run.

When double clicked, the AppImage opens a terminal window to show the server logs. This needs a terminal emulator, which every standard desktop (GNOME, KDE, XFCE, and others) already provides, so no setup is required. On a minimal system with no terminal emulator the log window is skipped, but the app still starts and opens the browser as usual.

Note: the desktop installers ship with CPU only PyTorch and llama-cpp-python. For NVIDIA (CUDA) or AMD (ROCm) GPU acceleration, use the pip installation below.

Installation (PyPI)

dashAI needs Python 3.10 or greater. We strongly recommend installing it inside an isolated environment (venv or conda) to avoid clashes with other packages.

Installing dashAI also installs PyTorch with the default build for your platform, which works out of the box on CPU. To enable GPU acceleration (NVIDIA CUDA or AMD ROCm), or to force a CPU only build, reinstall PyTorch from the matching index as shown in step 3. llama-cpp-python is required to run LLM models (GGUF / Llama, Mistral, Qwen, and similar) inside the app, but it is never installed automatically, so install it in step 3 if you need those models.

1. Create an environment

Linux / macOS (venv)

$ python3 -m venv .venv
$ source .venv/bin/activate

Windows (venv, PowerShell)

> python -m venv .venv
> .venv\Scripts\Activate.ps1

Any OS (conda)

$ conda create -n dashai python=3.12
$ conda activate dashai

2. Install dashAI

With the environment active:

$ pip install dashai

3. Select a PyTorch build and (optional) llama-cpp

This step is optional on CPU (step 2 already installed a working PyTorch). Run the section below that matches your hardware to pick a specific build.

CPU only (Linux / macOS / Windows)

On Linux the default PyTorch ships the large CUDA build; reinstall from the CPU index if you want a smaller CPU only install:

$ pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu

# Optional, for GGUF / Llama models (precompiled CPU wheel, no build tools):
$ pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu

NVIDIA GPU (CUDA 12.8)

# Torch CUDA 12.8 (prebuilt wheels)
$ pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128

# Llama compiled with CUDA offload (requires build tools, see below)
$ pip install llama-cpp-python -C cmake.args="-DGGML_CUDA=on"

AMD GPU (ROCm 6.4)

# Torch ROCm (prebuilt wheels)
$ pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6.4

# Llama compiled with HIP/ROCm offload (requires build tools, see below)
$ pip install llama-cpp-python -C cmake.args="-DGGML_HIP=on"

Build tools for GPU llama-cpp

The -C cmake.args=... commands above compile llama-cpp-python from source. They require:

• CMake (required to drive the build)

• A C compiler:

  • Linux: gcc or clang

  • Windows: Visual Studio (C++ build tools / MSVC) or MinGW

  • macOS: Xcode

• NVIDIA (CUDA): NVIDIA drivers and the NVIDIA CUDA Toolkit. Use version >=12.8 for RTX 5000 series GPUs to work.

• AMD (ROCm): the ROCm / HIP SDK and AMD drivers.

If you want to skip compilation, precompiled llama-cpp-python wheels are available for CPU and CUDA:

$ pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
$ pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/<cuda-version>

Replace <cuda-version> with your CUDA tag. Prebuilt wheels are published for cu118, cu121, cu122, cu123, cu124, cu125, cu130 and cu132 (for example cu124). See the llama-cpp-python installation docs for the available wheels and other backend options.

4. Run dashAI

Start the server and graphical interface with:

$ dashai

Then open http://localhost:8000/ in your browser to access the dashAI graphical interface.

Docker

dashAI can also run inside a container. Two Dockerfiles are provided at the repository root.

CPU image

Dockerfile builds a CPU only image (CPU PyTorch). Build and run it with:

$ docker build -t dashai .
$ docker run -p 8000:8000 dashai

NVIDIA GPU image (CUDA)

Dockerfile.cuda builds a CUDA enabled image (CUDA 12.8 PyTorch and llama-cpp-python compiled with CUDA offload). Build and run it with:

$ docker build -t dashai:cuda -f Dockerfile.cuda .
$ docker run --gpus all -p 8000:8000 dashai:cuda

Then open http://localhost:8000/ in your browser.

To pass the host GPU into the container with --gpus all you need the NVIDIA drivers plus the runtime that wires the GPU into Docker. How you get that runtime depends on your setup:

• Native Linux Docker: install the NVIDIA Container Toolkit on the host.

• Docker Desktop (Windows / macOS): the GPU runtime is bundled with the WSL 2 backend, so you only install the NVIDIA driver on Windows and enable the WSL 2 backend. See the Docker Desktop GPU docs.

• Docker Engine inside a WSL 2 distro (without Docker Desktop): install the NVIDIA Container Toolkit inside the WSL distro, following the CUDA on WSL guide.

Test datasets

Some datasets you can use to try dashAI are available here.

Development

To download and run the development version of dashAI, first, download the repository and switch to the developing branch:

$ git clone https://github.com/DashAISoftware/DashAI.git
$ git checkout develop

Frontend

Warning

All commands executed in this section must be run from DashAI/front. To move there, run:

$ cd DashAI/front

Prepare the environment

1. Install the LTS node version.

2. Install Yarn package manager following the instructions located on the yarn getting started page.

3. Move to DashAI/front and Install the project packages using yarn:

$ cd DashAI/front
$ yarn install

Running the frontend

Move to DashAI/front if you are not on that route:

$ cd DashAI/front

Then, launch the front-end development server by running the following command:

$ yarn start

Backend

Prepare the environment

First, set the python enviroment, for that you can use conda:

Later, install the requirements:

$ pip install -r requirements.txt
$ pip install -r requirements-dev.txt
$ pre-commit install

Running the Backend

There are two ways to run dashAI:

1. By executing dashAI as a module from the root of the repository:

$ python -m DashAI

2. Or, installing the default build:

$ pip install . -e
$ dashai

Optional Flags

Setting the local execution path

With the –local-path (alias -lp) option you can determine where dashAI will save its local files, such as datasets, experiments, runs and others. The following example shows how to set the folder in the local .DashAI directory:

$ python -m DashAI --local-path "~/.DashAI"

Setting the logging level

Through the –logging-level (alias -ll) parameter, you can set which logging level the dashAI backend server will have.

$ python -m DashAI --logging-level INFO

The possible levels available are: DEBUG, INFO, WARNING, ERROR, CRITICAL.

Note that the –logging-level not only affects the dashAI loggers, but also the datasets (which is set to the same level as dashAI) and the SQLAlchemy (which is only activated when logging level is DEBUG).

Disabling automatic browser opening

By default, dashAI will open a browser window pointing to the application after starting. If you prefer to disable this behavior, you can use the –no-browser (alias -nb) flag:

$ python -m DashAI --no-browser

Checking Available Options

You can check all available options through the command:

$ python -m DashAI --help

Database Migrations

Migrations are managed through Alembic.

They are automatically executed when starting dashAI. However, if you want to run them manually, you can do so using the following command (inside the DashAI/ folder):

$ alembic upgrade head

This command applies all pending migrations up to the latest revision.

—

Creating a New Migration

After modifying the database models, a new migration can be generated using:

$ alembic revision --autogenerate -m "<<Your message here>>"

Where <<Your message here>> is a brief description of the changes introduced (e.g., add model metadata table, update dataset schema).

Generated migrations are located in the alembic/versions directory and must be committed to the repository.

It is strongly recommended to review the autogenerated migration file before applying it, as Alembic may not always detect complex changes correctly.

—

Applying Migrations

To apply all pending migrations:

$ alembic upgrade head

To upgrade to a specific revision:

$ alembic upgrade <revision_id>

—

Downgrading Migrations

If you need to revert database changes, migrations can be downgraded using:

$ alembic downgrade -1

This command reverts the last applied migration.

To downgrade to a specific revision:

$ alembic downgrade <revision_id>

—

Checking Migration Status

To view the current migration applied to the database:

$ alembic current

To list the full migration history:

$ alembic history

—

Testing

Execute tests

dashAI uses pytest to perform the backend tests. To execute the backend tests

1. Move to DashAI/back

$ cd DashAI/back

2. Run:

$ pytest tests/

Note

The database session is parametrized in every endpoint as db: Session = Depends(get_db) so we can test endpoints on a test database without making changes to the main database.

Acknowledgments

This project is developed in collaboration with:

• University of Chile - Leading Institution

• CENIA - Associated Institution

• IMFD - Collaborator

• Unholster - Industry Partner

Supported by ANID through Fondef IDEA ID25I10330, Fondef VIU23P 0110, and grants supporting the centers CENIA (FB210017) and IMFD (ICN17_002). Developed by students of DCC UChile and UTFSM.

To see the full list of contributors, visit in Contributors the dashAI repository on Github.