fullOpen.fullExtensible.
An open-source no-code Visual ML platform. No paywall, no closed components, no external API keys.
Welcome to dashAI!
Pick a module to start
An open platform isn't one distributed for free: it's one that behaves openly.
dashAI is Full Open + Full Extensible. That's the editorial line.
Full Open.
Openness isn't a license statement: it's a measurable property of the system.
MIT licensed, no copyleft.
Integrable into institutional systems and commercial products. No obligation to release what you build on top.
No productive paywall.
Scheduling, deployment, RBAC and governance live in the core. The project you clone today is what reaches production.
No closed components.
Every official extension shares the core license. Auditable, modifiable, forkable under the same terms.
Native coverage, local execution.
⊳ Openness, in practiceClassification & regression
Thirty models over scikit-learn and boosting. Each one as a subclass of BaseModel.
- RandomForest
- XGBoost
- LightGBM
- SVM
- MLP
- +25
Transformers & seq-to-seq
Classifiers and neural translation models with native BLEU · ChrF · TER metrics.
- DistilBERT
- DeBERTa-v3
- ModernBERT
- NLLB
- OpusMT
- +19
Local LLMs
Llama, Mistral, Mixtral, Qwen and SmolLM on open weights. Generation as a native task.
- Llama
- Mistral
- Mixtral
- Qwen
- SmolLM
Text to image
PixArt-Sigma, SDXL, Stable Diffusion and ControlNet variants. Automatic CUDA detection.
- PixArt-Sigma
- SDXL
- SD v3
- SD v2
- ControlNet
- +6
The interface that emerges from the schema.
When you register a component, the UI isn't programmed: it's generated. The server exposes its Pydantic schema and the React frontend renders the form without prior knowledge.
RandomForestClassifier
Hyperparameters generated from MyForestSchema
Number of trees in the forest.
Maximum depth of the tree.
Function to measure split quality.
Use bootstrap samples when building each tree.
1. Schema → UI
Each form field is born from a Pydantic line. Type, range, default and description render automatically.
2. No frontend code
The new component touches zero React files. The UI shows up on its own once the backend registers it.
3. Browsable catalog
Tabular, NLP, translation, LLMs and generative vision coexist in one consistent UI — all on the same twelve abstractions.
Pick a model
Validation metrics
Orchestration
Full Extensible.
Each functional role has its base class. Twenty to forty lines for a new component. The UI emerges from the schema — it isn't programmed.
Schema-driven UI
The server exposes the Pydantic schema; the React frontend renders the form without prior knowledge.
Hot-install from the UI
One click installs an extension from PyPI. No restart, no external shell.
Public HTTP API
Any HTTP client — Python, Jupyter, CLI — queries components and launches jobs. GET /component/, POST /job/.
FastAPI + React, not desktop
The server runs anywhere, reachable from any browser — local, network or remote.
# A complete classifier in dashAI
from dashai.base import BaseModel
from dashai.core.schema_fields import schema_field, optimizer_int_field
from sklearn.ensemble import RandomForestClassifier
class MyForestSchema(BaseSchema):
n_estimators: schema_field(
optimizer_int_field(ge=1),
placeholder={
"optimize": False, "fixed_value": 100,
"lower_bound": 50, "upper_bound": 200,
},
description="Number of trees",
)
max_depth: schema_field(
optimizer_int_field(ge=1),
placeholder={
"optimize": False, "fixed_value": 2,
"lower_bound": 2, "upper_bound": 10,
},
description="Maximum depth",
)
class MyForest(BaseModel):
SCHEMA = MyForestSchema
def train(self, X, y):
self.model = RandomForestClassifier(
**self.params
).fit(X, y)
def predict(self, X):
return self.model.predict(X)Download dashAI.
git clone github.com/DashAISoftware/DashAILet's build this together.
dashAI is open source and open community. Join the conversation, propose components, share experiments.
Built across institutions.
dashAI is a Chilean academic project maintained by a network of research institutions and public funding bodies.