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-Jungle Grid is the execution layer for AI workloads and agents. Submit inference, training, fine-tuning, and batch jobs by intent. Jungle Grid handles GPU placement, routing, execution, logs, and recovery.
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Jungle Grid GitHub Defaults
-## Community Health Files
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Community health files, templates, and public profile content for Jungle Grid.
-This repository includes standard community health files in `.github/`.
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-**The execution layer for AI workloads and agents.**
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Jungle Grid
-Jungle Grid lets developers submit AI workloads by intent - inference, training, fine-tuning, and batch jobs - without manually choosing GPUs, regions, providers, pods, or infrastructure settings.
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The execution layer for AI workloads and agents.
-Instead of asking developers to think like cloud infrastructure engineers, Jungle Grid lets them say what they want to run. The platform handles placement, routing, execution, logs, lifecycle tracking, and recovery across available GPU capacity.
-
-Website: https://junglegrid.dev
-Docs: https://junglegrid.dev/docs
-
----
-
-## What is Jungle Grid?
-
-Jungle Grid is a GPU orchestration platform built for AI teams, developers, and agents.
-
-Modern AI workloads are hard to run because developers often have to answer low-level infrastructure questions before they can even execute a job:
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-- Which GPU should I pick?
-- Is this model too large for the available VRAM?
-- Which provider has capacity right now?
-- What happens if the node fails?
-- How do I track logs and job state?
-- How does my AI agent run real workloads safely?
-
-Jungle Grid abstracts that complexity.
-
-Developers describe the workload, and Jungle Grid handles the execution layer underneath.
-
----
-
-## Core idea
-
-Traditional GPU platforms start with hardware:
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-> Pick a GPU.
-> Pick a region.
-> Configure a pod.
-> Manage execution yourself.
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-Jungle Grid starts with workload intent:
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-> Run this inference job.
-> Fine-tune this model.
-> Execute this batch workload.
-> Let my agent submit and monitor this job.
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-Jungle Grid then decides how and where the workload should run.
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----
-
-## What Jungle Grid handles
-
-Jungle Grid provides:
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-- Workload-first job submission
-- Intent-based workload classification
-- GPU fit checks based on workload requirements
-- Routing across available compute capacity
-- Provider-aware placement
-- Managed job execution
-- Job lifecycle tracking
-- Live and historical logs
-- Retry and recovery behavior
-- Artifact handling
-- CLI-based developer workflow
-- API/service integration
-- MCP integration for AI agents
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----
-
-## Supported workload types
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-Jungle Grid is designed for AI workloads such as:
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-- Inference
-- Training
-- Fine-tuning
-- Batch jobs
-- Model evaluation
-- Data processing
-- Agent-triggered compute tasks
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----
-
-## Why Jungle Grid exists
-
-AI development is moving toward agents, automation, and workload-level execution.
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-But most compute platforms still expose infrastructure-first primitives. That means developers and agents still need to think about GPU types, pod management, regions, capacity failures, and operational details.
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-Jungle Grid exists to make compute feel closer to an execution layer:
-
-```text
-Intent -> Routing -> GPU placement -> Execution -> Logs -> Results
-```
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-The goal is simple:
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-Developers and agents should submit workloads, not manage GPU infrastructure.
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----
-
-## Quick start
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-Install and authenticate with the Jungle Grid CLI:
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-```bash
-npx @jungle-grid/cli@latest login
-```
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-This command runs the Jungle Grid CLI without requiring a global install and starts the login flow.
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-Check your authenticated user:
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-```bash
-npx @jungle-grid/cli@latest whoami
-```
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-This command confirms that the CLI is connected to your Jungle Grid account.
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-Submit an inference workload:
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-```bash
-npx @jungle-grid/cli@latest submit \
- --workload inference \
- --model-size 7 \
- --image pytorch/pytorch:2.4.0-cuda12.1-cudnn9-runtime \
- --name chat-infer
-```
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-This command submits an inference job, tells Jungle Grid the approximate model size, provides the container image to run, and gives the job a readable name.
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-Check jobs:
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-```bash
-npx @jungle-grid/cli@latest jobs
-```
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-This command lists your submitted workloads.
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-Check the status of a specific job:
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-```bash
-npx @jungle-grid/cli@latest status
-```
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-This command shows the current lifecycle state of a submitted job.
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----
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-## CLI workflow
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-The Jungle Grid CLI is built around a simple loop:
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-```text
-login -> submit workload -> inspect status -> view logs -> collect results
-```
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-The CLI lets developers interact with Jungle Grid from their terminal without manually managing GPU servers or provider dashboards.
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----
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-## MCP for agents
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-Jungle Grid includes an MCP layer so AI agents can submit and monitor workloads from inside agent workflows.
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-This allows agentic systems to move from planning to execution.
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-Instead of only suggesting that a workload should run, an agent can use Jungle Grid to:
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-- Estimate a workload
-- Submit a job
-- Monitor job state
-- Retrieve logs
-- Track completion
-- Reason over execution results
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-This makes Jungle Grid useful as an execution backend for AI coding agents, internal automation agents, research agents, and developer tools.
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