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Quick Start

This guide walks you from "nothing installed" to a published validation session in roughly fifteen minutes. The profiler is always operated against an EdgeFirst Studio session.

1. If you haven't already, log in to EdgeFirst Studio

  1. Go to EdgeFirst Studio
  2. If you do not have an account, create a free account on the landing page
  3. Log in with your username and password

2. Create your project in EdgeFirst Studio

  1. From the User Home Page screen, click the "Projects" button.

    Go To Projects
    Go To Projects

  2. Now that you are in the "Projects" page, create your first project by clicking on the "New Project" button on the top-right corner of the page.

    Create Project
    Create Project

  3. Provide a name and a description of the project as shown in the example below. Click the "Create" button to create your new project.

    Project Details
    Project Details

  4. Your created project will be shown like the example below.

    New Project
    New Project

3. Create an Ultralytics training session

Use your own project

Profiling must be run against a project you own. Running a profiling or validation session on the Sample Project — or any public, read-only project — will fail with an error. Make sure you created your own project in Step 2 before continuing.

  1. Click on "Model Experiments" of your created project

    Model Experiments Page
    Model Experiments Page

  2. Create a new experiment

    Model Experiments Page
    Model Experiments Page

  3. Navigate to the "Training Sessions"

    Training Sessions
    Training Sessions

  4. Create a new training session by clicking the "Actions" dropdown menu on the top right of the page and then click the "+ New" button

    New Session Button
    New Session Button

  5. Start a YOLOv8n detection model by following these settings. Once the settings are set, click on "Start Session" at the bottom of the window

    Ultralytics Training Settings
    Ultralytics Training Settings

    This will start a training session in progress.

    Ultralytics Training Progress
    Ultralytics Training Progress

    InsufficientInstanceCapacity

    InsufficientInstanceCapacity Error

    If you see this error after starting your training session, retry creating the session. This can happen when AWS reports that no EC2 instances are currently available to launch; the current workaround is to retry.

    No Training Charts

    No Training Charts

    Training sessions configured without epochs will not generate loss or metric charts, since the chart x-axis is epoch-based. This is expected behavior.

  6. The completed training session should look like the following

    Ultralytics Training Completed
    Ultralytics Training Completed

  7. We support model conversion and optimization workflows that enable trained models to be deployed on a wide range of target platforms and hardware architectures.

    Follow the model conversion workflow that corresponds to your target platform. If you are deploying to a Windows/Linux PC or macOS system, you can skip this section and proceed directly to the next step to validate the ONNX model, which is automatically generated as part of the training session outputs.

    If you are deploying to one of the supported hardware platforms listed below, follow the platform-specific conversion instructions for your target device.

    Converter Supported Targets Output Format Docs
    TFLite Converter NXP i.MX 8M Plus (VIP8000), generic CPU/NPU TFLite delegates .tflite flatbuffer TFLite Converter
    Neutron Converter NXP i.MX 95, i.MX 943/952, S32N79, MCX N54x/N94x, i.MX RT700, S32K5 .tflite flatbuffer with Neutron microcode Neutron Converter
    TensorRT Converter NVIDIA Jetson (Orin Nano Super validated; broader lineup in progress) .tensorrt.zip bundle (engine built on-device) TensorRT Converter
    Ara2 Converter NXP Ara240 DNPU .dvm Dataflow Virtual Machine binary Ara2 Converter
    Hailo Converter Hailo-8 (26 TOPS), Hailo-8L (13 TOPS) .hef Hailo Executable Format Hailo Converter

  8. All converted models should appear under the model artifacts of the training session card. Click on the training session card to expand for more details.

    YOLOv8n Detection Model Artifacts
    YOLOv8n Detection Model Artifacts

    Once you have converted your model, you can proceed towards profiling and validating the performance of your model next.

4. Install the profiler

The recommended convenience path is pip. The wheel ships the same native binary the platform installers deliver, and pulls in the few Python-side helpers needed for end-to-end workflows. Platform installers are provided for environments where Python is not available.

pip install edgefirst-profiler

curl -fsSL https://raw.githubusercontent.com/EdgeFirstAI/profiler-cli/main/install.sh | bash

irm https://raw.githubusercontent.com/EdgeFirstAI/profiler-cli/main/install.ps1 | iex

Confirm the install:

edgefirst-profiler --version

For per-target details (NPU delegates, runtime libraries, hardware-specific quirks) see the installation guides.

5. Sign in to EdgeFirst Studio (profiler CLI)

edgefirst-profiler login

The interactive prompt asks for server, username, and password. The credentials are saved to ~/.config/edgefirststudio/token and refresh automatically while you are using the profiler. For headless / CI flows, see Connecting to EdgeFirst Studio.

6. Launch the TUI

Running edgefirst-profiler with no subcommand launches the interactive terminal UI. Any explicit subcommand — validate, login, publish, report — bypasses the TUI and runs headlessly.

edgefirst-profiler

The TUI opens on the F1 Help screen, which lists the keybindings and the four screens available to you:

EdgeFirst Profiler — F1 Help screen, shown on launch
EdgeFirst Profiler — F1 Help screen, shown on launch

Four function keys switch between screens:

Key Screen Purpose
F1 Help Keybindings and a short orientation — the landing screen
F2 Studio Sign in to EdgeFirst Studio, browse projects, run validation sessions
F3 Files Browse the local filesystem
F4 Profiler Configure and run a profiling pass with a live dashboard

Press q to quit (disabled while typing into form fields). Ctrl-C always quits.

7. Run a validation session

The profiler is operated against a Studio validation session. Both paths produce the same Studio session card and the same set of accuracy charts.

Validation sessions need a writable project

Creating a validation session requires write access to the Studio project. You cannot validate against the read-only public Sample Project directly — first copy its dataset into a project you own (and add your model), then create the training and validation session there.

Press F2 to switch to the Studio screen. If you are not signed in, the login form appears first.

F2 Studio — login form
F2 Studio — login form

Once signed in, navigate the explorer:

Projects  →  Experiments  →  Training Sessions  →  Artifacts
F2 Studio — explorer drilling into a training session
F2 Studio — explorer drilling into a training session

Each artifact is prefixed with a colored dot indicating whether it can be deployed on the current host:

Indicator Status Meaning Example artifacts
🟢 Green Deployable Format recognized and all runtime requirements met on this host. .onnx (Generic ONNX), .tflite (Generic TFLite)
🟠 Orange Conditions not confirmed Known deployable format for a specific target, but the required hardware, runtime, or accelerator was not detected. .hef (Hailo-8L runtime absent), .dvm (NXP Ara240 not present), .engine (no CUDA host), .imx95.tflite (different SoC)
🔴 Red Not deployable Supporting file, archive, or unrecognized format — not a model the profiler can run directly. labels.txt, _saved_model.zip, .tensorrt.zip

Select an artifact and choose Validate. The profiler creates a new validation session in Studio, downloads anything missing, jumps to F4 Profiler, and starts the run.

F2 Studio — confirming Validate against a model artifact
F2 Studio — confirming Validate against a model artifact

Before the run starts, a launch dialog appears. For ONNX models it first asks which execution provider to use (CPU, CUDA if available, or CoreML on macOS). Then it shows four per-stage depth sliders — Capture, Preprocess, Inference, and Postprocess — each defaulting to Auto, which resolves to the value measured fastest for your model, runtime, and host. Press Enter to accept the Auto defaults and start immediately, or c to customize the depths. The dialog warns if a platform constraint (such as the i.MX 95 Neutron single-bind delegate) holds a stage to a single thread.

The F4 dashboard streams iteration-level latency, system metrics, and per-stage timings while the run executes:

EdgeFirst Profiler — F4 dashboard during a run
EdgeFirst Profiler — F4 dashboard during a run

The system-metrics row includes live power draw and on-board temperatures wherever the hardware exposes a sensor. The profiler shows a power meter only for the rails it can actually read — the CPU, GPU, Neural Engine, and DRAM rails on Apple Silicon, and the real board rails on a Linux device with a supported power monitor (for example an NVIDIA Jetson's board-input rail and its per-component breakdown). On a target with no power sensor, no power meters are shown — rather than a row pinned at 0 W — and the session report notes that power is unavailable. Temperatures are read from the platform's thermal zones and hwmon sensors, so the readout reflects what each board actually measures.

When the run finishes, a completion summary shows the headline numbers and the path to the trace file. The artifacts upload to Studio automatically and the cloud validator is triggered.

╔═ Profiling Complete ═════════════════════════════╗
║                                                  ║
║  Iterations: 100                                 ║
║                                                  ║
║  Mean:  43.76 ms                                 ║
║  P95:   44.12 ms                                 ║
║  P99:   44.51 ms                                 ║
║  Min:   43.21 ms  Max: 45.03 ms                  ║
║                                                  ║
║  Trace: ./results/trace.pftrace                  ║
║                                                  ║
║  [Enter] Dismiss                                 ║
╚══════════════════════════════════════════════════╝

For the full walkthrough see Validation from the Profiler.

Path B — start from Studio

Create a user-managed validation session in the Studio web UI (instructions). Make a note of the session ID, then on the target:

edgefirst-profiler validate --session-id v-1ce9

The profiler runs headlessly, prints progress bars for download/inference/upload, emits a formatted Session Report to stdout, and publishes results to Studio when the run completes.

8. View the results in Studio

Both paths land you at the same place: the validation session card in EdgeFirst Studio. The card shows progress while the cloud validator runs, then surfaces the accuracy charts and trace viewer when it completes.

EdgeFirst Studio — trace viewer on a completed validation session, showing pipeline stages and per-operator timing
EdgeFirst Studio — trace viewer on a completed validation session, showing pipeline stages and per-operator timing

See Object Detection Metrics and Segmentation Metrics for the metrics reference.

Next steps

  • Profile on edge hardware. The convenience install path works on desktop hosts and most target boards. Embedded targets with NPU/GPU acceleration have a few extra knobs — pick your target from the installation guides.
  • Pick the right Studio path. Validation from Studio (session created in the web UI) versus Validation from the Profiler (session created from the TUI).
  • Connect to a different Studio. See Connecting to EdgeFirst Studio for test / stage / saas server selection and headless credential handling.