Embedl Hub Python library
embedl-hub is the official Python client for
Embedl Hub — a platform for compiling, profiling, and
tracking machine learning models for edge devices.
The embedl-hub command-line tool takes a model from your machine to a real
device and records every step along the way:
- Compile a model for on-device execution through the TFLite, ONNX Runtime, or TensorRT toolchains.
- Profile latency and memory on a real device — in a device cloud, or on your own hardware over SSH.
- Invoke a compiled model on a device to run inference on real input data.
- Track every run, with its parameters, metrics, tags, and artifacts, in your Embedl Hub project — from the shell, or from your own Python code.
Each command records a run on Embedl Hub, so the artifact produced by one step feeds straight into the next and your results stay comparable and reproducible later.
- Guides and setup: https://hub.embedl.com/docs
- Create an account: https://hub.embedl.com/docs/setup
Requirements
Python 3.10 or newer.
Installation
pip install embedl-hub
The base install covers the tracking API and the whole run-control CLI — auth,
init, show, use, run, log, batch, and list-devices — and pulls in
no backend execution toolchain.
compile, profile, and invoke wrap external toolchains, so each one lives
behind an extra:
| Toolchain | Install command |
|---|---|
| TFLite | pip install 'embedl-hub[tflite]' |
| ONNX Runtime | pip install 'embedl-hub[onnxruntime]' |
| TensorRT | pip install 'embedl-hub[tensorrt]' |
| Everything | pip install 'embedl-hub[all]' |
Run a command whose extra is not installed and the CLI prints the pip install
line you need, instead of an import traceback.
On Linux aarch64, [tflite] skips the onnx2tf-based local conversion path and
the ai-edge-* quantization dependencies, because the upstream TensorFlow
package publishes no Linux aarch64 wheels. The Qualcomm AI Hub TFLite provider
still works on that platform.
Getting started
Create an API key under Personal API keys on your Embedl Hub profile page, then store it:
embedl-hub auth --api-key <your-key>
The key can also be supplied through the EMBEDL_HUB_API_KEY environment
variable.
Choose the project your runs are recorded in, and check the active context:
embedl-hub init --project my-project
embedl-hub show
Track a run from the shell
run start creates a run and prints its ID to stdout, so a shell script can
capture it and log against it:
run=$(embedl-hub run start --type eval --name "Evaluate baseline")
embedl-hub log param --run "$run" model resnet18
embedl-hub log metric --run "$run" accuracy 0.923 --step 1
embedl-hub log artifact --run "$run" results/predictions.json
embedl-hub run finish "$run"
Or bind the run to the current directory with --use, and drop --run
everywhere after that:
embedl-hub run start --type eval --use
embedl-hub log metric accuracy 0.923 --step 1
embedl-hub log tag stage baseline
embedl-hub run finish
embedl-hub use prints the defaults in effect and where each one came from;
use run, use project, and use batch set them, and use clear removes
them. EMBEDL_HUB_RUN, EMBEDL_HUB_PROJECT, and EMBEDL_HUB_BATCH override
the directory defaults, which makes the same scripts work unchanged in CI.
Read runs back without leaving the terminal:
embedl-hub run list # the project's runs, newest first
embedl-hub run show "$run" # one run with everything logged on it
embedl-hub run url "$run" # its address on the hub
run list --failed, --all, and -n/--limit narrow the listing, and both
run list and run show take --json for scripting.
Batch logging
When a job logs a lot, or runs somewhere without reliable network, queue the entries locally and send them in one request afterwards:
embedl-hub batch start --name "epoch metrics" --use
embedl-hub log metric accuracy 0.91 --step 1
embedl-hub log metric accuracy 0.93 --step 2
embedl-hub batch send
batch show inspects a queue before sending, batch list shows the queues on
this machine, and batch discard drops one. A single log command can bypass
an active batch with --no-batch.
Take a model to a device
List the devices you can target:
embedl-hub list-devices
Compile a model, then profile what you just compiled. --from-run latest picks
up the artifact from the previous run, so there are no files to move by hand:
pip install 'embedl-hub[tflite]'
embedl-hub compile tflite qai-hub -m model.onnx -s 1,3,224,224 -d "Samsung Galaxy S24"
embedl-hub profile tflite qai-hub --from-run latest -d "Samsung Galaxy S24"
Commands
| Command | Purpose | Base install |
|---|---|---|
embedl-hub auth |
Store your API key. | yes |
embedl-hub init |
Set the project and artifact directory. | yes |
embedl-hub show |
Print the active project and artifact directory. | yes |
embedl-hub use |
Bind default targets to the current directory. | yes |
embedl-hub run |
Start, finish, and inspect runs. | yes |
embedl-hub log |
Log metrics, params, tags, links, and artifacts. | yes |
embedl-hub batch |
Queue log entries locally and send them later. | yes |
embedl-hub list-devices |
List available target devices. | embedl devices |
embedl-hub compile |
Compile a model for on-device execution. | needs an extra |
embedl-hub profile |
Measure latency and memory on a device. | needs an extra |
embedl-hub invoke |
Run inference on a compiled model. | needs an extra |
run, log, batch, and use group their own subcommands:
| Group | Subcommands |
|---|---|
run |
start, finish, list, show, set-parent, url |
log |
metric, param, tag, link, artifact |
batch |
start, send, show, list, discard |
use |
run, project, batch, clear |
compile, profile, and invoke each take a toolchain, then a provider:
| Command | TFLite | ONNX Runtime | TensorRT |
|---|---|---|---|
compile |
local, qai-hub |
qai-hub, embedl-onnxruntime |
trtexec |
profile |
qai-hub, aws |
qai-hub, embedl-onnxruntime |
trtexec |
invoke |
qai-hub |
qai-hub, embedl-onnxruntime |
trtexec |
The embedl-onnxruntime and trtexec providers run on your own hardware over
SSH; the others use a device cloud. embedl-hub list-devices qai-hub lists the
Qualcomm AI Hub catalogue and needs the [onnxruntime] extra.
Run embedl-hub --help, or --help on any command, for the full option list.
The package installs two equivalent entry points: embedl-hub and ehub.
Tracking from Python
The tracking API is part of the base install and writes to the same projects and runs as the CLI, so you can log directly from your training or evaluation code:
from embedl_hub.tracking import Client
client = Client()
client.set_project("my-project")
with client.start_run("eval", name="Evaluate baseline"):
client.log_param("model", "resnet18")
client.log_param("dataset", "imagenet-val")
client.log_metric("accuracy", 0.923, step=1)
client.log_metric("latency_ms", 8.4, step=1)
client.log_tag("stage", "baseline")
client.log_artifact("results/predictions.json")
start_run is a context manager: the run is finished for you on the way out,
marked failed if the block raises, and killed if you interrupt it. Parameters
take string values, metrics take floats with an optional step, and
log_artifact uploads a local file to the run. The run type is a string or a
member of RunType, also exported from embedl_hub.tracking.
Open the project on Embedl Hub to see the run and everything logged to it.
The compile, profile, and invoke components are available as Python classes too, but each needs the extra for its toolchain installed. See the documentation for those.
Project status
embedl-hub is under active development. The public API may change between
releases in the 0.0.x series, so pin an exact version if you need a reproducible
build.
Support
For questions, bug reports, and feedback, contact support@embedl.com.
License
This software is subject to the Embedl Hub Software License Agreement.
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