remyxai 0.1.4

No project description provided

Remyx AI command-line client

Installation

To install the Remyx AI CLI in Python virtual environment, run:

pip install remyxai

Token authentication

Remyx AI API requires authentication token, which can be obtained on this page: https://engine.remyx.ai/account

Provide api key to the CLI through an environment variable REMYXAI_API_KEY.

export REMYXAI_API_KEY=<your-key-here>

Usage

Quickly get started with the following examples:

Model

List all models:

• cli command:

$ remyxai model list

• python command:

from remyxai.api import list_models
print(list_models())

Get the summary of a model:

• cli command:

$ remyxai model summarize --model_name=<your-model-name>

• python command:

from remyxai.api import get_model_summary
print(get_model_summary(model_name))

Delete a model by name:

• cli command:

$ remyxai model delete --model_name=<your-model-name>

• python command:

from remyxai.api import delete_model

model_name = "<your-model-name>"
print(delete_model(model_name))

Download and convert a model:

• cli command:

# possible model formats are "blob", "onnx", or "tflite"
$ remyxai model download --model_name=<your-model-name> --model_format="onnx"

• python command:

from remyxai.api import download_model 

model_name = "<your-model-name>"
model_format = "onnx"
print(download_model(model_name, model_format))

Tasks

Train an image classifier:

• cli command:

$ remyxai classify --model_name=<your-model-name> --labels="comma,separated,labels" --model_size=<int between 1-5>

add the optional --hf_dataset if you want to train with your own image dataset on 🤗. See the docs for more details

• python command:

from remyxai.api import train_classifier

model_name = "<your-model-name>"
labels = ["comma", "separated", "labels"]
model_size = 3 # use 1 for microcontrollers

# Optional HF dataset
hf_dataset = "your/hf-dataset"

print(train_classifier(model_name, labels, model_size, hf_dataset))

Train an object detector:

• cli command:

$ remyxai detect --model_name=<your-model-name> --labels="comma,separated,labels" --model_size=<int between 1-5>

add the optional --hf_dataset if you want to train with your own image dataset on 🤗. See the docs for more details

• python command:

from remyxai.api import train_detector

model_name = "<your-model-name>"
labels = ["comma", "separated", "labels"]
model_size = 3

# Optional HF dataset
hf_dataset = "your/hf-dataset"
print(train_detector(model_name, labels, model_size, hf_dataset))

User

Get user profile info:

• cli command:

$ remyxai user profile

• python command:

from remyxai.api import get_user_profile

print(get_user_profile())

Get user credit/subscription info:

• cli command:

$ remyxai user credits

• python command:

from remyxai.api import get_user_credits

print(get_user_credits())

Utils

New! Label images locally:

• cli command:

$ remyxai utils label --labels="comma,separated,labels" --image_dir="/path/to/image/dir"

• python command:

from remyxai.utils import labeler
model_name = "<your-model-name>"
labels = ["comma", "separated", "labels"]
image_dir = "/path/to/image/dir"
print(labeler(labels, image_dir, model_name))