SeeMe.ai Python SDK #

The Python SDK is a convenient wrapper to communicate with the SeeMe.ai API.

This document provides a detailed overview of all the methods and their parameters.

Installation #

Install the SDK from the command line:

$ pip install --upgrade seeme

or in your notebook:

!pip install --upgrade seeme

Verfiy the version you have installed:

import seeme
seeme.__version__

'0.13.8'

This document describes the 0.13.7 SDK

Register/Login #

Create a client #

Create a client to interact with the SeeMe.ai API, allowing you to manage models, datasets, predictions and training requests.

from seeme import Client

client = Client()

parameters:

• username (optional) : the username for the account you want to use;
• apikey (optional): the API key for the username you want user;
• backend (default provided): the backend the client communicates with, default “https://api.seeme.ai/api/v1.

Note:

Although username and apikey are optional, they need to be used together in order to be authenticated.

If you have a username and apikey, you can create a client that is ready to go:

my_username = ""
my_apikey = ""

client = Client(username=my_username, apikey=my_apikey)

Advanced: create a client that talks to a custom backend:

alternative_url = ""

client = Client(backend=alternative_url)

# or with auth
client = Client(username=my_username, apikey=my_apikey, backend=alternative_url)

Register #

Register a new user:

my_username =    # example: "my_username"
my_email =       # example: "jan.vandepoel@seeme.ai"
my_password =    # example: "supersecurepassword"
my_firstname =   # example: "Jan"
my_name =        # example: "Van de Poel"

client.register(
    username=my_username, 
    email=my_email, 
    password=my_password, 
    firstname=my_firstname, 
    name=my_name
)

Log in #

username = ""
password = ""

client.login(username, password)

Log out #

client.logout()

Models #

Get all models #

Get a list of all models you have access to.

models = client.get_models()

These models are divided in three groups:

• public: models that are available to everyone;
• owned: models you created;
• shared: models that are shared privately with you.

Your own models

A list of the models you created:

own_models = [ model for model in models if model["user_id"] == client.user_id]

Public models

Public models are provided by SeeMe.ai or other users.

public_models = [model for model in models if model["public"]]

Shared models

A list of models that others have shared with you in private.

shared_with_me = [model for model in models if model["shared_with_me"]]

Create model #

my_model = {
    "name": "Cats and dogs",
    "description": "Recognize cats and dogs in pictures."
}

my_model = client.create_model(my_model)

Every model has the following properties:

{
    'id': '22791060-71f5-4e71-935e-28b52ef4c047',
     'created_at': '2021-10-28T08:49:06.698391719Z',
     'updated_at': '2021-10-28T08:49:06.698391719Z',
     'name': 'Cats and dogs',
     'description': 'Recognize cats and dogs in pictures.',
     'active_version_id': 'fc92b1fe-2ee0-4d99-9a09-0a37d0c68ea1',
     'classes': '',
     'accuracy': 0,
     'user_id': 'd7159432-f218-44ac-aebe-e5d661d62862',
     'can_inference': False,
     'kind': '',
     'has_logo': False,
     'logo': '',
     'public': False,
     'config': '',
     'application_id': '',
     'has_ml_model': False,
     'has_onnx_model': False,
     'has_tflite_model': False,
     'has_labels_file': False,
     'shared_with_me': False,
     'auto_convert': True,
     'privacy_enabled': False
 }

Properties in more detail:

# deprecated
# just calls `create_model` underneath

my_model = client.create_full_model(my_model)

Get model #

Use the model id to get all the metadata of the model:

client.get_model(my_model["id"])

Update model #

Update any property of the model:

my_model["description"] = "Updated for demo purposes"

client.update_model(my_model)

Delete model #

Delete a model using its id.

client.delete_model(my_model["id"])

Upload model file #

You can upload the model file by calling the upload_model:

my_model = client.upload_model(my_model["id"], folder="directory/to/model", filename="cats_and_dogs.pkl")

This returns an updated my_model, where if successful the can_inference should now be set to True.

If auto_convert is enabled (default value: True) and depending on the used application_id (see Applications below), the model will have updated values for has_ml_model, has_tflite_model, has_onnx_model, has_labels_file.

Download model file(s) #

Download the file(s) associated with the current model. The method is called download_active_model, because it will download the currently active (in production) model files. (See Versions for more downloads)

client.download_active_model(my_model, asset_type="pkl")

If the asset_type exists, the model file will be downloaded to my_model["active_model_id"].{asset_type}. One exception, the labels file will receive a .txt extension.

Upload model logo #

my_model = client.upload_logo(my_model["id"], folder="directory/to/logo", filename="logo_filename.jpg")

Download model logo #

client.get_logo(my_model)

Make a prediction #

result = client.predict(model_id, item, input_type="image_classification")

On an image classification model:

item = "path/to/file.png"

result = client.predict(my_model["id"], item)

# Or
# result = client.predict(my_model["id"], item, input_type="image_classification")

On an object detection model:

item = "path/to/file.png"

result = client.predict(my_model["id"], item, input_type="object_detection")

On a text classification model:

item = "The text you want to classify."

result = client.predict(my_model["id"], item, input_type="text_classification")

On a strctured/tabular data model:

inputs = {
    "temperature": "30",
    "day": "Tuesday"
}

item = json.dumps(inputs)

result = client.predict(my_model["id"], item, input_type="structured")

An example reply for a prediction:

{
    'id': '891ed37a-fe77-415c-a4ab-3764e68aaa40',
    'created_at': '2021-10-28T12:33:17.277236872Z',
    'update_at': '2021-10-28T12:33:17.563600762Z',d
    'name': '10789.jpg',
    'description': '',
    'prediction': 'cats',
    'confidence': 0.9846015,
    'model_id': '56086c08-c552-4159-aafa-ac4b25a64fda',
    'model_version_id': '1a38b7ab-070f-4686-9a47-4ba7a76a5168',
    'extension': 'jpg',
    'user_id': 'd7159432-f218-44ac-aebe-e5d661d62862',
    'error_reported': False,
    'error': '',
    'application_id': '80ba88dd-91e9-445f-b7ab-38ebd9ab1e63',
    'inference_host': 'image-pt-1-8-1-fa-2-3-1',
    'inference_time': '284.418389ms',
    'end_to_end_time': '',
    'dataset_item_id': '',
    'result': '',
    'inference_items': None,
    'hidden': False,
    'privacy_enabled': False
 }

Properties in more detail:

# obsolete
# Used internally by `predict` but will be removed.
result = client.inference(my_model["id"], item, input_type="image_classification")

Model Versions #

An AI Model has one or multiple versions associated with it:

• the current live version
• previous versions
• future versions

Get all model versions #

Get a list of all versions for a specific model.

versions = client.get_model_versions(my_model["id"])

Create model version #

new_version = {
    "name": "A higher accuracy achieved",
    "application_id": "878aea66-16b7-4f10-9d82-a2a92a35728a"
}

new_version = client.create_model_version(my_model["id"], new_version)

Every model version has the following properties. Note that these are partially similar to the model properties:

{
    'id': '53535e2f-4b36-4235-b8d6-ed87a5d2f323',
    'created_at': '2021-10-29T14:16:46.603463402Z',
    'update_at': '2021-10-29T14:16:46.603463402Z',
    'name': 'A higher accuracy achieved',
    'description': '',
    'model_id': '22791060-71f5-4e71-935e-28b52ef4c047',
    'classes': '',
    'accuracy': 0,
    'user_id': 'd7159432-f218-44ac-aebe-e5d661d62862',
    'can_inference': False,
    'has_logo': False,
    'logo': '',
    'config': '',
    'application_id': '878aea66-16b7-4f10-9d82-a2a92a35728a',
    'version': '',
    'version_number': 4,
    'has_ml_model': False,
    'has_onnx_model': False,
    'has_tflite_model': False,
    'has_labels_file': False,
    'dataset_version_id': '',
    'training_request_id': ''
}

Properties in more detail.

Shared with the model entity:

Different from the model entity.

Get model version #

Use the model and version id to get the full model version:

model_version = client.get_model_version(my_model["id"], new_version["id"])

Update model version #

Update any property of the model version:

model_version["description"] = "We hit SOTA"

client.update_model_version(model_version)

Delete model version #

Delete a model version:

client.delete_model_version(my_model["id"], new_version["id"])

Upload model file for a version #

client.upload_model_version(new_version, folder="directory/to/model", filename="cats_and_dogs_v2.pkl")

Make a prediction on this version #

client.version_predict(model_version_id, item, input_type="image_classification")

On an image classification model version:

item = "path/to/file.png"

result = client.version_predict(new_version["id"], item)

# Or
# result = client.version_predict(new_version["id"], item, input_type="image_classification")

On an object detection model version:

item = "path/to/file.png"

result = client.version_predict(new_version["id"], item, input_type="object_detection")

On a text classification model version:

item = "The text you want to classify."

result = client.version_predict(new_version["id"], item, input_type="text_classification")

On a strctured/tabular data model version:

inputs = {
    "temperature": "30",
    "day": "Tuesday"
}

item = json.dumps(inputs)

result = client.version_predict(my_model["id"], item, input_type="structured")

An example reply for a prediction:

{
    'id': '891ed37a-fe77-415c-a4ab-3764e68aaa40',
    'created_at': '2021-10-28T12:33:17.277236872Z',
    'update_at': '2021-10-28T12:33:17.563600762Z',d
    'name': '10789.jpg',
    'description': '',
    'prediction': 'cats',
    'confidence': 0.9846015,
    'model_id': '56086c08-c552-4159-aafa-ac4b25a64fda',
    'model_version_id': '1a38b7ab-070f-4686-9a47-4ba7a76a5168',
    'extension': 'jpg',
    'user_id': 'd7159432-f218-44ac-aebe-e5d661d62862',
    'error_reported': False,
    'error': '',
    'application_id': '80ba88dd-91e9-445f-b7ab-38ebd9ab1e63',
    'inference_host': 'image-pt-1-8-1-fa-2-3-1',
    'inference_time': '284.418389ms',
    'end_to_end_time': '',
    'dataset_item_id': '',
    'result': '',
    'inference_items': None,
    'hidden': False,
    'privacy_enabled': False
 }

Properties in more detail:

# obsolete
# Used internally by `predict` but will be removed.
result = client.version_inference(my_model["id"], item, input_type="image_classification")

Datasets #

Get all datasets #

Get a list of all your datasets:

datasets = client.get_datasets()

The get_datasets() method does not take any parameters.

Create dataset #

my_dataset = {
    "name": "Cats & dogs dataset",
    "description": "A dataset with labelled images of cats and dogs.",
    "multi_label": False,
    "notes": "Cats and dogs is often used as a demo dataset.",
    "default_splits": True,
    "content_type": "images"
}

my_dataset = client.create_dataset(my_dataset)

Every dataset has the following properties:

{
    'id': '2bb8b9c1-c027-44c2-b438-6d50911964bd',
    'created_at': '2021-11-02T10:52:56.386712086Z',
    'update_at': '2021-11-02T10:52:56.386712086Z',
    'deleted_at': None,
    'name': 'Cats & dogs dataset',
    'description': 'A dataset with labelled images of cats and dogs.',
    'user_id': 'd7159432-f218-44ac-aebe-e5d661d62862',
    'notes': 'Cats and dogs is often used as a demo dataset.',
    'versions': [],
    'multi_label': False,
    'default_splits': True,
    'has_logo': False,
    'logo': '',
    'content_type': 'images'
}

Properties in more detail:

Get dataset #

dataset_id = "2bb8b9c1-c027-44c2-b438-6d50911964bd"

my_dataset = client.get_dataset(dataset_id)

Update dataset #

my_dataset["notes"] += "~25k labelled images of cats and dogs; 22500 for training, 2000 for validation."

client.update_dataset(my_dataset)

Delete dataset #

client.delete_dataset(my_dataset)

Upload dataset logo #

my_dataset = client.upload_dataset_logo(my_dataset["id"], folder="directory/to/logo", filename="logo_filename.jpg")

Download dataset logo #

client.get_dataset_logo(my_dataset)

Dataset Versions #

A dataset can have multiple versions.

Get all dataset versions #

dataset_versions = client.get_dataset_versions(my_dataset["id"])

Create dataset version #

new_dataset_version = {
    "name": "v2",
    "description": "Even more images of dogs and cats"
}

new_dataset_version = client.create_dataset_version(my_dataset["id"], new_dataset_version)

Every dataset_version has the following properties:

{
    'id': 'd774d5bd-40fd-4c5f-8043-f1a8ed88e0d2',
    'created_at': '2021-11-02T13:17:29.24027517Z',
    'update_at': '2021-11-02T13:17:29.24027517Z',
    'deleted_at': None,
    'name': 'v2',
    'description': 'Even more images of dogs and cats',
    'labels': None,
    'user_id': 'd7159432-f218-44ac-aebe-e5d661d62862',
    'dataset_id': '2bb8b9c1-c027-44c2-b438-6d50911964bd',
    'splits': None,
    'default_split': ''
}

Properties in more detail:

Get dataset version #

dataset_version = client.get_dataset_version(new_dataset_version["id"])

Update dataset version #

new_dataset_version["description"] = "Even more image of cats and dogs."

client.update_dataset_version(my_dataset["id"], new_dataset_version)

Delete dataset version #

client.delete_dataset_version(my_dataset["id"], new_dataset_version)

Dataset Splits #

A dataset version can have multiple splits, usually separating training, validation and test data.

Get all splits #

splits = client.get_dataset_splits(my_dataset["id"], new_dataset_version["id"])

Create split #

new_split = {
    "name": "train",
    "description": "training data for our model to learn from" 
}

new_split = client.create_dataset_split(my_dataset["id"], new_dataset_version["id"], new_split)

Every dataset split has the following properties:

{
    'id': '3faa6ddb-f5d6-4dda-92a9-beb383126072',
    'created_at': '2021-11-02T14:02:15.303555455Z',
    'updated_at': '2021-11-02T14:02:15.303555455Z',
    'name': 'train',
    'description': 'training data for our model to learn from',
    'user_id': 'd7159432-f218-44ac-aebe-e5d661d62862',
    'version_id': 'd774d5bd-40fd-4c5f-8043-f1a8ed88e0d2'
}
 

Properties in more detail:

Get split #

my_split = client.get_dataset_split(my_dataset["id"], new_dataset_version["id"], new_split["id"])

Update split #

my_split["description"] = "Training data"

client.update_dataset_split(my_dataset["id"], new_dataset_version["id"], my_split)

Delete split #

client.delete_dataset_split(my_dataset["id"], new_dataset_version["id"], my_split)

Dataset Labels #

A dataset version can have multiple labels.

Get all labels #

labels = client.get_dataset_labels(my_dataset["id"], new_dataset_version["id"])

Create label #

new_label = {
    "name": "Birds",
    "description": "Adding birds to the mix"
}

new_label = client.create_dataset_label(my_dataset["id"], new_dataset_version["id"], new_label)

Every dataset split has the following properties:

{
    'id': 'ea3be37d-ce48-44bc-4435-39d88a47a2d6',
    'created_at': '2021-11-02T14:24:06.187758716Z',
    'updated_at': '2021-11-02T14:24:06.187758716Z',
    'name': 'Birds',
    'description': 'Adding birds to the mix',
    'user_id': 'd7159432-f218-44ac-aebe-e5d661d62862',
    'version_id': 'd774d5bd-40fd-4c5f-8043-f1a8ed88e0d2',
    'color': '',
    'index': 2
}

Properties in more detail:

Get label #

my_label = client.get_dataset_label(my_dataset["id"], new_dataset_version["id"], new_label["id"])

Update label #

my_label["color"] = "#00ff00"

client.update_dataset_label(my_dataset["id"], new_dataset_version["id"], my_label)

Delete label #

client.delete_dataset_label(my_dataset["id"], new_dataset_version["id"], my_label)

Dataset Items #

A dataset version can contain many dataset items. Items are used to access and store the actual items in your dataset version such as images or text.

Get dataset items #

items = client.get_dataset_items(my_dataset["id"], new_dataset_version["id"])

# All params are optional, but here we combine them together for demo purposes. 
params = {
    "onlyUnlabelled": False,
    "pageSize": 25,
    "pageCount": 0,
    "labelId": new_label["id"],
    "splitId": my_split["id"]
}

client.get_dataset_items(my_dataset["id"], new_dataset_version["id"], params)

Create dataset item #

item = {
    "name": "An optional name",
}

item = client.create_dataset_item(my_dataset["id"], new_dataset_version["id"], item)

Every dataset item has the following properties:

{
     'id': 'e92dcc17-cbc2-4a1e-a332-4c62a3232ede',
     'created_at': '2021-11-02T16:07:15.88589873Z',
     'updated_at': '2021-11-02T16:07:15.88589873Z',
     'name': 'An optional name',
     'description': '',
     'user_id': 'd7159432-f218-44ac-aebe-e5d661d62862',
     'text': '',
     'splits': None,
     'annotations': None,
     'extension': ''
 }

Properties in more detail:

Get dataset item #

item = client.get_dataset_item(my_dataset["id"], new_dataset_version["id"], item["id"])

Update dataset item #

item["description"] = "A better description"

client.update_dataset_item(my_dataset["id"], new_dataset_version["id"], item)

Delete dataset item #

client.delete_dataset_item(my_dataset["id"], new_dataset_version["id"], item)

Upload dataset item image #

client.upload_dataset_item_image(my_dataset["id"], new_dataset_version["id"], item["id"], folder="directory/to/item", filename="item_filename.jpg")

Download dataset item image #

download_location = f"{item['id']}.{item['extension']}"

client.download_dataset_item_image(my_dataset["id"], new_dataset_version["id"], item["id"], download_location, thumbnail=False)

Export Dataset Version #

client.download_dataset(my_dataset["id"], new_dataset_version["id"], split_id="", extract_to_dir="data", download_file="dataset.zip", remove_download_file=True)

A note on the exported formats.

Image classification #

The .zip file contains a folder for every dataset split: “train”, “valid”, “test”.

Every dataset split folder contains a ‘label-named’ folder for every label: “cats”, “dogs”.

Every label folder contains all the images of the dataset items for the given label in the given dataset split.

Dataset items are named by the “name” and “extension” property of the dataset item. If the “name” property is empty, the “id” is used to name the file.

Dataset items that have no label, will be added to the split folder to which they belong.

.
+-- train/
|  +-- cats/
|     +-- cat1.jpg
|     +-- cat12.jpeg
|  +-- dogs/
|     +-- dog2.jpg
|     +-- dog4.png
|  +-- cat17.jpeg
|  +-- dog15.jpg
+-- valid/
|  +-- cats/
|     +-- cat4.jpg
|     +-- cat8.jpg
|  +-- dogs/
|     +-- dog9.jpg
|     +-- dog14.png
+-- test/
|  +-- cats/
|     +-- cat90.jpg
|     +-- cat34.jpeg
|  +-- dogs/
|     +-- dog81.jpg
|     +-- dog98.png

Text classification #

The .zip file contains a folder for every dataset split: “train”, “test”, “unsup”.

Every dataset split folder contains a ‘label-named’ folder for every label: “pos”, “neg”.

Every label folder contains all the images of the dataset items for the given label in the given dataset split.

Dataset items are named by the “name” and “extension” property of the dataset item. If the “name” property is empty, the “id” is used to name the file.

Dataset items that have no label, will be added to the split folder to which they belong.

.
+-- train/
|  +-- pos/
|     +-- 1.txt
|     +-- 3.txt
|  +-- neg/
|     +-- 2.txt
|     +-- 4.txt
+-- test/
|  +-- pos/
|     +-- 5.txt
|     +-- 7.txt
|  +-- neg/
|     +-- 6.txt
|     +-- 8.txt
+-- unsup/
|     +-- 13.txt
|     +-- 14.txt

Object detection #

Object detection datasets are exported in YOLO format.

For every dataset split a dataset_split_name.txt file gets created containing all the filenames for that dataset split.

Every dataset item will have an image and a txt file associated with it. The txt file contains a list of annotations in Yolo format: label_index relative_x relative_y relative_width relative_height.

The .names file contains the list of labels, where the index corresponds to the label_index in the annotation .txt files.

The config.json file contains a contains a json object with the color for every label.

.
+-- train.txt
+-- valid.txt
+-- test.txt
+-- 1.jpg
+-- 1.txt
+-- 3.jpg
+-- 3.txt
+-- ...
+-- dataset_version_id.names
+-- config.json

Import Dataset Version #

For image_classification and text_classification, you can upload a .zip of the dataset directly.

The zip file should be structured in the following way, for image_classification:

.
+-- train/
|  +-- cats/
|     +-- cat1.jpg
|     +-- cat12.jpeg
|  +-- dogs/
|     +-- dog2.jpg
|     +-- dog4.png
|  +-- cat17.jpeg
|  +-- dog15.jpg
+-- valid/
|  +-- cats/
|     +-- cat4.jpg
|     +-- cat8.jpg
|  +-- dogs/
|     +-- dog9.jpg
|     +-- dog14.png
+-- test/
|  +-- cats/
|     +-- cat90.jpg
|     +-- cat34.jpeg
|  +-- dogs/
|     +-- dog81.jpg
|     +-- dog98.png

and for text_classification:

.
+-- train/
|  +-- pos/
|     +-- 1.txt
|     +-- 3.txt
|  +-- neg/
|     +-- 2.txt
|     +-- 4.txt
+-- test/
|  +-- pos/
|     +-- 5.txt
|     +-- 7.txt
|  +-- neg/
|     +-- 6.txt
|     +-- 8.txt
+-- unsup/
|     +-- 13.txt
|     +-- 14.txt

This way all splits, labels, items, and annotations will automatically be imported.

client.upload_dataset_version(my_dataset["id"], new_dataset_version["id"], folder="data", filename="dataset.zip")

Annotations #

Manage annotations by adding, updating, and deleting them in the annotations list of the dataset item

Get Annotations #

item["annotations"]

Create annotation #

annotation = {
    "label_id": "1d57e9a8-8993-4de6-a69d-57192b27ab0e",
    "coordinates": "0.318984375 0.24546875 0.07359375 0.0665625"
}

item["annotations"].append(annotation)

item = client.update_dataset_item(my_dataset["id"], new_dataset_version["id"], item)

The dataset item has a list of annotations. Every annotation has the following properties:

{
    'id': '1d57e9a8-8993-4de6-a69d-57192b27ab0e',
    'created_at': '2021-11-03T13:15:17.237588533Z',
    'updated_at': '2021-11-03T13:15:17.237588533Z',
    'label_id': 'ea3be37d-ce48-44bc-4435-39d88a47a2d6',
    'item_id': 'e92dcc17-cbc2-4a1e-a332-4c62a3232ede',
    'coordinates': '0.318984375 0.24546875 0.07359375 0.0665625'
}

Properties in more detail.

Get annotation #

item["annotations"] = [{'id': '5308a7c7-b137-46e0-b718-08d1feb78b6d',
   'created_at': '2021-11-03T13:28:52.187604387Z',
   'updated_at': '2021-11-03T13:28:52.187604387Z',
   'label_id': 'ea3be37d-ce48-44bc-4435-39d88a47a2d6',
   'item_id': 'e92dcc17-cbc2-4a1e-a332-4c62a3232ede',
   'coordinates': ''},
  {'id': '5a44653c-89af-4317-abbe-fc0ae8d1b8bf',
   'created_at': '2021-11-03T13:28:52.187916713Z',
   'updated_at': '2021-11-03T13:28:52.187916713Z',
   'label_id': '1d57e9a8-8993-4de6-a69d-57192b27ab0e',
   'item_id': 'e92dcc17-cbc2-4a1e-a332-4c62a3232ede',
   'coordinates': '0.318984375 0.24546875 0.07359375 0.0665625'}]

item["annotations"][0]

Update annotation #

item["annotations"][0]["coordinates"] = "0.4234 0.12425 0.075634 0.0548990"

item = client.update_dataset_item(my_dataset["id"], new_dataset_version["id"], item)

Delete annotation #

del(item["annotations"][0])
client.update_dataset_item(my_dataset["id"], new_dataset_version["id"], item)

Applications #

SeeMe.ai supports multiple types of AI models, frameworks, and framework versions. To access, manage, and describe these, we use applications:

Get supported applications #

Print a list of the applications in your SeeMe.ai client:

client.applications

Every application has the following properties:

{
    'id': '878aea66-16b7-4f10-9d82-a2a92a35728a',
    'created_at': '2021-10-25T17:57:14.323965Z',
    'updated_at': '2021-10-25T17:57:14.323965Z',
    'base_framework': 'pytorch',
    'base_framework_version': '1.10.0',
    'framework': 'fastai',
    'framework_version': '2.5.3',
    'application': 'image_classification',
    'inference_host': 'image-pt-1-10-0-fa-2-5-3',
    'can_convert_to_onnx': True,
    'can_convert_to_coreml': True,
    'can_convert_to_tensorflow': True,
    'can_convert_to_tflite': True,
    'has_labels_file': True,
    'inference_extensions': 'pkl'
}

Properties in more detail.

You can update the local list of applications by:

client.applications = client.get_applications()

Get application id #

Before you can upload and use your model to make predictions, you need to add an application_id:

from torch import __version__ as torch_version
from fastai import __version__ as fastai_version

# Get the application_id for your framework (version).
application_id = client.get_application_id(
    base_framework="pytorch",
    framework="fastai",
    base_framework_version=torch_version,
    framework_version=fastai_version,
    application="image_classification"
)

If your combination is not supported, you will get a NotImplementedError.

Feedback #

For questions, feedback, corrections: contact jan.vandepoel@seeme.ai.