v0.22.1

pip install --upgrade seeme

!pip install --upgrade seeme

import seeme
print(seeme.__version__)

from seeme import Client

client = Client()

my_username  = "my_username"
my_email     = "my_email@mydomain.com"
my_password  = "supersecurepassword"
my_firstname = "firstname"
my_name      = "last name"

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

username = ""
password = ""

client.login(username, password)

my_username = ""
my_apikey = ""

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

client = Client(env_file=".my_env_file")

client.logout()

alternative_url = "http://seeme.yourdomain.com/api/v1"

client = Client(backend=alternative_url)

models = client.get_models()

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

public_models

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

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

application_id = client.get_application_id("pytorch", "fastai", "2.0.1", "2.7.12" )

my_model = {
    "name": "Cats and dogs",
    "description": "Recognize cats and dogs in pictures.",
    "privacy_enabled": False,
    "auto_convert": True,
    "application_id": application_id
}

my_model = client.create_model(my_model)

client.get_model(my_model["id"])

my_model = client.get_model(my_model["id"])

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

client.update_model(my_model)

client.delete_model(my_model["id"])

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

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

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

item = "path/to/file.png"

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

item = "path/to/file.png"

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

item = "The text you want to classify."

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

item = "The text where I want to extract information out of."

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

item = "The story of AI language models is"

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

import json

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

item = json.dumps(inputs)

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

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

client.get_logo(my_model)

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

new_version = {
    "name": "A higher accuracy achieved",
    "application_id": "b4b9aaf0-cb37-4629-8f9b-8877aeb09a53"
}

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

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

model_version["description"] = "SOTA comes and goes, but versions are forever!"

client.update_model_version(model_version)

client.upload_model_version(new_version, folder="directory/to/model", filename="your_exported_model_file_v2.pkl")

client.download_model(my_version, asset_type="pkl", download_folder="data")

# or interchangeable with client.download_model:
client.download_model_version(my_version, asset_type="pkl", download_folder="data")

# DEPRECATED
client.download_version(my_version, asset_type="pkl")

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

# or interchangeable with client.version_inference():
client.version_predict(model_version_id, item, input_type="image_classification")

item = "path/to/file.png"

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

item = "path/to/file.png"

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

item = "The text you want to classify."

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

import json

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

item = json.dumps(inputs)

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

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

datasets = client.get_datasets()

from seeme import DATASET_CONTENT_TYPE_IMAGES

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": DATASET_CONTENT_TYPE_IMAGES
}

my_dataset = client.create_dataset(my_dataset)

my_dataset = client.get_dataset(my_dataset["id"])

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

client.update_dataset(my_dataset)

client.delete_dataset(my_dataset)

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

client.get_dataset_logo(my_dataset)

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

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)

new_dataset_version

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

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

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

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

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

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)

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

my_split["description"] = "Training data"

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

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

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

labels

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)

new_label

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

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

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

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

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": True,
    "pageSize": 25,
    "pageCount": 0,
    "labelId": new_label["id"],
    "splitId": my_split["id"]
}

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

my_split

item = {
    "name": "An optional name",
    "splits": [my_split]
}

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

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

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

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

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

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

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)

annotation = {
    "label_id": my_label["id"],
    "split_id": my_split["id"],
    "item_id": item["id"]
}

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

annotation["coordinates"] = "14 20 34 48"

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

client.delete_annotation(my_dataset["id"], new_dataset_version["id"], annotation)

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

{
    "multi_label": false,
    "label_column": "labels",
    "split_column": "split",
    "label_separator": " ",
    "filename": "dataset_version_id.csv",
    "csv_separator": ","
}

{
    "splits": [
        "train",
        "valid",
        "test"
    ],
    "colors": {
        "label_name": "#82ebfd",
        "label_name2": "#e95211"
    }
}

[
    {
        "id": "the_dataset_item_id",
        "name": "the_original_filename" ,
        "text": "The textual content of the file that has been annotated.",
        "annotations": [{
            "start": 4,
            "end": 11,
            "label": "label_name",
        },
         {
             ...
         }
        ]
    },
    {
        ...
    }
]

{
    "multi_label": false,
    "label_column": "labels",
    "split_column": "split",
    "label_separator": " ",
    "filename": "dataset.csv",
    "csv_separator": ","
}

{
    "splits": [
        "train",
        "valid",
        "test"
    ],
    "colors": {
        "label_name": "#82ebfd",
        "label_name2": "#e95211"
    }
}

[{
    "id": "the_dataset_item_id",
    "name": "the_original_filename" ,
    "text": "The textual content of the file that has been annotated.",
    "annotations": [{
        "start": 4,
        "end": 11,
        "label": "label_name",
    },
     {
         ...
     }
    ]
},
{
    ...
}
]

from seeme import JOB_STATUS_WAITING, JOB_STATUS_STARTED, JOB_STATUS_FINISHED, JOB_STATUS_ERROR, JOB_TYPE_TRAINING


jobs = client.get_jobs(
    application_id="", 
    states=[JOB_STATUS_WAITING, JOB_STATUS_STARTED, JOB_STATUS_FINISHED, JOB_STATUS_ERROR],
    job_types=[JOB_TYPE_TRAINING]
)

from seeme import JOB_TYPE_TRAINING

my_job = {
    "name": "Train a new model for cats and dogs",
    "description": "",
    "job_type": JOB_TYPE_TRAINING, # "training"
    "application_id": "acf26cf4-e19f-425e-b5cb-031830a46df4", # See Applications to get the correct application_id for your job
    "dataset_id": "8037b73a-5512-4a45-89e2-29761771fff6", # Update to your dataset_id
    "dataset_version_id": "1d3bf8d6-e39b-498e-9c08-680d2f8a3c47", # Update to your dataset_version_id
    "items": [
        {
            "name": "image_size",
            "value": "224",
            "value_type": "number",
            "label": "Image Size"
        },
        {
            "name": "arch",
            "value": "resnet50",
            "value_type": "text",
            "label": "Architecture"
        },
        {
            "name": "batch_size",
            "value": "50",
            "value_type": "number",
            "label": "Batch size"
        }
    ]
}

my_job = client.create_job(my_job)

job_item = {
    "name": "arch",
    "value": "resnet50",
    "value_type": "text",
    "label": "Architecture"
}

job_item = {
    "name": "image_size",
    "value": "224",
    "value_type": "number",
    "label": "Image Size"
}

job_item = {
    "name": "image_resize",
    "value": "460",
    "value_type": "number",
    "label": "Image Resize"
}

job_item = {
    "name": "batch_size",
    "value": "32",
    "value_type": "number",
    "label": "Batch Size"
}

job_item = {
    "name": "nb_epochs",
    "value": "50",
    "value_type": "number",
    "label": "Number of epochs"
}

job_item = {
    "name": "mixup",
    "value": "0",
    "value_type": "number",
    "label": "Mix up %"
}

job_item = {
    "name": "seed",
    "value": "12",
    "value_type": "number",
    "label": "Seed"
}

job_item = {
    "name": "arch",
    "value": "yolov4-tiny",
    "value_type": "number",
    "label": "Architecture"
}

job_item = {
    "name": "image_size",
    "value": "416",
    "value_type": "number",
    "label": "Image Size"
}

job_item = {
    "name": "batch_size",
    "value": "64",
    "value_type": "number",
    "label": "Batch Size"
}

job_item = {
    "name": "subdivisions",
    "value": "8",
    "value_type": "number",
    "label": "Subdivisions"
}

job_item = {
    "name": "language",
    "value": "en",
    "value_type": "text",
    "label": "Language"
}

job_item = {
    "name": "optimize",
    "value": "accuracy",
    "value_type": "multi",
    "label": "Optimize for"
}

job_item = {
    "name": "arch",
    "value": "[200 - 100]",
    "value_type": "text",
    "label": "Architecture"
}

job_item = {
    "name": "batch_size",
    "value": "32",
    "value_type": "number",
    "label": "Batch Size"
}

job_item = {
    "name": "nb_epochs",
    "value": "50",
    "value_type": "number",
    "label": "Number of epochs"
}

job_item = {
    "name": "min_feat_importance",
    "value": "0",
    "value_type": "number",
    "label": "Minimal feature importance (%)"
}

job_item = {
    "name": "add_info",
    "value": True,
    "value_type": "boolean",
    "label": "Add info"
}

job_item = {
    "name": "booster",
    "value": "gbtree", # "gbtree", "dart", "gblinear"
    "value_type": "multi",
    "label": "Booster"
}

job_item = {
    "name": "nb_estimators",
    "value": "100",
    "value_type": "number",
    "label": "Nb Estimators"
}

job_item = {
    "name": "max_depth",
    "value": "6",
    "value_type": "number",
    "label": "Number of epochs"
}

job_item = {
    "name": "learning_rate",
    "value": "0.3",
    "value_type": "number",
    "label": "Learning rate (eta)"
}

job_item = {
    "name": "subsample",
    "value": "0.5",
    "value_type": "number",
    "label": "Subsample (0 - 1)"
}

job_item = {
    "name": "min_feat_importance",
    "value": "0",
    "value_type": "number",
    "label": "Minimal feature importance (%)"
}

job_item = {
    "name": "add_info",
    "value": True,
    "value_type": "boolean",
    "label": "Add info"
}

job_item = {
    "name": "nb_estimators",
    "value": "100",
    "value_type": "number",
    "label": "Nb Estimators"
}

job_item = {
    "name": "learning_rate",
    "value": "0.1",
    "value_type": "number",
    "label": "Learning rate"
}

job_item = {
    "name": "min_feat_importance",
    "value": "0",
    "value_type": "number",
    "label": "Minimal feature importance (%)"
}

job_item = {
    "name": "add_info",
    "value": True,
    "value_type": "boolean",
    "label": "Add info"
}

job_item = {
    "name": "booster",
    "value": "gbdt", # "gbdt", "dart", "rf"
    "value_type": "multi",
    "label": "Boosting Type"
}

job_item = {
    "name": "nb_estimators",
    "value": "100",
    "value_type": "number",
    "label": "Nb Estimators"
}

job_item = {
    "name": "max_depth",
    "value": "-1",
    "value_type": "number",
    "label": "Number of epochs"
}

Learning rate:

```Python
job_item = {
    "name": "learning_rate",
    "value": "0.1",
    "value_type": "number",
    "label": "Learning rate"
}

job_item = {
    "name": "num_leaves",
    "value": "31",
    "value_type": "number",
    "label": "Max number of leaves"
}

job_item = {
    "name": "min_feat_importance",
    "value": "0",
    "value_type": "number",
    "label": "Minimal feature importance (%)"
}

job_item = {
    "name": "add_info",
    "value": True,
    "value_type": "boolean",
    "label": "Add info"
}

my_job = client.get_job(my_job["id"])

my_job["description"] = "Update any property"

my_job = client.update_job(my_job)

client.delete_job(my_job["id"])

client.applications

client.applications = client.get_applications()

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"
)