Upload and download models

tip

Recommended tutorials before starting starting uploading and downloading models.

• Stochastic platform introduction
• Installation
• Quickstart

1. Sign in

Before you start using the CLI, you need to log in with your username and password.

Python

from stochasticx.auth.auth import Stochastic

username = "your_email@email.com"
password = "your_password"
Stochastic().login(username, password)

CLI

stochasticx login --username "my_email@email.com" --password "my_password"

Platform

You can log in on the Stochastic platform website: https://app.stochastic.ai/login

2. Create and upload models

Download a Hugging Face model and save it in the disk. In this tutorial, bert-large-uncased-whole-word-masking-finetuned-squad model will be downloaded from the Hugging Face Hub. It is a model finetuned on the SQuAD dataset.

from transformers import AutoTokenizer, AutoModelForQuestionAnswering

tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
tokenizer.save_pretrained("./bert-squad")

model = AutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
model.save_pretrained("./bert-squad")

Now you can upload this model to the Stochastic platform. Use the following command:

Python

from stochasticx.models.models import Model, ModelType

model_to_upload = Model(
    name="bert-squad",
    dir_path="bert-squad/",
    type=ModelType.HUGGINGFACE
)

print(model_to_upload)

model_to_upload.upload()
print(model_to_upload)

Output

Model ID: None ; Name: bert-squad ; Directory path: bert-squad/ ; Model type: hf ; Uploaded: False
Model ID: 62e3a5aab5beb3002644093e ; Name: bert-squad ; Directory path: bert-squad/ ; Model type: hf ; Uploaded: True

CLI

stochasticx models upload \
    --name "bert-squad" \
    --dir_path "./bert-squad" \
    --type "hf"

Platform

Go to the menu on the left and click on Models. Once there, you should see a Add model button in the top left corner.

Once you have clicked on Add model a new window will be showed. You have to enter a model name, the model type (select HuggingFace) and select the folder containing your BERT model that you downloaded from the HuggingFace Hub.

info

• name: will allow you to identify the model later.
• dir_path: directory path where your model is located.
• type: model type. In this case a Hugging Face model

3. List and inspect your models

You can list your models with the following command:

Python

from stochasticx.models.models import Models

models = Models.get_models()

for model in models:
    print(model)

CLI output

Model ID: 62e3a5aab5beb3002644093e ; Name: bert-squad ; Directory path: None ; Model type: hf ; Uploaded: True

CLI

stochasticx models ls

CLI output

[+] Collecting uploaded models

┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━┳━━━━━━━━━━┓
┃ Id                       ┃ Name                    ┃ Directory path ┃ Type ┃ Uploaded ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━╇━━━━━━━━━━┩
│ 62e3a5aab5beb3002644093e │ bert-squad              │                │ hf   │ True     │
└──────────────────────────┴─────────────────────────┴────────────────┴──────┴──────────┘

Platform

You have uploaded your first model in the Stochastic Platform. Now you should see your model listed on the models page. Here we can see the model is in status pending, it will become successful once we ensured all the files are correct. Only successfully verified models can be used for running jobs.

Inspect a model to get more details about it:

Python

model = Models.get_model("62e3a5aab5beb3002644093e")
model.get_model_info()

CLI output

{
   "model_type":"bert",
   "id2label":"None",
   "label2id":"None",
   "max_position_embeddings":512,
   "torch_dtype":"float32",
   "vocab_size":30522,
   "hugging_face_tasks":[
      "BertForQuestionAnswering"
   ],
   "model_size":1336507185,
   "specific_model_info":{
      "_name_or_path":"bert-large-uncased-whole-word-masking-finetuned-squad",
      "attention_probs_dropout_prob":0.1,
      "classifier_dropout":"None",
      "hidden_act":"gelu",
      "hidden_dropout_prob":0.1,
      "hidden_size":1024,
      "initializer_range":0.02,
      "intermediate_size":4096,
      "layer_norm_eps":1e-12,
      "num_attention_heads":16,
      "num_hidden_layers":24,
      "pad_token_id":0,
      "position_embedding_type":"absolute",
      "transformers_version":"4.18.0",
      "type_vocab_size":2,
      "use_cache":true
   }
}

CLI

stochasticx models inspect --id "62e3a5aab5beb3002644093e"

CLI output

Model ID: 62e3a5aab5beb3002644093e ; Name: bert-squad ; Directory path: None ; Model type: hf ; Uploaded: True


{
   "model_type":"bert",
   "id2label":"None",
   "label2id":"None",
   "max_position_embeddings":512,
   "torch_dtype":"float32",
   "vocab_size":30522,
   "hugging_face_tasks":[
      "BertForQuestionAnswering"
   ],
   "model_size":1336507185,
   "specific_model_info":{
      "_name_or_path":"bert-large-uncased-whole-word-masking-finetuned-squad",
      "attention_probs_dropout_prob":0.1,
      "classifier_dropout":"None",
      "hidden_act":"gelu",
      "hidden_dropout_prob":0.1,
      "hidden_size":1024,
      "initializer_range":0.02,
      "intermediate_size":4096,
      "layer_norm_eps":1e-12,
      "num_attention_heads":16,
      "num_hidden_layers":24,
      "pad_token_id":0,
      "position_embedding_type":"absolute",
      "transformers_version":"4.18.0",
      "type_vocab_size":2,
      "use_cache":true
   }
}

Platform

Click on a model card to get more information about it.

4. Download a specific model

You can also download these models in your local system:

Python

model = Models.get_model("62e3a5aab5beb3002644093e")
model.download(local_path="downloaded_model.zip")

CLI output

100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 248.0M/248.0M [00:02<00:00, 6.87MiB/s]

CLI

stochasticx models download \
    --id "62e9195598855200266c0497" \
    --path "./downloaded_model.zip"

CLI output

100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 248.0M/248.0M [00:02<00:00, 6.87MiB/s]

Platform

Click on the download icon of a model.

info

• model_id: model ID you want to download.
• local_path: local path where you want to save this model. Note that the downloaded model will be a zip file.