# configurations
.idea

# crawlee and apify storage folders
apify_storage
crawlee_storage
storage

# installed files
node_modules

# git folder
.git


.dockerignore

FROM huggingface/transformers-pytorch-gpu

COPY requirements.txt ./

RUN echo "Python version:" \
 &amp;&amp; python3 --version \
 &amp;&amp; echo "Pip version:" \
 &amp;&amp; pip --version \
 &amp;&amp; echo "Installing dependencies from requirements.txt:" \
 &amp;&amp; pip install -r requirements.txt \
 &amp;&amp; echo "All installed Python packages:" \
 &amp;&amp; pip freeze

COPY . ./

RUN python3 -c 'from transformers import pipeline; generator = pipeline("text-generation", model="gpt2")'

CMD python3 main.py

Dockerfile

{
 "title": "Generate text",
 "description": "This is actor input schema",
 "type": "object",
 "schemaVersion": 1,
 "properties": {
 "prompt": {
 "title": "Initial prompt",
 "type": "string",
 "description": "The initial prompt used as basis for text generation",
 "default": "My name is Apify and I like to",
 "editor": "textarea"
 },
 "max_length": {
 "title": "Maximum sequence length",
 "type": "integer",
 "description": "The maximum length of the generated sequence",
 "default": 30,
 "editor": "number"
 }
 },
 "required": [
 "prompt",
 "max_length"
 ]
}

INPUT_SCHEMA.json

# GPT-2 text generation

This actor uses the GPT-2 language model to generate text.

For more information about the model, see [https://huggingface.co/gpt2](https://huggingface.co/gpt2).

README.md

import os
from transformers import pipeline
from apify_client import ApifyClient

if __name__ == '__main__':
 client = ApifyClient(os.environ['APIFY_TOKEN'], api_url=os.environ['APIFY_API_BASE_URL'])

 default_kv_store_client = client.key_value_store(os.environ['APIFY_DEFAULT_KEY_VALUE_STORE_ID'])

 actor_input = default_kv_store_client.get_record(os.environ['APIFY_INPUT_KEY'])['value']

 default_dataset_client = client.dataset(os.environ['APIFY_DEFAULT_DATASET_ID'])

 generator = pipeline('text-generation', model='gpt2')
 output = generator(actor_input["prompt"], max_length=actor_input["max_length"], num_return_sequences=1)
 default_dataset_client.push_items(output)

main.py

# Add your dependencies here.
# See https://pip.pypa.io/en/latest/cli/pip_install/#requirements-file-format
# for how to format them

apify_client

requirements.txt

FROM huggingface/transformers-pytorch-gpu

COPY requirements.txt ./

RUN echo "Python version:" \
 && python3 --version \
 && echo "Pip version:" \
 && pip --version \
 && echo "Installing dependencies from requirements.txt:" \
 && pip install -r requirements.txt \
 && echo "All installed Python packages:" \
 && pip freeze

COPY . ./

RUN python3 -c 'from transformers import pipeline; generator = pipeline("text-generation", model="gpt2")'

CMD python3 main.py


The maximum length of the generated sequence

Maximum sequence length

The initial prompt used as basis for text generation

Initial prompt

Generate text

# GPT-2 text generation

This actor uses the GPT-2 language model to generate text.

For more information about the model, see [https://huggingface.co/gpt2](https://huggingface.co/gpt2).

import os
from transformers import pipeline
from apify_client import ApifyClient

if __name__ == '__main__':
    client = ApifyClient(os.environ['APIFY_TOKEN'], api_url=os.environ['APIFY_API_BASE_URL'])

    default_kv_store_client = client.key_value_store(os.environ['APIFY_DEFAULT_KEY_VALUE_STORE_ID'])

    actor_input = default_kv_store_client.get_record(os.environ['APIFY_INPUT_KEY'])['value']

    default_dataset_client = client.dataset(os.environ['APIFY_DEFAULT_DATASET_ID'])

    generator = pipeline('text-generation', model='gpt2')
    output = generator(actor_input["prompt"], max_length=actor_input["max_length"], num_return_sequences=1)
    default_dataset_client.push_items(output)


Initial prompt

Maximum sequence length

Where next?