This project was generated with the Hypergol framework
Please see documentation for instructions: https://hypergol.readthedocs.io/en/latest/
Hypergol is heavily integrated with git, all projects must be in a git repository to ensure code and data lineage (to record which data was created by which version of the code).
Initialise git with:
git init .
Create the first commit (datasets record the last commit when they are created and without this there is nothing to record):
git commit -m "First Commit!"
The project now (and any time a file is changed but the change is not committed to the repo) is in a "dirty" stage. If you run a pipeline or train a model, the last commit will be recorded but that commit will not represent the code that is running! Add changes and commit:
git add .
git commit -m "All the files!"
If there are files that shouldn't be checked in ever to git they should be to the .gitignore file before git add .
Alternatively individual files can be added to git with git add <filename>.
Having dedicated virtual environment fully described by the projects requirements.txt is the recommended practice. Don't forget to deactivate the current virtual environment! Files from the environment are included in the projects .gitignore file and will ignored by git.
deactivate
./make_venv.sh
source .venv/bin/activate
sys.path.insert(0, '<project_directory>/example')
from hypergol import HypergolProject
from data_models.data_type import DataType
project = HypergolProject(
projectDirectory='<project_directory>/example',
dataDirectory='<data_directory>',
force=True
)
dataTypeDataset = project.datasetFactory.get(dataType=DataType, name='data_types')
with dataTypeDataset.open('r') as dsr:
dataTypes = [value.to_data() for value in islice(dsr, 10)]
# Or convert straight into pandas
import pandas as pd
dataTypeDataframe = pd.DataFrame([value.to_data() for value in islice(dataTypeDataset.open('r'), 10)])
<project_directory> is the repo's directory.
<data_directory> is the parent data directory.
If the project is called my_project and the code is located in ~/my_project and the project data is in ~/data/my_project, <data_directory> is ~/data.
Set branch argument in datasetFactory.get() if you need anything else other than the current branch.
The force argument allows you to load the data even if your repo has uncommitted code, this is usually not a problem unless you plan to write into dataframes from Jupyter.
This will list all existing datasets that matches pattern as self contained executable code.
project.list_datasets(pattern='.*', asCode=True);
It is recommended to start it in a screen session (screen -S tensorboard) so you can close the terminal window or if you disconnect from a remote Linux machine (reconnect with screen -x tensorboard). In the project directory:
screen -S tensorboard
source .venv/bin/activate
tensorboard --logdir=<data_directory>/example/tensorboard/
After implementing all components and required functions:
./train_example.sh
This will execute the model manager's run() function with the prescribed schedule (training steps, evaluation steps, etc.). Training can be stopped with Ctrl-C, this will won't result in the corruption of the output dataset (datasets must be closed properly to generate their chk file after they are read only). This is possible because the entire training happen in a try/finally block.
In the generated models/serve_example.py function specify the directory of the model to be served at:
MODEL_DIRECTORY = '<data_directory>/example/<branch>/models/<ModelName>/<epoch_number>'
then start serving with (port and host can be set in the shell script):
./serve_example.sh
import requests
response = json.loads(requests.get('http://0.0.0.0:8000', headers={'accept': 'application/json'}).text)
modelLongName = response['model']
This allows to verify if indeed the intended model is served. The generated training script sets training day and the commit hash at that point to be part of the long name and to ensure that the exact conditions of training are available at serving. Long name should be used in logging to identify which model created an output. From v0.0.10 the long name is returned in the header of the response of /output endpoint as well in the x-model-long-name field.
To get the response of the model to a list of objects, see example below. Replace ExampleOutput with the correct output type and load a dataset into ds, use list_datasets from above to do this.
sys.path.insert(0, '<project_directory>/example')
import requests
from itertools import islice
from data_models.example_model_output import ExampleModelOutput
with ds.open('r') as dsr:
values = [value.to_data() for value in islice(dsr, 10)]
response = requests.post(
'http://0.0.0.0:8000/output',
headers={
'accept': 'application/json',
'Content-Type': 'application/json',
},
data=json.dumps(values)
)
outputs = [ExampleModelOutput.from_data(v) for v in json.loads(response.text)]
modelLongName = response.headers['x-model-long-name']
It is not recommended to do large scale evaluation through the API as the overhead per object is too high and it is single threaded.