tensorflow / tensorflow/tensorboard
Add TensorBoard server to Flask endpoint
Nobody has claimed this yet.
- Dominant language
- TypeScript
- Stars
- 7.2k
- Forks
- 1.7k
- Avg merge
- 4d 22h
- Merged PRs (30d)
- 1
Description
I have a Flask App and a Tensorboad server. Is there a way by which I can map the Tensorboard server to one of the endpoints of Flask so that as soon as I hit that endpoint it triggers the Tensorboard server?
Flask application
from flask import Flask, jsonify, request
app = Flask(__name__)
@app.route('/hello-world', methods=['GET', 'POST'])
def say_hello():
return jsonify({'result': 'Hello world'})
if __name__ == "__main__":
app.run(host=host, port=5000)
Tensorboard server code:
from tensorboard.program import TensorBoard, setup_environment
def tensorboard_main(host, port, logdir):
configuration = list([""])
configuration.extend(["--host", host])
configuration.extend(["--port", port])
configuration.extend(["--logdir", logdir])
tensorboard = TensorBoard()
tensorboard.configure(configuration)
tensorboard.main()
if __name__ == "__main__":
host = "0.0.0.0"
port = "7070"
logdir = '/tmp/logdir'
tensorboard_main(host, port, logdir)
I tried creating an endpoint in Flask app and then added tensorboard_main(host, port, logdir) in the hope that if I hit the endpoint then the server will start but I got no luck.
I had posted this issue on StackOverflow but didn't get a valid reply hence posting it here.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the Flask application and the tensorboard_main entry point shown in the issue, then review how TensorBoard is configured and started. Reproduce the endpoint-triggered startup attempt and determine the supported server-lifecycle behavior. Done should mean a clearly defined, working way to expose or trigger TensorBoard from a Flask endpoint, with validation or documentation of the result.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- flask, python, tensorflow
- Domain
- backend, data-visualization
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100