tfc | Tinfoil Chat - Onion-routed , endpoint secure messaging | Router library

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All the files in the same directory tree as entry_point will be packaged in the docker image created, along with the entry_point file.

Using the entry point, it puts the wheel into the tar and copies it to the bucket.

The "COPY" exists but comes after the "pip install" command, so pip does not see the wheel.

You have to patch tensorflow-cloud in containerize.py:

move the lines

So, I figured this out and it is not in the documentation. A workspace that is a triggered by another workspace will not automatically update it's terrafrom plan.

Normally when I edit the code in github (or another repo) terraform cloud will automatically run a plan once you have saved that new code. A workspace that is triggered by another will not do that. So, even though I changed the code, I had to manually go to TF Cloud discard the current run on that triggered workspace, and re-run the plan. After this, the run trigger would successfully run.

It was a weird thing...

join:

Ok so I did this awhile back(last year) so I might have forgotten something but you can just refer to the code here, uses Expo and makes predictions on a live video feed just pardon the really bad code(I write better code now).

Anyway this a simple update on what you need to do which is mainly about handleCameraStream(). You will need to run two different useEffect hooks, one for initially loading up the model and the other one for canceling the animation frames which you will need to use when make predictions constantly.

Set the model into state then you can access it using model from any part in the file. I also did the same for predictions.

I have also added the ability to make predictions every N number of frames , by setting makePredictionsEveryNFrames to 1 it basically passes the tensors from TensorCamera to the function to make predictions every single frame. After making predictions you will also want to dispose of the tensors using tf.dispose(). This function loop() needs to be run infinitely to make predictions on oncoming frames continuously.

You can't mix and match callback-based async with promise-based (async/await) async.

Generally, you can promisify callback-based functions, but for fs, there's fs/promises already (since Node 10).

Your code can thus be simplified to something like this. It won't read the files in parallel, though; if you had very many files, it'd be easy to cause "Too many open files" without adding something like p-queue or p-limit into the mix.

Turns out it was a problem with my GCP configuration Here are the steps I made to make it work:

• Create an s3 bucket and make all files inside it public so the train job can access them

• Include these two in the requirements fsspec and gcsfs

• remove the 'engine' parameter from panda.readCsv like so

  dataset = pandas.read_csv('gs:///USDJPY.fx5.csv', usecols=[2, 3, 4, 5])

Since you are uploading the python file to GCP a good way to organize your code it to put all of the training logic into a method and then called it conditionally on the cloud train flag:

I using pdfminer to extract pdf. You can refer example code.