deeplearning | Model training , evaluation and prediction related codes | Continous Integration library

You're getting different results because you're evaluating the learner using different train and test data. If I use the same 3-fold CV, I get the same results:

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It seems that by default, the size of the shared memory is limited to 64mb. The solution to this error therefore, as shown in this issue is to increase the size of shared memory.

Hence, the first idea that comes to mind would be simply defining something like shm_size: 9gb in the YAML file of the stack. However, this might not work as shown for e.g in this issue.

Therefore, in the end, I had to use the following workaround (also described here, but poorly documented):

I would distinguish the forecasting problem from the data sampling time problem. You are dealing substantially with missing data.

1. Forecasting problem: You may use any machine learning technique just ignoring missing data. If you are not familiar with machine learning, I would suggest you to use LASSO (least absolute shrinkage and selection operator), which has been demonstrated to have predicting power (see "Sparse Signals in the Cross-Section of Returns" by ALEX CHINCO, ADAM D. CLARK-JOSEPH, and MAO YE).

2. Missing imputation problem: In the first place you should consider the reason why you have missing data. Sometime it makes no sense to impute values because the information that the value is missing is itself important and should not be overridden. Otherwise you have multiple options, other than linear interpolation, to estimate the missing values. For example check the MATLAB function fillmissing.

that error is actually refering to the weights of the conv layer which are in float32 by default when the matrix multiplication is called. Since your input is double(float64 in pytorch) while the weights in conv are float
So the solution in your case is :

I didn't find why the error occured but I found a solution to remove it. Instead of cloning master then pulling the branch, I directly clone the branch I want to use.

The cloning line is now :

Thanks to everybody who commented on this question.

You can run python scripts in GCP Deeplearning notebook VM in the background through nohup.

You are on Windows which is why you need to escape the backslashes or use raw strings to store file paths, i.e.:

From the documentation of h2o.init() (emphasis mine):

This method first checks if H2O is connectible. If it cannot connect and startH2O = TRUE with IP of localhost, it will attempt to start an instance of H2O with IP = localhost, port = 54321. Otherwise, it stops immediately with an error. When initializing H2O locally, this method searches for h2o.jar in the R library resources [...], and if the file does not exist, it will automatically attempt to download the correct version from Amazon S3. The user must have Internet access for this process to be successful. Once connected, the method checks to see if the local H2O R package version matches the version of H2O running on the server. If there is a mismatch and the user indicates she wishes to upgrade, it will remove the local H2O R package and download/install the H2O R package from the server.

So, h2o.init() with the default setting ip = "127.0.0.1", as here, connects the R session with the H2O instance (sometimes referred to as "server") in your local machine. If all the necessary package files are in place and up to date, no internet connection is necessary; the package will attempt to connect to the internet only to download stuff in case something is not present or up to date. No data is uploaded anywhere.