maml | Materials Machine Learning , Materials Descriptors | Machine Learning library

The only difference is that in second approach you'll have to keep much more stuff in memory - until you call backward you'll have all unrolled parameters fnet.parameters(time=T) (along with intermediate computation tensors) for each of task_num iterations as part of the graph for the aggregated meta_loss. If you call backward on every task then you only need to keep full set of unrolled parameters (and other pieces of the graph) for one task.

So to answer your question's title: because in this case the memory footprint is task_num times bigger.

In a nutshell what you're doing is similar to comparing loopA(N) and loopB(N) in the following code. Here loopA will get as much memory as it can and OOM with sufficiently large N, while loopB will use about same amount of memory for any large N:

Solution here:

The if/else can return only a single TRUE/FALSE and is not vectorized for length > 1. It may be suitable to use ifelse (but that is also not required and would be less efficient compared to direct coersion of logical vector to binary (as.integer). In the OP's code, the 'close' column elements are looped (sapply) and subtracted from the whole 'open' column. The intention might be to do elementwise subtraction. In that case, - between the columns is much cleaner and efficient (as these operations are vectorized)

Welcome to R!

The reason your code is not working as expected is because the first four lines in your script are assigning vectors. This would work fine if you were using base R subsetting (try ?'[' at the console) and performing logic tests in the columns as vectors.

dplyr works somewhat differently. The metaphor is closer to SQL, treating each "column" in the dataset as a SQL field. So, you can work with your variables directly, without subsetting them out into vectors.

Try:

The countcolumn is not calculated by the function apriori()in this version of the package arules, so I calculated it in this way, using the inverse formula to calculate the support:

Hi I had the same problem 2 days ago with the function pull(), always of the package dplyr. The problem is that the both version of R (CRAN R 3.1.0 and Microsoft R open 3.2.2) supported by Azure Machine Learning Studio, does not support the version 0.7.4 of package dplyr. If you read the documentation related to the package dplyr you can see that the package is installable only for R versions >= 3.1.2.

Then you must wait for the R version used by Azure Machine Learning Studio be updated, or find an alternative solution to your function.

You can author custom R Modules.

Here is some documentation: https://blogs.technet.microsoft.com/machinelearning/2015/04/23/build-your-own-r-modules-in-azure-ml/ https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-custom-r-modules

One thing you can do is combining two data-sets together and selecting the appropriate fields using the R script. That would be an easy workaround.

To scale your Azure Redis Cache instances using the Microsoft Azure Management Libraries (MAML), call the IRedisOperations.CreateOrUpdate method and pass in the new size for the RedisProperties.SKU.Capacity.