systems | systems is a set of tools

You are close. You need to tell the autodiff pipeline what you want to take the derivative with respect to. In this case, I believe you want

The ASHWID provides a strong binding between the app/packag, which is not affected by OS re-installs and version updates for an app. But sometimes there are also other reasons can affect it, it is difficult to know which causes the change of the ASHWID.

Therefore, if you want to get the unique id to distinguish the device, maybe you could use SystemIdentification.GetSystemIdForPublisher method, the identifier returned by this method is specific to the app publisher on the current device. In other words, all apps by the same publisher will get the same value for this ID (for all users).

Oh, if you can change your data structure please do.

Here I'm wondering about the line [in s_client]
New, TLSv1/SSLv3, Cipher is ECDHE-RSA-AES256-GCM-SHA384

You're apparently using OpenSSL 1.0.2, where that's a basically useless relic. Back in the days when OpenSSL supported SSLv2 (mostly until 2010, although almost no one used it much after 2000), the ciphersuite values used for SSLv3 and up (including all TLS, but before 2014 OpenSSL didn't implement higher than TLS1.0) were structured differently than those used for SSLv2, so it was important to qualify the ciphersuite by the 'universe' it existed in. It has almost nothing to do with the protocol version actually used, which appears later in the session-param decode:

Unfortunately doing this directly by attempting to web scrape the search results using UrlFetchApp will not work. You can use third party tools to get the number of search results, however.

I tested this out using an exponential backoff method which sometimes is able to get past 429 errors when a fetch request is invoked by UrlFetchApp.

When using UrlFetchApp to either web scrape or to connect to an API, it can happen that the server denies the request on the grounds of too many requests - or HTTP Error 429.

Google Apps Script runs in the cloud, from a set of IP addresses in a pool that Google own. You can actually see all the IP ranges here. Most websites (especially large companies such as Google) have architecture in place to prevent the use of bots scraping their websites and slowing down traffic.

Sometimes it's possible to get past this error, using a mixture of exponential backoff and random time intervals as shown for the Binance API (Full Disclosure: this GitHub repository was written by me.)

I assume that either Google directly blocks the Apps Script IP pool, or there are simply too many people trying the same thing - because with the same techniques I was unable to get any response that didn't involve entering a captcha as we discussed in the comments above and can be seen in the log of the page string.

There are many third party APIs that you can use to do this, and I suggest searching for one that meets your needs.

I tested out one called Authoritas which returns search engine indexing for different keywords. The API is asynchornous, so can take up to a minute to get a response, so a Web App solution needs to be made.

The flow I used is as follows:

• Obtain API key from Authoritas (free)
• Create a new Apps Script project to make an API call:

Can you try this?

During the initialization and update processes, the platform looks for ImpEx files in the /resources/impex folder. In particular:

For essential data: The platform scans the /resources/impex folders for files with names that match the pattern essentialdata*.impex and imports the files during the essential data creation.

For project data: The platform scans the /resources/impex folders for files with names that match the pattern projectdata*.impex and imports the files during the project data creation.

The ImpEx directory does not exist by default. You must create it and copy files to it.

For example, if you have the following folder structure:

resources/impex/essentialdataOne.impex

resources/impex/essDataOne.impex

You could try:

While I do not know how to deal with this problem directly, I had a somewhat similar issue(and solved). The difference is:

• I use fairseq
• I can run my code on google colab with 1 GPU
• Got RuntimeError: unable to mmap 280 bytes from file : Cannot allocate memory (12) immediately when I tried to run it on multiple GPUs.

From the other people's code, I found that he uses python -m torch.distributed.launch -- ... to run fairseq-train, and I added it to my bash script and the RuntimeError is gone and training is going.

So I guess if you can run with 21000 samples, you may use torch.distributed to make whole data into small batches and distribute them to several workers.