pbs | PYBOSSA command line client | Command Line Interface library

you receive tweet in stream.on listener, so it's just tweet, instead of req.body.postContent:

To run your job as-is, you could simply request ncpu=32 and then in your python script set num_cores = 2. Obviously this has you paying for 32 cores and then leaving 30 of them idle, which is wasteful.

The real problem here is that your current algorithm is memory-bound, not CPU-bound. You should be going to great lengths to read only chunks of your files into memory, operating on the chunks, and then writing the result chunks to disk to be organized later.

Fortunately Dask is built to do exactly this kind of thing. As a first step, you can take out the parallelize_dataframe function and directly load and map your some_algorithm with a dask.dataframe and dask.array:

What could be the reason?

In order to be interoperable with g++, pgc++ (aka nvc++) uses the g++ STL and system header files. Since the location of these headers can vary, on installation a configuration file, "localrc", is created to store these locations.

What could be happening here is that on install, a single system install was selected and hence the generated localrc is for the system from which the compilers were installed, not the remote system.

If this is the case, consider re-installing with the "network" option. In this case, the creation of the localrc is delayed until the first compiler invocation with a unique localrc generated for each system.

Another possibility is that creating of the localrc file failed for some unknown reason. Possibly a permission issue. To check, you can run the 'makelocalrc' utility to see if any errors occurred.

Note for newer versions of nvc++, we are moving away from using pre-generated config files and instead determining these config items each time the compiler is invoked. The pervious concern being the overhead involved in generating the config, but this has become less of a problem.

If you are simply printing the objects and looking at that output, the string representations of API v2 models/objects only include the default attributes that are guaranteed to exist.

The objects themselves still include the relevant data, which you can access as attributes or by key, like a dictionary.

https://tweepy.readthedocs.io/en/v4.6.0/faq.html#why-am-i-not-getting-expansions-or-fields-data-with-api-v2-using-client

Try this if you want to run mpirun -n 32 twice in parallel:

Incidentally, this is not a stupid question at all, you have just started learning. First of all, I advise you to consider a separate

This error message...

There are a couple of things to note here:

• in Twitter API v2, at the time of writing this answer in Feb 2022, the video URL is not currently available. This is a known omission that Twitter is working on.

• in Twitter API v1.1, you generally should be able to get the video URLs. There are two additional things to know about...

  • if the Tweet is a longer (more than 140 characters) Tweet, then you will need to add tweet_mode=extended to your API call to ensure that the whole Tweet data and extended entities are included. You can tell if you have not retrieved the whole Tweet if the truncated field value is true. This is the case in your example Tweet ID above. Using tweet_mode=extended you will see the video URLs.
  • if the Tweet is from the Ads platform, the video may be part of a Ads card format, and these are not served via the API. You can tell this by checking the source field for evidence that the Tweet was posted as an ad. The advertiser is able to mark the media as not for wider syndication / API access in that case, and the video URL is not returned in the API.

The count is increasing for each user because, the count state that you are maintaining is not specific to a user. It is a general, common counter.

What you could instead do is,

1. Get rid of the count state.
2. Add a users state, which will be an array of users, with a count for each user. Something like:

this.state = Items.map(i => ({...i, count: 0}))

1. Pass an id to the incremento(id) and decremento(id) methods.
2. This way now, you can update the count of only the user for which the up/down button was clicked inside your incremento() and decremento() methods
3. And in the render method, you can sort the Items array by the count field, and then render each user.

This way, you can have the count increased only for the clicked user, and users sorted by counts in descending order.