checkpoint | Centralized identity store and authentication broker | Authentication library

I'm not sure if it completely addresses your needs, but package checkpoint seems appropriate here. It allows you to download source packages from a snapshot of CRAN taken at a specified date, going back to 2014-09-17. R 4.0.0 was released around 2020-04-24, so the snapshot from 2020-04-01 should work for your purposes.

Here is a reproducible example:

FirebaseFirestore.instance.collection('users').doc(user.uid).where('your filed', isEqualTo: 1).get();

Here is a minimal working example:

Use Run.create_children method which will start child runs that are “local” to the parent run, and don’t need authentication.

For AMLcompute max_concurrent_runs map to maximum number of nodes that will be used to run a hyperparameter tuning run. So there would be 1 execution per node.

single service deployed but you can load multiple model versions in the init then the score function, depending on the request’s param, uses particular model version to score. or with the new ML Endpoints (Preview). What are endpoints (preview) - Azure Machine Learning | Microsoft Docs

The configurations spark.streaming.[...] you are referring to belong to the Direct Streaming (aka Spark Streaming) and not to Structured Streaming.

In case you are unaware of the difference, I recommend to have a look at the separate programming guides:

• Structured Streaming: processing structured data streams with relation queries (using Datasets and DataFrames, newer API than DStreams)
• Spark Streaming: processing data streams using DStreams (old API)

Structured Streaming does not provide a backpressure mechanism. As you are consuming from Kafka you can use (as you are already doing) the option maxOffsetsPerTrigger to set a limit on read messages on each trigger. This option is documented in the Structured Streaming and Kafka Integration Guide as:

"Rate limit on maximum number of offsets processed per trigger interval. The specified total number of offsets will be proportionally split across topicPartitions of different volume."

In case you are still interested in the title question

How is spark.streaming.kafka.maxRatePerPartition related to spark.streaming.backpressure.enabled in case of spark streaming with Kafka?

This relation is explained in the documentation on Spark's Configuration:

"Enables or disables Spark Streaming's internal backpressure mechanism (since 1.5). This enables the Spark Streaming to control the receiving rate based on the current batch scheduling delays and processing times so that the system receives only as fast as the system can process. Internally, this dynamically sets the maximum receiving rate of receivers. This rate is upper bounded by the values spark.streaming.receiver.maxRate and spark.streaming.kafka.maxRatePerPartition if they are set (see below)."

All details on the backpressure mechanism available in Spark Streaming (DStream, not Structured Streaming) are explained in the blog that you have already linked Enable Back Pressure To Make Your Spark Streaming Application Production Ready.

Typically, if you enable backpressure you would set spark.streaming.kafka.maxRatePerPartition to be 150% ~ 200% of the optimal estimated rate.

The exact calculation of the PID controller can be found in the code within the class PIDRateEstimator.

As you asked for an example, here is one that I have done in one of my productive applications:

• Kafka topic has 16 partitions
• Spark runs with 16 worker cores, so each partitions can be consumed in parallel
• Using Spark Streaming (not Structured Streaming)
• Batch interval is 10 seconds
• spark.streaming.backpressure.enabled set to true
• spark.streaming.kafka.maxRatePerPartition set to 10000
• spark.streaming.backpressure.pid.minRate kept at default value of 100
• The job can handle around 5000 messages per second per partition
• Kafka topic contains multiple millions of messages in each partitions before starting the streaming job

• In the very first batch the streaming job fetches 16000 (= 10 seconds * 16 partitions * 100 pid.minRate) messages.
• The job is processing these 16000 message quite fast, so the PID controller estimates an optimal rate of something larger than the masRatePerPartition of 10000.
• Therefore, in the second batch, the streaming job fetches 16000 (= 10 seconds * 16 partitions * 10000 maxRatePerPartition) messages.
• Now, it takes around 22 seconds for the second batch to finish
• Because our batch interval was set to 10 seconds, after 10 seconds the streaming job schedules already the third micro-batch with again 1600000. The reason is that the PID controller can only use performance information from finished micro-batches.
• Only in the sixth or seventh micro-batch the PID controller finds the optimal processing rate of around 5000 messages per second per partition.

• Inference

  By default, an inference on your model will allocate memory to store the activations of each layer (activation as in intermediate layer inputs). This is needed for backpropagation where those tensors are used to compute the gradients. A simple but effective example is a function defined by f: x -> x². Here, df/dx = 2x, i.e. in order to compute df/dx you are required to keep x in memory.

  If you use the torch.no_grad() context manager, you will allow PyTorch to not save those values thus saving memory. This is particularly useful when evaluating or testing your model, i.e. when backpropagation is performed. Of course, you won't be able to use this during training!

• Backward propagation

  The backward pass call will allocate additional memory on the device to store each parameter's gradient value. Only leaf tensor nodes (model parameters and inputs) get their gradient stored in the grad attribute. This is why the memory usage is only increasing between the inference and backward calls.

• Model parameter update

  Since you are using a stateful optimizer (Adam), some additional memory is required to save some parameters. Read related PyTorch forum post on that. If you try with a stateless optimizer (for instance SGD) you should not have any memory overhead on the step call.

All three steps can have memory needs. In summary, the memory allocated on your device will effectively depend on three elements:

1. The size of your neural network: the bigger the model, the more layer activations and gradients will be saved in memory.

2. Whether you are under the torch.no_grad context: in this case, only the state of your model needs to be in memory (no activations or gradients necessary).

3. The type of optimizer used: whether it is stateful (saves some running estimates during parameter update, or stateless (doesn't require to).

whether you require to do back

broadcast() is used to cache data on each executor (instead of sending the data with every task) but it's not working too well with very large amounts of data. It seems here that 17M rows was a bit too much.

Pre-partitionning your source data before the join could also help if the partitioning of the source data is not optimized for the join. You'll want partition around the column you use for the join. Usually data should be partitionned depending on how it's consumed.

The Reformer model was proposed in the paper Reformer: The Efficient Transformer by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya. The paper contains a method for factorization gigantic matrix which is resulted of working with very long sequences! This factorization is relying on 2 assumptions

1. the parameter config.axial_pos_embds_dim is set to a tuple (d1,d2) which sum has to be equal to config.hidden_size
2. config.axial_pos_shape is set to a tuple (n1s,n2s) which product has to be equal to config.max_embedding_size (more on these here!)

Finally your question ;)

• I'm almost sure your session crushed duo to ram overflow
• you can change any config parameter during model instantiation like the official documentation!

If you can inline the svg inside the html and prepare it with groups that represent the parallax scrolling planes you can do something like the snippet below.

Due to svg structure these groups are already in order from back to front (farthest to nearest). So you can insert into id attribute of groups the parallax factor like prefixNN.NNN.

Javascript-side you only need to match the groups, extract the parallax factor removing the prefix, and parsing the rest of the value as float.

Multiplying the parallax factor by the distance between the vertical center of the SVG and the center of the current view you will get the vertical translation to be applied to each group (with a multiplier to be adjusted if necessary).

Here the result: https://jsfiddle.net/t50qo9cp/

Sorry I can only attach the javascript example code due to post characters limits.