This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

mapreduce.fileoutputcommitter.algorithm.version=2 # Use the latest committer version
mapreduce.job.reduce.slowstart.completedmaps=0.99 # 99% map, then reduce
mapreduce.reduce.shuffle.input.buffer.percent=0.9 # Min % buffer in RAM
mapreduce.reduce.shuffle.merge.percent=0.9 # Minimum % merges in RAM
mapreduce.reduce.speculative=false # Disable speculation for reducing
mapreduce.task.io.sort.factor=999 # Threshold before writing to disk

net.ipv4.tcp_max_syn_backlog=16384
net.core.wmem_max=4194304
net.core.rmem_max=4194304
net.core.rmem_default=4194304
net.core.wmem_default=4194304
net.ipv4.tcp_rmem="4096 87380 4194304"
net.ipv4.tcp_wmem="4096 65536 4194304"

# Reduce CPU utilization
net.ipv4.tcp_timestamps=0

# Increase throughput
net.ipv4.tcp_sack=1

# Low latency mode for TCP
net.ipv4.tcp_low_latency=1

# The following variable is used to tell the kernel how 

		}
	}
}

// Test for reduceErrs, reduceErr reduces collection
// of errors into a single maximal error with in the list.
func TestReduceErrs(t *testing.T) {
	canceledErrs := make([]error, 0, 5)
	for i := range 5 {
		canceledErrs = append(canceledErrs, fmt.Errorf("error %d: %w", i, context.Canceled))
	}
	// List all of all test cases to validate various cases of reduce errors.
	testCases := []struct {
		errs        []error

/// warning

The supporting additional functions `fake_password_hasher` and `fake_save_user` are just to demo a possible flow of the data, but they of course are not providing any real security.

///

## Reduce duplication { #reduce-duplication }

Reducing code duplication is one of the core ideas in **FastAPI**.

and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers
compression will not only reduce disk usage but also help increase system throughput.
Typically, enabling compression on spinning disk systems
will increase speed when the content can be compressed.

## Get Started

### 1. Prerequisites

//     Some of the Veeam products use Multi Delete operations. This setting can reduce how many objects are included in one
//     multi-delete operation. The same registry key setting overwrites the storage-defined setting.
//     Optional value, default 1000, range: 1-unlimited (S3 standard maximum is 1000 and should not be set higher)
//
//   - <StorageConcurrentTasksLimit>

   - Use `[WIP]` for in-progress PRs to avoid premature merging or choose GitHub draft PRs.

2. **Commits**:
   - Write clear messages: what changed and why (e.g., “Refactor S3 API handler to reduce latency so that requests process 20% faster”).
   - Rebase to tidy commits before submitting (e.g., `git rebase -i main` to squash typos or reword messages), unless multiple contributors worked on the branch.

That way I could find the best ways to reduce code duplication as much as possible, to have completion everywhere, type and error checks, etc.

All in a way that provided the best development experience for all the developers.

## Requirements { #requirements }

		// a row, then make sure we print it to call attention.
		if m[err.Error()] > 2 {
			peersLogAlwaysIf(ctx, fmt.Errorf("Following error has been printed %d times.. %w", m[err.Error()], err))
			// Reduce the count to introduce further delay in printing
			// but let it again print after the 2th attempt
			m[err.Error()]--
			m[err.Error()]--
		}
		m[err.Error()]++
	}
}()