# Do not use swap at all
vm.swappiness=0
vm.vfs_cache_pressure=50

# Start writeback at 3% memory
vm.dirty_background_ratio=3
# Force writeback at 10% memory
vm.dirty_ratio=10

# Quite a few memory map
# areas may be consumed
vm.max_map_count=524288

# Default is 500000 = 0.5ms
kernel.sched_migration_cost_ns=5000000

# stalled hdd io threads
kernel.hung_task_timeout_secs=85

# network tuning for bigger throughput

    for (int i = 0; i < 11; i++) {
      limiter.acquire(); // #7, showing off the warmup starting from totally cold
    }

    // make sure the areas (times) remain the same, while permits are different
    assertEvents(
        "R0.00, R1.38, R1.13, R0.88, R0.63, R0.50, R0.50, R0.50", // #1
        "U4.50", // #2

    for (int i = 0; i < 11; i++) {
      limiter.acquire(); // #7, showing off the warmup starting from totally cold
    }

    // make sure the areas (times) remain the same, while permits are different
    assertEvents(
        "R0.00, R1.38, R1.13, R0.88, R0.63, R0.50, R0.50, R0.50", // #1
        "U4.50", // #2

Including how to handle lifespan state that can be used in other areas of your code.

///

## Sub Applications { #sub-applications }

Incluindo como manipular estado do lifespan que pode ser usado em outras áreas do seu código.

///

## Sub Aplicações

the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)
and [C++](https://www.tensorflow.org/api_docs/cc) APIs, as well as a
non-guaranteed backward compatible API for

Incluyendo cómo manejar el estado de lifespan que puede ser usado en otras áreas de tu código.

///

## Sub Aplicaciones

    void testWireFormatStructure() {
        byte[] buffer = new byte[256];
        Arrays.fill(buffer, (byte) 0xCC); // Fill with pattern to detect unwritten areas

        int written = request.writeBytesWireFormat(buffer, 0);

        // Verify structure according to SMB2 FLUSH specification
        // Structure Size (2 bytes) - should be 24

`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are
tenant isolation, resource allocation, model sharing and hardware attacks.

### Tenant isolation

Since any tenants or users providing models, graphs or checkpoints can execute

            assertEquals(0x1, buffer[offset + 12]);
            // Note: Padding byte at offset + 13 is not guaranteed to be 0 after encode

            // Verify areas before offset are untouched
            for (int i = 0; i < offset; i++) {
                assertEquals((byte) 0xFF, buffer[i], "Buffer corrupted before offset at position " + i);
            }
        }
    }