This document is relevant for: Inf1, Inf2, Trn1, Trn1n

Neuron Runtime Configuration#

Neuron Runtime is responsible for executing ML models on Neuron Devices. Neuron Runtime determines which NeuronCore will execute which model and how to execute it. Configuration of the Neuron Runtime is controlled through the use of Environment variables at the process level. By default, Neuron framework extensions will take care of Neuron Runtime configuration on the user’s behalf. Explicit configurations are also possible when attempting to achieve a desired behavior.

This guide provides an overview of the different environment variables available to configure Neuron Runtime behavior.

Environment Variables#

Name

Description

Type

Expected Values

Default Value

RT Version

NEURON_RT_VISIBLE_CORES

Range of specific NeuronCores needed by the process

Integer range (like 1-3)

Any value or range between 0 to Max NeuronCore in the system.

None

2.0+

NEURON_RT_NUM_CORES

Number of NeuronCores required by the process.

Integer

A value from 1 to Max NeuronCore in the system.

0, which is interpretted as “all”

2.0+

NEURON_RT_LOG_LOCATION

Runtime log location

string

console or syslog

console

2.0+

NEURON_RT_LOG_LEVEL

Runtime log verbose level

string

ERROR, WARNING, INFO, DEBUG, TRACE

ERROR

2.0+

NEURON_RT_EXEC_TIMEOUT

Timeout for execution in seconds

Integer

0 to INT_MAX

30

2.0+

NEURON_RT_VALIDATE_HASH

Validate NEFF contents before loading into accelerator

Boolean

TRUE or FALSE

FALSE

2.0+

NEURON_RT_MULTI_INSTANCE_SHARED_WEIGHTS

Share weights when loading multiple instance versions of the same model on different NeuronCores

Boolean

TRUE or FALSE

FALSE

2.11+

NeuronCore Allocation#

Important

NEURONCORE_GROUP_SIZES is being deprecated, if your application is using NEURONCORE_GROUP_SIZES please see Migrate your application to Neuron Runtime 2.x (libnrt.so) for more details.

By default, Neuron Runtime initializes all the cores present in the system and reserves them for the current process.

Note

Once a NeuronCore is reserved for a process, it cannot be used by another process at all, until the process reserving that NeuronCore is terminated.

Using NEURON_RT_VISIBLE_CORES#

For parallel processing, NEURON_RT_VISIBLE_CORES can be used to control which NeuronCores each process would reserve. This variable is specified with a single NeuronCore index or an inclusive range value.

For example, if a process (myapp.py) requires one NeuronCore, then it can be started with NEURON_RT_VISIBLE_CORES=0 to limit the process to NeuronCore 0. For parallel processing, multiple process can be started (without any change to myapp.py code) with different NEURON_RT_VISIBLE_CORES values. Here is an example that runs myapp.py on inf1.xlarge in parallel across the four different NeuronCores available in the inf1.xlarge.

NEURON_RT_VISIBLE_CORES=0 myapp.py &
NEURON_RT_VISIBLE_CORES=1 myapp.py &
NEURON_RT_VISIBLE_CORES=2 myapp.py &
NEURON_RT_VISIBLE_CORES=3 myapp.py &

If myapp.py required 3 NeuronCores and was running on a inf1.6xlarge (16 NeuronCores maximum), the first instance of myapp.py could use NeuronCores 0-2, the next instance could use 3-5 and so on:

NEURON_RT_VISIBLE_CORES=0-2 myapp.py &
NEURON_RT_VISIBLE_CORES=3-5 myapp.py &
NEURON_RT_VISIBLE_CORES=6-8 myapp.py &
NEURON_RT_VISIBLE_CORES=9-11 myapp.py &
NEURON_RT_VISIBLE_CORES=12-14 myapp.py &

Using NEURON_RT_NUM_CORES#

If NEURON_RT_NUM_CORES is set to a value between 1 and the maximum number of NeuronCores in the instance, Neuron Runtime will attempt to automatically reserve the number of free NeuronCores specified for the process. The difference between NEURON_RT_VISIBLE_CORES and NEURON_RT_NUM_CORES is that, NEURON_RT_VISIBLE_CORES specifies exact NeuronCores to allocate where as NEURON_RT_NUM_CORES specifies the number of NeuronCores needed and Neuron Runtime selects free NeuronCores.

Using the same example earlier where myapp.py needed 3 cores, but _which_ 3 cores was of no concern, the same application could be executed in parallel up to 5 times on an inf1.6xlarge (16 NeuronCore max):

NEURON_RT_NUM_CORES=3 myapp.py &
NEURON_RT_NUM_CORES=3 myapp.py &
NEURON_RT_NUM_CORES=3 myapp.py &
NEURON_RT_NUM_CORES=3 myapp.py &
NEURON_RT_NUM_CORES=3 myapp.py &

Executing a 6th NEURON_RT_NUM_CORES=3 myapp.py & in the above example would fail as there is only a single NeuronCore still free.

Notes#

  1. Number of NeuronCores in a inferentia device is 4

  2. Number of inferentia is depends on the instance size.

  3. The NeuronCore index in NEURON_RT_VISIBLE_CORES starts from 0 and ends at (number of NeuronDevices * number of NeuronCores) - 1.

  4. By default, NEURON_RT_NUM_CORES is set to 0, which indicates to RT that all cores are to be used.

  5. NEURON_RT_VISIBLE_CORES takes precedence over NEURON_RT_NUM_CORES. If specified, all cores within the range will be assigned to the owning process.

Logging and debug-ability#

By default, Neuron Runtime logs to syslog with verbose level of INFO and only ERROR s are logged in console. The following code snippet shows ways to increase/decrease the log level.

NEURON_RT_LOG_LEVEL=INFO myapp.py         # Sets the log level for syslog and console to INFO
NEURON_RT_LOG_LOCATION=console NEURON_RT_LOG_LEVEL=QUIET myapp.py    # Completely disables console logging.

By default, Neuron Runtime expects the NeuronCore to complete execution of any model with in 2 seconds. If NeuronCore didnt complete the execution within 2 seconds then runtime would fail the execution with timeout error. Most of the models takes few milliseconds to complete so 2 seconds(2000 milliseconds) is more than adequate. However if your model is expected to run more than 2 seconds then you can increase the timeout with NEURON_RT_EXEC_TIMEOUT.

NEURON_RT_EXEC_TIMEOUT=5 myapp.py       # increases the timeout to 5 seconds

Checksum#

To execute a model(NEFF), Neuron Runtime needs to load the NEFF file into NeuronCore and run. Neuron Runtime provides a way to do checksum validation on each NEFF file while loading to validate the file is not corrupted. This option is off by default to avoid performance penalty during model load time(~50%).

NEURON_RT_VALIDATE_HASH=true myapp1.py     # enables model checksum validation while loading
NEURON_RT_VALIDATE_HASH=false myapp2.py    # disables(default) model checksum validation while loading

Shared Weights (NEURON_RT_MULTI_INSTANCE_SHARED_WEIGHTS)#

By default, Neuron Runtime will make copies of modle weights when loading the same instance of a model to multiple NeuronCores. Changing this default to a weight sharing mechanism is possible with Neuron Runtime 2.11 or higher by setting NEURON_RT_MULTI_INSTANCE_SHARED_WEIGHTS=TRUE. Use of this flag will allow for more models to be loaded by reducing the memory requirements, but will potentially come at a cost of throughput by forcing the execution across cores to compete for memory bandwidth.

Note: the use of this flag requires the model to be loaded with the multi-instance feature.

NEURON_RT_MULTI_INSTANCE_SHARED_WEIGHTS=TRUE myapp1.py     # enables model weight sharing
NEURON_RT_MULTI_INSTANCE_SHARED_WEIGHTS=FALSE myapp2.py    # disables(default) model weight sharing

This document is relevant for: Inf1, Inf2, Trn1, Trn1n