Performance settings
thread_count
Command-line: -T, --thread-count
Description
The number of threads to use during the training.
Python package, Command-line
-
For CPU
Optimizes the speed of execution. This parameter doesn't affect results.
-
For GPU
The given value is used for reading the data from the hard drive and does not affect the training.During the training one main thread and one thread for each GPU are used.
R package
Optimizes the speed of execution. This parameter doesn't affect results.
Type
int
Default value
-1 (the number of threads is equal to the number of processor cores)
Supported processing units
CPU and GPU
used_ram_limit
Command-line: --used-ram-limit
Description
Attempt to limit the amount of used CPU RAM.
Alert
- This option affects only the CTR calculation memory usage.
- In some cases it is impossible to limit the amount of CPU RAM used in accordance with the specified value.
Format:
<size><measure of information>
Supported measures of information (non case-sensitive):
- MB
- KB
- GB
For example:
2gb
Type
int
Default value
None (memory usage is no limited)
Supported processing units
CPU
gpu_ram_part
Command-line: --gpu-ram-part
Description
How much of the GPU RAM to use for training.
Type
float
Default value
0.95
Supported processing units
CPU
pinned_memory_size
Command-line: --pinned-memory-size
Description
How much pinned (page-locked) CPU RAM to use per GPU.
The value should be a positive integer or inf. Measure of information can be defined for integer values.
Format:
<size><measure of information>
Supported measures of information (non case-sensitive):
- MB
- KB
- GB
For example:
2gb
Type
int
Default value
1073741824
Supported processing units
CPU
gpu_cat_features_storage
Command-line: --gpu-cat-features-storage
Description
The method for storing the categorical features' values.
Possible values:
- CpuPinnedMemory
- GpuRam
Note
Use the CpuPinnedMemory value if feature combinations are used and the available GPU RAM is not sufficient.
Type
string
Default value
Python package
None (set to GpuRam)
Command-line
GpuRam
Supported processing units
CPU
data_partition
Command-line: --data-partition
Description
The method for splitting the input dataset between multiple workers.
Possible values:
-
FeatureParallel — Split the input dataset by features and calculate the value of each of these features on a certain GPU.
For example:
- GPU0 is used to calculate the values of features indexed 0, 1, 2
- GPU1 is used to calculate the values of features indexed 3, 4, 5, etc.
-
DocParallel — Split the input dataset by objects and calculate all features for each of these objects on a certain GPU. It is recommended to use powers of two as the value for optimal performance.
For example:
- GPU0 is used to calculate all features for objects indexed
object_1,object_2 - GPU1 is used to calculate all features for objects indexed
object_3,object_4, etc.
- GPU0 is used to calculate all features for objects indexed
Type
string
Default value
Depends on the learning mode and the input dataset
Supported processing units
CPU