Variables controlling the execution environment¶

• MXNET_LIBRARY_PATH Absolute path indicating where the mxnet dynamic library is to be located, this would be the absolute path to libmxnet.so or libmxnet.dll depending on the platform. The logic for loading the library is in python/mxnet/libinfo.py

Set the Number of Threads¶

• MXNET_GPU_WORKER_NTHREADS
  • Values: Int (default=2)
  • The maximum number of threads to use on each GPU. This parameter is used to parallelize the computation within a single GPU card.
• MXNET_GPU_COPY_NTHREADS
  • Values: Int (default=2)
  • The maximum number of concurrent threads that do the memory copy job on each GPU.
• MXNET_CPU_WORKER_NTHREADS
  • Values: Int (default=1)
  • The maximum number of scheduling threads on CPU. It specifies how many operators can be run in parallel.
• MXNET_CPU_PRIORITY_NTHREADS
  • Values: Int (default=4)
  • The number of threads given to prioritized CPU jobs.
• MXNET_CPU_NNPACK_NTHREADS
  • Values: Int (default=4)
  • The number of threads used for NNPACK. NNPACK package aims to provide high-performance implementations of some layers for multi-core CPUs. Checkout NNPACK to know more about it.
• MXNET_MP_WORKER_NTHREADS
  • Values: Int (default=1)
  • The number of scheduling threads on CPU given to multiprocess workers. Enlarge this number allows more operators to run in parallel in individual workers but please consider reducing the overall num_workers to avoid thread contention (not available on Windows).
• MXNET_MP_OPENCV_NUM_THREADS
  • Values: Int (default=0)
  • The number of OpenCV execution threads given to multiprocess workers. OpenCV multithreading is disabled if MXNET_MP_OPENCV_NUM_THREADS < 1 (default). Enlarge this number may boost the performance of individual workers when executing underlying OpenCV functions but please consider reducing the overall num_workers to avoid thread contention (not available on Windows).

Memory Options¶

• MXNET_EXEC_ENABLE_INPLACE
  • Values: true or false (default=true)
  • Whether to enable in-place optimization in symbolic execution. Checkout in-place optimization to know more about it.
• NNVM_EXEC_MATCH_RANGE
  • Values: Int (default=16)
  • The approximate matching scale in the symbolic execution memory allocator.
  • Set this to 0 if you don’t want to enable memory sharing between graph nodes(for debugging purposes).
  • This variable has impact on the result of memory planning. So, MXNet sweep between [1, NNVM_EXEC_MATCH_RANGE], and selects the best value.
• MXNET_EXEC_NUM_TEMP
  • Values: Int (default=1)
  • The maximum number of temporary workspaces to allocate to each device. This controls space replicas and in turn reduces the memory usage.
  • Setting this to a small number can save GPU memory. It will also likely decrease the level of parallelism, which is usually acceptable.
  • MXNet internally uses graph coloring algorithm to optimize memory consumption.
  • This parameter is also used to get number of matching colors in graph and in turn how much parallelism one can get in each GPU. Color based match usually costs more memory but also enables more parallelism.
• MXNET_GPU_MEM_POOL_RESERVE
  • Values: Int (default=5)
  • The percentage of GPU memory to reserve for things other than the GPU array, such as kernel launch or cudnn handle space.
  • If you see a strange out-of-memory error from the kernel launch, after multiple iterations, try setting this to a larger value.
• MXNET_GPU_MEM_POOL_TYPE
  • Values: String (default=Naive)
  • The type of memory pool.
  • Choices:
    • Naive: A simple memory pool that allocates memory for the exact requested size and cache memory buffers. If a buffered memory chunk matches the size of a new request, the chunk from the memory pool will be returned and reused.
    • Round: A memory pool that always rounds the requested memory size and allocates memory of the rounded size. MXNET_GPU_MEM_POOL_ROUND_LINEAR_CUTOFF defines how to round up a memory size. Caching and allocating buffered memory works in the same way as the naive memory pool.
• MXNET_GPU_MEM_POOL_ROUND_LINEAR_CUTOFF
  • Values: Int (default=24)
  • The cutoff threshold that decides the rounding strategy. Let’s denote the threshold as T. If the memory size is smaller than 2 ** T (by default, it’s 2 ** 24 = 16MB), it rounds to the smallest 2 ** n that is larger than the requested memory size; if the memory size is larger than 2 ** T, it rounds to the next k * 2 ** T.
• MXNET_GPU_MEM_LARGE_ALLOC_ROUND_SIZE
  • Values: Int (default=2097152)
  • When using the naive pool type, memory allocations larger than this threshhold are rounded up to a multiple of this value.
  • The default was chosen to minimize global memory fragmentation within the GPU driver. Set this to 1 to disable.

Engine Type¶

• MXNET_ENGINE_TYPE
  • Values: String (default=ThreadedEnginePerDevice)
  • The type of underlying execution engine of MXNet.
  • Choices:
    • NaiveEngine: A very simple engine that uses the master thread to do the computation synchronously. Setting this engine disables multi-threading. You can use this type for debugging in case of any error. Backtrace will give you the series of calls that lead to the error. Remember to set MXNET_ENGINE_TYPE back to empty after debugging.
    • ThreadedEngine: A threaded engine that uses a global thread pool to schedule jobs.
    • ThreadedEnginePerDevice: A threaded engine that allocates thread per GPU and executes jobs asynchronously.

Execution Options¶

• MXNET_EXEC_BULK_EXEC_INFERENCE
  • Values: 0(false) or 1(true) (default=1)
  • If set to 1, during inference MXNet executes the entire computation graph in bulk mode, which reduces kernel launch gaps in between symbolic operators.
• MXNET_EXEC_BULK_EXEC_TRAIN
  • Values: 0(false) or 1(true) (default=1)
  • If set to 1, during training MXNet executes the computation graph as several subgraphs in bulk mode.
• MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN
  • Values: Int (default=15)
  • The maximum number of nodes in the subgraph executed in bulk during training(not inference). Setting this to a larger number may reduce the degree of parallelism for multi-GPU training.

Control the Data Communication¶

• MXNET_KVSTORE_REDUCTION_NTHREADS
  • Values: Int (default=4)
  • The number of CPU threads used for summing up big arrays on a single machine
  • This will also be used for dist_sync kvstore to sum up arrays from different contexts on a single machine.
  • This does not affect summing up of arrays from different machines on servers.
  • Summing up of arrays for dist_sync_device kvstore is also unaffected as that happens on GPUs.
• MXNET_KVSTORE_BIGARRAY_BOUND
  • Values: Int (default=1000000)
  • The minimum size of a “big array”.
  • When the array size is bigger than this threshold, MXNET_KVSTORE_REDUCTION_NTHREADS threads are used for reduction.
  • This parameter is also used as a load balancer in kvstore. It controls when to partition a single weight to all the servers. If the size of a single weight is less than MXNET_KVSTORE_BIGARRAY_BOUND then, it is sent to a single randomly picked server otherwise it is partitioned to all the servers.
• MXNET_KVSTORE_USETREE
  • Values: 0(false) or 1(true) (default=0)
  • If true, MXNet tries to use tree reduction for Push and Pull communication.
  • Otherwise, MXNet uses the default Push and Pull implementation.
  • Tree reduction technology has been shown to be faster than the standard --kv-store device Push/Pull and --kv-store nccl Push/Pull for small batch sizes.
• MXNET_KVSTORE_LOGTREE
  • Values: 0(false) or 1(true) (default=0)
  • If true and MXNET_KVSTORE_USETREE is set to 1, MXNet will log the reduction trees that have been generated.
• MXNET_KVSTORE_TREE_ARRAY_BOUND
  • Values: Int (default=10000000)
  • The minimum size of a “big array”.
  • When the array size is bigger than this threshold and MXNET_KVSTORE_USETREE is set to 1, multiple trees are used to load balance the big gradient being communicated in order to better saturate link bandwidth.
  • Note: This environmental variable only takes effect if Tree KVStore is being used (MXNET_KVSTORE_USETREE=1).
• MXNET_KVSTORE_TREE_BACKTRACK
  • Values: 0(false) or 1(true) ```(default=0)
  • If true and MXNET_KVSTORE_USETREE is set to 1, MXNet tries to use backtracking to generate the trees required for tree reduction.
  • If false and MXNET_KVSTORE_USETREE is set to 1, MXNet tries to use Kernighan-Lin heuristic to generate the trees required for tree reduction.
• MXNET_KVSTORE_TREE_LINK_USAGE_PENALTY
  • Values: Float (default=0.7)
  • The multiplicative penalty term to a link being used once.
• MXNET_ENABLE_GPU_P2P
  • Values: 0(false) or 1(true) (default=1)
  • If true, MXNet tries to use GPU peer-to-peer communication, if available on your device, when kvstore’s type is device.

Memonger¶

• MXNET_BACKWARD_DO_MIRROR
  • Values: 0(false) or 1(true) (default=0)
  • MXNet uses mirroring concept to save memory. Normally backward pass needs some forward input and it is stored in memory but you can choose to release this saved input and recalculate it in backward pass when needed. This basically trades off the computation for memory consumption.
  • This parameter decides whether to do mirror during training for saving device memory.
  • When set to 1, during forward propagation, graph executor will mirror some layer’s feature map and drop others, but it will re-compute this dropped feature maps when needed.
  • MXNET_BACKWARD_DO_MIRROR=1 will save 30%~50% of device memory, but retains about 95% of running speed.
  • One extension of mirror in MXNet is called memonger technology, it will only use O(sqrt(N)) memory at 75% running speed. Checkout the code here.

Control the profiler¶

When USE_PROFILER is enabled in Makefile or CMake, the following environments can be used to profile the application without changing code. Execution options may affect the granularity of profiling result. If you need profiling result of every operator, please set MXNET_EXEC_BULK_EXEC_INFERENCE, MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN and MXNET_EXEC_BULK_EXEC_TRAIN to 0.

• MXNET_PROFILER_AUTOSTART
  • Values: 0(false) or 1(true) (default=0)
    • Set to 1, MXNet starts the profiler automatically. The profiling result is stored into profile.json in the working directory.
• MXNET_PROFILER_MODE
  • Values: 0(false) or 1(true) (default=0)
    • If set to ‘0’, profiler records the events of the symbolic operators.
    • If set to ‘1’, profiler records the events of all operators.

Other Environment Variables¶

• MXNET_CUDNN_AUTOTUNE_DEFAULT
  • Values: 0, 1, or 2 (default=1)
  • The default value of cudnn auto tuning for convolution layers.
  • Value of 0 means there is no auto tuning to pick the convolution algo
  • Performance tests are run to pick the convolution algo when value is 1 or 2
  • Value of 1 chooses the best algo in a limited workspace
  • Value of 2 chooses the fastest algo whose memory requirements may be larger than the default workspace threshold
• MXNET_CUDA_ALLOW_TENSOR_CORE
  • 0(false) or 1(true) (default=1)
    • If set to ‘0’, disallows Tensor Core use in CUDA ops.
    • If set to ‘1’, allows Tensor Core use in CUDA ops.
  • This variable can only be set once in a session.
• MXNET_CUDA_TENSOR_OP_MATH_ALLOW_CONVERSION
  • 0(false) or 1(true) (default=0)
    • If set to ‘0’, disallows implicit type conversions to Float16 to use Tensor Cores
    • If set to ‘1’, allows CUDA ops like RNN and Convolution to use TensorCores even with Float32 input data by using implicit type casting to Float16. Only has an effect if MXNET_CUDA_ALLOW_TENSOR_CORE is 1.
• MXNET_GLUON_REPO
  • Values: String (default='https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/'
  • The repository url to be used for Gluon datasets and pre-trained models.
• MXNET_HOME
  • Data directory in the filesystem for storage, for example when downloading gluon models.
  • Default in *nix is .mxnet APPDATA/mxnet in windows.
• MXNET_MKLDNN_ENABLED
  • Values: 0, 1 (default=1)
  • Flag to enable or disable MKLDNN accelerator. On by default.
  • Only applies to mxnet that has been compiled with MKLDNN (pip install mxnet-mkl or built from source with USE_MKLDNN=1)
• MXNET_MKLDNN_CACHE_NUM
  • Values: Int (default=-1)
  • Flag to set num of elements that MKLDNN cache can hold. Default is -1 which means cache size is unbounded. Should only be set if your model has variable input shapes, as cache size may grow unbounded. The number represents the number of items in the cache and is proportional to the number of layers that use MKLDNN and different input shape.
• MXNET_ENFORCE_DETERMINISM
  • Values: 0(false) or 1(true) (default=0)
  • If set to true, MXNet will only use deterministic algorithms in forward and backward computation. If no such algorithm exists given other constraints, MXNet will error out. This variable affects the choice of CUDNN convolution algorithms. Please see CUDNN developer guide for more details.
• MXNET_CPU_PARALLEL_COPY_SIZE
  • Values: Int (default=200000)
  • The minimum size to call parallel copy by OpenMP in CPU2CPU mode.
  • When the array size is bigger than or equal to this threshold, NDArray::Copy(from, to) is implemented by OpenMP with the Recommended OMP Thread Count.
  • When the array size is less than this threshold, NDArray::Copy(from , to)) is implemented by memcpy in single thread.

Settings for Minimum Memory Usage¶

• Make sure min(MXNET_EXEC_NUM_TEMP, MXNET_GPU_WORKER_NTHREADS) = 1
  • The default setting satisfies this.

Settings for More GPU Parallelism¶

• Set MXNET_GPU_WORKER_NTHREADS to a larger number (e.g., 2)
  • To reduce memory usage, consider setting MXNET_EXEC_NUM_TEMP.
  • This might not speed things up, especially for image applications, because GPU is usually fully utilized even with serialized jobs.

Settings for controlling OMP tuning¶

• Set MXNET_USE_OPERATOR_TUNING=0 to disable Operator tuning code which decides whether to use OMP or not for operator

  • Values: String representation of MXNET_ENABLE_OPERATOR_TUNING environment variable

  •        0=disable all
    

  •        1=enable all
    

  •        float32, float16, float32=list of types to enable, and disable those not listed
    

  • refer : https://github.com/apache/incubator-mxnet/blob/master/src/operator/operator_tune-inl.h#L444

• Set MXNET_USE_NUM_CORES_OPERATOR_TUNING to define num_cores to be used by operator tuning code.

  • This reduces operator tuning overhead when there are multiple instances of mxnet running in the system and we know that each mxnet will take only partial num_cores available with system.
  • refer: https://github.com/apache/incubator-mxnet/pull/13602