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List of environment variables that control tinygrad behavior.

This is a list of environment variable that control the runtime behavior of tinygrad and its examples. Most of these are self-explanatory, and are usually used to set an option at runtime.

Example: GPU=1 DEBUG=4 python3 -m pytest

The columns are: Variable, Possible Value(s) and Description.

  • A # means that the variable can take any integer value.

Global Variables

These control the behavior of core tinygrad even when used as a library.

Variable Possible Value(s) Description
DEBUG [1-4] enable debugging output, with 4 you get operations, timings, speed, generated code and more
GPU [1] enable the GPU backend
CUDA [1] enable CUDA backend
CPU [1] enable CPU backend
MPS [1] enable MPS device (for Mac M1 and after)
METAL [1] enable Metal backend (for Mac M1 and after)
METAL_XCODE [1] enable Metal using macOS Xcode SDK
TORCH [1] enable PyTorch backend
CLANG [1] enable Clang backend
LLVM [1] enable LLVM backend
LLVMOPT [1] enable slightly more expensive LLVM optimizations
LAZY [1] enable lazy operations (this is the default)
OPT [1-4] optimization level
GRAPH [1] create a graph of all operations (requires graphviz)
GRAPHPATH [/path/to] where to put the generated graph
PRUNEGRAPH [1] prune MovementOps and LoadOps from the graph
PRINT_PRG [1] print program code
IMAGE [1] enable 2d specific optimizations
FLOAT16 [1] use float16 for images instead of float32
ENABLE_METHOD_CACHE [1] enable method cache (this is the default)
EARLY_STOPPING [# > 0] stop after this many kernels
DISALLOW_ASSIGN [1] disallow assignment of tensors
CL_EXCLUDE [name0,name1] comma-separated list of device names to exclude when using OpenCL GPU backend (like CL_EXCLUDE=gfx1036)
CL_PLATFORM [# >= 0] index of the OpenCL platform to run on. Defaults to 0.
RDNA [1] enable the specialized RDNA 3 assembler for AMD 7000-series GPUs. If not set, defaults to generic OpenCL codegen backend.
PTX [1] enable the specialized PTX assembler for Nvidia GPUs. If not set, defaults to generic CUDA codegen backend.

File Specific Variables

These are variables that control the behavior of a specific file, these usually don't affect the library itself. Most of the time these will never be used, but they are here for completeness.

accel/ane/2_compile/hwx_parse.py

Variable Possible Value(s) Description
PRINTALL [1] print all ANE registers

extra/onnx.py

Variable Possible Value(s) Description
ONNXLIMIT [#] set a limit for ONNX
DEBUGONNX [1] enable ONNX debugging

extra/thneed.py

Variable Possible Value(s) Description
DEBUGCL [1-4] enable Debugging for OpenCL
PRINT_KERNEL [1] Print OpenCL Kernels

extra/kernel_search.py

Variable Possible Value(s) Description
OP [1-3] different operations
NOTEST [1] enable not testing AST
DUMP [1] enable dumping of intervention cache
REDUCE [1] enable reduce operations
SIMPLE_REDUCE [1] enable simpler reduce operations
BC [1] enable big conv operations
CONVW [1] enable convw operations
FASTCONV [1] enable faster conv operations
GEMM [1] enable general matrix multiply operations
BROKEN [1] enable a kind of operation
BROKEN3 [1] enable a kind of operation

examples/vit.py

Variable Possible Value(s) Description
LARGE [1] enable larger dimension model

examples/llama.py

Variable Possible Value(s) Description
WEIGHTS [1] enable loading weights

examples/mlperf

Variable Possible Value(s) Description
MODEL [resnet,retinanet,unet3d,rnnt,bert,maskrcnn] what models to use

examples/benchmark_train_efficientnet.py

Variable Possible Value(s) Description
CNT [10] the amount of times to loop the benchmark
BACKWARD [1] enable backward pass
TRAINING [1] set Tensor.training
CLCACHE [1] enable cache for OpenCL

examples/hlb_cifar10.py

Variable Possible Value(s) Description
TORCHWEIGHTS [1] use torch to initialize weights
DISABLE_BACKWARD [1] don't do backward pass

examples/benchmark_train_efficientnet.py & examples/hlb_cifar10.py

Variable Possible Value(s) Description
ADAM [1] use the Adam optimizer

examples/hlb_cifar10.py & xamples/hlb_cifar10_torch.py

Variable Possible Value(s) Description
STEPS [0-10] number of steps
FAKEDATA [1] enable to use random data

examples/train_efficientnet.py

Variable Possible Value(s) Description
STEPS [# % 1024] number of steps
TINY [1] use a tiny convolution network
IMAGENET [1] use imagenet for training

examples/train_efficientnet.py & examples/train_resnet.py

Variable Possible Value(s) Description
TRANSFER [1] enable to use pretrained data

examples & test/external/external_test_opt.py

Variable Possible Value(s) Description
NUM [18, 2] what ResNet[18] / EfficientNet[2] to train

test/test_ops.py

Variable Possible Value(s) Description
PRINT_TENSORS [1] print tensors
FORWARD_ONLY [1] use forward operations only

test/test_speed_v_torch.py

Variable Possible Value(s) Description
TORCHCUDA [1] enable the torch cuda backend

test/external/external_test_gpu_ast.py

Variable Possible Value(s) Description
KOPT [1] enable kernel optimization
KCACHE [1] enable kernel cache

test/external/external_test_opt.py

Variable Possible Value(s) Description
ENET_NUM [-2,-1] what EfficientNet to use

test/test_dtype.py & test/extra/test_utils.py & extra/training.py

Variable Possible Value(s) Description
CI [1] disables some tests for CI

examples & extra & test

Variable Possible Value(s) Description
BS [8, 16, 32, 64, 128] batch size to use

datasets/imagenet_download.py

Variable Possible Value(s) Description
IMGNET_TRAIN [1] download also training data with imagenet