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marcusbuffett / condition.txt
Last active November 29, 2023 23:50
overflow of large sea orm condition
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[src/query_cacher.rs:70] &condition = Condition {
negate: false,
condition_type: Any,
conditions: [
SimpleExpr(
Binary(
Binary(
Column(
TableColumn(
SeaRc(
"line"
"1.e4 c5 2.Nf3 d6 3.d4 cxd4 4.Nxd4"
"1.e4 c5 2.Nf3 d6 3.d4 cxd4 4.Nxd4 Nf6 5.Nc3"
"1.e4 c5 2.Nf3 d6 3.d4 cxd4 4.Nxd4 Nf6"
"1.e4 e5 2.Nf3 Nc6 3.Bb5 a6 4.Ba4"
"1.e4 e5 2.Nf3 Nc6 3.Bb5 a6 4.Ba4 Nf6 5.O-O"
"1.e4 e5 2.Nf3 Nc6 3.Bb5 a6 4.Ba4 Nf6"
"1.d4 Nf6 2.c4 g6 3.Nc3 Bg7 4.e4"
"1.e4 c5 2.Nf3 d6 3.d4 cxd4 4.Nxd4 Nf6 5.Nc3 a6"
"1.e4 c5 2.Nf3 e6 3.d4 cxd4 4.Nxd4"
This file has been truncated, but you can view the full file.
validate checksum key=ReduceAutotuneKey - ElemInput: Float(F32), ElemOutput: Float(F32), ElemAcc: Float(F32), PotentialLineSize: 4, AxisIsContiguous: true, ReduceAxisShape: 4096, ReduceCount: 64, checksum=a14a9dbdfd906e71ae310b4829971734
00d104e8138e13e2dd8e06
0129d16,
),
info: Some (
(
CubeDim {
x: 16,
y: 16,
z: 1,
2025-07-23T10:24:36.343588Z INFO burn_train::learner::train_val: Fitting the model:
OXIModel {
encoder: ChessResNet {
input_conv: Conv2d {stride: [1, 1], kernel_size: [3, 3], dilation: [1, 1], groups: 1, padding: Same, params: 9280}
input_bn: BatchNorm {num_features: 64, momentum: 0.1, epsilon: 0.00001, params: 256}
blocks: Vec<0..6> {
0: BasicBlock {
conv1: Conv2d {stride: [1, 1], kernel_size: [3, 3], dilation: [1, 1], groups: 1, padding: Same, params: 36928}
bn1: BatchNorm {num_features: 64, momentum: 0.1, epsilon: 0.00001, params: 256}
conv2: Conv2d {stride: [1, 1], kernel_size: [3, 3], dilation: [1, 1], groups: 1, padding: Same, params: 36928}