diff --git a/.clang-format b/.clang-format
new file mode 100644
index 000000000..5191313a3
--- /dev/null
+++ b/.clang-format
@@ -0,0 +1,5 @@
+---
+BasedOnStyle: Mozilla
+IndentWidth: 4
+
+...
diff --git a/Makefile b/Makefile
index 2a7c48e45..7e28ea46b 100644
--- a/Makefile
+++ b/Makefile
@@ -198,7 +198,9 @@ win_build_xxd:
 	cd external/bin/ && gcc.exe -o xxd.exe xxd.c -DCYGWIN
 
 format:
-	$(CLANG_FORMAT_BIN) -i -style="{BasedOnStyle: mozilla, IndentWidth: 4}" src/*.cpp src/include/kompute/*.hpp test/*cpp
+	for val in "examples single_include src test" ; do \
+    	find $$val -depth -iname *.h -or -iname *.c -or -iname *.hpp -or -iname *.cpp | grep -v "shaders" | xargs $(CLANG_FORMAT_BIN) -style=file -i; \
+	done
 
 static_scan:
 	cppcheck --project=build/compile_commands.json -iexternal/
diff --git a/examples/android/android-simple/app/src/main/cpp/KomputeJniNative.cpp b/examples/android/android-simple/app/src/main/cpp/KomputeJniNative.cpp
index 6aa270193..92939c4de 100644
--- a/examples/android/android-simple/app/src/main/cpp/KomputeJniNative.cpp
+++ b/examples/android/android-simple/app/src/main/cpp/KomputeJniNative.cpp
@@ -12,17 +12,16 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-
-// Includes the Jni utilities for Android to be able to create the 
-// relevant bindings for java, including JNIEXPORT, JNICALL , and 
+// Includes the Jni utilities for Android to be able to create the
+// relevant bindings for java, including JNIEXPORT, JNICALL , and
 // other "j-variables".
 #include <jni.h>
 
-// The ML class exposing the Kompute ML workflow for training and 
+// The ML class exposing the Kompute ML workflow for training and
 // prediction of inference data.
 #include "KomputeModelML.hpp"
 
-// Allows us to use the C++ sleep function to wait when loading the 
+// Allows us to use the C++ sleep function to wait when loading the
 // Vulkan library in android
 #include <unistd.h>
 
@@ -30,86 +29,92 @@
 #define KOMPUTE_VK_INIT_RETRIES 5
 #endif
 
-static std::vector<float> jfloatArrayToVector(JNIEnv *env, const jfloatArray & fromArray) {
-    float *inCArray = env->GetFloatArrayElements(fromArray, NULL);
-    if (NULL == inCArray) return std::vector<float>();
+static std::vector<float>
+jfloatArrayToVector(JNIEnv* env, const jfloatArray& fromArray)
+{
+    float* inCArray = env->GetFloatArrayElements(fromArray, NULL);
+    if (NULL == inCArray)
+        return std::vector<float>();
     int32_t length = env->GetArrayLength(fromArray);
 
     std::vector<float> outVector(inCArray, inCArray + length);
     return outVector;
 }
 
-static jfloatArray vectorToJFloatArray(JNIEnv *env, const std::vector<float> & fromVector) {
+static jfloatArray
+vectorToJFloatArray(JNIEnv* env, const std::vector<float>& fromVector)
+{
     jfloatArray ret = env->NewFloatArray(fromVector.size());
-    if (NULL == ret) return NULL;
+    if (NULL == ret)
+        return NULL;
     env->SetFloatArrayRegion(ret, 0, fromVector.size(), fromVector.data());
     return ret;
 }
 
-extern "C" {
+extern "C"
+{
 
-JNIEXPORT jboolean JNICALL
-Java_com_ethicalml_kompute_KomputeJni_initVulkan(JNIEnv *env, jobject thiz) {
+    JNIEXPORT jboolean JNICALL
+    Java_com_ethicalml_kompute_KomputeJni_initVulkan(JNIEnv* env, jobject thiz)
+    {
 
-    KP_LOG_INFO("Initialising vulkan");
+        KP_LOG_INFO("Initialising vulkan");
 
-    uint32_t totalRetries = 0;
+        uint32_t totalRetries = 0;
 
-    while (totalRetries < KOMPUTE_VK_INIT_RETRIES) {
-        KP_LOG_INFO("VULKAN LOAD TRY NUMBER: %u", totalRetries);
-        if(InitVulkan()) {
-            break;
+        while (totalRetries < KOMPUTE_VK_INIT_RETRIES) {
+            KP_LOG_INFO("VULKAN LOAD TRY NUMBER: %u", totalRetries);
+            if (InitVulkan()) {
+                break;
+            }
+            sleep(1);
+            totalRetries++;
         }
-        sleep(1);
-        totalRetries++;
+
+        return totalRetries < KOMPUTE_VK_INIT_RETRIES;
     }
 
-    return totalRetries < KOMPUTE_VK_INIT_RETRIES;
-}
-
-
-JNIEXPORT jfloatArray JNICALL
-Java_com_ethicalml_kompute_KomputeJni_kompute(
-        JNIEnv *env,
-        jobject thiz,
-        jfloatArray xiJFloatArr,
-        jfloatArray xjJFloatArr,
-        jfloatArray yJFloatArr) {
-
-    KP_LOG_INFO("Creating manager");
-
-    std::vector<float> xiVector = jfloatArrayToVector(env, xiJFloatArr);
-    std::vector<float> xjVector = jfloatArrayToVector(env, xjJFloatArr);
-    std::vector<float> yVector = jfloatArrayToVector(env, yJFloatArr);
-
-    KomputeModelML kml;
-    kml.train(yVector, xiVector, xjVector);
-
-    std::vector<float> pred = kml.predict(xiVector, xjVector);
-
-    return vectorToJFloatArray(env, pred);
-}
-
-JNIEXPORT jfloatArray JNICALL
-Java_com_ethicalml_kompute_KomputeJni_komputeParams(
-        JNIEnv *env,
-        jobject thiz,
-        jfloatArray xiJFloatArr,
-        jfloatArray xjJFloatArr,
-        jfloatArray yJFloatArr) {
-
-    KP_LOG_INFO("Creating manager");
-
-    std::vector<float> xiVector = jfloatArrayToVector(env, xiJFloatArr);
-    std::vector<float> xjVector = jfloatArrayToVector(env, xjJFloatArr);
-    std::vector<float> yVector = jfloatArrayToVector(env, yJFloatArr);
-
-    KomputeModelML kml;
-    kml.train(yVector, xiVector, xjVector);
-
-    std::vector<float> params = kml.get_params();
-
-    return vectorToJFloatArray(env, params);
-}
-
+    JNIEXPORT jfloatArray JNICALL
+    Java_com_ethicalml_kompute_KomputeJni_kompute(JNIEnv* env,
+                                                  jobject thiz,
+                                                  jfloatArray xiJFloatArr,
+                                                  jfloatArray xjJFloatArr,
+                                                  jfloatArray yJFloatArr)
+    {
+
+        KP_LOG_INFO("Creating manager");
+
+        std::vector<float> xiVector = jfloatArrayToVector(env, xiJFloatArr);
+        std::vector<float> xjVector = jfloatArrayToVector(env, xjJFloatArr);
+        std::vector<float> yVector = jfloatArrayToVector(env, yJFloatArr);
+
+        KomputeModelML kml;
+        kml.train(yVector, xiVector, xjVector);
+
+        std::vector<float> pred = kml.predict(xiVector, xjVector);
+
+        return vectorToJFloatArray(env, pred);
+    }
+
+    JNIEXPORT jfloatArray JNICALL
+    Java_com_ethicalml_kompute_KomputeJni_komputeParams(JNIEnv* env,
+                                                        jobject thiz,
+                                                        jfloatArray xiJFloatArr,
+                                                        jfloatArray xjJFloatArr,
+                                                        jfloatArray yJFloatArr)
+    {
+
+        KP_LOG_INFO("Creating manager");
+
+        std::vector<float> xiVector = jfloatArrayToVector(env, xiJFloatArr);
+        std::vector<float> xjVector = jfloatArrayToVector(env, xjJFloatArr);
+        std::vector<float> yVector = jfloatArrayToVector(env, yJFloatArr);
+
+        KomputeModelML kml;
+        kml.train(yVector, xiVector, xjVector);
+
+        std::vector<float> params = kml.get_params();
+
+        return vectorToJFloatArray(env, params);
+    }
 }
diff --git a/examples/android/android-simple/app/src/main/cpp/KomputeModelML.cpp b/examples/android/android-simple/app/src/main/cpp/KomputeModelML.cpp
old mode 100755
new mode 100644
index e9fc67d68..3f674a783
--- a/examples/android/android-simple/app/src/main/cpp/KomputeModelML.cpp
+++ b/examples/android/android-simple/app/src/main/cpp/KomputeModelML.cpp
@@ -1,15 +1,15 @@
 
 #include "KomputeModelML.hpp"
 
-KomputeModelML::KomputeModelML() {
+KomputeModelML::KomputeModelML() {}
 
-}
+KomputeModelML::~KomputeModelML() {}
 
-KomputeModelML::~KomputeModelML() {
-
-}
-
-void KomputeModelML::train(std::vector<float> yData, std::vector<float> xIData, std::vector<float> xJData) {
+void
+KomputeModelML::train(std::vector<float> yData,
+                      std::vector<float> xIData,
+                      std::vector<float> xJData)
+{
 
     std::vector<float> zerosData;
 
@@ -42,17 +42,19 @@ void KomputeModelML::train(std::vector<float> yData, std::vector<float> xIData,
                                                             bIn, bOut,  lOut };
 
         std::vector<uint32_t> spirv = std::vector<uint32_t>(
-                (uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
-                (uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv +
-                        kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
-
+          (uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
+          (uint32_t*)(kp::shader_data::
+                        shaders_glsl_logisticregression_comp_spv +
+                      kp::shader_data::
+                        shaders_glsl_logisticregression_comp_spv_len));
 
         std::shared_ptr<kp::Algorithm> algorithm = mgr.algorithm(
-                params, spirv, kp::Workgroup({ 5 }), std::vector<float>({ 5.0 }));
+          params, spirv, kp::Workgroup({ 5 }), std::vector<float>({ 5.0 }));
 
         mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
 
-        std::shared_ptr<kp::Sequence> sq = mgr.sequence()
+        std::shared_ptr<kp::Sequence> sq =
+          mgr.sequence()
             ->record<kp::OpTensorSyncDevice>({ wIn, bIn })
             ->record<kp::OpAlgoDispatch>(algorithm)
             ->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
@@ -79,7 +81,9 @@ void KomputeModelML::train(std::vector<float> yData, std::vector<float> xIData,
     }
 }
 
-std::vector<float> KomputeModelML::predict(std::vector<float> xI, std::vector<float> xJ) {
+std::vector<float>
+KomputeModelML::predict(std::vector<float> xI, std::vector<float> xJ)
+{
 
     KP_LOG_INFO("Running prediction inference");
 
@@ -93,9 +97,8 @@ std::vector<float> KomputeModelML::predict(std::vector<float> xI, std::vector<fl
     for (size_t i = 0; i < xI.size(); i++) {
         float xIVal = xI[i];
         float xJVal = xJ[i];
-        float result = (xIVal * this->mWeights[0]
-                        + xJVal * this->mWeights[1]
-                        + this->mBias[0]);
+        float result = (xIVal * this->mWeights[0] + xJVal * this->mWeights[1] +
+                        this->mBias[0]);
 
         // Instead of using sigmoid we'll just return full numbers
         float var = result > 0 ? 1 : 0;
@@ -107,13 +110,15 @@ std::vector<float> KomputeModelML::predict(std::vector<float> xI, std::vector<fl
     return retVector;
 }
 
-std::vector<float> KomputeModelML::get_params() {
+std::vector<float>
+KomputeModelML::get_params()
+{
 
     KP_LOG_INFO("Displaying results");
 
     std::vector<float> retVector;
 
-    if(this->mWeights.size() + this->mBias.size() == 0) {
+    if (this->mWeights.size() + this->mBias.size() == 0) {
         return retVector;
     }
 
diff --git a/examples/android/android-simple/app/src/main/cpp/KomputeModelML.hpp b/examples/android/android-simple/app/src/main/cpp/KomputeModelML.hpp
old mode 100755
new mode 100644
index 558760e36..779c6db7f
--- a/examples/android/android-simple/app/src/main/cpp/KomputeModelML.hpp
+++ b/examples/android/android-simple/app/src/main/cpp/KomputeModelML.hpp
@@ -2,28 +2,30 @@
 #ifndef KOMPUTEMODELML_HPP
 #define KOMPUTEMODELML_HPP
 
-#include <vector>
-#include <string>
 #include <memory>
+#include <string>
+#include <vector>
 
 #include "kompute/Kompute.hpp"
 
-class KomputeModelML {
+class KomputeModelML
+{
 
-public:
+  public:
     KomputeModelML();
     virtual ~KomputeModelML();
 
-    void train(std::vector<float> yData, std::vector<float> xIData, std::vector<float> xJData);
+    void train(std::vector<float> yData,
+               std::vector<float> xIData,
+               std::vector<float> xJData);
 
     std::vector<float> predict(std::vector<float> xI, std::vector<float> xJ);
 
     std::vector<float> get_params();
 
-private:
+  private:
     std::vector<float> mWeights;
     std::vector<float> mBias;
-
 };
 
 static std::string LR_SHADER = R"(
@@ -83,4 +85,4 @@ void main() {
 }
 )";
 
-#endif //ANDROID_SIMPLE_KOMPUTEMODELML_HPP
+#endif // ANDROID_SIMPLE_KOMPUTEMODELML_HPP
diff --git a/examples/array_multiplication/src/Main.cpp b/examples/array_multiplication/src/Main.cpp
old mode 100755
new mode 100644
index be1be9d5d..4ba17cef1
--- a/examples/array_multiplication/src/Main.cpp
+++ b/examples/array_multiplication/src/Main.cpp
@@ -5,23 +5,27 @@
 
 #include "kompute/Kompute.hpp"
 
-static
-std::vector<uint32_t>
-compileSource(
-  const std::string& source)
+static std::vector<uint32_t>
+compileSource(const std::string& source)
 {
     std::ofstream fileOut("tmp_kp_shader.comp");
-	fileOut << source;
-	fileOut.close();
-    if (system(std::string("glslangValidator -V tmp_kp_shader.comp -o tmp_kp_shader.comp.spv").c_str()))
+    fileOut << source;
+    fileOut.close();
+    if (system(
+          std::string(
+            "glslangValidator -V tmp_kp_shader.comp -o tmp_kp_shader.comp.spv")
+            .c_str()))
         throw std::runtime_error("Error running glslangValidator command");
     std::ifstream fileStream("tmp_kp_shader.comp.spv", std::ios::binary);
     std::vector<char> buffer;
-    buffer.insert(buffer.begin(), std::istreambuf_iterator<char>(fileStream), {});
-    return {(uint32_t*)buffer.data(), (uint32_t*)(buffer.data() + buffer.size())};
+    buffer.insert(
+      buffer.begin(), std::istreambuf_iterator<char>(fileStream), {});
+    return { (uint32_t*)buffer.data(),
+             (uint32_t*)(buffer.data() + buffer.size()) };
 }
 
-int main()
+int
+main()
 {
 #if KOMPUTE_ENABLE_SPDLOG
     spdlog::set_level(
@@ -53,21 +57,23 @@ int main()
         }
       )");
 
-    std::vector<std::shared_ptr<kp::Tensor>> params = { tensorInA, tensorInB, tensorOut };
+    std::vector<std::shared_ptr<kp::Tensor>> params = { tensorInA,
+                                                        tensorInB,
+                                                        tensorOut };
 
-    std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(params, compileSource(shader));
+    std::shared_ptr<kp::Algorithm> algo =
+      mgr.algorithm(params, compileSource(shader));
 
     mgr.sequence()
-        ->record<kp::OpTensorSyncDevice>(params)
-        ->record<kp::OpAlgoDispatch>(algo)
-        ->record<kp::OpTensorSyncLocal>(params)
-        ->eval();
+      ->record<kp::OpTensorSyncDevice>(params)
+      ->record<kp::OpAlgoDispatch>(algo)
+      ->record<kp::OpTensorSyncLocal>(params)
+      ->eval();
 
     // prints "Output {  0  4  12  }"
-    std::cout<< "Output: {  ";
+    std::cout << "Output: {  ";
     for (const float& elem : tensorOut->vector()) {
-      std::cout << elem << "  ";
+        std::cout << elem << "  ";
     }
     std::cout << "}" << std::endl;
 }
-
diff --git a/examples/godot_examples/custom_module/kompute_summator/KomputeSummatorNode.cpp b/examples/godot_examples/custom_module/kompute_summator/KomputeSummatorNode.cpp
index f937e7668..cef2d26d6 100644
--- a/examples/godot_examples/custom_module/kompute_summator/KomputeSummatorNode.cpp
+++ b/examples/godot_examples/custom_module/kompute_summator/KomputeSummatorNode.cpp
@@ -4,55 +4,63 @@
 
 #include "KomputeSummatorNode.h"
 
-static
-std::vector<uint32_t>
-compileSource(
-  const std::string& source)
+static std::vector<uint32_t>
+compileSource(const std::string& source)
 {
     std::ofstream fileOut("tmp_kp_shader.comp");
-	fileOut << source;
-	fileOut.close();
-    if (system(std::string("glslangValidator -V tmp_kp_shader.comp -o tmp_kp_shader.comp.spv").c_str()))
+    fileOut << source;
+    fileOut.close();
+    if (system(
+          std::string(
+            "glslangValidator -V tmp_kp_shader.comp -o tmp_kp_shader.comp.spv")
+            .c_str()))
         throw std::runtime_error("Error running glslangValidator command");
     std::ifstream fileStream("tmp_kp_shader.comp.spv", std::ios::binary);
     std::vector<char> buffer;
-    buffer.insert(buffer.begin(), std::istreambuf_iterator<char>(fileStream), {});
-    return {(uint32_t*)buffer.data(), (uint32_t*)(buffer.data() + buffer.size())};
+    buffer.insert(
+      buffer.begin(), std::istreambuf_iterator<char>(fileStream), {});
+    return { (uint32_t*)buffer.data(),
+             (uint32_t*)(buffer.data() + buffer.size()) };
 }
 
-
-KomputeSummatorNode::KomputeSummatorNode() {
+KomputeSummatorNode::KomputeSummatorNode()
+{
     this->_init();
 }
 
-void KomputeSummatorNode::add(float value) {
+void
+KomputeSummatorNode::add(float value)
+{
     // Set the new data in the local device
-    this->mSecondaryTensor->setData({value});
+    this->mSecondaryTensor->setData({ value });
     // Execute recorded sequence
     if (std::shared_ptr<kp::Sequence> sq = this->mSequence) {
         sq->eval();
-    }
-    else {
+    } else {
         throw std::runtime_error("Sequence pointer no longer available");
     }
 }
 
-void KomputeSummatorNode::reset() {
-}
+void
+KomputeSummatorNode::reset()
+{}
 
-float KomputeSummatorNode::get_total() const {
+float
+KomputeSummatorNode::get_total() const
+{
     return this->mPrimaryTensor->data()[0];
 }
 
-void KomputeSummatorNode::_init() {
+void
+KomputeSummatorNode::_init()
+{
     std::cout << "CALLING INIT" << std::endl;
     this->mPrimaryTensor = this->mManager.tensor({ 0.0 });
     this->mSecondaryTensor = this->mManager.tensor({ 0.0 });
     this->mSequence = this->mManager.sequence();
 
     // We now record the steps in the sequence
-    if (std::shared_ptr<kp::Sequence> sq = this->mSequence)
-    {
+    if (std::shared_ptr<kp::Sequence> sq = this->mSequence) {
 
         std::string shader(R"(
             #version 450
@@ -68,40 +76,38 @@ void KomputeSummatorNode::_init() {
             }
         )");
 
-        std::shared_ptr<kp::Algorithm> algo =
-          this->mManager.algorithm(
-                { this->mPrimaryTensor, this->mSecondaryTensor },
-                compileSource(shader));
-
+        std::shared_ptr<kp::Algorithm> algo = this->mManager.algorithm(
+          { this->mPrimaryTensor, this->mSecondaryTensor },
+          compileSource(shader));
 
         // First we ensure secondary tensor loads to GPU
         // No need to sync the primary tensor as it should not be changed
-        sq->record<kp::OpTensorSyncDevice>(
-                { this->mSecondaryTensor });
+        sq->record<kp::OpTensorSyncDevice>({ this->mSecondaryTensor });
 
         // Then we run the operation with both tensors
         sq->record<kp::OpAlgoDispatch>(algo);
 
-        // We map the result back to local 
-        sq->record<kp::OpTensorSyncLocal>(
-                { this->mPrimaryTensor });
+        // We map the result back to local
+        sq->record<kp::OpTensorSyncLocal>({ this->mPrimaryTensor });
 
-    }
-    else {
+    } else {
         throw std::runtime_error("Sequence pointer no longer available");
     }
 }
 
-void KomputeSummatorNode::_process(float delta) {
+void
+KomputeSummatorNode::_process(float delta)
+{}
 
-}
-
-void KomputeSummatorNode::_bind_methods() {
-    ClassDB::bind_method(D_METHOD("_process", "delta"), &KomputeSummatorNode::_process);
+void
+KomputeSummatorNode::_bind_methods()
+{
+    ClassDB::bind_method(D_METHOD("_process", "delta"),
+                         &KomputeSummatorNode::_process);
     ClassDB::bind_method(D_METHOD("_init"), &KomputeSummatorNode::_init);
 
     ClassDB::bind_method(D_METHOD("add", "value"), &KomputeSummatorNode::add);
     ClassDB::bind_method(D_METHOD("reset"), &KomputeSummatorNode::reset);
-    ClassDB::bind_method(D_METHOD("get_total"), &KomputeSummatorNode::get_total);
+    ClassDB::bind_method(D_METHOD("get_total"),
+                         &KomputeSummatorNode::get_total);
 }
-
diff --git a/examples/godot_examples/custom_module/kompute_summator/KomputeSummatorNode.h b/examples/godot_examples/custom_module/kompute_summator/KomputeSummatorNode.h
index 1d94da9a5..0acfbf590 100644
--- a/examples/godot_examples/custom_module/kompute_summator/KomputeSummatorNode.h
+++ b/examples/godot_examples/custom_module/kompute_summator/KomputeSummatorNode.h
@@ -6,10 +6,11 @@
 
 #include "scene/main/node.h"
 
-class KomputeSummatorNode : public Node {
+class KomputeSummatorNode : public Node
+{
     GDCLASS(KomputeSummatorNode, Node);
 
-public:
+  public:
     KomputeSummatorNode();
 
     void add(float value);
@@ -19,13 +20,12 @@ public:
     void _process(float delta);
     void _init();
 
-protected:
+  protected:
     static void _bind_methods();
 
-private:
+  private:
     kp::Manager mManager;
     std::shared_ptr<kp::Sequence> mSequence;
     std::shared_ptr<kp::Tensor> mPrimaryTensor;
     std::shared_ptr<kp::Tensor> mSecondaryTensor;
 };
-
diff --git a/examples/godot_examples/custom_module/kompute_summator/register_types.cpp b/examples/godot_examples/custom_module/kompute_summator/register_types.cpp
index c04b3ecc9..61fd32ca6 100644
--- a/examples/godot_examples/custom_module/kompute_summator/register_types.cpp
+++ b/examples/godot_examples/custom_module/kompute_summator/register_types.cpp
@@ -2,13 +2,17 @@
 
 #include "register_types.h"
 
-#include "core/class_db.h"
 #include "KomputeSummatorNode.h"
+#include "core/class_db.h"
 
-void register_kompute_summator_types() {
+void
+register_kompute_summator_types()
+{
     ClassDB::register_class<KomputeSummatorNode>();
 }
 
-void unregister_kompute_summator_types() {
-   // Nothing to do here in this example.
+void
+unregister_kompute_summator_types()
+{
+    // Nothing to do here in this example.
 }
diff --git a/examples/godot_examples/custom_module/kompute_summator/register_types.h b/examples/godot_examples/custom_module/kompute_summator/register_types.h
index eeef27432..2c8cb4b7e 100644
--- a/examples/godot_examples/custom_module/kompute_summator/register_types.h
+++ b/examples/godot_examples/custom_module/kompute_summator/register_types.h
@@ -1,6 +1,8 @@
 /* register_types.h */
 #pragma once
 
-void register_kompute_summator_types();
-void unregister_kompute_summator_types();
+void
+register_kompute_summator_types();
+void
+unregister_kompute_summator_types();
 /* yes, the word in the middle must be the same as the module folder name */
diff --git a/examples/godot_examples/gdnative_shared/src/KomputeGdNative.cpp b/examples/godot_examples/gdnative_shared/src/KomputeGdNative.cpp
index d0b83940a..d06ba9811 100644
--- a/examples/godot_examples/gdnative_shared/src/KomputeGdNative.cpp
+++ b/examples/godot_examples/gdnative_shared/src/KomputeGdNative.cpp
@@ -1,14 +1,20 @@
 #include "KomputeSummator.hpp"
 
-extern "C" void GDN_EXPORT godot_gdnative_init(godot_gdnative_init_options *o) {
+extern "C" void GDN_EXPORT
+godot_gdnative_init(godot_gdnative_init_options* o)
+{
     godot::Godot::gdnative_init(o);
 }
 
-extern "C" void GDN_EXPORT godot_gdnative_terminate(godot_gdnative_terminate_options *o) {
+extern "C" void GDN_EXPORT
+godot_gdnative_terminate(godot_gdnative_terminate_options* o)
+{
     godot::Godot::gdnative_terminate(o);
 }
 
-extern "C" void GDN_EXPORT godot_nativescript_init(void *handle) {
+extern "C" void GDN_EXPORT
+godot_nativescript_init(void* handle)
+{
     godot::Godot::nativescript_init(handle);
 
     godot::register_class<godot::KomputeSummator>();
diff --git a/examples/godot_examples/gdnative_shared/src/KomputeSummator.cpp b/examples/godot_examples/gdnative_shared/src/KomputeSummator.cpp
index ac3398cb7..277fb69dd 100644
--- a/examples/godot_examples/gdnative_shared/src/KomputeSummator.cpp
+++ b/examples/godot_examples/gdnative_shared/src/KomputeSummator.cpp
@@ -1,49 +1,59 @@
 /* summator.cpp */
 
-#include <vector>
 #include <iostream>
+#include <vector>
 
 #include "KomputeSummator.hpp"
 
-static
-std::vector<uint32_t>
-compileSource(
-  const std::string& source)
+static std::vector<uint32_t>
+compileSource(const std::string& source)
 {
     std::ofstream fileOut("tmp_kp_shader.comp");
-	fileOut << source;
-	fileOut.close();
-    if (system(std::string("glslangValidator -V tmp_kp_shader.comp -o tmp_kp_shader.comp.spv").c_str()))
+    fileOut << source;
+    fileOut.close();
+    if (system(
+          std::string(
+            "glslangValidator -V tmp_kp_shader.comp -o tmp_kp_shader.comp.spv")
+            .c_str()))
         throw std::runtime_error("Error running glslangValidator command");
     std::ifstream fileStream("tmp_kp_shader.comp.spv", std::ios::binary);
     std::vector<char> buffer;
-    buffer.insert(buffer.begin(), std::istreambuf_iterator<char>(fileStream), {});
-    return {(uint32_t*)buffer.data(), (uint32_t*)(buffer.data() + buffer.size())};
+    buffer.insert(
+      buffer.begin(), std::istreambuf_iterator<char>(fileStream), {});
+    return { (uint32_t*)buffer.data(),
+             (uint32_t*)(buffer.data() + buffer.size()) };
 }
 
-
 namespace godot {
 
-KomputeSummator::KomputeSummator() {
+KomputeSummator::KomputeSummator()
+{
     std::cout << "CALLING CONSTRUCTOR" << std::endl;
     this->_init();
 }
 
-void KomputeSummator::add(float value) {
+void
+KomputeSummator::add(float value)
+{
     // Set the new data in the local device
-    this->mSecondaryTensor->setData({value});
+    this->mSecondaryTensor->setData({ value });
     // Execute recorded sequence
     this->mSequence->eval();
 }
 
-void KomputeSummator::reset() {
-}
+void
+KomputeSummator::reset()
+{}
 
-float KomputeSummator::get_total() const {
+float
+KomputeSummator::get_total() const
+{
     return this->mPrimaryTensor->data()[0];
 }
 
-void KomputeSummator::_init() {
+void
+KomputeSummator::_init()
+{
     std::cout << "CALLING INIT" << std::endl;
     this->mPrimaryTensor = this->mManager.tensor({ 0.0 });
     this->mSecondaryTensor = this->mManager.tensor({ 0.0 });
@@ -70,33 +80,34 @@ void KomputeSummator::_init() {
         // First we ensure secondary tensor loads to GPU
         // No need to sync the primary tensor as it should not be changed
         this->mSequence->record<kp::OpTensorSyncDevice>(
-                { this->mSecondaryTensor });
+          { this->mSecondaryTensor });
 
         // Then we run the operation with both tensors
         this->mSequence->record<kp::OpAlgoCreate>(
-            { this->mPrimaryTensor, this->mSecondaryTensor }, 
-            compileSource(shader));
+          { this->mPrimaryTensor, this->mSecondaryTensor },
+          compileSource(shader));
 
-        // We map the result back to local 
+        // We map the result back to local
         this->mSequence->record<kp::OpTensorSyncLocal>(
-                { this->mPrimaryTensor });
+          { this->mPrimaryTensor });
 
         this->mSequence->end();
     }
 }
 
-void KomputeSummator::_process(float delta) {
+void
+KomputeSummator::_process(float delta)
+{}
 
-}
+void
+KomputeSummator::_register_methods()
+{
+    register_method((char*)"_process", &KomputeSummator::_process);
+    register_method((char*)"_init", &KomputeSummator::_init);
 
-void KomputeSummator::_register_methods() {
-    register_method((char *)"_process", &KomputeSummator::_process);
-    register_method((char *)"_init", &KomputeSummator::_init);
-
-    register_method((char *)"add", &KomputeSummator::add);
-    register_method((char *)"reset", &KomputeSummator::reset);
-    register_method((char *)"get_total", &KomputeSummator::get_total);
+    register_method((char*)"add", &KomputeSummator::add);
+    register_method((char*)"reset", &KomputeSummator::reset);
+    register_method((char*)"get_total", &KomputeSummator::get_total);
 }
 
 }
-
diff --git a/examples/godot_examples/gdnative_shared/src/KomputeSummator.hpp b/examples/godot_examples/gdnative_shared/src/KomputeSummator.hpp
index 7f6b42e82..3aafa73d9 100644
--- a/examples/godot_examples/gdnative_shared/src/KomputeSummator.hpp
+++ b/examples/godot_examples/gdnative_shared/src/KomputeSummator.hpp
@@ -8,11 +8,12 @@
 #include "kompute/Kompute.hpp"
 
 namespace godot {
-class KomputeSummator : public Node2D {
-private:
+class KomputeSummator : public Node2D
+{
+  private:
     GODOT_CLASS(KomputeSummator, Node2D);
 
-public:
+  public:
     KomputeSummator();
 
     void add(float value);
@@ -24,7 +25,7 @@ public:
 
     static void _register_methods();
 
-private:
+  private:
     kp::Manager mManager;
     std::shared_ptr<kp::Sequence> mSequence;
     std::shared_ptr<kp::Tensor> mPrimaryTensor;
diff --git a/examples/godot_logistic_regression/custom_module/kompute_model_ml/KomputeModelMLNode.cpp b/examples/godot_logistic_regression/custom_module/kompute_model_ml/KomputeModelMLNode.cpp
index 081315a4b..bf9b9ef85 100644
--- a/examples/godot_logistic_regression/custom_module/kompute_model_ml/KomputeModelMLNode.cpp
+++ b/examples/godot_logistic_regression/custom_module/kompute_model_ml/KomputeModelMLNode.cpp
@@ -4,12 +4,15 @@
 
 #include "KomputeModelMLNode.h"
 
-KomputeModelMLNode::KomputeModelMLNode() {
+KomputeModelMLNode::KomputeModelMLNode()
+{
     std::cout << "CALLING CONSTRUCTOR" << std::endl;
     this->_init();
 }
 
-void KomputeModelMLNode::train(Array yArr, Array xIArr, Array xJArr) {
+void
+KomputeModelMLNode::train(Array yArr, Array xIArr, Array xJArr)
+{
 
     assert(yArr.size() == xIArr.size());
     assert(xIArr.size() == xJArr.size());
@@ -52,15 +55,19 @@ void KomputeModelMLNode::train(Array yArr, Array xIArr, Array xJArr) {
 
         {
             std::vector<uint32_t> spirv(
-                        (uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
-                        (uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv
-                            + kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
+              (uint32_t*)
+                kp::shader_data::shaders_glsl_logisticregression_comp_spv,
+              (uint32_t*)(kp::shader_data::
+                            shaders_glsl_logisticregression_comp_spv +
+                          kp::shader_data::
+                            shaders_glsl_logisticregression_comp_spv_len));
 
             std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(params, spirv);
 
             mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
 
-            std::shared_ptr<kp::Sequence> sq = mgr.sequence()
+            std::shared_ptr<kp::Sequence> sq =
+              mgr.sequence()
                 ->record<kp::OpTensorSyncDevice>({ wIn, bIn })
                 ->record<kp::OpAlgoDispatch>(algo)
                 ->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
@@ -88,20 +95,22 @@ void KomputeModelMLNode::train(Array yArr, Array xIArr, Array xJArr) {
     }
 }
 
-Array KomputeModelMLNode::predict(Array xI, Array xJ) {
+Array
+KomputeModelMLNode::predict(Array xI, Array xJ)
+{
     assert(xI.size() == xJ.size());
 
     Array retArray;
 
     // We run the inference in the CPU for simplicity
-    // BUt you can also implement the inference on GPU 
+    // BUt you can also implement the inference on GPU
     // GPU implementation would speed up minibatching
     for (size_t i = 0; i < xI.size(); i++) {
         float xIVal = xI[i];
         float xJVal = xJ[i];
-        float result = (xIVal * this->mWeights.data()[0]
-                + xJVal * this->mWeights.data()[1]
-                + this->mBias.data()[0]);
+        float result =
+          (xIVal * this->mWeights.data()[0] + xJVal * this->mWeights.data()[1] +
+           this->mBias.data()[0]);
 
         // Instead of using sigmoid we'll just return full numbers
         Variant var = result > 0 ? 1 : 0;
@@ -111,12 +120,14 @@ Array KomputeModelMLNode::predict(Array xI, Array xJ) {
     return retArray;
 }
 
-Array KomputeModelMLNode::get_params() {
+Array
+KomputeModelMLNode::get_params()
+{
     Array retArray;
 
     KP_LOG_INFO(this->mWeights.size() + this->mBias.size());
 
-    if(this->mWeights.size() + this->mBias.size() == 0) {
+    if (this->mWeights.size() + this->mBias.size() == 0) {
         return retArray;
     }
 
@@ -128,20 +139,27 @@ Array KomputeModelMLNode::get_params() {
     return retArray;
 }
 
-void KomputeModelMLNode::_init() {
+void
+KomputeModelMLNode::_init()
+{
     std::cout << "CALLING INIT" << std::endl;
 }
 
-void KomputeModelMLNode::_process(float delta) {
+void
+KomputeModelMLNode::_process(float delta)
+{}
 
-}
-
-void KomputeModelMLNode::_bind_methods() {
-    ClassDB::bind_method(D_METHOD("_process", "delta"), &KomputeModelMLNode::_process);
+void
+KomputeModelMLNode::_bind_methods()
+{
+    ClassDB::bind_method(D_METHOD("_process", "delta"),
+                         &KomputeModelMLNode::_process);
     ClassDB::bind_method(D_METHOD("_init"), &KomputeModelMLNode::_init);
 
-    ClassDB::bind_method(D_METHOD("train", "yArr", "xIArr", "xJArr"), &KomputeModelMLNode::train);
-    ClassDB::bind_method(D_METHOD("predict", "xI", "xJ"), &KomputeModelMLNode::predict);
-    ClassDB::bind_method(D_METHOD("get_params"), &KomputeModelMLNode::get_params);
+    ClassDB::bind_method(D_METHOD("train", "yArr", "xIArr", "xJArr"),
+                         &KomputeModelMLNode::train);
+    ClassDB::bind_method(D_METHOD("predict", "xI", "xJ"),
+                         &KomputeModelMLNode::predict);
+    ClassDB::bind_method(D_METHOD("get_params"),
+                         &KomputeModelMLNode::get_params);
 }
-
diff --git a/examples/godot_logistic_regression/custom_module/kompute_model_ml/KomputeModelMLNode.h b/examples/godot_logistic_regression/custom_module/kompute_model_ml/KomputeModelMLNode.h
index 85c3310f9..bdc2ce85d 100644
--- a/examples/godot_logistic_regression/custom_module/kompute_model_ml/KomputeModelMLNode.h
+++ b/examples/godot_logistic_regression/custom_module/kompute_model_ml/KomputeModelMLNode.h
@@ -6,10 +6,11 @@
 
 #include "scene/main/node.h"
 
-class KomputeModelMLNode : public Node {
+class KomputeModelMLNode : public Node
+{
     GDCLASS(KomputeModelMLNode, Node);
 
-public:
+  public:
     KomputeModelMLNode();
 
     void train(Array y, Array xI, Array xJ);
@@ -21,10 +22,10 @@ public:
     void _process(float delta);
     void _init();
 
-protected:
+  protected:
     static void _bind_methods();
 
-private:
+  private:
     kp::Tensor mWeights;
     kp::Tensor mBias;
 };
@@ -85,4 +86,3 @@ void main() {
     lout[idx] = calculateLoss(yHat, yCurr);
 }
 )";
-
diff --git a/examples/godot_logistic_regression/custom_module/kompute_model_ml/register_types.cpp b/examples/godot_logistic_regression/custom_module/kompute_model_ml/register_types.cpp
index a85141263..f8e4fc92c 100644
--- a/examples/godot_logistic_regression/custom_module/kompute_model_ml/register_types.cpp
+++ b/examples/godot_logistic_regression/custom_module/kompute_model_ml/register_types.cpp
@@ -2,13 +2,17 @@
 
 #include "register_types.h"
 
-#include "core/class_db.h"
 #include "KomputeModelMLNode.h"
+#include "core/class_db.h"
 
-void register_kompute_model_ml_types() {
+void
+register_kompute_model_ml_types()
+{
     ClassDB::register_class<KomputeModelMLNode>();
 }
 
-void unregister_kompute_model_ml_types() {
-   // Nothing to do here in this example.
+void
+unregister_kompute_model_ml_types()
+{
+    // Nothing to do here in this example.
 }
diff --git a/examples/godot_logistic_regression/custom_module/kompute_model_ml/register_types.h b/examples/godot_logistic_regression/custom_module/kompute_model_ml/register_types.h
index ad09fb8cc..734f1aa43 100644
--- a/examples/godot_logistic_regression/custom_module/kompute_model_ml/register_types.h
+++ b/examples/godot_logistic_regression/custom_module/kompute_model_ml/register_types.h
@@ -1,6 +1,8 @@
 /* register_types.h */
 #pragma once
 
-void register_kompute_model_ml_types();
-void unregister_kompute_model_ml_types();
+void
+register_kompute_model_ml_types();
+void
+unregister_kompute_model_ml_types();
 /* yes, the word in the middle must be the same as the module folder name */
diff --git a/examples/godot_logistic_regression/gdnative_shared/src/KomputeGdNative.cpp b/examples/godot_logistic_regression/gdnative_shared/src/KomputeGdNative.cpp
index 15364ca88..06dd72452 100644
--- a/examples/godot_logistic_regression/gdnative_shared/src/KomputeGdNative.cpp
+++ b/examples/godot_logistic_regression/gdnative_shared/src/KomputeGdNative.cpp
@@ -1,14 +1,20 @@
 #include "KomputeModelML.hpp"
 
-extern "C" void GDN_EXPORT godot_gdnative_init(godot_gdnative_init_options *o) {
+extern "C" void GDN_EXPORT
+godot_gdnative_init(godot_gdnative_init_options* o)
+{
     godot::Godot::gdnative_init(o);
 }
 
-extern "C" void GDN_EXPORT godot_gdnative_terminate(godot_gdnative_terminate_options *o) {
+extern "C" void GDN_EXPORT
+godot_gdnative_terminate(godot_gdnative_terminate_options* o)
+{
     godot::Godot::gdnative_terminate(o);
 }
 
-extern "C" void GDN_EXPORT godot_nativescript_init(void *handle) {
+extern "C" void GDN_EXPORT
+godot_nativescript_init(void* handle)
+{
     godot::Godot::nativescript_init(handle);
 
     godot::register_class<godot::KomputeModelML>();
diff --git a/examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.cpp b/examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.cpp
index 1222fe867..2efbbfddc 100644
--- a/examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.cpp
+++ b/examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.cpp
@@ -1,19 +1,22 @@
 #pragma once
 
-#include <vector>
-#include <string>
 #include <iostream>
+#include <string>
+#include <vector>
 
 #include "KomputeModelML.hpp"
 
 namespace godot {
 
-KomputeModelML::KomputeModelML() {
+KomputeModelML::KomputeModelML()
+{
     std::cout << "CALLING CONSTRUCTOR" << std::endl;
     this->_init();
 }
 
-void KomputeModelML::train(Array yArr, Array xIArr, Array xJArr) {
+void
+KomputeModelML::train(Array yArr, Array xIArr, Array xJArr)
+{
 
     assert(yArr.size() == xIArr.size());
     assert(xIArr.size() == xJArr.size());
@@ -56,15 +59,19 @@ void KomputeModelML::train(Array yArr, Array xIArr, Array xJArr) {
 
         {
             std::vector<uint32_t> spirv(
-                        (uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
-                        (uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv
-                            + kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
+              (uint32_t*)
+                kp::shader_data::shaders_glsl_logisticregression_comp_spv,
+              (uint32_t*)(kp::shader_data::
+                            shaders_glsl_logisticregression_comp_spv +
+                          kp::shader_data::
+                            shaders_glsl_logisticregression_comp_spv_len));
 
             std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(params, spirv);
 
             mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
 
-            std::shared_ptr<kp::Sequence> sq = mgr.sequence()
+            std::shared_ptr<kp::Sequence> sq =
+              mgr.sequence()
                 ->record<kp::OpTensorSyncDevice>({ wIn, bIn })
                 ->record<kp::OpAlgoDispatch>(algo)
                 ->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
@@ -92,20 +99,22 @@ void KomputeModelML::train(Array yArr, Array xIArr, Array xJArr) {
     }
 }
 
-Array KomputeModelML::predict(Array xI, Array xJ) {
+Array
+KomputeModelML::predict(Array xI, Array xJ)
+{
     assert(xI.size() == xJ.size());
 
     Array retArray;
 
     // We run the inference in the CPU for simplicity
-    // BUt you can also implement the inference on GPU 
+    // BUt you can also implement the inference on GPU
     // GPU implementation would speed up minibatching
     for (size_t i = 0; i < xI.size(); i++) {
         float xIVal = xI[i];
         float xJVal = xJ[i];
-        float result = (xIVal * this->mWeights->data()[0]
-                + xJVal * this->mWeights->data()[1]
-                + this->mBias->data()[0]);
+        float result =
+          (xIVal * this->mWeights->data()[0] +
+           xJVal * this->mWeights->data()[1] + this->mBias->data()[0]);
 
         // Instead of using sigmoid we'll just return full numbers
         Variant var = result > 0 ? 1 : 0;
@@ -115,12 +124,14 @@ Array KomputeModelML::predict(Array xI, Array xJ) {
     return retArray;
 }
 
-Array KomputeModelML::get_params() {
+Array
+KomputeModelML::get_params()
+{
     Array retArray;
 
     KP_LOG_INFO(this->mWeights->size() + this->mBias->size());
 
-    if(this->mWeights->size() + this->mBias->size() == 0) {
+    if (this->mWeights->size() + this->mBias->size() == 0) {
         return retArray;
     }
 
@@ -132,22 +143,25 @@ Array KomputeModelML::get_params() {
     return retArray;
 }
 
-void KomputeModelML::_init() {
+void
+KomputeModelML::_init()
+{
     std::cout << "CALLING INIT" << std::endl;
 }
 
-void KomputeModelML::_process(float delta) {
+void
+KomputeModelML::_process(float delta)
+{}
 
-}
+void
+KomputeModelML::_register_methods()
+{
+    register_method((char*)"_process", &KomputeModelML::_process);
+    register_method((char*)"_init", &KomputeModelML::_init);
 
-void KomputeModelML::_register_methods() {
-    register_method((char *)"_process", &KomputeModelML::_process);
-    register_method((char *)"_init", &KomputeModelML::_init);
-
-    register_method((char *)"train", &KomputeModelML::train);
-    register_method((char *)"predict", &KomputeModelML::predict);
-    register_method((char *)"get_params", &KomputeModelML::get_params);
+    register_method((char*)"train", &KomputeModelML::train);
+    register_method((char*)"predict", &KomputeModelML::predict);
+    register_method((char*)"get_params", &KomputeModelML::get_params);
 }
 
 }
-
diff --git a/examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.hpp b/examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.hpp
index 69bab4f19..017d890c0 100644
--- a/examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.hpp
+++ b/examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.hpp
@@ -1,19 +1,20 @@
 #pragma once
 
+#include <Array.hpp>
 #include <Godot.hpp>
 #include <Node2D.hpp>
-#include <Array.hpp>
 
 #include <memory>
 
 #include "kompute/Kompute.hpp"
 
 namespace godot {
-class KomputeModelML : public Node2D {
-private:
+class KomputeModelML : public Node2D
+{
+  private:
     GODOT_CLASS(KomputeModelML, Node2D);
 
-public:
+  public:
     KomputeModelML();
 
     void train(Array y, Array xI, Array xJ);
@@ -27,7 +28,7 @@ public:
 
     static void _register_methods();
 
-private:
+  private:
     std::shared_ptr<kp::Tensor> mWeights;
     std::shared_ptr<kp::Tensor> mBias;
 };
diff --git a/examples/logistic_regression/src/Main.cpp b/examples/logistic_regression/src/Main.cpp
old mode 100755
new mode 100644
index ec52439ed..8feee258f
--- a/examples/logistic_regression/src/Main.cpp
+++ b/examples/logistic_regression/src/Main.cpp
@@ -5,7 +5,8 @@
 
 #include "kompute/Kompute.hpp"
 
-int main()
+int
+main()
 {
 #if KOMPUTE_ENABLE_SPDLOG
     spdlog::set_level(
@@ -36,16 +37,18 @@ int main()
                                                         bIn, bOut,  lOut };
 
     std::vector<uint32_t> spirv(
-                (uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
-                (uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv
-                    + kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
+      (uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
+      (uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv +
+                  kp::shader_data::
+                    shaders_glsl_logisticregression_comp_spv_len));
 
     std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(
-            params, spirv, kp::Workgroup({ 5 }), std::vector<float>({ 5.0 }));
+      params, spirv, kp::Workgroup({ 5 }), std::vector<float>({ 5.0 }));
 
     mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
 
-    std::shared_ptr<kp::Sequence> sq = mgr.sequence()
+    std::shared_ptr<kp::Sequence> sq =
+      mgr.sequence()
         ->record<kp::OpTensorSyncDevice>({ wIn, bIn })
         ->record<kp::OpAlgoDispatch>(algo)
         ->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
@@ -67,4 +70,3 @@ int main()
     std::cout << "w2: " << wIn->data()[1] << std::endl;
     std::cout << "b: " << bIn->data()[0] << std::endl;
 }
-
diff --git a/single_include/AggregateHeaders.cpp b/single_include/AggregateHeaders.cpp
index e1c7f4afb..0498fc01a 100644
--- a/single_include/AggregateHeaders.cpp
+++ b/single_include/AggregateHeaders.cpp
@@ -1,15 +1,15 @@
 #pragma once
-#include "kompute/shaders/shaderopmult.hpp"
-#include "kompute/shaders/shaderlogisticregression.hpp"
-#include "kompute/Core.hpp"
-#include "kompute/Tensor.hpp"
 #include "kompute/Algorithm.hpp"
+#include "kompute/Core.hpp"
+#include "kompute/Manager.hpp"
+#include "kompute/Sequence.hpp"
+#include "kompute/Tensor.hpp"
+#include "kompute/operations/OpAlgoDispatch.hpp"
 #include "kompute/operations/OpBase.hpp"
 #include "kompute/operations/OpMemoryBarrier.hpp"
+#include "kompute/operations/OpMult.hpp"
 #include "kompute/operations/OpTensorCopy.hpp"
 #include "kompute/operations/OpTensorSyncDevice.hpp"
 #include "kompute/operations/OpTensorSyncLocal.hpp"
-#include "kompute/operations/OpAlgoDispatch.hpp"
-#include "kompute/operations/OpMult.hpp"
-#include "kompute/Sequence.hpp"
-#include "kompute/Manager.hpp"
+#include "kompute/shaders/shaderlogisticregression.hpp"
+#include "kompute/shaders/shaderopmult.hpp"
diff --git a/single_include/kompute/Kompute.hpp b/single_include/kompute/Kompute.hpp
old mode 100755
new mode 100644
index ebba78628..fa51631f7
--- a/single_include/kompute/Kompute.hpp
+++ b/single_include/kompute/Kompute.hpp
@@ -25,128 +25,128 @@
 namespace kp {
 namespace shader_data {
 static const unsigned char shaders_glsl_opmult_comp_spv[] = {
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+    0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30,
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+    0x11, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
+    0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00,
+    0xc2, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00,
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+    0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x48, 0x00, 0x05, 0x00,
+    0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x47, 0x00, 0x03, 0x00, 0x19, 0x00, 0x00, 0x00,
+    0x03, 0x00, 0x00, 0x00, 0x47, 0x00, 0x04, 0x00, 0x1b, 0x00, 0x00, 0x00,
+    0x22, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x47, 0x00, 0x04, 0x00,
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+    0x04, 0x00, 0x00, 0x00, 0x48, 0x00, 0x05, 0x00, 0x21, 0x00, 0x00, 0x00,
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+    0x47, 0x00, 0x04, 0x00, 0x23, 0x00, 0x00, 0x00, 0x22, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x47, 0x00, 0x04, 0x00, 0x23, 0x00, 0x00, 0x00,
+    0x21, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x47, 0x00, 0x04, 0x00,
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+    0x20, 0x00, 0x04, 0x00, 0x07, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00,
+    0x06, 0x00, 0x00, 0x00, 0x17, 0x00, 0x04, 0x00, 0x09, 0x00, 0x00, 0x00,
+    0x06, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x20, 0x00, 0x04, 0x00,
+    0x0a, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00,
+    0x3b, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x00, 0x00,
+    0x01, 0x00, 0x00, 0x00, 0x2b, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00,
+    0x0c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x00, 0x04, 0x00,
+    0x0d, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00,
+    0x16, 0x00, 0x03, 0x00, 0x10, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,
+    0x1d, 0x00, 0x03, 0x00, 0x11, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
+    0x1e, 0x00, 0x03, 0x00, 0x12, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00,
+    0x20, 0x00, 0x04, 0x00, 0x13, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
+    0x12, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, 0x13, 0x00, 0x00, 0x00,
+    0x14, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x15, 0x00, 0x04, 0x00,
+    0x15, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
+    0x2b, 0x00, 0x04, 0x00, 0x15, 0x00, 0x00, 0x00, 0x16, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x1d, 0x00, 0x03, 0x00, 0x18, 0x00, 0x00, 0x00,
+    0x10, 0x00, 0x00, 0x00, 0x1e, 0x00, 0x03, 0x00, 0x19, 0x00, 0x00, 0x00,
+    0x18, 0x00, 0x00, 0x00, 0x20, 0x00, 0x04, 0x00, 0x1a, 0x00, 0x00, 0x00,
+    0x02, 0x00, 0x00, 0x00, 0x19, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00,
+    0x1a, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
+    0x20, 0x00, 0x04, 0x00, 0x1d, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
+    0x10, 0x00, 0x00, 0x00, 0x1d, 0x00, 0x03, 0x00, 0x20, 0x00, 0x00, 0x00,
+    0x10, 0x00, 0x00, 0x00, 0x1e, 0x00, 0x03, 0x00, 0x21, 0x00, 0x00, 0x00,
+    0x20, 0x00, 0x00, 0x00, 0x20, 0x00, 0x04, 0x00, 0x22, 0x00, 0x00, 0x00,
+    0x02, 0x00, 0x00, 0x00, 0x21, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00,
+    0x22, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
+    0x32, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x29, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x32, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00,
+    0x2a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x32, 0x00, 0x04, 0x00,
+    0x06, 0x00, 0x00, 0x00, 0x2b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x2b, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00,
+    0x01, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x06, 0x00, 0x09, 0x00, 0x00, 0x00,
+    0x2d, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00,
+    0x2c, 0x00, 0x00, 0x00, 0x36, 0x00, 0x05, 0x00, 0x02, 0x00, 0x00, 0x00,
+    0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
+    0xf8, 0x00, 0x02, 0x00, 0x05, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00,
+    0x07, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00,
+    0x41, 0x00, 0x05, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00,
+    0x0b, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00,
+    0x06, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00,
+    0x3e, 0x00, 0x03, 0x00, 0x08, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00,
+    0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x17, 0x00, 0x00, 0x00,
+    0x08, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00,
+    0x1c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00,
+    0x1d, 0x00, 0x00, 0x00, 0x1e, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00,
+    0x16, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00,
+    0x10, 0x00, 0x00, 0x00, 0x1f, 0x00, 0x00, 0x00, 0x1e, 0x00, 0x00, 0x00,
+    0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00,
+    0x08, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, 0x1d, 0x00, 0x00, 0x00,
+    0x25, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x00, 0x16, 0x00, 0x00, 0x00,
+    0x24, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x10, 0x00, 0x00, 0x00,
+    0x26, 0x00, 0x00, 0x00, 0x25, 0x00, 0x00, 0x00, 0x85, 0x00, 0x05, 0x00,
+    0x10, 0x00, 0x00, 0x00, 0x27, 0x00, 0x00, 0x00, 0x1f, 0x00, 0x00, 0x00,
+    0x26, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, 0x1d, 0x00, 0x00, 0x00,
+    0x28, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x16, 0x00, 0x00, 0x00,
+    0x17, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x03, 0x00, 0x28, 0x00, 0x00, 0x00,
+    0x27, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00
 };
 static const unsigned int shaders_glsl_opmult_comp_spv_len = 1464;
 }
@@ -179,408 +179,408 @@ static const unsigned int shaders_glsl_opmult_comp_spv_len = 1464;
 namespace kp {
 namespace shader_data {
 static const unsigned char shaders_glsl_logisticregression_comp_spv[] = {
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+    0x38, 0x00, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00, 0x37, 0x00, 0x00, 0x00,
+    0x7f, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x39, 0x00, 0x00, 0x00,
+    0x38, 0x00, 0x00, 0x00, 0xfe, 0x00, 0x02, 0x00, 0x39, 0x00, 0x00, 0x00,
+    0x38, 0x00, 0x01, 0x00
 };
 static const unsigned int shaders_glsl_logisticregression_comp_spv_len = 4816;
 }
@@ -1409,16 +1409,12 @@ namespace kp {
 class OpBase
 {
   public:
-
     /**
      * Default destructor for OpBase class. This OpBase destructor class should
      * always be called to destroy and free owned resources unless it is
      * intended to destroy the resources in the parent class.
      */
-    virtual ~OpBase()
-    {
-        KP_LOG_DEBUG("Kompute OpBase destructor started");
-    }
+    virtual ~OpBase() { KP_LOG_DEBUG("Kompute OpBase destructor started"); }
 
     /**
      * The record function is intended to only send a record command or run
@@ -1430,24 +1426,24 @@ class OpBase
     virtual void record(const vk::CommandBuffer& commandBuffer) = 0;
 
     /**
-     * Pre eval is called before the Sequence has called eval and submitted the commands to
-     * the GPU for processing, and can be used to perform any per-eval setup steps
-     * required as the computation iteration begins. It's worth noting that 
-     * there are situations where eval can be called multiple times, so the 
-     * resources that are created should be idempotent in case it's called multiple
-     * times in a row.
+     * Pre eval is called before the Sequence has called eval and submitted the
+     * commands to the GPU for processing, and can be used to perform any
+     * per-eval setup steps required as the computation iteration begins. It's
+     * worth noting that there are situations where eval can be called multiple
+     * times, so the resources that are created should be idempotent in case
+     * it's called multiple times in a row.
      *
      * @param commandBuffer The command buffer to record the command into.
      */
     virtual void preEval(const vk::CommandBuffer& commandBuffer) = 0;
 
     /**
-     * Post eval is called after the Sequence has called eval and submitted the commands to
-     * the GPU for processing, and can be used to perform any tear-down steps
-     * required as the computation iteration finishes. It's worth noting that 
-     * there are situations where eval can be called multiple times, so the 
-     * resources that are destroyed should not require a re-init unless explicitly
-     * provided by the user.
+     * Post eval is called after the Sequence has called eval and submitted the
+     * commands to the GPU for processing, and can be used to perform any
+     * tear-down steps required as the computation iteration finishes. It's
+     * worth noting that there are situations where eval can be called multiple
+     * times, so the resources that are destroyed should not require a re-init
+     * unless explicitly provided by the user.
      *
      * @param commandBuffer The command buffer to record the command into.
      */
@@ -1461,7 +1457,7 @@ class OpBase
 namespace kp {
 
 /**
- * Operation that provides a general abstraction that simplifies the use of 
+ * Operation that provides a general abstraction that simplifies the use of
  * algorithm and parameter components which can be used with shaders.
  * It exposes the pipeline barrier functionality specifically for memory
  * barriers that can be configured through the respective source and destination
@@ -1470,29 +1466,32 @@ namespace kp {
 class OpMemoryBarrier : public OpBase
 {
   public:
-
     /**
-     * Constructor that stores tensors as well as memory barrier parameters to be
-     * used to create a pipeline barrier on the respective primary or staging tensor.
+     * Constructor that stores tensors as well as memory barrier parameters to
+     * be used to create a pipeline barrier on the respective primary or staging
+     * tensor.
      *
      * @param tensors The tensors to apply the memory barriers on
      * @param srcAccessMask The kp::AccessFlagBits for the source access mask
-     * @param dstAccessMask The kp::AccessFlagBits for the destination access mask
-     * @param srcStageMask The kp::PipelineStageFlagBits for the source stage mask
-     * @param dstStageMask The kp::PipelineStageFlagBits for the destination stage mask
-     * @param barrierOnPrimary Boolean to select primary or secondary buffers on tensors
+     * @param dstAccessMask The kp::AccessFlagBits for the destination access
+     * mask
+     * @param srcStageMask The kp::PipelineStageFlagBits for the source stage
+     * mask
+     * @param dstStageMask The kp::PipelineStageFlagBits for the destination
+     * stage mask
+     * @param barrierOnPrimary Boolean to select primary or secondary buffers on
+     * tensors
      */
-    OpMemoryBarrier(
-            const std::vector<std::shared_ptr<Tensor>>& tensors,
-            const vk::AccessFlagBits& srcAccessMask,
-            const vk::AccessFlagBits& dstAccessMask,
-            const vk::PipelineStageFlagBits& srcStageMask,
-            const vk::PipelineStageFlagBits& dstStageMask,
-            bool barrierOnPrimary = true);
+    OpMemoryBarrier(const std::vector<std::shared_ptr<Tensor>>& tensors,
+                    const vk::AccessFlagBits& srcAccessMask,
+                    const vk::AccessFlagBits& dstAccessMask,
+                    const vk::PipelineStageFlagBits& srcStageMask,
+                    const vk::PipelineStageFlagBits& dstStageMask,
+                    bool barrierOnPrimary = true);
 
     /**
-     * Default destructor, which is in charge of destroying the reference to the tensors
-     * and all the relevant access / stage masks created
+     * Default destructor, which is in charge of destroying the reference to the
+     * tensors and all the relevant access / stage masks created
      */
     virtual ~OpMemoryBarrier() override;
 
@@ -1518,7 +1517,7 @@ class OpMemoryBarrier : public OpBase
      */
     virtual void postEval(const vk::CommandBuffer& commandBuffer) override;
 
-private:
+  private:
     const vk::AccessFlagBits mSrcAccessMask;
     const vk::AccessFlagBits mDstAccessMask;
     const vk::PipelineStageFlagBits mSrcStageMask;
@@ -1534,30 +1533,30 @@ private:
 namespace kp {
 
 /**
- * Operation that copies the data from the first tensor to the rest of the tensors 
- * provided, using a record command for all the vectors. This operation does not 
- * own/manage the memory of the tensors passed to it. The operation must only 
- * receive tensors of type 
-*/
+ * Operation that copies the data from the first tensor to the rest of the
+ * tensors provided, using a record command for all the vectors. This operation
+ * does not own/manage the memory of the tensors passed to it. The operation
+ * must only receive tensors of type
+ */
 class OpTensorCopy : public OpBase
 {
   public:
     /**
-     * Default constructor with parameters that provides the core vulkan resources 
-     * and the tensors that will be used in the operation.
+     * Default constructor with parameters that provides the core vulkan
+     * resources and the tensors that will be used in the operation.
      *
      * @param tensors Tensors that will be used to create in operation.
      */
     OpTensorCopy(const std::vector<std::shared_ptr<Tensor>>& tensors);
 
     /**
-     * Default destructor. This class does not manage memory so it won't be 
+     * Default destructor. This class does not manage memory so it won't be
      * expecting the parent to perform a release.
      */
     ~OpTensorCopy() override;
 
     /**
-     * Records the copy commands from the first tensor into all the other 
+     * Records the copy commands from the first tensor into all the other
      * tensors provided. Also optionally records a barrier.
      *
      * @param commandBuffer The command buffer to record the command into.
@@ -1572,7 +1571,8 @@ class OpTensorCopy : public OpBase
     virtual void preEval(const vk::CommandBuffer& commandBuffer) override;
 
     /**
-     * Copies the local vectors for all the tensors to sync the data with the gpu.
+     * Copies the local vectors for all the tensors to sync the data with the
+     * gpu.
      *
      * @param commandBuffer The command buffer to record the command into.
      */
@@ -1590,32 +1590,34 @@ class OpTensorCopy : public OpBase
 namespace kp {
 
 /**
- * Operation that syncs tensor's device by mapping local data into the device memory. 
- * For TensorTypes::eDevice it will use a record operation for the memory to be syncd 
- * into GPU memory which means that the operation will be done in sync with GPU commands. 
- * For TensorTypes::eHost it will only map the data into host memory which will 
- * happen during preEval before the recorded commands are dispatched.
-*/
+ * Operation that syncs tensor's device by mapping local data into the device
+ * memory. For TensorTypes::eDevice it will use a record operation for the
+ * memory to be syncd into GPU memory which means that the operation will be
+ * done in sync with GPU commands. For TensorTypes::eHost it will only map the
+ * data into host memory which will happen during preEval before the recorded
+ * commands are dispatched.
+ */
 class OpTensorSyncDevice : public OpBase
 {
   public:
     /**
-     * Default constructor with parameters that provides the core vulkan resources 
-     * and the tensors that will be used in the operation. The tensos provided cannot 
-     * be of type TensorTypes::eStorage.
+     * Default constructor with parameters that provides the core vulkan
+     * resources and the tensors that will be used in the operation. The tensos
+     * provided cannot be of type TensorTypes::eStorage.
      *
      * @param tensors Tensors that will be used to create in operation.
      */
     OpTensorSyncDevice(const std::vector<std::shared_ptr<Tensor>>& tensors);
 
     /**
-     * Default destructor. This class does not manage memory so it won't be expecting the parent to perform a release.
+     * Default destructor. This class does not manage memory so it won't be
+     * expecting the parent to perform a release.
      */
     ~OpTensorSyncDevice() override;
 
     /**
-     * For device tensors, it records the copy command for the tensor to copy the 
-     * data from its staging to device memory.
+     * For device tensors, it records the copy command for the tensor to copy
+     * the data from its staging to device memory.
      *
      * @param commandBuffer The command buffer to record the command into.
      */
@@ -1647,34 +1649,34 @@ class OpTensorSyncDevice : public OpBase
 namespace kp {
 
 /**
- * Operation that syncs tensor's local memory by mapping device data into the 
- * local CPU memory. For TensorTypes::eDevice it will use a record operation 
- * for the memory to be syncd into GPU memory which means that the operation 
- * will be done in sync with GPU commands. For TensorTypes::eHost it will 
- * only map the data into host memory which will happen during preEval before 
+ * Operation that syncs tensor's local memory by mapping device data into the
+ * local CPU memory. For TensorTypes::eDevice it will use a record operation
+ * for the memory to be syncd into GPU memory which means that the operation
+ * will be done in sync with GPU commands. For TensorTypes::eHost it will
+ * only map the data into host memory which will happen during preEval before
  * the recorded commands are dispatched.
-*/
+ */
 class OpTensorSyncLocal : public OpBase
 {
   public:
     /**
-     * Default constructor with parameters that provides the core vulkan resources 
-     * and the tensors that will be used in the operation. The tensors provided 
-     * cannot be of type TensorTypes::eStorage.
+     * Default constructor with parameters that provides the core vulkan
+     * resources and the tensors that will be used in the operation. The tensors
+     * provided cannot be of type TensorTypes::eStorage.
      *
      * @param tensors Tensors that will be used to create in operation.
      */
     OpTensorSyncLocal(const std::vector<std::shared_ptr<Tensor>>& tensors);
 
     /**
-     * Default destructor. This class does not manage memory so it won't be expecting 
-     * the parent to perform a release.
+     * Default destructor. This class does not manage memory so it won't be
+     * expecting the parent to perform a release.
      */
     ~OpTensorSyncLocal() override;
 
     /**
-     * For device tensors, it records the copy command for the tensor to copy the 
-     * data from its device to staging memory.
+     * For device tensors, it records the copy command for the tensor to copy
+     * the data from its device to staging memory.
      *
      * @param commandBuffer The command buffer to record the command into.
      */
@@ -1688,7 +1690,8 @@ class OpTensorSyncLocal : public OpBase
     virtual void preEval(const vk::CommandBuffer& commandBuffer) override;
 
     /**
-     * For host tensors it performs the map command from the host memory into local memory.
+     * For host tensors it performs the map command from the host memory into
+     * local memory.
      *
      * @param commandBuffer The command buffer to record the command into.
      */
@@ -1706,7 +1709,7 @@ class OpTensorSyncLocal : public OpBase
 namespace kp {
 
 /**
- * Operation that provides a general abstraction that simplifies the use of 
+ * Operation that provides a general abstraction that simplifies the use of
  * algorithm and parameter components which can be used with shaders.
  * By default it enables the user to provide a dynamic number of tensors
  * which are then passed as inputs.
@@ -1714,7 +1717,6 @@ namespace kp {
 class OpAlgoDispatch : public OpBase
 {
   public:
-
     /**
      * Constructor that stores the algorithm to use as well as the relevant
      * push constants to override when recording.
@@ -1724,7 +1726,7 @@ class OpAlgoDispatch : public OpBase
      */
     template<typename T = float>
     OpAlgoDispatch(const std::shared_ptr<kp::Algorithm>& algorithm,
-            const std::vector<T>& pushConstants = {})
+                   const std::vector<T>& pushConstants = {})
     {
         KP_LOG_DEBUG("Kompute OpAlgoDispatch constructor");
 
@@ -1773,7 +1775,7 @@ class OpAlgoDispatch : public OpBase
      */
     virtual void postEval(const vk::CommandBuffer& commandBuffer) override;
 
-private:
+  private:
     // -------------- ALWAYS OWNED RESOURCES
     std::shared_ptr<Algorithm> mAlgorithm;
     void* mPushConstantsData = nullptr;
@@ -1796,7 +1798,6 @@ namespace kp {
 class OpMult : public OpAlgoDispatch
 {
   public:
-
     /**
      * Default constructor with parameters that provides the bare minimum
      * requirements for the operations to be able to create and manage their
@@ -1806,19 +1807,21 @@ class OpMult : public OpAlgoDispatch
      * @param algorithm An algorithm that will be overridden with the OpMult
      * shader data and the tensors provided which are expected to be 3
      */
-    OpMult(std::vector<std::shared_ptr<Tensor>> tensors, std::shared_ptr<Algorithm> algorithm)
-        : OpAlgoDispatch(algorithm)
+    OpMult(std::vector<std::shared_ptr<Tensor>> tensors,
+           std::shared_ptr<Algorithm> algorithm)
+      : OpAlgoDispatch(algorithm)
     {
         KP_LOG_DEBUG("Kompute OpMult constructor with params");
 
         if (tensors.size() != 3) {
-            throw std::runtime_error("Kompute OpMult expected 3 tensors but got " + tensors.size());
+            throw std::runtime_error(
+              "Kompute OpMult expected 3 tensors but got " + tensors.size());
         }
 
         std::vector<uint32_t> spirv(
           (uint32_t*)shader_data::shaders_glsl_opmult_comp_spv,
           (uint32_t*)(shader_data::shaders_glsl_opmult_comp_spv +
-            kp::shader_data::shaders_glsl_opmult_comp_spv_len));
+                      kp::shader_data::shaders_glsl_opmult_comp_spv_len));
 
         algorithm->rebuild<>(tensors, spirv);
     }
@@ -1827,7 +1830,8 @@ class OpMult : public OpAlgoDispatch
      * Default destructor, which is in charge of destroying the algorithm
      * components but does not destroy the underlying tensors
      */
-    virtual ~OpMult() override {
+    virtual ~OpMult() override
+    {
         KP_LOG_DEBUG("Kompute OpMult destructor started");
     }
 };
@@ -2374,33 +2378,55 @@ class Manager
 /**
  * fmt fromater for kp::Tensor::TensorDataTypes.
  */
-template <> struct fmt::formatter<kp::Tensor::TensorDataTypes>: formatter<std::string> {
-  template <typename FormatContext>
-  auto format(kp::Tensor::TensorDataTypes dt, FormatContext& ctx) {
-    std::string name = "unknown";
-    switch (dt) {
-      case kp::Tensor::TensorDataTypes::eBool: name = "eBool"; break;
-      case kp::Tensor::TensorDataTypes::eDouble: name = "eDouble"; break;
-      case kp::Tensor::TensorDataTypes::eFloat: name = "eFloat"; break;
-      case kp::Tensor::TensorDataTypes::eInt: name = "eInt"; break;
-      case kp::Tensor::TensorDataTypes::eUnsignedInt: name = "eUnsignedInt"; break;
+template<>
+struct fmt::formatter<kp::Tensor::TensorDataTypes> : formatter<std::string>
+{
+    template<typename FormatContext>
+    auto format(kp::Tensor::TensorDataTypes dt, FormatContext& ctx)
+    {
+        std::string name = "unknown";
+        switch (dt) {
+            case kp::Tensor::TensorDataTypes::eBool:
+                name = "eBool";
+                break;
+            case kp::Tensor::TensorDataTypes::eDouble:
+                name = "eDouble";
+                break;
+            case kp::Tensor::TensorDataTypes::eFloat:
+                name = "eFloat";
+                break;
+            case kp::Tensor::TensorDataTypes::eInt:
+                name = "eInt";
+                break;
+            case kp::Tensor::TensorDataTypes::eUnsignedInt:
+                name = "eUnsignedInt";
+                break;
+        }
+        return formatter<std::string>::format(name, ctx);
     }
-    return formatter<std::string>::format(name, ctx);
-  }
 };
 
 /**
  * fmt fromater for kp::Tensor::TensorTypes.
  */
-template <> struct fmt::formatter<kp::Tensor::TensorTypes>: formatter<std::string> {
-  template <typename FormatContext>
-  auto format(kp::Tensor::TensorTypes dt, FormatContext& ctx) {
-    std::string name = "unknown";
-    switch (dt) {
-      case kp::Tensor::TensorTypes::eDevice: name = "eDevice"; break;
-      case kp::Tensor::TensorTypes::eHost: name = "eHost"; break;
-      case kp::Tensor::TensorTypes::eStorage: name = "eStorage"; break;
+template<>
+struct fmt::formatter<kp::Tensor::TensorTypes> : formatter<std::string>
+{
+    template<typename FormatContext>
+    auto format(kp::Tensor::TensorTypes dt, FormatContext& ctx)
+    {
+        std::string name = "unknown";
+        switch (dt) {
+            case kp::Tensor::TensorTypes::eDevice:
+                name = "eDevice";
+                break;
+            case kp::Tensor::TensorTypes::eHost:
+                name = "eHost";
+                break;
+            case kp::Tensor::TensorTypes::eStorage:
+                name = "eStorage";
+                break;
+        }
+        return formatter<std::string>::format(name, ctx);
     }
-    return formatter<std::string>::format(name, ctx);
-  }
 };
diff --git a/src/Algorithm.cpp b/src/Algorithm.cpp
index a3ecee004..a354157cd 100644
--- a/src/Algorithm.cpp
+++ b/src/Algorithm.cpp
@@ -305,7 +305,8 @@ Algorithm::createPipeline()
     this->mFreePipeline = true;
 #else
     vk::Pipeline pipeline =
-      this->mDevice->createComputePipeline(*this->mPipelineCache, pipelineInfo).value;
+      this->mDevice->createComputePipeline(*this->mPipelineCache, pipelineInfo)
+        .value;
     this->mPipeline = std::make_shared<vk::Pipeline>(pipeline);
     this->mFreePipeline = true;
 #endif
diff --git a/src/Manager.cpp b/src/Manager.cpp
index 8a99665f8..8e8d0cdca 100644
--- a/src/Manager.cpp
+++ b/src/Manager.cpp
@@ -291,7 +291,7 @@ Manager::createDevice(const std::vector<uint32_t>& familyQueueIndices,
 
     // Getting an integer that says how many vuklan devices we have
     std::vector<vk::PhysicalDevice> physicalDevices =
-            this->mInstance->enumeratePhysicalDevices();
+      this->mInstance->enumeratePhysicalDevices();
     uint32_t deviceCount = physicalDevices.size();
 
     // This means there are no devices at all
diff --git a/src/include/kompute/Tensor.hpp b/src/include/kompute/Tensor.hpp
index 3b562564c..05c402ac4 100644
--- a/src/include/kompute/Tensor.hpp
+++ b/src/include/kompute/Tensor.hpp
@@ -345,33 +345,55 @@ class TensorT : public Tensor
 /**
  * fmt fromater for kp::Tensor::TensorDataTypes.
  */
-template <> struct fmt::formatter<kp::Tensor::TensorDataTypes>: formatter<std::string> {
-  template <typename FormatContext>
-  auto format(kp::Tensor::TensorDataTypes dt, FormatContext& ctx) {
-    std::string name = "unknown";
-    switch (dt) {
-      case kp::Tensor::TensorDataTypes::eBool: name = "eBool"; break;
-      case kp::Tensor::TensorDataTypes::eDouble: name = "eDouble"; break;
-      case kp::Tensor::TensorDataTypes::eFloat: name = "eFloat"; break;
-      case kp::Tensor::TensorDataTypes::eInt: name = "eInt"; break;
-      case kp::Tensor::TensorDataTypes::eUnsignedInt: name = "eUnsignedInt"; break;
+template<>
+struct fmt::formatter<kp::Tensor::TensorDataTypes> : formatter<std::string>
+{
+    template<typename FormatContext>
+    auto format(kp::Tensor::TensorDataTypes dt, FormatContext& ctx)
+    {
+        std::string name = "unknown";
+        switch (dt) {
+            case kp::Tensor::TensorDataTypes::eBool:
+                name = "eBool";
+                break;
+            case kp::Tensor::TensorDataTypes::eDouble:
+                name = "eDouble";
+                break;
+            case kp::Tensor::TensorDataTypes::eFloat:
+                name = "eFloat";
+                break;
+            case kp::Tensor::TensorDataTypes::eInt:
+                name = "eInt";
+                break;
+            case kp::Tensor::TensorDataTypes::eUnsignedInt:
+                name = "eUnsignedInt";
+                break;
+        }
+        return formatter<std::string>::format(name, ctx);
     }
-    return formatter<std::string>::format(name, ctx);
-  }
 };
 
 /**
  * fmt fromater for kp::Tensor::TensorTypes.
  */
-template <> struct fmt::formatter<kp::Tensor::TensorTypes>: formatter<std::string> {
-  template <typename FormatContext>
-  auto format(kp::Tensor::TensorTypes dt, FormatContext& ctx) {
-    std::string name = "unknown";
-    switch (dt) {
-      case kp::Tensor::TensorTypes::eDevice: name = "eDevice"; break;
-      case kp::Tensor::TensorTypes::eHost: name = "eHost"; break;
-      case kp::Tensor::TensorTypes::eStorage: name = "eStorage"; break;
+template<>
+struct fmt::formatter<kp::Tensor::TensorTypes> : formatter<std::string>
+{
+    template<typename FormatContext>
+    auto format(kp::Tensor::TensorTypes dt, FormatContext& ctx)
+    {
+        std::string name = "unknown";
+        switch (dt) {
+            case kp::Tensor::TensorTypes::eDevice:
+                name = "eDevice";
+                break;
+            case kp::Tensor::TensorTypes::eHost:
+                name = "eHost";
+                break;
+            case kp::Tensor::TensorTypes::eStorage:
+                name = "eStorage";
+                break;
+        }
+        return formatter<std::string>::format(name, ctx);
     }
-    return formatter<std::string>::format(name, ctx);
-  }
 };
diff --git a/src/include/kompute/operations/OpAlgoDispatch.hpp b/src/include/kompute/operations/OpAlgoDispatch.hpp
index 48acd6014..e91598f05 100644
--- a/src/include/kompute/operations/OpAlgoDispatch.hpp
+++ b/src/include/kompute/operations/OpAlgoDispatch.hpp
@@ -1,15 +1,15 @@
 // SPDX-License-Identifier: Apache-2.0
 #pragma once
 
-#include "kompute/Core.hpp"
 #include "kompute/Algorithm.hpp"
+#include "kompute/Core.hpp"
 #include "kompute/Tensor.hpp"
 #include "kompute/operations/OpBase.hpp"
 
 namespace kp {
 
 /**
- * Operation that provides a general abstraction that simplifies the use of 
+ * Operation that provides a general abstraction that simplifies the use of
  * algorithm and parameter components which can be used with shaders.
  * By default it enables the user to provide a dynamic number of tensors
  * which are then passed as inputs.
@@ -17,7 +17,6 @@ namespace kp {
 class OpAlgoDispatch : public OpBase
 {
   public:
-
     /**
      * Constructor that stores the algorithm to use as well as the relevant
      * push constants to override when recording.
@@ -27,7 +26,7 @@ class OpAlgoDispatch : public OpBase
      */
     template<typename T = float>
     OpAlgoDispatch(const std::shared_ptr<kp::Algorithm>& algorithm,
-            const std::vector<T>& pushConstants = {})
+                   const std::vector<T>& pushConstants = {})
     {
         KP_LOG_DEBUG("Kompute OpAlgoDispatch constructor");
 
@@ -76,7 +75,7 @@ class OpAlgoDispatch : public OpBase
      */
     virtual void postEval(const vk::CommandBuffer& commandBuffer) override;
 
-private:
+  private:
     // -------------- ALWAYS OWNED RESOURCES
     std::shared_ptr<Algorithm> mAlgorithm;
     void* mPushConstantsData = nullptr;
@@ -85,4 +84,3 @@ private:
 };
 
 } // End namespace kp
-
diff --git a/src/include/kompute/operations/OpBase.hpp b/src/include/kompute/operations/OpBase.hpp
index 884e6e962..737670846 100644
--- a/src/include/kompute/operations/OpBase.hpp
+++ b/src/include/kompute/operations/OpBase.hpp
@@ -1,9 +1,9 @@
 // SPDX-License-Identifier: Apache-2.0
 #pragma once
 
+#include "kompute/Algorithm.hpp"
 #include "kompute/Core.hpp"
 #include "kompute/Tensor.hpp"
-#include "kompute/Algorithm.hpp"
 
 namespace kp {
 
@@ -18,16 +18,12 @@ namespace kp {
 class OpBase
 {
   public:
-
     /**
      * Default destructor for OpBase class. This OpBase destructor class should
      * always be called to destroy and free owned resources unless it is
      * intended to destroy the resources in the parent class.
      */
-    virtual ~OpBase()
-    {
-        KP_LOG_DEBUG("Kompute OpBase destructor started");
-    }
+    virtual ~OpBase() { KP_LOG_DEBUG("Kompute OpBase destructor started"); }
 
     /**
      * The record function is intended to only send a record command or run
@@ -39,24 +35,24 @@ class OpBase
     virtual void record(const vk::CommandBuffer& commandBuffer) = 0;
 
     /**
-     * Pre eval is called before the Sequence has called eval and submitted the commands to
-     * the GPU for processing, and can be used to perform any per-eval setup steps
-     * required as the computation iteration begins. It's worth noting that 
-     * there are situations where eval can be called multiple times, so the 
-     * resources that are created should be idempotent in case it's called multiple
-     * times in a row.
+     * Pre eval is called before the Sequence has called eval and submitted the
+     * commands to the GPU for processing, and can be used to perform any
+     * per-eval setup steps required as the computation iteration begins. It's
+     * worth noting that there are situations where eval can be called multiple
+     * times, so the resources that are created should be idempotent in case
+     * it's called multiple times in a row.
      *
      * @param commandBuffer The command buffer to record the command into.
      */
     virtual void preEval(const vk::CommandBuffer& commandBuffer) = 0;
 
     /**
-     * Post eval is called after the Sequence has called eval and submitted the commands to
-     * the GPU for processing, and can be used to perform any tear-down steps
-     * required as the computation iteration finishes. It's worth noting that 
-     * there are situations where eval can be called multiple times, so the 
-     * resources that are destroyed should not require a re-init unless explicitly
-     * provided by the user.
+     * Post eval is called after the Sequence has called eval and submitted the
+     * commands to the GPU for processing, and can be used to perform any
+     * tear-down steps required as the computation iteration finishes. It's
+     * worth noting that there are situations where eval can be called multiple
+     * times, so the resources that are destroyed should not require a re-init
+     * unless explicitly provided by the user.
      *
      * @param commandBuffer The command buffer to record the command into.
      */
diff --git a/src/include/kompute/operations/OpMemoryBarrier.hpp b/src/include/kompute/operations/OpMemoryBarrier.hpp
index d784f3bd7..4a2322323 100644
--- a/src/include/kompute/operations/OpMemoryBarrier.hpp
+++ b/src/include/kompute/operations/OpMemoryBarrier.hpp
@@ -1,15 +1,15 @@
 // SPDX-License-Identifier: Apache-2.0
 #pragma once
 
-#include "kompute/Core.hpp"
 #include "kompute/Algorithm.hpp"
+#include "kompute/Core.hpp"
 #include "kompute/Tensor.hpp"
 #include "kompute/operations/OpBase.hpp"
 
 namespace kp {
 
 /**
- * Operation that provides a general abstraction that simplifies the use of 
+ * Operation that provides a general abstraction that simplifies the use of
  * algorithm and parameter components which can be used with shaders.
  * It exposes the pipeline barrier functionality specifically for memory
  * barriers that can be configured through the respective source and destination
@@ -18,29 +18,32 @@ namespace kp {
 class OpMemoryBarrier : public OpBase
 {
   public:
-
     /**
-     * Constructor that stores tensors as well as memory barrier parameters to be
-     * used to create a pipeline barrier on the respective primary or staging tensor.
+     * Constructor that stores tensors as well as memory barrier parameters to
+     * be used to create a pipeline barrier on the respective primary or staging
+     * tensor.
      *
      * @param tensors The tensors to apply the memory barriers on
      * @param srcAccessMask The kp::AccessFlagBits for the source access mask
-     * @param dstAccessMask The kp::AccessFlagBits for the destination access mask
-     * @param srcStageMask The kp::PipelineStageFlagBits for the source stage mask
-     * @param dstStageMask The kp::PipelineStageFlagBits for the destination stage mask
-     * @param barrierOnPrimary Boolean to select primary or secondary buffers on tensors
+     * @param dstAccessMask The kp::AccessFlagBits for the destination access
+     * mask
+     * @param srcStageMask The kp::PipelineStageFlagBits for the source stage
+     * mask
+     * @param dstStageMask The kp::PipelineStageFlagBits for the destination
+     * stage mask
+     * @param barrierOnPrimary Boolean to select primary or secondary buffers on
+     * tensors
      */
-    OpMemoryBarrier(
-            const std::vector<std::shared_ptr<Tensor>>& tensors,
-            const vk::AccessFlagBits& srcAccessMask,
-            const vk::AccessFlagBits& dstAccessMask,
-            const vk::PipelineStageFlagBits& srcStageMask,
-            const vk::PipelineStageFlagBits& dstStageMask,
-            bool barrierOnPrimary = true);
+    OpMemoryBarrier(const std::vector<std::shared_ptr<Tensor>>& tensors,
+                    const vk::AccessFlagBits& srcAccessMask,
+                    const vk::AccessFlagBits& dstAccessMask,
+                    const vk::PipelineStageFlagBits& srcStageMask,
+                    const vk::PipelineStageFlagBits& dstStageMask,
+                    bool barrierOnPrimary = true);
 
     /**
-     * Default destructor, which is in charge of destroying the reference to the tensors
-     * and all the relevant access / stage masks created
+     * Default destructor, which is in charge of destroying the reference to the
+     * tensors and all the relevant access / stage masks created
      */
     virtual ~OpMemoryBarrier() override;
 
@@ -66,7 +69,7 @@ class OpMemoryBarrier : public OpBase
      */
     virtual void postEval(const vk::CommandBuffer& commandBuffer) override;
 
-private:
+  private:
     const vk::AccessFlagBits mSrcAccessMask;
     const vk::AccessFlagBits mDstAccessMask;
     const vk::PipelineStageFlagBits mSrcStageMask;
@@ -76,4 +79,3 @@ private:
 };
 
 } // End namespace kp
-
diff --git a/src/include/kompute/operations/OpMult.hpp b/src/include/kompute/operations/OpMult.hpp
index 2d6b88057..d148087c7 100644
--- a/src/include/kompute/operations/OpMult.hpp
+++ b/src/include/kompute/operations/OpMult.hpp
@@ -21,7 +21,6 @@ namespace kp {
 class OpMult : public OpAlgoDispatch
 {
   public:
-
     /**
      * Default constructor with parameters that provides the bare minimum
      * requirements for the operations to be able to create and manage their
@@ -31,19 +30,21 @@ class OpMult : public OpAlgoDispatch
      * @param algorithm An algorithm that will be overridden with the OpMult
      * shader data and the tensors provided which are expected to be 3
      */
-    OpMult(std::vector<std::shared_ptr<Tensor>> tensors, std::shared_ptr<Algorithm> algorithm)
-        : OpAlgoDispatch(algorithm)
+    OpMult(std::vector<std::shared_ptr<Tensor>> tensors,
+           std::shared_ptr<Algorithm> algorithm)
+      : OpAlgoDispatch(algorithm)
     {
         KP_LOG_DEBUG("Kompute OpMult constructor with params");
 
         if (tensors.size() != 3) {
-            throw std::runtime_error("Kompute OpMult expected 3 tensors but got " + tensors.size());
+            throw std::runtime_error(
+              "Kompute OpMult expected 3 tensors but got " + tensors.size());
         }
 
         std::vector<uint32_t> spirv(
           (uint32_t*)shader_data::shaders_glsl_opmult_comp_spv,
           (uint32_t*)(shader_data::shaders_glsl_opmult_comp_spv +
-            kp::shader_data::shaders_glsl_opmult_comp_spv_len));
+                      kp::shader_data::shaders_glsl_opmult_comp_spv_len));
 
         algorithm->rebuild<>(tensors, spirv);
     }
@@ -52,7 +53,8 @@ class OpMult : public OpAlgoDispatch
      * Default destructor, which is in charge of destroying the algorithm
      * components but does not destroy the underlying tensors
      */
-    virtual ~OpMult() override {
+    virtual ~OpMult() override
+    {
         KP_LOG_DEBUG("Kompute OpMult destructor started");
     }
 };
diff --git a/src/include/kompute/operations/OpTensorCopy.hpp b/src/include/kompute/operations/OpTensorCopy.hpp
index a51021975..968c1065a 100644
--- a/src/include/kompute/operations/OpTensorCopy.hpp
+++ b/src/include/kompute/operations/OpTensorCopy.hpp
@@ -10,30 +10,30 @@
 namespace kp {
 
 /**
- * Operation that copies the data from the first tensor to the rest of the tensors 
- * provided, using a record command for all the vectors. This operation does not 
- * own/manage the memory of the tensors passed to it. The operation must only 
- * receive tensors of type 
-*/
+ * Operation that copies the data from the first tensor to the rest of the
+ * tensors provided, using a record command for all the vectors. This operation
+ * does not own/manage the memory of the tensors passed to it. The operation
+ * must only receive tensors of type
+ */
 class OpTensorCopy : public OpBase
 {
   public:
     /**
-     * Default constructor with parameters that provides the core vulkan resources 
-     * and the tensors that will be used in the operation.
+     * Default constructor with parameters that provides the core vulkan
+     * resources and the tensors that will be used in the operation.
      *
      * @param tensors Tensors that will be used to create in operation.
      */
     OpTensorCopy(const std::vector<std::shared_ptr<Tensor>>& tensors);
 
     /**
-     * Default destructor. This class does not manage memory so it won't be 
+     * Default destructor. This class does not manage memory so it won't be
      * expecting the parent to perform a release.
      */
     ~OpTensorCopy() override;
 
     /**
-     * Records the copy commands from the first tensor into all the other 
+     * Records the copy commands from the first tensor into all the other
      * tensors provided. Also optionally records a barrier.
      *
      * @param commandBuffer The command buffer to record the command into.
@@ -48,7 +48,8 @@ class OpTensorCopy : public OpBase
     virtual void preEval(const vk::CommandBuffer& commandBuffer) override;
 
     /**
-     * Copies the local vectors for all the tensors to sync the data with the gpu.
+     * Copies the local vectors for all the tensors to sync the data with the
+     * gpu.
      *
      * @param commandBuffer The command buffer to record the command into.
      */
@@ -60,4 +61,3 @@ class OpTensorCopy : public OpBase
 };
 
 } // End namespace kp
-
diff --git a/src/include/kompute/operations/OpTensorSyncDevice.hpp b/src/include/kompute/operations/OpTensorSyncDevice.hpp
index 09c755b01..3a1792ac6 100644
--- a/src/include/kompute/operations/OpTensorSyncDevice.hpp
+++ b/src/include/kompute/operations/OpTensorSyncDevice.hpp
@@ -2,39 +2,40 @@
 #pragma once
 
 #include "kompute/Core.hpp"
-#include "kompute/operations/OpBase.hpp"
 #include "kompute/Tensor.hpp"
 #include "kompute/operations/OpBase.hpp"
 
 namespace kp {
 
 /**
- * Operation that syncs tensor's device by mapping local data into the device memory. 
- * For TensorTypes::eDevice it will use a record operation for the memory to be syncd 
- * into GPU memory which means that the operation will be done in sync with GPU commands. 
- * For TensorTypes::eHost it will only map the data into host memory which will 
- * happen during preEval before the recorded commands are dispatched.
-*/
+ * Operation that syncs tensor's device by mapping local data into the device
+ * memory. For TensorTypes::eDevice it will use a record operation for the
+ * memory to be syncd into GPU memory which means that the operation will be
+ * done in sync with GPU commands. For TensorTypes::eHost it will only map the
+ * data into host memory which will happen during preEval before the recorded
+ * commands are dispatched.
+ */
 class OpTensorSyncDevice : public OpBase
 {
   public:
     /**
-     * Default constructor with parameters that provides the core vulkan resources 
-     * and the tensors that will be used in the operation. The tensos provided cannot 
-     * be of type TensorTypes::eStorage.
+     * Default constructor with parameters that provides the core vulkan
+     * resources and the tensors that will be used in the operation. The tensos
+     * provided cannot be of type TensorTypes::eStorage.
      *
      * @param tensors Tensors that will be used to create in operation.
      */
     OpTensorSyncDevice(const std::vector<std::shared_ptr<Tensor>>& tensors);
 
     /**
-     * Default destructor. This class does not manage memory so it won't be expecting the parent to perform a release.
+     * Default destructor. This class does not manage memory so it won't be
+     * expecting the parent to perform a release.
      */
     ~OpTensorSyncDevice() override;
 
     /**
-     * For device tensors, it records the copy command for the tensor to copy the 
-     * data from its staging to device memory.
+     * For device tensors, it records the copy command for the tensor to copy
+     * the data from its staging to device memory.
      *
      * @param commandBuffer The command buffer to record the command into.
      */
@@ -60,5 +61,3 @@ class OpTensorSyncDevice : public OpBase
 };
 
 } // End namespace kp
-
-
diff --git a/src/include/kompute/operations/OpTensorSyncLocal.hpp b/src/include/kompute/operations/OpTensorSyncLocal.hpp
index 365cabdf8..4216003e5 100644
--- a/src/include/kompute/operations/OpTensorSyncLocal.hpp
+++ b/src/include/kompute/operations/OpTensorSyncLocal.hpp
@@ -10,34 +10,34 @@
 namespace kp {
 
 /**
- * Operation that syncs tensor's local memory by mapping device data into the 
- * local CPU memory. For TensorTypes::eDevice it will use a record operation 
- * for the memory to be syncd into GPU memory which means that the operation 
- * will be done in sync with GPU commands. For TensorTypes::eHost it will 
- * only map the data into host memory which will happen during preEval before 
+ * Operation that syncs tensor's local memory by mapping device data into the
+ * local CPU memory. For TensorTypes::eDevice it will use a record operation
+ * for the memory to be syncd into GPU memory which means that the operation
+ * will be done in sync with GPU commands. For TensorTypes::eHost it will
+ * only map the data into host memory which will happen during preEval before
  * the recorded commands are dispatched.
-*/
+ */
 class OpTensorSyncLocal : public OpBase
 {
   public:
     /**
-     * Default constructor with parameters that provides the core vulkan resources 
-     * and the tensors that will be used in the operation. The tensors provided 
-     * cannot be of type TensorTypes::eStorage.
+     * Default constructor with parameters that provides the core vulkan
+     * resources and the tensors that will be used in the operation. The tensors
+     * provided cannot be of type TensorTypes::eStorage.
      *
      * @param tensors Tensors that will be used to create in operation.
      */
     OpTensorSyncLocal(const std::vector<std::shared_ptr<Tensor>>& tensors);
 
     /**
-     * Default destructor. This class does not manage memory so it won't be expecting 
-     * the parent to perform a release.
+     * Default destructor. This class does not manage memory so it won't be
+     * expecting the parent to perform a release.
      */
     ~OpTensorSyncLocal() override;
 
     /**
-     * For device tensors, it records the copy command for the tensor to copy the 
-     * data from its device to staging memory.
+     * For device tensors, it records the copy command for the tensor to copy
+     * the data from its device to staging memory.
      *
      * @param commandBuffer The command buffer to record the command into.
      */
@@ -51,19 +51,16 @@ class OpTensorSyncLocal : public OpBase
     virtual void preEval(const vk::CommandBuffer& commandBuffer) override;
 
     /**
-     * For host tensors it performs the map command from the host memory into local memory.
+     * For host tensors it performs the map command from the host memory into
+     * local memory.
      *
      * @param commandBuffer The command buffer to record the command into.
      */
     virtual void postEval(const vk::CommandBuffer& commandBuffer) override;
 
-
   private:
     // -------------- ALWAYS OWNED RESOURCES
     std::vector<std::shared_ptr<Tensor>> mTensors;
 };
 
 } // End namespace kp
-
-
-
diff --git a/test/TestDestroy.cpp b/test/TestDestroy.cpp
index ba3052631..8ae824756 100644
--- a/test/TestDestroy.cpp
+++ b/test/TestDestroy.cpp
@@ -27,16 +27,15 @@ TEST(TestDestroy, TestDestroyTensorSingle)
         {
             kp::Manager mgr;
 
-            const std::vector<float> initialValues = {0.0f, 0.0f, 0.0f};
+            const std::vector<float> initialValues = { 0.0f, 0.0f, 0.0f };
 
             tensorA = mgr.tensor(initialValues);
 
             std::shared_ptr<kp::Algorithm> algo =
-              mgr.algorithm({tensorA}, spirv);
+              mgr.algorithm({ tensorA }, spirv);
 
             // Sync values to and from device
-            mgr.sequence()
-            ->eval<kp::OpTensorSyncDevice>(algo->getTensors());
+            mgr.sequence()->eval<kp::OpTensorSyncDevice>(algo->getTensors());
 
             EXPECT_EQ(tensorA->vector(), initialValues);
 
@@ -45,7 +44,7 @@ TEST(TestDestroy, TestDestroyTensorSingle)
               ->eval()
               ->eval<kp::OpTensorSyncLocal>(algo->getTensors());
 
-            const std::vector<float> expectedFinalValues = {1.0f, 1.0f, 1.0f};
+            const std::vector<float> expectedFinalValues = { 1.0f, 1.0f, 1.0f };
             EXPECT_EQ(tensorA->vector(), expectedFinalValues);
 
             tensorA->destroy();
diff --git a/test/utils/kompute_test/Shader.hpp b/test/utils/kompute_test/Shader.hpp
index 03789130e..33f81f122 100644
--- a/test/utils/kompute_test/Shader.hpp
+++ b/test/utils/kompute_test/Shader.hpp
@@ -2,31 +2,34 @@
 
 #pragma once
 
+#include <fstream>
 #include <iostream>
 #include <vector>
-#include <fstream>
 
 /**
  * Compile a single glslang source from string value. This is only meant
  * to be used for testing as it's non threadsafe, and it had to be removed
- * from the glslang dependency and now can only run the CLI directly due to 
+ * from the glslang dependency and now can only run the CLI directly due to
  * license issues: see https://github.com/KomputeProject/kompute/pull/235
  *
  * @param source An individual raw glsl shader in string format
  * @return The compiled SPIR-V binary in unsigned int32 format
  */
-static
-std::vector<uint32_t>
-compileSource(
-  const std::string& source)
+static std::vector<uint32_t>
+compileSource(const std::string& source)
 {
     std::ofstream fileOut("tmp_kp_shader.comp");
-	fileOut << source;
-	fileOut.close();
-    if (system(std::string("glslangValidator -V tmp_kp_shader.comp -o tmp_kp_shader.comp.spv").c_str()))
+    fileOut << source;
+    fileOut.close();
+    if (system(
+          std::string(
+            "glslangValidator -V tmp_kp_shader.comp -o tmp_kp_shader.comp.spv")
+            .c_str()))
         throw std::runtime_error("Error running glslangValidator command");
     std::ifstream fileStream("tmp_kp_shader.comp.spv", std::ios::binary);
     std::vector<char> buffer;
-    buffer.insert(buffer.begin(), std::istreambuf_iterator<char>(fileStream), {});
-    return {(uint32_t*)buffer.data(), (uint32_t*)(buffer.data() + buffer.size())};
+    buffer.insert(
+      buffer.begin(), std::istreambuf_iterator<char>(fileStream), {});
+    return { (uint32_t*)buffer.data(),
+             (uint32_t*)(buffer.data() + buffer.size()) };
 }