chore: bump deps (#60) (#61)

denosaurs · Sep 7, 2024 · 7d5d206 · 7d5d206
1 parent 7cc0b82
commit 7d5d206
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Showing 21 changed files with 193 additions and 287 deletions.
diff --git a/Cargo.lock b/Cargo.lock
diff --git a/Cargo.toml b/Cargo.toml
@@ -2,3 +2,12 @@
 package.version = "0.4.0"
 members = ["crates/*"]
 resolver = "2"
+
+[workspace.dependencies]
+cudarc = "0.9.14" 
+ndarray = "0.16.1"
+ndarray-rand = "0.15.0"
+safetensors = "0.4.5"
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+thiserror = "1.0.49"
diff --git a/bench/netsaur_cpu.ts b/bench/netsaur_cpu.ts
@@ -11,72 +11,34 @@ import {
 
 await setupBackend(CPU);
 
-Deno.bench(
-  { name: "xor 10000 epochs", permissions: "inherit" },
-  async () => {
-    const net = new Sequential({
-      size: [4, 2],
-      silent: true,
-      layers: [
-        DenseLayer({ size: [3] }),
-        SigmoidLayer(),
-        DenseLayer({ size: [1] }),
-        SigmoidLayer(),
-      ],
-      cost: Cost.MSE,
-    });
-
-    net.train(
-      [
-        {
-          inputs: tensor2D([
-            [0, 0],
-            [1, 0],
-            [0, 1],
-            [1, 1],
-          ]),
-          outputs: tensor2D([[0], [1], [1], [0]]),
-        },
-      ],
-      10000,
-    );
-
-    console.log((await net.predict(tensor1D([0, 0]))).data);
-    console.log((await net.predict(tensor1D([1, 0]))).data);
-    console.log((await net.predict(tensor1D([0, 1]))).data);
-    console.log((await net.predict(tensor1D([1, 1]))).data);
-  },
+const net = new Sequential({
+  size: [4, 2],
+  silent: true,
+  layers: [
+    DenseLayer({ size: [3] }),
+    SigmoidLayer(),
+    DenseLayer({ size: [1] }),
+    SigmoidLayer(),
+  ],
+  cost: Cost.MSE,
+});
+
+net.train(
+  [
+    {
+      inputs: tensor2D([
+        [0, 0],
+        [1, 0],
+        [0, 1],
+        [1, 1],
+      ]),
+      outputs: tensor2D([[0], [1], [1], [0]]),
+    },
+  ],
+  10000,
 );
 
-// const net = new NeuralNetwork({
-//   size: [4, 2],
-//   silent: true,
-//   layers: [
-//     DenseLayer({ size: [3], activation: Activation.Sigmoid }),
-//     DenseLayer({ size: [1], activation: Activation.Sigmoid }),
-//   ],
-//   cost: Cost.MSE,
-// });
-
-// const time = performance.now();
-
-// net.train(
-//   [
-//     {
-//       inputs: tensor2D([
-//         [0, 0],
-//         [1, 0],
-//         [0, 1],
-//         [1, 1],
-//       ]),
-//       outputs: tensor2D([[0], [1], [1], [0]]),
-//     },
-//   ],
-//   10000,
-// )
-
-// console.log(`training time: ${performance.now() - time}ms`);
-// console.log((await net.predict(tensor2D([[0, 0]]))).data);
-// console.log((await net.predict(tensor2D([[1, 0]]))).data);
-// console.log((await net.predict(tensor2D([[0, 1]]))).data);
-// console.log((await net.predict(tensor2D([[1, 1]]))).data);
+console.log((await net.predict(tensor1D([0, 0]))).data);
+console.log((await net.predict(tensor1D([1, 0]))).data);
+console.log((await net.predict(tensor1D([0, 1]))).data);
+console.log((await net.predict(tensor1D([1, 1]))).data);
diff --git a/bench/netsaur_wasm.ts b/bench/netsaur_wasm.ts
@@ -11,72 +11,34 @@ import {
 
 await setupBackend(WASM);
 
-Deno.bench(
-  { name: "xor 10000 epochs", permissions: "inherit" },
-  async () => {
-    const net = new Sequential({
-      size: [4, 2],
-      silent: true,
-      layers: [
-        DenseLayer({ size: [3] }),
-        SigmoidLayer(),
-        DenseLayer({ size: [1] }),
-        SigmoidLayer(),
-      ],
-      cost: Cost.MSE,
-    });
-
-    net.train(
-      [
-        {
-          inputs: tensor2D([
-            [0, 0],
-            [1, 0],
-            [0, 1],
-            [1, 1],
-          ]),
-          outputs: tensor2D([[0], [1], [1], [0]]),
-        },
-      ],
-      10000,
-    );
-
-    console.log((await net.predict(tensor1D([0, 0]))).data);
-    console.log((await net.predict(tensor1D([1, 0]))).data);
-    console.log((await net.predict(tensor1D([0, 1]))).data);
-    console.log((await net.predict(tensor1D([1, 1]))).data);
-  },
+const net = new Sequential({
+  size: [4, 2],
+  silent: true,
+  layers: [
+    DenseLayer({ size: [3] }),
+    SigmoidLayer(),
+    DenseLayer({ size: [1] }),
+    SigmoidLayer(),
+  ],
+  cost: Cost.MSE,
+});
+
+net.train(
+  [
+    {
+      inputs: tensor2D([
+        [0, 0],
+        [1, 0],
+        [0, 1],
+        [1, 1],
+      ]),
+      outputs: tensor2D([[0], [1], [1], [0]]),
+    },
+  ],
+  10000,
 );
 
-// const net = new NeuralNetwork({
-//   size: [4, 2],
-//   silent: true,
-//   layers: [
-//     DenseLayer({ size: [3], activation: Activation.Sigmoid }),
-//     DenseLayer({ size: [1], activation: Activation.Sigmoid }),
-//   ],
-//   cost: Cost.MSE,
-// });
-
-// const time = performance.now();
-
-// net.train(
-//   [
-//     {
-//       inputs: tensor2D([
-//         [0, 0],
-//         [1, 0],
-//         [0, 1],
-//         [1, 1],
-//       ]),
-//       outputs: tensor2D([[0], [1], [1], [0]]),
-//     },
-//   ],
-//   10000,
-// )
-
-// console.log(`training time: ${performance.now() - time}ms`);
-// console.log((await net.predict(tensor2D([[0, 0]]))).data);
-// console.log((await net.predict(tensor2D([[1, 0]]))).data);
-// console.log((await net.predict(tensor2D([[0, 1]]))).data);
-// console.log((await net.predict(tensor2D([[1, 1]]))).data);
+console.log((await net.predict(tensor1D([0, 0]))).data);
+console.log((await net.predict(tensor1D([1, 0]))).data);
+console.log((await net.predict(tensor1D([0, 1]))).data);
+console.log((await net.predict(tensor1D([1, 1]))).data);
diff --git a/bench/node/brain_cpu.js b/bench/node/brain_cpu.js
diff --git a/bench/node/brain_gpu.js b/bench/node/brain_gpu.js
diff --git a/bench/node/package.json b/bench/node/package.json
diff --git a/bench/node/tfjs_cpu.js b/bench/node/tfjs_cpu.js
diff --git a/crates/core-gpu/Cargo.toml b/crates/core-gpu/Cargo.toml
@@ -6,12 +6,11 @@ version = { workspace = true }
 [lib]
 crate-type = ["cdylib"]
 
-
 [dependencies]
-ndarray = "0.15.6"
-ndarray-rand = "0.14.0"
-serde = {version = "1.0", features = ["derive"]}
-serde_json = "1.0"
-safetensors = "0.4.0"
-cudarc = "0.9.14" 
-thiserror = "1.0.49"
+cudarc = { workspace = true }
+ndarray = { workspace = true }
+ndarray-rand = { workspace = true }
+safetensors = { workspace = true }
+serde = { workspace = true }
+serde_json = { workspace = true }
+thiserror = { workspace = true }