🌐 [translation-sync] [numpy_vs_numba_vs_jax] Fix deprecated device= argument on jax.jit (#68)

* Update translation: lectures/numpy_vs_numba_vs_jax.md

* Update translation: .translate/state/numpy_vs_numba_vs_jax.md.yml

Author: mmcky

.translate/state/numpy_vs_numba_vs_jax.md.yml

@@ -1,5 +1,5 @@
-source-sha: d08a73d48a409509d7d6f6585b99c2c8909c9a28
-synced-at: "2026-05-14"
+source-sha: d37b1d8adbf6e18b17e125cca761a6eb2ccd9041
+synced-at: "2026-06-19"
 model: claude-sonnet-4-6
 mode: UPDATE
 section-count: 3

lectures/numpy_vs_numba_vs_jax.md

@@ -467,7 +467,10 @@ Numba 非常高效地处理了这个顺序运算。
 ```{code-cell} ipython3
 cpu = jax.devices("cpu")[0]
 
-@partial(jax.jit, static_argnames=("n",), device=cpu)
+# Pin the input to the CPU, which keeps the whole computation there
+x0_cpu = jax.device_put(0.1, cpu)
+
+@partial(jax.jit, static_argnames=("n",))
 def qm_jax_fori(x0, n, α=4.0):
 
     x = jnp.empty(n + 1).at[0].set(x0)
@@ -481,7 +484,7 @@ def qm_jax_fori(x0, n, α=4.0):
 ```
 
 * 我们将 `n` 设为静态，因为它影响数组大小，JAX 希望在编译代码中针对其值进行特化处理。
-* 我们通过 `device=cpu` 将计算固定到 CPU，因为这种顺序工作负载由许多小型运算组成，几乎没有机会利用 GPU 并行性。
+* 我们通过 `jax.device_put` 将输入固定到 CPU（从而使整个计算保持在 CPU 上），因为这种顺序工作负载由许多小型运算组成，几乎没有机会利用 GPU 并行性。
 
 重要提示：虽然 `at[t].set` 看起来在每一步都创建了一个新数组，但在 JIT 编译的函数内部，编译器会检测到旧数组不再需要，并就地执行更新！
 
@@ -490,7 +493,7 @@ def qm_jax_fori(x0, n, α=4.0):
 ```{code-cell} ipython3
 with qe.Timer():
     # First run
-    x_jax = qm_jax_fori(0.1, n)
+    x_jax = qm_jax_fori(x0_cpu, n)
     # Hold interpreter
     x_jax.block_until_ready()
 ```
@@ -500,7 +503,7 @@ with qe.Timer():
 ```{code-cell} ipython3
 with qe.Timer():
     # Second run
-    x_jax = qm_jax_fori(0.1, n)
+    x_jax = qm_jax_fori(x0_cpu, n)
     # Hold interpreter
     x_jax.block_until_ready()
 ```
@@ -514,7 +517,7 @@ JAX 对于这种顺序运算也相当高效！
 这种替代方案可以说更符合 JAX 的函数式风格——尽管语法难以记忆。
 
 ```{code-cell} ipython3
-@partial(jax.jit, static_argnames=("n",), device=cpu)
+@partial(jax.jit, static_argnames=("n",))
 def qm_jax_scan(x0, n, α=4.0):
     def update(x, t):
         x_new = α * x * (1 - x)
@@ -531,7 +534,7 @@ def qm_jax_scan(x0, n, α=4.0):
 ```{code-cell} ipython3
 with qe.Timer():
     # First run
-    x_jax = qm_jax_scan(0.1, n)
+    x_jax = qm_jax_scan(x0_cpu, n)
     # Hold interpreter
     x_jax.block_until_ready()
 ```
@@ -541,7 +544,7 @@ with qe.Timer():
 ```{code-cell} ipython3
 with qe.Timer():
     # Second run
-    x_jax = qm_jax_scan(0.1, n)
+    x_jax = qm_jax_scan(x0_cpu, n)
     # Hold interpreter
     x_jax.block_until_ready()
 ```