Filter out the warnings

Author: avik-pal

docs/Project.toml

@@ -3,6 +3,7 @@ DeepEquilibriumNetworks = "6748aba7-0e9b-415e-a410-ae3cc0ecb334"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DocumenterCitations = "daee34ce-89f3-4625-b898-19384cb65244"
 LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
+LoggingExtras = "e6f89c97-d47a-5376-807f-9c37f3926c36"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
 LuxCUDA = "d0bbae9a-e099-4d5b-a835-1c6931763bda"
 MLDataUtils = "cc2ba9b6-d476-5e6d-8eaf-a92d5412d41d"
@@ -20,6 +21,7 @@ DeepEquilibriumNetworks = "2"
 Documenter = "1"
 DocumenterCitations = "1"
 LinearSolve = "2"
+LoggingExtras = "1"
 Lux = "0.5"
 LuxCUDA = "0.3"
 MLDataUtils = "0.5"

docs/src/tutorials/basic_mnist_deq.md

@@ -4,7 +4,7 @@ We will train a simple Deep Equilibrium Model on MNIST. First we load a few pack
 
 ```@example basic_mnist_deq
 using DeepEquilibriumNetworks, SciMLSensitivity, Lux, NonlinearSolve, OrdinaryDiffEq,
-    Statistics, Random, Optimisers, LuxCUDA, Zygote, LinearSolve
+    Statistics, Random, Optimisers, LuxCUDA, Zygote, LinearSolve, LoggingExtras
 using MLDatasets: MNIST
 using MLDataUtils: LabelEnc, convertlabel, stratifiedobs, batchview
 
@@ -20,6 +20,18 @@ const cdev = cpu_device()
 const gdev = gpu_device()
 ```
 
+SciMLBase introduced a warning instead of depwarn which pollutes the output. We can suppress
+it with the following logger
+
+```@example basic_mnist_deq
+function remove_syms_warning(log_args)
+    return log_args.message !=
+           "The use of keyword arguments `syms`, `paramsyms` and `indepsym` for `SciMLFunction`s is deprecated. Pass `sys = SymbolCache(syms, paramsyms, indepsym)` instead."
+end
+
+filtered_logger = ActiveFilteredLogger(remove_syms_warning, global_logger())
+```
+
 We can now construct our dataloader.
 
 ```@example basic_mnist_deq
@@ -175,15 +187,19 @@ and end up using solvers like `Broyden`, but we can simply slap in any of the fa
 from NonlinearSolve.jl. Here we will use Newton-Krylov Method:
 
 ```@example basic_mnist_deq
-train_model(NewtonRaphson(; linsolve=KrylovJL_GMRES()), :regdeq)
+with_logger(filtered_logger) do
+    train_model(NewtonRaphson(; linsolve=KrylovJL_GMRES()), :regdeq)
+end
 nothing # hide
 ```
 
 We can also train a continuous DEQ by passing in an ODE solver. Here we will use `VCAB3()`
 which tend to be quite fast for continuous Neural Network problems.
 
 ```@example basic_mnist_deq
-train_model(VCAB3(), :deq)
+with_logger(filtered_logger) do
+    train_model(VCAB3(), :deq)
+end
 nothing # hide
 ```
 

docs/src/tutorials/reduced_dim_deq.md

@@ -6,7 +6,7 @@ same MNIST example as before, but this time we will use a reduced state size.
 
 ```@example reduced_dim_mnist
 using DeepEquilibriumNetworks, SciMLSensitivity, Lux, NonlinearSolve, OrdinaryDiffEq,
-    Statistics, Random, Optimisers, LuxCUDA, Zygote, LinearSolve
+    Statistics, Random, Optimisers, LuxCUDA, Zygote, LinearSolve, LoggingExtras
 using MLDatasets: MNIST
 using MLDataUtils: LabelEnc, convertlabel, stratifiedobs, batchview
 
@@ -16,6 +16,13 @@ ENV["DATADEPS_ALWAYS_ACCEPT"] = true
 const cdev = cpu_device()
 const gdev = gpu_device()
 
+function remove_syms_warning(log_args)
+    return log_args.message !=
+           "The use of keyword arguments `syms`, `paramsyms` and `indepsym` for `SciMLFunction`s is deprecated. Pass `sys = SymbolCache(syms, paramsyms, indepsym)` instead."
+end
+
+filtered_logger = ActiveFilteredLogger(remove_syms_warning, global_logger())
+
 function onehot(labels_raw)
     return convertlabel(LabelEnc.OneOfK, labels_raw, LabelEnc.NativeLabels(collect(0:9)))
 end
@@ -168,11 +175,15 @@ Now we can train our model. We can't use `:regdeq` here currently, but we will s
 in the future.
 
 ```@example reduced_dim_mnist
-train_model(NewtonRaphson(; linsolve=KrylovJL_GMRES()), :skipdeq)
+with_logger(filtered_logger) do
+    train_model(NewtonRaphson(; linsolve=KrylovJL_GMRES()), :skipdeq)
+end
 nothing # hide
 ```
 
 ```@example reduced_dim_mnist
-train_model(NewtonRaphson(; linsolve=KrylovJL_GMRES()), :deq)
+with_logger(filtered_logger) do
+    train_model(NewtonRaphson(; linsolve=KrylovJL_GMRES()), :deq)
+end
 nothing # hide
 ```