Avoid XGBoost 1.3.0 which seems broken

dev/extra-ml-requirements.txt

@@ -23,7 +23,8 @@ torch
 torchvision
 pytorch_lightning>=1.0.2
 # Required by mlflow.xgboost
-xgboost>=0.82
+# Exclude 1.3.0 to avoid this issue: https://github.com/dmlc/xgboost/issues/6481
+xgboost>=0.82,!=1.3.0
 # Required by mlflow.lightgbm
 lightgbm
 # Required by mlflow.statsmodels

mlflow/R/mlflow/.create-test-env.R

@@ -11,6 +11,7 @@ keras::install_keras(method = "conda", envname = mlflow:::mlflow_conda_env_name(
 # TODO: unpin after we use tensorflow >= 2.4
 reticulate::conda_install("'h5py<3.0.0'", envname = mlflow:::mlflow_conda_env_name(), pip = TRUE)
 reticulate::conda_install(Sys.getenv("MLFLOW_HOME", "../../../../."), envname = mlflow:::mlflow_conda_env_name(), pip = TRUE)
-reticulate::conda_install("xgboost", envname = mlflow:::mlflow_conda_env_name())
+# Exclude 1.3.0 to avoid this issue: https://github.com/dmlc/xgboost/issues/6481
+reticulate::conda_install("xgboost!=1.3.0", envname = mlflow:::mlflow_conda_env_name())
 # Pin h2o to prevent version-mismatch between python and R
 reticulate::conda_install("h2o==3.30.1.3", envname = mlflow:::mlflow_conda_env_name(), pip = TRUE)

mlflow/R/mlflow/R/model-xgboost.R

@@ -19,7 +19,7 @@ mlflow_save_model.xgb.Booster <- function(model,
   )
 
   conda_env <- create_default_conda_env_if_absent(
-    path, conda_env, default_pip_deps = list("mlflow", paste("xgboost>=", version, sep = ""))
+    path, conda_env, default_pip_deps = list("mlflow", paste("xgboost==", version, sep = ""))
   )
   xgboost_conf <- list(
     xgboost = list(xgb_version = version, data = model_data_subpath)