Update user docs to drop config file path

Author: dipannita08

PREFLIGHT.md

@@ -1,35 +1,39 @@
 # Optimization 1: Multihost recommended network settings
-We included all the recommended network settings in [rto_setup.sh](https://github.com/google/maxtext/blob/main/src/dependencies/scripts/rto_setup.sh). 
+
+We included all the recommended network settings in [rto_setup.sh](https://github.com/google/maxtext/blob/main/src/dependencies/scripts/rto_setup.sh).
 
 [preflight.sh](https://github.com/google/maxtext/blob/main/src/dependencies/scripts/preflight.sh) will help you apply them based on GCE or GKE platform.
 
 Before you run ML workload on Multihost with GCE or GKE, simply apply `bash preflight.sh PLATFORM=[GCE or GKE]` to leverage the best DCN network performance.
 
 Here is an example for GCE:
+
 ```
-bash src/dependencies/scripts/preflight.sh PLATFORM=GCE && python3 -m maxtext.trainers.pre_train.train run_name=${YOUR_JOB_NAME?}
+bash preflight.sh PLATFORM=GCE && python3 -m maxtext.trainers.pre_train.train run_name=${YOUR_JOB_NAME?}
 ```
 
 Here is an example for GKE:
+
 ```
-bash src/dependencies/scripts/preflight.sh PLATFORM=GKE && python3 -m maxtext.trainers.pre_train.train run_name=${YOUR_JOB_NAME?}
+bash preflight.sh PLATFORM=GKE && python3 -m maxtext.trainers.pre_train.train run_name=${YOUR_JOB_NAME?}
 ```
 
 # Optimization 2: Numa binding (You can only apply this to v4 and v5p)
+
 NUMA binding is recommended for enhanced performance, as it reduces memory latency and maximizes data throughput, ensuring that your high-performance applications operate more efficiently and effectively.
 
-For GCE, 
+For GCE,
 [preflight.sh](https://github.com/google/maxtext/blob/main/src/dependencies/scripts/preflight.sh) will help you install `numactl` dependency, so you can use it directly, here is an example:
 
 ```
-bash src/dependencies/scripts/preflight.sh PLATFORM=GCE && numactl --membind 0 --cpunodebind=0 python3 -m maxtext.trainers.pre_train.train run_name=${YOUR_JOB_NAME?}
+bash preflight.sh PLATFORM=GCE && numactl --membind 0 --cpunodebind=0 python3 -m maxtext.trainers.pre_train.train run_name=${YOUR_JOB_NAME?}
 ```
 
 For GKE,
 `numactl` should be built into your docker image from [maxtext_tpu_dependencies.Dockerfile](https://github.com/google/maxtext/blob/main/src/dependencies/dockerfiles/maxtext_tpu_dependencies.Dockerfile), so you can use it directly if you built the maxtext docker image. Here is an example
 
 ```
-bash src/dependencies/scripts/preflight.sh PLATFORM=GKE && numactl --membind 0 --cpunodebind=0 python3 -m maxtext.trainers.pre_train.train run_name=${YOUR_JOB_NAME?}
+bash preflight.sh PLATFORM=GKE && numactl --membind 0 --cpunodebind=0 python3 -m maxtext.trainers.pre_train.train run_name=${YOUR_JOB_NAME?}
 ```
 
 1. `numactl`: This is the command-line tool used for controlling NUMA policy for processes or shared memory. It's particularly useful on multi-socket systems where memory locality can impact performance.

docs/tutorials/first_run.md

@@ -39,6 +39,15 @@ multiple hosts but is a good way to learn about MaxText.
 2. For instructions on installing MaxText on your VM, please refer to the [official documentation](https://maxtext.readthedocs.io/en/latest/install_maxtext.html).
 3. After installation completes, run training on synthetic data with the following command:
 
+```sh
+python3 -m venv ~/venv-maxtext
+source ~/venv-maxtext/bin/activate
+bash tools/setup/setup.sh
+pre-commit install
+```
+
+4. After installation completes, run training on synthetic data with the following command:
+
 ```sh
 python3 -m maxtext.trainers.pre_train.train \
   run_name=${YOUR_JOB_NAME?} \

docs/tutorials/posttraining/sft_on_multi_host.md

@@ -113,7 +113,7 @@ xpk workload create \
 --workload=${WORKLOAD_NAME?} \
 --tpu-type=${TPU_TYPE?} \
 --num-slices=${TPU_SLICE?} \
---command "python3 -m maxtext.trainers.post_train.sft.train_sft run_name=${WORKLOAD_NAME?} base_output_directory=${OUTPUT_PATH?} model_name=${MODEL?} load_parameters_path=${MAXTEXT_CKPT_PATH?} hf_access_token=${HF_TOKEN?}  per_device_batch_size=1 steps=${STEPS?} profiler=xplane hf_path=${DATASET_NAME?} train_split=${TRAIN_SPLIT?} train_data_columns=${TRAIN_DATA_COLUMNS?}"
+--command "python3 -m maxtext.trainers.post_train.sft.train_sft run_name=${WORKLOAD_NAME?} base_output_directory=${OUTPUT_PATH?} model_name=${MODEL_NAME?} load_parameters_path=${MODEL_CHECKPOINT_PATH?} hf_access_token=${HF_TOKEN?} tokenizer_path=${TOKENIZER_PATH?} per_device_batch_size=1 steps=${STEPS?} profiler=xplane hf_path=${DATASET_NAME?} train_split=${TRAIN_SPLIT?} train_data_columns=${TRAIN_DATA_COLUMNS?}"
 ```
 
 Once the fine-tuning is completed, you can access your model checkpoints at `$OUTPUT_PATH/$WORKLOAD_NAME/checkpoints`.
@@ -131,7 +131,7 @@ xpk workload create-pathways \
 --workload=${WORKLOAD_NAME?} \
 --tpu-type=${TPU_TYPE?} \
 --num-slices=${TPU_SLICE?} \
---command="JAX_PLATFORMS=proxy JAX_BACKEND_TARGET=grpc://127.0.0.1:29000 ENABLE_PATHWAYS_PERSISTENCE=1 python3 -m maxtext.trainers.post_train.sft.train_sft run_name=${WORKLOAD_NAME?} base_output_directory=${OUTPUT_PATH?} model_name=${MODEL?} load_parameters_path=${MAXTEXT_CKPT_PATH?} hf_access_token=${HF_TOKEN?} per_device_batch_size=1 steps=${STEPS?} profiler=xplane checkpoint_storage_use_zarr3=$((1 - USE_PATHWAYS)) checkpoint_storage_use_ocdbt=$((1 - USE_PATHWAYS)) enable_single_controller=True"
+--command="JAX_PLATFORMS=proxy JAX_BACKEND_TARGET=grpc://127.0.0.1:29000 ENABLE_PATHWAYS_PERSISTENCE=1 python3 -m maxtext.trainers.post_train.sft.train_sft run_name=${WORKLOAD_NAME?} base_output_directory=${OUTPUT_PATH?} model_name=${MODEL_NAME?} load_parameters_path=${MODEL_CHECKPOINT_PATH?} hf_access_token=${HF_TOKEN?} tokenizer_path=${TOKENIZER_PATH?} per_device_batch_size=1 steps=${STEPS?} profiler=xplane checkpoint_storage_use_zarr3=False checkpoint_storage_use_ocdbt=False enable_single_controller=True"
 ```
 
 Once the fine-tuning is completed, you can access your model checkpoints at `$OUTPUT_PATH/$WORKLOAD_NAME/checkpoints`.